Create a Data Management Roadmap

Make sure the right information gets to the right people, at the right time.

ANALYST PERSPECTIVE

IT needs to spend more time on “I” and less on “T.” (Harvard Business Review)

"Insightful data-focused strategies are driving today’s business agendas. There is a growing gap between the exceptional leaders in each market and their market followers. Data is used in ways never before imagined, calling to action the need for relevant, complete, consistent, transparent, precise, timely, and accurate data. This requires an enterprise-wide approach to data management founded in business strategy. Aligning your business’s data strategies with key data management enablers will ensure that your data management practice is fit-for-business purpose and is right-sized to meet your unique business data needs."

Steven J. Wilson,

Senior Director, Data Management, BI, EI Practice

Info-Tech Research Group

Confirm this data management blueprint aligns with your project’s mission and expected outcomes

This Research is Designed For:

• Data management professionals looking to improve the organization’s ability to leverage data in value-added ways
• Data governance managers and data analysts looking to improve the effectiveness and value of their organization’s data management practice

This Research Will Help You

• Align data management plans with business requirements and strategic plans
• Create a collaborative plan that unites IT and the business in managing its data assets
• Design a data management program that can scale and evolve over time

This Research Will Also Assist:

• Business leaders create plans for leveraging data in their strategic planning and business processes.
• IT professionals looking to improve the environment that manages and delivers data

This Research Will Help Them

• Perform data strategy planning and incorporate data capabilities and plans into their broader plans
• Identify gaps in current data services and the supporting environment and determine effective corrective actions

Executive Summary

Situation

• Organizational appetite for data is increasing, with growing demands for data to better support business processes and inform decision making.
• For data to be accessible and trustworthy for the business it must be effectively managed throughout its lifecycle.

Complication

• Despite the growing focus on data, many organizations struggle to develop an effective strategy for managing their data assets.
• Successful management and consistent delivery of data assets throughout their lifecycle requires the collaboration of the business and IT and the balance of technology, process, and resourcing solutions.

Resolution

• Incremental Approach: Building a mature and optimized practice doesn’t occur overnight – it takes time and effort. Use this blueprint’s approach and roadmap results, to support your organization in building a practice that prioritizes scope, increases the effectiveness of your data management practice, and improves your alignment with business data needs.
• Build Smart: Don’t do data management for data management’s sake, but instead align it to business requirements and the business’s vision for the organization’s data. Ensure initiatives and program investments best align to business priorities and support the organization in becoming more data driven and data centric.
• Culture of Collaboration: For a data management practice to be successful and have longevity, it must actively engage the business during its planning and implementation. Through this cross-functional and collaborative approach, data assets can be best managed and leveraged by the business.

Info-Tech Insight

1. Data management is not one size fits all. Cut through the noise related to data management and create a strategy and process that is right for your organization.
2. Take a data-oriented approach. People stop thinking about data and the “art of the possible” when you bring up business functions. Maintain a data focus during a project’s planning, delivery, and evaluation stages.
3. Quickly generate business returns. Take immediate action on quick resolution issues uncovered in business interviews to help overcome the common inhibitors to strategic planning projects.

Data is a business asset and needs to be treated like one

Data management is an enabler of the business and therefore needs to be driven by business goals and objectives. For data to be a strategic asset of the business, the business and IT processes that support its delivery and management must be mature and clearly executed.

Business Drivers

1. Client Intimacy / Service Excellence
2. Product and Service Innovations
3. Operational Excellence
4. Risk and Compliance Management

Data Management Enablers

• Data Governance
• Data Strategy Planning
• Data Architecture
• Data Operations Management
• Data Risk Management
• Data Quality Management

Industry Spotlight: Risk Management in the Financial Services Sector

Regulatory Compliance

Regulations are the #1 driver for risk management.

$11M USD Fine: Fine incurred by a well-known Wall Street firm after using inaccurate data to execute short sales orders.

In 2008, the world experienced the worst financial crisis since the Great Depression. Major financial institutions were badly affected by risky assumptions and poorly informed decisions. One common cause among many of those worst affected was being unaware of the risk they were exposed to. Robust data management is imperative to accurately understanding your risk, as well as maintaining compliance with ever-expanding regulations.

Industry Spotlight: Operational Excellence in the Public Sector

GOVERNMENT TRANSPARENCY

With frequent government scandals and corruption dominating the news, transparency to the public is quickly becoming a widely adopted practice at every level of government. Open government is the guiding principle that the public has access to the documents and proceedings of government to allow for effective public oversight. With growing regulations and pressure from the public, governments must adopt a comprehensive data management strategy to ensure they remain accountable to their rate payers, residents, businesses, and other constituents.

1. Transparency

Traansparency is not just about access, it’s about sharing and reuse.

2. Social and Commercial Value

Everything from finding your local post office to building a search engine requires access to data.

3. Participatory Government

By opening up data, citizens are enabled to be more directly informed and involved in decision making.

Industry Spotlight: Operational Excellence and Client Intimacy in Major League Sports

SPORTS ANALYTICS

A professional sports team is essentially a business that is looking for wins to maximize revenue. While they hope for a successful post-season, they also need strong quarterly results, just like you. Sports teams are renowned for adopting data-driven decision making across their organizations to do everything from improving player performance to optimizing tickets sales. At the end of the day, to enable analytics you must have top notch information management.

Team Performance Benefits

1. Optimal line-ups
2. Player salary optimization
3. Real time play calling
4. Gathering proprietary data
5. Draft analysis

Customer Intelligence and Revenue Benefits

6. Dynamic ticket pricing
7. Marketing optimization
8. Fan loyalty analytics

(Davenport, 2014)

Industry leaders cite data, and the insights they glean from it, as their means of standing apart from their competitors.

Industry Spotlight: Operational Excellence and Service Delivery within manufacturing and supply chain services

Supply Chain Efficiency

Data offers key insights and opportunities when it comes to supply chain management. The supply chain is where the business strategy gets converted to operational service delivery of the business. Proper data management enables business processes to become more efficient, productive, and profitable through the greater availability of quality data and analysis.

97% of executives have an understanding of how data can improve their supply chain, but only 17% reported implementing any analytics in a supply chain function.

(Lewis, 2014)

Involving Data in Your Supply Chain

6x: Leaders are 6 times more likely to report an increase in supply chain efficiency of 10% or greater.

5x: Leaders are 5 times more likely to report shortened order-to-delivery cycle times.

5x: Leaders are 5 times more likely to report an improvement in demand-driven opportunities.

(Accenture, 2015)

Industry Spotlight: Intelligent product innovation and strong product portfolios differentiate consumer retailers and CPGs

Informed Product Development

Consumer shopping habits and preferences are notoriously variable, making the development of a well-received product a challenge. Supporting the probability of a successful outcome requires information and insights into consumer trends, shopping preferences, and market analysis.

Maintaining a Product Portfolio

What is selling? What is not selling?

Product Development

Based on current consumer buying patterns, what will they buy next?

How will this product be received by consumers?

What characteristics do consumers find important?

The ability to accurately answer these questions requires a combination of operational and analytical data.

Internal Data

• Organizational sales performance

External Data

• Competitor performance
• Market analysis
• Consumer trends and preferences

3%: The number of product launches deemed successful based on first year revenue performance.

(Schneider and Hall, 2011)

Changes in business and technology are changing how organizations use and manage data

The world moves a lot faster today.

Businesses of today operate in real time. To maintain a competitive edge, businesses must identify and respond quickly to opportunities and events. To effectively do this the business must have accurate and up-to-date data at their fingertips. To support the new demands around data consumption, improved Data Velocity (pace in which data is captured, organized, and analyzed) must also accelerate.

Data Management Implications

• Strong integration capabilities
• Intelligent and efficient systems
• Embedded data quality management
• Strong transparency into the history of data and its transformation

Studies and projections show a clear case of how data and its usage will grow and evolve.

Zettabyte Era

44x More Data: Annual Data Production in 2020 is expected to be 44 times greater than levels in 2009. (CSC, 2012)

Evolving Technologies

1/3 Cloud Proliferation: Cloud is not a new technology, but its rise as a mainstay storage and application platform will have ripple effects in data management practices. It is projected that, by 2020, one-third of data created will live in or pass through the Cloud. (CSC, 2012)

To differentiate and remain competitive in today’s marketplace organizations are becoming more data-driven

Analytic Competitor

Leveraging data as a competitive advantage makes data a central enabler to an organization’s operations and strategy.

(Davenport and Harris, 2007.)

Data and the use of data analytics has become a centerpiece to effective modern business. Top-performing organizations across a variety of industries have been cited as using analytics five times more than lower performers.

Source: MIT Sloan Management Review

Business executives see data management as a core IT service

Top IT Services for Business Stakeholders

1. Network Infrastructure
2. Service Desk
3. Business Applications
4. Data Quality
5. Devices
6. Analytical Capability
7. Client-Facing Technology
8. Work Orders
9. Innovation Leadership
10. Projects

Info-Tech Research Group, N=21,367 July 2015

Top Upcoming Technology Innovations for CEOs

1. Mobile for Employees
2. Big Data – Analytics
3. Internal Collaboration Tools
4. Mobile for Customers
5. Social Media for Engagement
6. Big Data – Collection
7. Cloud for Application Functionality
8. Cloud for Agile Infrastructure
9. Social Media for Acquisition
10. Internet of Things for Product Innovation

Info-Tech Research Group, N=215 July 2015

Learn more about the CIO Business Vision program.

Info-Tech’s Data Management Framework

What is Data Management?

Data management is the planning, execution, and oversight of policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information assets. (DAMA, 2009)

Info-Tech’s Approach

Info-Tech’s Data Management Framework is designed to show how an organization’s business model sits as the foundation of its data management practice. Drawing from the requirements of the underpinning model, a practice is designed and maintained through the creation and application of the enablers and dimensions of data management.

Build a data management practice that is centered on supporting the business and its use of key data assets

Decoding Info-Tech’s Data Management Framework – Layer 1

Business Resources

Data subject areas provide high-level views of the data assets that are used in business processes and enable an organization to perform its business functions. Classified by specific subjects, these groups reflect data elements that, when used effectively, are able to support analytical and operational use cases of data. This layer is representative of the delivery of the data assets and the business’s consumption of the data.

Data is an integral business asset that exists across all areas of an organization

Trustworthy and Usable Data + Well-Designed and Executed Processes = Business Capabilities and Functions

For a data management practice to be effective it ultimately must show how its capabilities and operations better support the business in accessing and leveraging its key data assets.*

*This project focuses on an organization’s plans to build its capabilities for data management. Leverage our Data Quality Management research to support you in assessing the performance of this model.

Information Dimensions support the different types of data present within an organization’s environment

Decoding Info-Tech’s Data Management Framework – Layer 2

Information Dimensions

Components at the Information Dimensions layer manage the different types of data and information present with an environment. At this layer, data is managed based on its type and how the business is looking to use and access the data.

Custom capabilities are developed at this level to support:

• Structured data
• Semi-structured data
• Unstructured data

The types, formats, and structure of the data are managed at this level using the data management enablers to support their successful execution and performance.

Build a data management practice with strong process capabilities

Decoding Info-Tech’s Data Management Framework – Layer 3

Use these guiding principles to contextualize the purpose and value for each data management enabler.

Data Management Enablers

Info-Tech categorizes data management enablers as the processes that guide the management of the organization’s data assets and support the delivery.

Use Info-Tech’s assessment framework to support your organization’s data management planning

Info-Tech employs a consumer-driven approach to requirements gathering in order to support a data management practice. This will create a vision and strategic plan that will help to make data an enabler to the business as it looks to achieve its strategic objectives.

Data Strategy Planning

To support the project in building an accurate understanding of the organization’s data requirements and the role of data in its operations (current and future), the framework first guides organizations on a business and subject area assessment. By focusing on data usage and strategies for unique data subject areas, the project team will be better able to craft a data management practice with capabilities that will generate the greatest value and proactively handle evolving data requirements.

Data Management Assessment

To support the design of a fit-for-purpose data management practice that aligns with the business’s data requirements this assessment will guide you in:

• Determining the target capabilities for the different dimensions of data management
• Identifying the interaction dependencies and coordination efforts required to build a successful data management practice.

This blueprint’s two-phase structure guides clients in building a business-aligned data management practice

Info-Tech delivers

Use the step-by-step advice within this blueprint to adopt an agile and iterative approach to creating data strategies for the business and building a plan for creating a well-aligned and fit-for-purpose data management practice. Follow the phases outlined in the blueprint to support your organization in developing information strategies and building a roadmap that will enable end users to:

• Have access to quality data
• Have data to support the analytical and operational use for each relevant business area
• Use data that is properly secured and protected

BLUEPRINT RESOURCES

• Step-by-step instructions for performing data strategy planning
• Data Management Assessment and Planning Tool
• Initiative Definition Tool
• Data Management Roadmap Template

*This blueprint contains additional resources that support the creation of interim deliverables and the execution of project steps.

Use this blueprint and its diagnostic as a jumping off point into Info-Tech’s data management research library

Use the diagnostic analysis and planning that results from this project to guide you in focusing your efforts on building capabilities in critical and high-return areas of data management.

Leverage Info-Tech’s supporting research within the data management space to help you design the necessary capabilities and implement them into your organization’s environment.

Modernize Data Architecture For Measurable Business Results

Establish Effective Data Governance

Use these blueprints and the additional research available on Info-Tech’s website to support your team in translating the plans from this project into reality.

Launch the project by engaging the necessary participants and defining project expectations

Recommended Project Manager

The head of the organization’s data management practice.

(Depending on organizational size this role could be the Chief Data Officer [CDO], Applications Director, Data/Data Governance Manager, or Data Management Analyst/Manager)

Recommended Oversight

Maintain business and IT oversight in this project by engaging both senior management from IT and the business unit’s developing information strategies

Estimated Time for this Project

1-3 months

(Depending on full-time/part-time project staffing and the number of business areas being engaged)

Project Scope

1-3 data subject areas of the business (depending on business participation and size) and the underpinning data management practices that support the business’s data consumption

Standing on the shoulders of IM giants

As part of our research process, we leveraged the frameworks of COBIT5, Mike 2.0, and DAMA DMBOK2. Contextualizing data management within these frameworks clarifies its importance and role and ensures that our assessment tool is focused on key priority areas.

The DMBOK2 Data Management framework by the Data Asset Management Association (DAMA) provided a starting point for our classification of the components in our IM framework.

Mike 2.0 is a data management framework that helped guide the development of our framework through their core solutions and composite solutions.

The Cobit 5 framework and its business enablers were used as a starting point for assessing the performance capabilities of the different components of information management.

Info-Tech offers various levels of support to best suit your needs

DIY Toolkit

"Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful."

Guided Implementation

"Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keeps us on track."

Workshop

"We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place."

Consulting

"Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project."

Diagnostics and consistent frameworks are used throughout all four options.

Create a Data Management Roadmap – Project Overview

Data Management Workshop Overview

Contact your account representative or email Workshops@InfoTech.com for more information.

Navigate the two phases of the blueprint using this table of contents

Phase 1: Align the Business

Step 1: Understand Industry and Organizational Definitions and Components of Data Management

Step 2: Perform Data Strategy Planning

Template: Data Strategy Planning Interview Guide

Tool: Data Management Assessment and Planning Tool

Appendix

Project Management Guidance

Template: Project Charter Template

Template: Business Case / Communication Template

Tool: Data Management Work Breakdown Structure

Tool: Stakeholder Register

Phase 2: Plan for the Future

Step 1: Evaluate Data Management Capabilities

Step 2: Develop Alignment Strategies and Improvement Plans

Tool: Initiative Definition Tool

Step 3: Create a Roadmap and Plan of Action for Data Management

Template: Data Management Roadmap Template

Tool: Track and Measure Benefits Tool

Appendix

Additional Research

PHASE 1

Align with the Business

Phase 1 Overview

Detailed Overview

Step 1: Understand Industry and Organizational Definitions and Components of Data Management

Step 2: Perform Data Strategy Planning

Appendix

• Project Management Guidance
• Key Terms and Definitions

Outcomes

• Increased organizational learning and understanding of Data Management components, principles, and how they apply to the organization’s current environment and practices
• Identification of data strategies related to the business’s vision for its data consumption and usage
• Development of data management strategies that will support the realization of the business’s strategic plans

Benefits

• Alignment of objectives and plans between IT and the business
• Completion of critical foundational planning and analytical steps that will ensure value-added and impactful capability planning and practice development.

Use advisory support to accelerate your completion of phase 1 activities

Call 1-888-670-8889 or email GuidedImplementations@InfoTech.com for more information.

Complete these steps on your own, or call us to complete a guided implementation. A guided implementation is a series of 2-3 advisory calls that help you execute each phase of a project. They are included in most advisory memberships.

STEP 1

Understand Industry and Organizational Definitions and Components of Data Management

1.1 Understand industry and organizational definitions and components of data management

Step Objectives

• Understand the definitions and roles of each component of data management
• Contextualize data management components to your organization’s environment and current practices

Step Activities

1.1.1 Overview of data management and its components

1.1.2. Contextualize data management components

1.1.3 Craft your vision and mission statements

1.1.4 Create guiding principles for your organization’s data management practice

Outcomes

• Defined terms
• Solid foundational understanding of data management

Research Support

• Info-Tech’s Data Management Framework

Proposed Participants in this Step

• Project Manager
• Data Architect(s) or Enterprise Architect
• Project Team

1.1.1 Overview of data management

What is Data Management?

Data Management is the planning, execution, and oversight of policies, practices, and projects that acquire, control, protect, deliver, and enhance the value of data and information assets. (DAMA, 2009)

Mission of a Data Management Practice

• Data services add direct value to business functions and processes.
• Data is accessible and used to improve the competitive position of the organization.
• Data usage and management does not expose the business to undue risk.

Performing effective data management requires a multi-faceted approach that includes investments in people, processes, and technology.

Many components are included under the umbrella of data management, all working in concert to:

• Deliver data and allow it to support the data appetites of the business
• Successfully support data through its lifecycle
• Ensure it is appropriately treated as it flows through the organization’s environment

Use the proceeding slides to build your understanding of the purpose, value, and capabilities associated with each component.

Data governance

Guiding Principles and Value

Governs and directs the data management practices supporting the organization’s treatment of data assets.

Overview

The exercise of authority, control, and shared decision making in terms of planning, monitoring, and enforcement over the management of data assets. It can also be reviewed as “high-level, executive data stewardship.” (Data Management Book of Knowledge, 2009)

The value of data governance comes from its ability to set policies and standards, and ensure their adherence in both business and IT environments. It governs:

• How the business consumes and stewards data assets
• How data management professionals execute components of data management

Key Objective

Governance of data assets will become embedded into the processes and perspectives of the business.

How to get there:

• Self-governing processes, assisted by tools and widely adopted governance principles
• A data-oriented culture where the business owns the data and takes responsibility for its management

In most organizations, data governance takes on a form similar to a representative government, with different functions operating as separate branches.

Legislative Functions:

• Policy and standard creation

Executive Functions:

• Policy enforcement
• Administration of role and responsibilities into the environment
• Implementation of data architecture artifacts and the provision of data management services

Judicial Functions:

• Issue management

(DAMA, 2009)

Separated roles and responsibilities help to create a system of checks and balances that ensures policies alignment to internal and external requirements for data management.

Data strategy planning

Guiding Principles and Value

Aligns the business’s and data management practice’s perspectives and vision for data.

Plans the data strategies and identifies high-level data management enabling factors to support their achievement.

Overview

This component includes the steps of identifying and translating business objectives and capability goals into strategies for improving data usage by the business and enhancing the capabilities of data management.

*Use Step 2 of this project to guide your project in executing data strategy planning.

Steps to Data Strategy Planning

• Analyze business strategies
• Identify requirements for data
• Develop data strategies
• Identify direct and indirect data management enablers
• Design data management strategy

"If you don’t maintain the link to the business objectives throughout the initiative, it quickly becomes just an IT initiative."

– Mario Cantin,

Chief Data Strategist, Prodago

Key Terms

Data Strategy

A description of how the organization is looking to be able to view and use data in order to support the achievement of a business capability or strategic goal.

Data Management Strategy

Planning derived from the business’s data strategies that identifies the data management sub-practices and capabilities that support the data strategy in being realized. The activity and the planning outcomes are derived from analysis of the data barriers and the vision for the “to be” environment of the business.

Data architecture

Guiding Principles and Value

Aligns with business capabilities and frameworks established by EA.

Plans the models and artifacts to help design and execute components of data management.

Definition

A description of the structure and interaction of the enterprise’s major types and sources of data, logical data assets, physical data assets, and data management resources. (TOGAF 9)

The execution of data architecture falls under the scope of enterprise architecture. But for most organizations, its ongoing management and the usage of its artifacts are leveraged by the data management program.

Objectives of Data Architecture

• Defines and clearly communicates a single, consistent, and authoritative view of an organization’s data (as is, to be, and transitional views) through an integrated set of models/blueprints.
• Crafts a well-positioned and authoritative data delivery environment.

Value of Data Architecture

• Well-architected data meets evolving business needs and supports operational, tactical, and strategic data usage.
• Data architecture artifacts are used as blueprints and references for data managements practices.

Data Architecture

• Information Value Chains
• Authoritative Data Delivery Architecture
• Enterprise Data Model
• Related Data Architectures

These critical DA artifacts are critical guides for designing and building data management components and delivering trustworthy data.

Data operations management

Guiding Principles and Value

Operate, deliver, and support data to the organization’s data consumers and business areas.

Overview

The planning, control, and support for data assets across the data lifecycle from creation and acquisition, through archival to purge. (Data Management Book of Knowledge, 2009)

Objectives of Data Operations Management

• Implement and follow policies and procedures to manage data at each stage of its lifecycle.
• Maintain the technology supporting the flow and delivery of data (applications, databases, systems, etc.).
• Control the delivery of data within the system environment.

Indicators of Successful Data Operations Management

• Effective delivery of data assets to end users.
• Successful maintenance and performance of the technical environment that collects, stores, delivers, and purges organizational data.

This Data Management Enabler has a heavy focus on the management and performance of data systems and applications.

This component works closely with the organization’s technical architecture in order to support successful data delivery and lifecycle management (e.g. data warehouses, repositories, databases, networks, etc.).

Data risk management

Guiding Principles and Value

Operate, deliver, and support. Data risk management is responsible for ensuring data is protected and secure during its management, delivery, and disposition within the IT and business environments it resides.

Overview Data risk management ensures data assets are sufficiently protected and able to be accessed in secure and controlled manners throughout their lifecycle and as they sit at rest or in flight within the organization’s IT infrastructure and business environment.

Objectives of Data Risk Management

• Data is managed with consideration and adherence to the confidentiality, integrity, and availability needs of the business.
• A balance exists between managing risk of data usage and making data widely visible and available to the business.
• Data assets within the organization’s environment adhere to regulatory standards.
• The IT environment storing the data is proactively addressing data concerns and evolving its protection policies and management practices to address evolving technologies and new threats/concerns.

Critical Success Factors for Data Risk Management

• Sensitive data assets (e.g. PCI data) and areas with high vulnerabilities and downside risks are classified and have controlled access and tight management.
• Security awareness training is present across the organization.
• Data is protected throughout its lifecycle.
• Decisions of technology usage and data access take into account security principles and incorporate security controls during their planning and implementations.
• The organization proactively tests its environment for security threats/weaknesses.

Data quality management

Guiding Principles and Value

Monitor the fitness of data in the environment.

Improves the quality of data through ongoing cleansing efforts and correction plans.

Overview

Data quality encompasses planning, implementation, and control activities that apply quality management techniques to measure, assess, improve, and ensure the fitness of data for use. (Data Management Book of Knowledge, 2009)

CONSIDER

What does data quality mean for your organization? How does your business determine data is of the necessary quality?

Value of Data Quality Management

Data quality and its management is integral to the performance and perception of the entire data management program:

• Aligns integrity and fitness of data to the business’s requirements
• Gauges the quality of data and the performance of the data management practices that support its delivery

Objectives of Data Quality Management

• Ensure usable and trustworthy data is available to the business and its processes.
• Analyze and recommend improvements to data management components in order to support improved data quality.
• Coordinate efforts between IT and the business that manage and maintain the quality of data assets on an ongoing basis.
• Investigate data quality issues, determining root cause and designing corrective plans that enable the maintained integrity of data assets.

Critical Success Factors for Effective Data Quality Management

• Activities of data quality are recognized as a business function that must be supported by executives and executed tactically by business and IT staff
• Continual cleansing of data assets (automated and manual)
• Periodical reviews and improvements of the management practices for data

Practice evolution

Monitor and improve the components and capacities of the practice over time.

Overview

The organization’s data management practice is continuously improved in order to better support the delivery of existing data assets and enable the business to respond to new opportunities and requirements related to the capture, management, and consumption of data.

Value of Practice Evolution

• Data is never static. Therefore, the practices managing and supporting it need to be dynamic and evolve.
• A continuous improvement mentality allows natural updates to occur and enables the organization to take advantage of new opportunities related to data.

Mission of Practice Evolution

• Ensure improvements to the capabilities of the data management practice are made over time.
• Evolve the capabilities of data management practices on both the sides of IT and the business.
• Ensure new data sources/types and business requirements trigger re-evaluations and improvements to practice capabilities.

Objectives and Methods to Incorporating Continuous Improvement into Data Management

Short-Term and Long-Term Practice Planning

• Create and manage a data management roadmap
• Conduct performance tuning on practice operations

Improving Business and IT Processes

• Leverage business process transformations and process engineering to create more effective management and use of data in the business environment
• Formalize and mature IT procedures and processes

Respond to Evolving Data Usage and Data Types

• Re-evaluate capability requirements and practice performance against the organization’s data strategies and evolving business strategies
• Identify how to effectively respond to the addition of new data sources and types.

Metadata Management

Overview

Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource. Metadata is often called data about data or information about information (NISO)

Metadata Management is the function that manages and maintains the technology and processes that creates, processes, and stores metadata created by business processes and data.

93%. The projected amount of the unstructured data in the digital universe and an organization’s environment by 2020.

Source: Baseline Magazine, 2015, and IDG, 2014

As data becomes more unstructured, complex, and manipulated, the importance and value of metadata will grow exponentially and support improved:

• Data consumption
• Quality Management
• Risk Management

Value of Effective Metadata Management

• Supports the traceability of data through an environment
• Creates standards and logging that enable information and data to be searchable and catalogued
• Metadata schemas enable easier transferring and distribution of data across different environments.

Data about data – the true value of metadata and the management practices supporting it is its ability to provide deeper understanding and auditability to the data assets and processes of the business

Metadata supports the use of:

• Big Data (Unstructured data)
• Content and Documents (Unstructured and Semi-structured data)
• Structured data (Master, Reference, etc.)

Critical Success Factors of Metadata Management

• Consistent and documented data standards and definitions
• Architectural planning for metadata
• Incorporation of metadata into system design and the processing of data
• Technology to support metadata creation, collection, storage, and reviews (meta-data repository, meta marts, etc.)

Reference and master data management (MDM)

Definitions

Master Data

An organization’s data about:

• Parties (customers, employees, etc.)
• Products
• Finances
• Locations

Reference Data

Data with standardized formats and restricted value options (e.g. zip codes or country codes).

Master Data Management

The control over master data values to enable consistent, shared, contextual use across systems of the most accurate, timely, and relevant version of truth about essential business entities. (DAMA, 2009)

Objectives of MDM

• Improve the quality and accessibility of the organization’s most valuable and widely used data assets.
• Identify singular versions of the truth (a singular authoritative master file) for data elements on an enterprise-wide level.

Critical Success Factors for MDM

• Business buy-in and leadership in executing an MDM project
• A deep understanding of how master and reference data elements are created and consumed by business processes
• Relationships and architectural planning is planned, documented, and applied to processes and IT systems
• A successful balance of processes, resourcing, and technology that supports an MDM program
• An interoperable environment that successfully integrates systems and propagates master files when necessary

MDM is often viewed as a dimension or approach to data quality – and is generally centered around improving the fitness, consistency, and usability of master data assets.

Implementation approaches to MDM will vary heavily based on an organization’s environment and data culture.

Enterprise content and document management

Overview

Enterprise content management (ECM) is the strategies, methods, and tools used to capture, manage, store, preserve, and deliver content and documents (e.g. electronic files and physical records) related to organizational processes. (www.aiim.org)

Value of Content and Document Management

• Unstructured data assets are managed effectively and available to the business based on the business’s requirements through its lifecycle.
• The unstructured data generated and captured by business processes are able to be effectively used on an immediate basis and stored and maintained to provide long-standing business value.

Objectives of Content and Document Management

• Documents and content records are created in forms and structures that make them useful to the organization.
• Documents and content are searchable and able to be successfully retrieved.
• Content is able to be effectively maintained and remains accurate over time.

Document and Content Management oversees the treatment of unstructured data assets such as:

• Videos
• Text messages
• Images
• Audio recordings
• Faxes

Critical Success Factors for Content and Document Management

• Governance practices are in place to oversee the treatment and use of documents and content throughout their lifecycle.
• Documents and content are tagged with enterprise taxonomies and have metadata for them.
• Support technology (content and records management tools) have capabilities that enable storage, searchability, and usage of unstructured assets.
• Standards for creating, storing, and retrieving documents and contents are incorporated into business processes.

Big data management

Overview

Big data management is the organization, administration, and governance of large volumes of both structured and unstructured data. (TechTarget)

KPMG’s Technology Trend’s Index found that while organizations and media are talking about big data, there is a growing amount of activity related to deals (market) and usage by organizations.

Key Index Findings big data and analytics is the second-most buzzed about topic. Across 8 industries there is an increasing trend of usage, versus talk about big data capture and analytics

Data snapshot as of October 15, 2015

Consider - Big data Analytics is the second-most talked about upcoming technology by CEOs. (Info-Tech, 2015)

Is big data something your organization is currently leveraging or has incorporated into its strategic plans?

Have you been able to effectively scale up existing applications and databases to support an introduction of big data or is a commercial Hadoop distribution tool necessary?

Objectives of Big Data Management

Big data is effectively captured, stored, and managed in the organization’s environment and available for BI and big data analytics.

Scholarly Perspective “Big Data is less about data that is big than it is about a capacity to search, aggregate, and cross-reference large data sets.”

Indicators of a Valuable and Scalable Big Data Program

• Quality management and metadata for big data assets allows for the data to be reliable and trustworthy
• A technology environment that is able to support the velocity, veracity, volume, and variety of big data being captured, stored, and utilized by the organization
• Analytic capabilities have evolved to generate valuable business insights from big data scenarios

Corporate enterprise integration

Definition of Corporate Enterprise Integration

The integration environment of the organization is designed, implemented, and maintained to facilitate the successful flow of data across the environment and create an interoperable and scalable network that can effectively manage inter- and intra- organization integration scenarios. (Includes integration practices at both the data and application layers.)

The integration environment is:

• Interoperable
• Scalable
• Manageable

Shared data is:

• Consistent
• Trustworthy
• Secure

Objectives for a well-designed and well-executed integration environment:

• It is scalable to fit increasing data processing volumes and introductions of new systems into the IT environment.
• It is able to manage the entry of data from external environments.
• It is able to effectively process and deliver data within the time span required for the data.

Indicators of an effective enterprise integration strategy and program:

• The integration environment includes comprehensive services for all enterprise information.
• Computing power and toolsets support a consistent and scalable delivery of data
• Integration tools and middleware
• Common protocols and languages
• Integration and data access is designed in alignment with the business’s data privacy and security framework (effectively protecting all data-in-motion).
• The integration environment is open, product-neutral, and supports intra/inter-organizational integration.

Business intelligence and analytics

Overview

Business Intelligence (BI). Analytical capabilities centered around metrics and measures that gauge past performance and guide business planning.

Advanced Analytics. Uses sophisticated techniques to predict events, uncover trends and patterns, and garner additional insights from data that could not be uncovered from traditional BI practices

Business Intelligence

• Reports and dashboards
• OLAP queries
• Data discovery

Questions asked in BI scenarios

• What happened?
• How many?
• How often did it happen?

Analytics

• Descriptive modelling
• Predictive analytics
• Text analytics
• Multimedia analytics
• Optimization and simulation

Questions asked in advanced analytic scenarios

• Why is this happening?
• What if…
• What will happen next?
• What is the best outcome?

(Source: Rapidminer.com)

Objectives of BI and Analytics Programs/Practices

• To provide insight and drive business planning through the exploration and investigation of business data
• Business user are able to access valuable data faster through reports and dashboards
• The organization’s decision makers are able to have improved value to cost and identify new business opportunities from data-driven decision making

Critical Success Factors For Effective BI/Analytics

A Balance of Business Acumen and Technical Expertise

• A program balances technical expertise with a deep understanding of business requirements and data usage.

Multiple Delivery Mechanisms

• Processes are in place to support the creation of custom and scheduled reports to business users.
• Self-service BI capabilities allow the business greater autonomy in accessing and leveraging data for decision making.

Transparent and Consistent Report Creation

• The logic, back-end calculations, and data elements should adhere to statistical principles and organization’s data model/data definitions while also aligning to the business’s report request and ultimate business action/requirement.

1.1.2 Review how the different components of data management fit into the organization’s current data management practices

~ 45 minutes

Consider

Effective, consistent, and reliable delivery of data to end users and effective management throughout its lifecycle will require a multi-faceted approach that encompasses most or all components of data management. Frame this discussion on reviewing the relevance and value of all the components, and don’t just default to the commonly discussed items. (BI and Advanced Analytics, Data Governance, and Data Quality).

Instructions

1. On a high level, discuss how data management is currently performed at your organization.
2. Review Info-Tech’s data management framework and discuss how the components are managed and reflected in your own organization’s environment. Consider the following:
• Do specific components stand out to you as being especially important, troublesome, or valuable considering your organization’s current data issues and data appetite?
3. Inventory if certain components are currently absent, informal, or formally present in the environment and discuss their current implications considering the project’s drivers and wants.

Activity

Brainstorming session / whiteboarding exercise

Output

Project framing

Participants

• Project manager
• Project sponsor

As needed, additional DM professionals

• Business analysts
• Data architect
• Data stewards
• Database administrators
• Etc.

Later Usage and Downstream Value of This Exercise

This preliminary analysis of current definitions and data management practices at your organization will help frame your data management strategy planning, identify direct and indirect enablers, and streamline current statement capability analysis.

Create compelling vision and mission statements for the organization’s future data management practice

A vision represents the way your organization intends to be in the future.

A clear vision statement helps align the entire organization to the same end goal. Your vision should be brief, concise, and inspirational; it is attempting to say a lot in a few words so be very thoughtful and careful with the words you choose. Consider your IT department’s strengths, the customers of your IT services, and your current/future commitments to service quality. Remember that a vision statement is internally facing for other members of your company throughout the process.

"A vision is a picture of the future you seek to create, described in the present tense, as if it were happening. A statement of our vision shows where we want to go and what we will be like when we get there."

– Senge et al. 1994

A mission expresses why you exist.

While your vision is a declaration of where your organization aspires to be in the future, your mission statement should communicate the fundamental purpose of the data management practice. It identifies the function of IT, what it produces, and its high-level goals that are linked to delivering timely, high quality, relevant, and valuable data to business processes and end users. Consider if the practice is responsible for providing data for analytical and/or operational use cases. A mission statement should be concise, and provide a clear statement of purpose for both internal and external stakeholders.

"The mission statement provides a valuable starting point for establishing, afterwards, more specific objectives and strategies."

– Hannagan, 2002

Craft your vision and mission statement

~ 45 minutes

Overview

Gather a collection of project stakeholders and project team members together to create consensus on a singular vision and mission for the organization’s data management practice.

Instructions

Get the conversation started:

Ask everyone to complete the following sentence:

• Five years from now, our data management will include ______________.

1. Have each participant create a statement of purpose (1-5 lines) describing the future data management practice. Have them consider the following:

Vision:

• What does an IT department with a high-functioning data management program look like?
• How will our organization benefit and grow from improved data management?
• What are our customers saying, feeling, and doing? Reflect on current state data collection.

Mission:

• Why does this program exist?
• What problems are we trying to solve?
• Who will benefit from this program?
• How will we reach our target?
2. Ask each participant to present their vision and mission. Discuss common themes and then develop a concise vision statement that incorporates the group’s ideas.
3. Consolidate the finding and document the results.

Activity

• Brainstorming session / Whiteboarding exercise

Output

• DM practice vision statement
• DM practice mission statement

Participants

• Project manager
• Project sponsor

As needed, additional DM professionals

• Business analysts
• Data architect
• Data stewards
• Database administrators
• Etc.

1.1.3 A provincial government planned how to improve the management, delivery, and sharing of its data

CASE STUDY

Industry: Government

Source: Government of Alberta, 2012

The Vision

The Government of Alberta is a trusted steward of information held on behalf of Albertans.

INFORMATION IS: COLLECTED ONCE; MANAGED DIGITALLY IN AN OPEN AND SECURE ENVIRONMENT; ACCESSIBLE; AND USED TO ITS FULLEST POTENTIAL

Source: Information Management Strategy of Alberta, 2012

Government of Alberta

The government of Alberta is responsible for a wide variety of government services and agencies that all create and manage a high volume of data. Alberta saw its data and information as integral to good decision making and the ability to deliver services to the Alberta population. To support the management of data and information across the different agencies, the Government of Alberta created an overarching Information Management Strategy* to guide the practices of the agencies underneath it.

Information Management Strategy (abridged synopsis)

The IM strategy created by the government was designed to support its broader strategic plans and objectives, including:

• The execution of responsible government
• Transparency of government operations, its use of public funds, etc.
• Open data: the public availability of data created and collected by different agencies of the province.

The overarching strategic planning outlined in this document was then used to guide the data management practices of each agency within the province of Alberta and align them to a common mission and set of priorities for data.

*In this case Alberta’s use of the term Information Management synonymous with Info-Tech’s definition for Data Management

1.1.3 Create guiding principles for your organization’s data management practice

Info-Tech Recommends

Devise a set of guiding principles that speak to your organization’s data management program and your current culture (or desired culture). The following exercise will help you create a set of guiding principles.

Value of Clearly Defined Data Principles

• Guiding principles help define the culture and characteristics of your practice by describing your beliefs and philosophy.
• Guiding principles act as the heart of your data management – helping to shape initiative plans and day-to-day behaviors related to data management and treatment of the organization’s data assets.

Leverage the industry reference below as you interpret your own values and developing a clear set of guiding principles for your data management practice.

Mike 2.0 Information Management Guiding Principle

Principle #1

Fact-based decision making

Principle #2

Integrated data with consistent definitions

Principle #3

Appropriate retention of detailed data

Principle #4

Quality of data will be measured

Principle #5

Appropriate enterprise access

Principle #6

Every data item has one person or role as ultimate custodian

Source: Mike 2.0

Create guiding principles for your organization’s data management practice

~ 45 minutes

Skip this activity if your organization has already established data principles within its data architecture practice. Update the principles if you do not find them reflective of the vision and objectives of the planned data management practice.

Instructions

1. As a group, brainstorm a list of principles and values related to key business objectives of data and the goals of the data management practice – these will help you develop the practice’s ultimate principles. Attempt to brainstorm between 5 and 10 values.

What is the business looking to get from its data?	How is the practice looking to manage data?
Quality data? Accessible data? Real time?	Keep it secure? Create a scalable environment? Create a culture of accountability and stewardship?

2. Divide into small groups, with each group taking one of the values. For each value, determine:
• Why is it important to data management?
• How will it help to improve the management?
• Complete one of the following sentences:
• Data is…
• The business is able to…
• The data management…
3. Get back together as a group to discuss the principles:
• How can these principles help to guide the practice’s planning?
• How will these principles help to correct and guide staff behavior?
4. Finalize and document your guiding principles.

Output

• Guiding principles

Materials

• Materials

Participants

• Project manager
• Project team
• Data stewards and business representatives

STEP 2

Perform Data Strategy Planning

1.2 Perform Data Strategy Planning

Step Objectives

• Scope the project (identify data elements being evaluated and included in planning steps).
• Identify how the business is looking to leverage data as a business enabler.

Step Activities

Outcomes

• Subject area modelling and analysis
• Data strategy planning (by subject area)
• Data management strategies
• Prioritized subject areas and project scope
• Project approach

Research Support

• Data Management Assessment and Planning Tool

Proposed Participants in This Step

Project manager

Data architect(s) or enterprise architect

Project team

Business function representatives

• Power users
• Business leaders

1.2 Create effective data management by first understanding and aligning to the business and its data consumption needs

Starting your evaluation looking at business plans and data requirements that stem from them will ensure your end product and data management practice understand consumer needs and deliver the right services and data to business.

Build your practice with a solid foundation – the business itself

Approach

1. Establish Business Context
• What is the business’s vision, strategic plans, KPIs, etc.?
2. Identify the data behind the business’s strategic and capability plans
• What data is needed to facilitate the change and enables the business to attain its goals?
3. Define the data strategies and enablers supporting their attainment
• What are the current challenges around data? What components of data management will allow consumers to use data in these strategic ways?
4. Evaluate and define the data management capabilities
• Where do data management practices sit today? What capabilities must be in place to support the data strategies of the business?

1.2 Leverage the Data Management Assessment and Planning Tool to guide your project across each of its assessments

Data Management Assessment and Planning Tool

Follow Info-Tech’s process of first analyzing the business, then data, and finally data management itself to support your team in building a practice that will support the strategic goals of the business.

Objective: Understand the business and its strategic plans

Tab 2. Business Alignment

Step

• Prioritize and evaluate business drivers and their requirements for data

Purpose: Define project scope and define data strategies

Tab 3. Data Planning

Step

• Define data strategies and the challenges surrounding their achievement
• Identify data management strategies related to data strategies

Purpose: Assess and plan data management capabilities

Tabs 4-9

Step

• Evaluate current and target capabilities or data management
• Analyze performance gaps
• Develop plans for building DM practice capabilities

1.2.1 Begin your data management project by first ensuring you have a deep understanding of the business

Develop a deep understanding of the business, its goals, and its plans. This understanding will ensure that the project’s planning and outcomes around data and data management are able to track directly to cases of business value and support of key strategic priorities.

Key Items to Consider

What are the driving forces behind changes and decisions within the business?

1. Client Intimacy / Service Excellence
2. Product and Service Innovations
3. Operational Excellence
4. Risk and Compliance Management

Questions to Ask Business Stakeholders

1. Is the organization’s business model changing?
2. Are business operations evolving and changing?
3. Are regulations causing your organization to re-evaluate how data is used and managed by the business?

1.2.1 Establish the business context behind this data management project

Variable time commitment based on the number of interviews performed

Instructions

1. Develop a firm understanding of the strategic plans and goals of the business. Before proceeding with an evaluation of the data itself, ensure you understand the business’s: vision, goals, objectives, KPIs, strategies, priorities, and key drivers

(Leverage your project sponsor to streamline this information-gathering process).

2. Use the techniques outlined below to support you in establishing a clear business context and alignment as you kick off your data management project.
3. Input results and complete Tab 2. Business Drivers of the Data Management Assessment and Planning Tool.

Recommended Techniques

SWOT Analysis (Use Activity 1.2.1a for support)

Interview business stakeholders (Use Activity 1.2.1b for support)

Review key business artifacts

Typical Review Inputs:

• Business vision and mandate
• 3-5-year roadmaps
• Department-level business plans

Output

Use the findings of your analysis of the business to support your identification of:

• Organizational data assets
• Data scope for the project
• Data strategies
• Target state capabilities for data management
• Implementation plans

1.2.1a Perform SWOT analysis

~ 45 minutes

Objective

Improve the team’s understanding of the business organizational environment and the drivers that sit behind its broader plans and more specific data requirements.

Instructions

1. Draw a SWOT grid on the board.
2. Break the team into two groups
• Assign Group “A” Strengths and Weaknesses
• Assign Group “B” Opportunities and Threats
3. Have each team brainstorm the items for their assigned grids.
4. Have each team present their findings to the group.
5. Conduct a full group discussion on the contents of the SWOT; add additional items and considerations uncovered in the larger debate.
6. Document the findings.

Activity

Brainstorming session / Whiteboarding exercise

Participants

Core project team

Output

• SWOT analysis
• Team understanding of the organizational environment

1.2.1b Interview business stakeholders

30-60 Minute Interview Sessions

Objective

Increase the team’s understanding of organizational strategic plans and how the organization would like to use data as a strategic enabler.

Activity

Panel and individual interviews with the business

Materials

Data Strategy Planning Interview Guide

Instructions

1. Identify which members of the business you would like to interview to gather an understanding of their current data issues and desired data usage capabilities. (Recommendation to gather a diverse set of individuals to help build a broader and more holistic knowledge of data consumption wants/requirements.
2. Prepare your interview questions (Use the Data Strategy Planning Interview Guide as a starting point).
3. Interview the identified members of the business.
4. Debrief and document results.

Tactical Tips

• Include members of your team to help heighten their firsthand knowledge of the business.
• Identify a team member to operate as the formal scribe.
• Keep the discussion as free flowing as possible; it will likely enable the business to share more.
• Don’t get defensive – one of the goals of the interviews is to open communication lines and identify opportunities for changes, not create tension between IT and the business.

Info-Tech Insight

Consider interviewing as a panel to help save time and promote more discussion. Creating a group discussion in the interviews can help individuals feel more comfortable contributing and enable them to build on each other’s comments.

Info-Tech Insight

Prevent the interviews from being just a venue for the business to complain about data. Open the discussion by having them share current concerns, then focus the second half on what they would like to do with data and how they see data assets supporting their strategic plans.

1.2.2 Identify data subject areas

~ 30 minutes

Objective

Identify data subject areas and their dimensions.

Instructions

Take a consumer viewpoint and continue to map the data present within the organization and used in business processes to support business capabilities.

1. Brainstorm and document the types of data used by the organization.
2. Organize the types of data considering high-level subject areas. (Use the provided example and the subject areas documented on the following slide to help organize data.)

Facilitation Support

Get a greater sense of the total data in the environment by drawing back to business capabilities.

• Identify key business capabilities and activities and identify the data assets that are used to execute them.
• Ask individuals what data business leaders are asking for to support them in making business decisions.
• Walk through the departments of the organization and consider what their primary responsibilities are. Follow-up the identification of responsibilities by identifying data that used to support the activities.

Activity

Brainstorming session / Whiteboarding exercise

Output

• High-level subject areas
• Identification of key data assets

Materials

Data Management Assessment and Planning Tool

Participants

• Project manager
• Project sponsor

As needed

• Business analysts
• Data architect

1.2.2 A marketing company brainstormed and organized their data assets

Sample Activity Outcome

Scenario: Before identifying data strategies and building their data management practice, a marketing company sought to understand the type of data assets present within their environment. Understanding both client services and internal processes and departments, the team began to brainstorm the data present within their environment and organized it into subject areas.

Brainstorming

• Client Accounts
• Leads and conversions (Marketing campaigns)
• Client Renewal Data
• Company Leads (Internal client sales process)
• Client Invoicing Data
• Third-Party Industry Data
• Year-End Reporting
• Mail Campaign Data
• Third-Party Client Data
• Employee Records
• Marketing Distribution Templates and Designs
• Corporate Financial Data
• Data on marketing Campaigns
• Email Campaign Data

Subject Area Documentation

Customer

• Client accounts
• Third-party client data
• Third-party industry data

Quotes and Orders

• Client renewal data

Financials

• Year-end reporting
• Corporate financial data

Employees

• Employee records

Prospects

• Company leads (internal client sales process)

Products and Services

• Leads and conversions (marketing campaigns)
• Data on marketing campaigns
• Mail campaign data
• Email campaign data
• Marketing distribution templates and designs

Note: this sample reflects only a sub-set of the data identified in the environment.

1.2.3a Determine the data strategies for key data assets

~ 45 minutes

Business Consumption Evaluation

Objective

Determine the vision and plans for how different functions of the business are looking to use key data assets.

Recommendation

Adopt a consumption viewpoint to identify data elements and their desired usage and value to the business.

Method (Interviews from Activity 1.2.1b )

Interviews with key business leaders, data consumers, and data stewards. Expand interviews to address scope of data assets being analyzed.

Instructions

1. For selected subject areas of data, identify the following:
• The current challenges around the data for the business
• How the business would like to use the data
2. Document results in Tab 3. Data Planning of the Data Management Assessment and Planning Tool.

Columns B-F of tab 3. Data Planning

Output

Data strategies for key data assets

Materials

Data Management Assessment and Planning Tool

Participants

Project Team

• Project manager and analysts

Business

• Business leaders
• Data stewards
• Power users

1.2.3b Determine the underlying data management strategies supporting the actualization of the business's data strategies

Data Management Practice Evaluation and Planning

Objectives

Build data management strategies and identify the underlying capabilities and components that will allow the business to success on realizing its data strategy plans.

Instructions

1. Evaluate the data strategies and objectives of the business related to the consumption and utilization of key data assets. Discuss what data management components and capabilities will need to be in place to support these data consumption objectives.
2. Discuss and identify what high level data management components and capabilities would have to be in place to support this strategy.
3. Map direct and indirect components and capabilities to the overarching data management strategies.
4. Document results in tab 3. Data Planning of the Data Management Assessment and Planning Tool

Columns G-I of tab 3. Data Planning

Output

Data management strategies

Materials

Data Management Assessment and Planning Tool

Participants

Project Team

• Project manager and analysts
• Relevant data management professionals

1.2.3c Generate immediate results by taking quick action on issues with simple and low effort resolutions (quick wins)

~ 15-30 minutes

Recommendation

Don’t sit on valuable information uncovered in the project that can provide quick wins for your DM practice. Where possible, take action on problems with simple resolutions.

Instructions

1. When performing business stakeholder interviews for data strategy planning, highlight barriers and business issues with data that relate to a simple root cause.
2. Perform a quick review of the effort and steps necessary to implement the fix.
3. Take steps to implement changes. (Where required, follow change and release management procedures.)
4. Record steps taken and the business benefits from the quick win. (Use these when finalizing the roadmap or creating a business case to help sell the organization on the idea and value of a data management program.

Example

Output

• Improved goodwill
• DM success stories

Info-Tech Insight

Planning and strategy projects often lose momentum and buy-in if they don’t prove the value of their work and the subject they are focusing upon. Taking immediate action on quick resolution issues can help overcome this common Achilles heel to planning intensive projects and improve goodwill for a DM program.

Seeking to improve their customer intelligence, an organization built a data strategy plan for their customer data

CASE STUDY

Industry: Professional Services

Source: Client Interview

Overview

A professional services organization that sells products and support services to public and private organizations was having an issue around tracking their client interactions and improving how they target clients for service offerings based on improved customer profiling and customer intelligence.

Business Driver/Need

Operational Excellence

Senior management felt the organization was missing out on revenue opportunities due to the low revenue-per-client numbers revealed from their CRM dashboards and reports.

Client Intimacy and Service Excellence

More and more clients were complaining of not getting value from the products sold to them and didn’t feel they were adequately supported in how to best utilize and maintain the products with supplementary offerings.

Business Strategy Planning

To address concerns about losses of revenue opportunities and poor customer service, the organization sought to improve its client servicing. To support these plans for improved client intelligence, an understanding of changes to data management and how data is consumed by the business was identified.

Next Steps

Analyze customer data, specifically customer records within the CRM.

The organization analyzed its business’s needs related to its customer data

CASE STUDY

Data Consumption and Needs Analysis

Before proceeding to evaluate and build data management capabilities, further investigation of data requirements and the vision for data was performed.

Subject Area Analysis

Customer Data: Client records in the CRM system

Definition

Client record data pertaining to organizations with active accounts and up-to-date invoicing with the organization

Action Taken

A series of interviews with business consumers of customer data from a variety of business functions uncovered the following key challenges and objectives for improved customer data usage.

Data Challenges

• The organization is not able get an accurate count of number of current clients
• Difficulties seeing historical data on clients
• Data on client demographics is often stale and incomplete
• It is difficult to see the source of data contained in records
• Inability to see change history and see information from a specific date and time on the account
• Critical data on customer profiles is in open fields and unable to be put into reports

Business Vision for Customer Data

• The organization is able to analyze customer segments by industry and size dimensions
• Management is able to see the history of changes to a client’s information and a full breakdown of its purchase of services and interaction with the organization (sales and delivery teams)
• All customer data is contained in one system
• All the organization’s information on a customer is included or linked to the customer’s profile in the CRM system
• Complete data fields allow for customer profiles to be viewed and analyzed in a variety of different analytical models

Data strategies and data management strategies were developed to support improved utilization of customer data

CASE STUDY

Strategy Planning

Info-Tech interpreted the results to help address how to build data management capabilities that will support improved data delivery.

Data strategies and the data management strategies that underpin them were developed after the business’s current challenges and vision for customer data usage were analyzed.

Data Strategies

• The organization is able to see all of their current customers in a consolidated report/view.
• The organization is able to compare current data in customer records to historical data.
• Dashboards are able to support increased insight into the performance of sales operations.
• Dashboards and reports are able to break down customer base performance along a variety of size, demographic, industry, service-level dimensions.

Data Management Strategies

• Create data unification for customer data to enable an integrated view of current and past customers.
• Improve the functionality of system, storage, traceability, and capture of data to allow historical views of data and year-over-year reviews.
• Improve the delivery of custom and standard reports on customer information
• Expand the dimensions and elements evaluated and shared in sales dashboards and weekly reporting summaries.

Building Blocks

Direct Enablers

• Master data management
• Data quality management (data entry, data validation, data cleansing, etc.)
• Business intelligence program

Indirect Enablers

• Data governance (stewardship and change management)
• Data architecture (data modelling and design)

Deconstructing Info-Tech’s framework on data management

Info-Tech’s layered model allows you to identify and assess the needs and goals of the business to inform your evaluation and planning of data management capabilities. Maintaining alignment throughout the project and in practice implementation will help to ensure data is available and valuable to the business.

Layer 3 Data Management Enablers

The top-most layer of the framework defines the practice components and key processes. Each process and component has a specific purposes (left column) that supports the building of a comprehensive data management practice that will ensure proper data delivery and management of data across its entire lifecycle.

Layer 2 Information Dimensions

The middle layer is the information dimensions. These are the different types of information found in your organization and the way they are going to be accessed and used by data consumers.

Layer 1 Business Subject Areas

At the very foundation of the framework is the business. This is reflected in the types of data used by the business in executing business processes and achieving key capabilities.

1.1.4a Select the approach for analyzing and building a fit-for-purpose data management practice

~ 15-30 minutes

Overview

Data is pervasive and flowing throughout the environments of IT and the business. As a result, it may be difficult and time consuming to analyze and design practices that span across the entire organization.

Instructions

1. Identify if your project is evaluating an enterprise-wide view of how all data is managed, or if practices and capabilities for defined data elements are being evaluated. Consider:
• The size of the organization environment and the different types of data and usages to help determine a reasonable scope.
• Different data use cases and data elements may be supported and require different capabilities, making individual evaluations necessary and an enterprise-wide review work intensive and potentially less valuable if it is not tailored to subject areas.
2. Review the different approaches and select an approach for the project.

Activity

Brainstorming session and planning meeting

Input

• Environmental factors
• Project resourcing
• Business buy-in

Output

• Project approach

Info-Tech Insight

Regardless of the approach you take (enterprise or defined subject area), focus on the data, rather than business function as you proceed with the project. If a project shifts to talking about business process, it is not longer about data orientation, but rather about process. For a project dedicated to evaluating and building data management capabilities, data itself, and the processes around it should be the focal point. Improved business process performance should be an outcome, especially if strategic planning is properly incorporated from the outset.

1.1.4a Select an approach that aligns with the size of the organization and the expectations for the project

1.1.4b Prioritize data elements and determine subject area scope for the data management project

~ 15-30 minutes

Pay particular attention to this selection if you are taking an incremental or agile approach to data management.

Objectives

Identify data elements (subject areas) and the business drivers that will serve as the scope and direction for the data management project.

Instructions

1. Review the subject areas and data elements documented in 1.1.3 and their significance to the business.
2. Consider what subject areas will serve as the best starting point/focus area for building the capabilities of the data management practice. Consider:
• How the subject area data is consumed by the business. Data elements that span multiple business functions provide a strong opportunity to improve the perception of data and the future data management practice, while also providing some of the best opportunities for providing value to multiple areas and stakeholders.
• Consider subject areas that can be effectively evaluated and having planning that will reflect and support the evolution of multiple dimensions and components of data management.
3. After reviewing the benefits and barriers related to different subject areas select the scope of the project.
4. Document selection in the Data Management Assessment and Planning Tool.

Activity

Brainstorming session and planning meeting

Output

• Business context findings
• Subject area analysis
• Data strategy planning
• Key stakeholder preferences

Example

Customer Data

An organization’s customer data often supports multiple business areas: marketing, sales, executive strategy planning, product development, etc.

1.1.5 Speak the language of the business: Create strong communication for the project and practice

~ 30 minutes

Key Messages to Convey to the Business

Principles of Data Management

• Data management built on a partnership between IT and the business
• Data is a corporate business asset
• Data management is not a nice to have – but a must have

Business Value

• Expected improvements of data trust and access
• How the project’s outcomes will beneficially impact different data consumers
• How the project relates back to: Client Intimacy; Service Excellence; Product and Service Innovation; Operational Excellence; and/or Risk Management

Inputs

• Project mission and value
• Vision for the DM practice
• Project goals and objectives
• Project overview
• Outline of plan/stages
• Expected outcomes

Input

• Data strategy planning outputs
• Project documentation (Use the Project Management Guidance in the Appendix for support

Output

Project and practice messaging

Materials

Data Management Communication/Business Case Template

This messaging should live past the lifecycle of the project and be used to help sustain momentum for the ongoing data management practice and continued IT–business collaboration.

Data management and the state of processes around data can be a sensitive business topic. To overcome issues of resistance from the operational or strategic level, create a carefully crafted message.

Do you have a clear understanding of the business’s data strategies and a clear data model?

Self-Auditing Guidelines

☐ Is the project starting with a strong foundation and understanding of the business’s strategic plans and the role of data?

☐ Does the project have a clear understanding of the organization’s plans and needs around key subject areas?

☐ Is the data strategy planning and enabler identification tied to strategic plans and business capability objectives? Will the expected project outcomes and improvement plans clearly link back to business value.

Tactical Tips

Perform subject area modelling for the purpose of project scoping and creating a high level plan of data, rather than building a full data architecture artifact. Eventually, if the organization plans to evolve the artifacts of its data architecture, this modeling can be revisited as a starting point.

If you want additional support, have our analysts guide you through this phase as part of an Info-Tech workshop

Book a workshop with our Info-Tech analysts:

• To accelerate this project, engage your IT team in an Info-Tech workshop with an Info-Tech analyst team.
• Info-Tech analysts will join you and your team onsite at your location or welcome you to Info-Tech’s historic Toronto office to participate in an innovative onsite workshop.
• Contact your account manager (www.infotech.com/account), or email Workshops@InfoTech.com for more information.

The following are sample activities that will be conducted by Info-Tech analysts with your team:

What does data management mean to your organization?

Definitions and implementations of data management vary greatly depending on environments and organizational consumption of data. To begin the data management workshop, the facilitator will frame the four-day engagement by understanding the current practices of data management and how it is positioned in the organization.

Discuss Info-Tech’s Data Management Framework

To support the upcoming data strategy planning and identification of DM enablers for data strategies, the facilitator will walk workshop participants through the different components of Info-Tech’s data management framework and discuss the presence and state of each item and how it ultimately supports the organization’s data delivery requirements.

Establish business context

To develop an improved understanding of the business’s strategic plans, current data usage, and data requirements, workshop participants will lead interviews with business stakeholders.

Debrief business interviews and discuss the implications

The Info-Tech facilitator will guide workshop participants in evaluating the findings of the business interviews, discuss their implications on the project’s plans, and identify key takeaways.

Brainstorm and discuss key data assets

Although the scope of the workshop will likely be centered on one key data asset, additional data assets will be discussed and evaluated based on the findings of interviews and the business priorities. This list and priorities related to the data assets will be documented and used to help plan the gradual scope increase of the data management practice.

Create a system context diagram

To provide an improved understanding of the business process and technical environment of the selected data scope, the facilitator will support the participants in building a system context diagram.

Perform data strategy planning

Considering the interview findings, workshop prep work, and exercise findings, the facilitator will support participants in determining the business’s vision for the selected data asset and the data strategies that will support their consumption requirements. *If time permits additional data assets strategy planning will be performed.

Create data management strategies for key data strategies

Building on the findings of the data strategy planning, the facilitator will discuss data management implications of the business’s expressed data requirements. These findings will help to frame the future state planning of data management practice and identify capabilities across the sub-practices that will be necessary.

Appendix A

Project Management Guidance Definition of Key Terms

Project Management Guidance

Use this section to create the initial planning and outline the ongoing project management activities for the data management project

Project management guidance

This section will support you in the following activities:

• Perform the necessary planning for your data management project
• Identify the objectives and expected outcomes of the project
• Outline the plans for ongoing project management and communication of project progress

Step Activities

PM1 Define project’s value proposition

PM2 Create the messaging for the project

PM3 Define goals and expectations

PM4 Determine success criteria

PM5 Create project plan

PM6 Identify project stakeholders and create a plan for effective stakeholder management

Outcomes

• Project definition
• Stakeholder engagement
• Project launch

Research Support

• Data Management Project Charter Template
• Data Management Planning Work Breakdown Structure Template
• Structure Template Data Management Communication/Business Case Template

Proposed Participants in This Step

• Project Manager
• Project Sponsor
• Project Team

Use Info-Tech’s project charter to support your team in organizing its information strategy plans

Data Management Project Charter Template

Use your charter as a project management tool.

Use this master document to centralize the critical information regarding the objectives, staffing, timeline, budget, and expected outcome of the project.

Prior to project launch, prevent confusion by creating a clear plan that outlines the essential information and project steps.

Consider the common pitfalls that were mentioned earlier that can cause IT projects to fail. Plan and take clear steps to avoid or mitigate these concerns.

Build project management in at the beginning using these areas.

☐ Goals and Objectives

☐ Domain Selection

☐ Rationale

☐ Scoping and Timeline

☐ Key Milestones

☐ RACI Chart

☐ Executive Signatures

INFO-TECH DELIVERABLE

Data Management Project Charter Template

Use business language and drivers to outline the value behind executing this data management project

Project Manager and Project Sponsor

Overview

Define the value proposition behind addressing data strategies and developing the organization’s data management practice.

Instructions

1. Craft a clear statement/message outlining the business value associated with executing the project.
2. Identify the business benefits expected from the outcomes of the project (recommendation to consider the strategic business drivers)
• Mapping project and DM practice benefits back to business drivers, not IT pain points, will help to make a meaningful case to how data management is a key business enabler and a valuable project to undertake.

Consider

Why is this project critical for the business?

Why should this project be done now?

Sample Mission Statements

Using the insight from analyzing [company’s] customer data, the project will provide opportunities for improved customer service customer intelligence through enhancements in the supporting data management practices and business. The mission of the project is to make sure the right information gets to the right people, at the right time.

Output

Key project messaging

Participants

• Project manager
• Project sponsor

Results Documentation

Project Planning

Document results in the relevant sections of the project charter

Project Messaging

Incorporate these statements of value and benefits in the Data Management Communication/Business Case Template

Define your data management goals and objectives

Goals & Objectives

It is important to be clear and transparent about the goals and objectives of your IM project, for those on the team as well as stakeholders and key decision makers. This is not going to be easy; make sure to start with the end in mind.

Your goals and objectives should be practical and measurable (we will address metrics and KPIs shortly). Goals and objectives should be mapped back to the business drivers we identified in the Executive Brief. It is okay if your drivers are not below, just be sure to keep the business in mind when defining your goals.

1. Client Intimacy / Service Excellence
2. Product and Service Innovations
3. Operational Excellence
4. Risk and Compliance Management

Example Objectives

• Creating a roadmap that will enable you to stage your organization’s IM practice incrementally
• A fit-for-purpose and iterative approach that first addresses high priority and impact data assets and designs a practice to support them
• Align the organization’s IT and business capabilities in IM for the requirements of the organization’s business processes and the data that supports it

Document results in your charter

Define the success criteria and expected outcomes of the project

Overview

Data management as a concept can be ambiguous, with definitions and expectations varying heavily for individuals. Ensure alignment at the outset of the project by outlining and attaining agreement on the expectations and expected outcomes (deliverables) of the project.

Instructions

Clarify the expectations of the project and determine the final outcomes expected at the end of the project.

1. Project Management (Project Sponsor and Manager) discuss and determine what successful completion of the project looks like.
• What deliverables must be completed at the end of the project?
• What evaluation and planning must be completed?
2. Document expected outcomes of the project in the charter.

Recommended Outcomes

• A completed and approved roadmap
• Outline of an action plan (immediate next steps)
• Documented data strategies
• Collaboration plans between IT and the business related to data management

Input

Stakeholder expectations

Output

Defined success criteria

Materials

Project charter

Participants

• Project manager
• Project sponsor

Measurements of ROI and the quantitative benefits of the planning performed at this stage will come later on with the implementation of initiatives identified in the roadmap and realization of data strategies.

Outline the plan for completing the project

Instructions

1. As a group, discuss and document the following:
• Scope of the project
• Identify both in-scope and out-of-scope items
• Be sure to identify the data subject areas being evaluated
• Project plan
• Include project steps and milestones
• Project expectations and outcomes
• Expected project deliverables and artifacts
2. Document the plans for your project in the associated sections of the project charter and customize the Data Management Work Breakdown Structure to align with the outcomes and deliverables associated with the project.

Recommended project scope based on the blueprint content and structure:

1. Align Data Management Practices with the Business

Step: Perform data strategy planning

Step: Determine the business’s data requirements

2. Evaluate Data Management Capabilities

Step: Perform an evidence-based assessment of current practices

Step: Determine target capability levels

3. Create a Roadmap

Step: Develop alignment strategies and improvement plans

Step: Create a strategic roadmap and plan of action

Use the sample material in the Charter and Work Breakdown Structure to support you in developing your project plans.

Activity

• Planning meeting

Output

• Project scope
• Outcome expectations
• Project plan
• List of expected deliverables and artifacts

Materials

• Data Management Project Charter Template
• Data Management Work Breakdown Structure Template

Participants

• Project manager
• Project sponsor

Identify the resourcing requirements for your project

Activity: Project Planning Meeting

Objective

Create a project team that has representation of both IT and the business. (This will help improve alignment and downstream implementation planning.)

Instructions

1. Determine the resourcing plan for the project.
2. Identify at what steps project participants and members will be engaged.
3. Outline the responsibilities for each member of the project team:
• Support the formal identification of responsibilities by associating resources with items in the Data Management Work Breakdown Structure.
4. Evaluate resourcing plan against additional project and workload expectations to ensure realistic expectations are in place.

Use the structure of the project charter for further support. Document results in your charter.

Business roles to engage

• Data owners (for subject area data)
• Data stewards who are custodians of business data (related to subject areas evaluated)
• Data scientists or other power users who are heavy consumers of data

If a business user says they do not have the time to participate in a hands-on capacity, check to see if they are willing to participate in an interview to help identify data requirements.

IT roles to engage

• Data architect(s)
• Any data management professionals who are involved in data modeling, managing data assets, or supporting the systems in which the data resides
• Database administrators or data warehousing architects with a deep knowledge of data operations
• Individuals responsible for data governance

Ensure proper support and stakeholder engagement exists for your data management project

Identify the Business Champion or Sponsor Behind the Project

An ideal sponsor:

• Recognizes the strategic value of data on a corporate level and sees cases for how it could be a key enabler for their department
• Is willing to stand behind the project, helping to support: messaging, business case approval, potential budgetary needs, and change management

The following two business leaders are common and often strong champions of data management on the side of the business:

CFO

The CFO is responsible for the key business metrics and cost control. IM is on the CFO’s radar as it can be utilized for both metrics measurements and cost optimization. The CFO is looking to make the company more efficient and effective. IM is a practice that enables analyzing the corporate data and finding inefficiencies.

CMO

The CMO is usually seen as the CSO (chief strategy officer). CMOs are often tasked with growing the company and formulating effective strategies.

Stakeholder Management Checklist

• Identification of project sponsors (at project’s outset)
• Identification of informal and formal project influencers
• Communication plan related to key stakeholders
• Adherence to communication plan

"Organizational appetite for having access to data has intensified, with specific departments having high demands and extensive use cases for data."

– Andy Wozybun, Executive Advisor, Info-Tech Research Group

Identify the sponsors and stakeholders for the project (both those influencing and impacted by project outcomes)

INSTRUCTIONS

1. As a team discuss and identify the different stakeholders related to the team. At this stage document both sponsors and stakeholders of the project.
2. For each stakeholder, identify the following in the Stakeholder Register template:
• Title and department
• Degree they are impacted by the project
• Degree they may influence the project
• Budget authority, if applicable
• Level of support for the project
• Power map classification
• Method of communication
• Documentation to supply them
• Involvement level and interaction points
3. As you progress with the project, update the information in the register to help maintain the necessary communication, stakeholder support, and momentum required to complete the project.
4. Use this register as your project documentation to ensure proper stakeholder management and communication throughout the project.

Output

Project stakeholder list (including analysis and communication plan)

Participants

• Project manager
• Project sponsor

As needed:

• Business area owner
• Project team

Materials

• Stakeholder Register
• Data Management Project Charter Template

Research support

Leverage the following research in Info-Tech’s library to help you create the most successfully planned, executed, and well-received data management project.

Create Project Management Success

Use this holistic PM research to effectively plan your data management project and deliver its desired outcomes.

Adopt Organizational Change Management Best Practices

Changes to data management impact and require engagement from both IT and the business in order to be successful. Create a strong plan for communicating with project stakeholders and maintaining buy-in and momentum for a more business-aligned and mature data management practice.

Are you ready to proceed with evaluating and designing your data management practice?

Self-Auditing Guidelines

☐ Influence: Do you have the necessary stakeholder buy-in?

☐ Culture: Does the project team have a reasonable understanding of the culture around data in the organization?

☐ Scope:Are the data subject areas identified for the first iteration reasonable and able to be modelled in the given timeline?

☐ Resourcing: Is project staffing in line with the level of work required?

☐ Competencies: Is the necessary knowledge on data management present on the team?

If you have performed the necessary project scoping and planning and performed the necessary review and approval measures, proceed to launching the project.

Next Steps: Assess data management capabilities and outline desired future capabilities for the practice.

PHASE 2

Plan for the Future

Phase 2 Overview

Detailed Overview

Step 1: Evaluate Data Management Capabilities

Step 2: Develop Alignment Strategies and Improvement Plans

Step 3: Create a Roadmap and Plan of Action for Data Management

Appendix

Outcomes

• Completed assessment
• Documented initiatives and improvement plans
• Completed data management roadmap
• Immediate action plan

Benefits

• Creation and approval of roadmap that aligns the envisioned data management practice with the business’ strategic plans and data requirements
• Short-term and long-term plans to support improved delivery of data and management of data throughout its lifecycle

Use advisory support to achieve the fastest and most comprehensive outcomes for your DM initiative

Call 1-888-670-8889 or email GuidedImplementations@InfoTech.com for more information.

Complete these steps on your own, or call us to complete a guided implementation. A guided implementation is a series of 2-3 advisory calls that help you execute each phase of a project. They are included in most advisory memberships.

Use Info-Tech’s tools and templates to guide you in designing, organizing, and promoting a comprehensive DM program

Assessment and Planning Resources

Data Management Assessment and Planning Tool

Initiative Definition Tool

Final Documentation

Data Management Roadmap Template

STEP 1

Evaluate Data Management Capabilities

2.1 Evaluate data management capabilities

Step Objectives

• Develop an accurate understanding of the organization’s current capabilities of data management
• Determine the target capabilities for each component of the organization’s data management practice

Step Activities

2.1.1a Assess the current capabilities of data management Enablers and Information Dimensions

2.1.1b Determine the target capabilities for the organization’s envisioned data management practice

2.1.2 Review how the proliferation of cloud services will impact your organization’s environment

Outcomes

• Insight into components supporting a data management practice
• Current data management capability analysis
• Target data management capability levels

Research Support

Data Management Assessment and Planning Tool

Proposed Participants in this Step

Project manager

Project team

Relevant data management/data governance professionals

Continue your evaluation by assessing data management capabilities against data consumption needs

Use tabs 4-7 of the Data Management Assessment and Planning Tool to evaluate the following for each component of data management

• Current capability levels
• Target capability levels
• Priority and value analysis for capability gaps

Building on your understanding of business drivers and requirements of data, continue evaluating the information dimensions and data management enablers that will allow your practice to align its capabilities to the organization’s data strategies.

Use these results to inform and guide your initiative planning.

Assess the current state of data management for your organization

~ 1-4 hours minutes

Instructions

1. Evaluate the current capabilities and state of each component of your data management practice. You can either complete this assessment directly in the tool or use the Data Management Assessment Printout as a means to complete it with your team in a facilitated and collaborative working session. Use the following legend to guide your scoring of each applicable capability:

Assessment Legend (Based on CMMI)

1 = Initial/Ad hoc

2 = Developing

3 = Defined

4 = Managed and Measureable

5 = Optimized

2. Analyze and debrief the results: Review the individual capability findings and use the average maturity levels results to guide your team in having a comprehensive understanding of the current state of your data management practice.

Facilitation Options

• Have key members of the team each complete the assessment and take an average of the results (via Excel or the printouts).
• Have one person complete the assessment on behalf of the group.
• Complete the results line by line in a group working session.

Activity

• Excel-based assessment
• Team debrief and discussion

Output

Current state results

Materials

• Data Management Assessment and Planning Tool

Tab 4. Current State Assessment

Tab 5. Current State Results

• Data Management Assessment Printout

Participants

• Project manager
• Project team and related DM professionals

Determine the target capabilities for the organization’s envisioned data management practice

~ 30-90 minutes

Overview

Use the findings of your data strategy planning to guide you in determining what capabilities and attributes are present in your envisioned data management practice.

Instructions

1. As a group, discuss the capabilities that you would like to see in the organization’s future data management practice. Document these capabilities (recommendation: use the Data Management Strategy and the direct and indirect enablers as a guide).
2. Evaluate each component of data management, and identify and document the target levels and need for each of the relevant capabilities on Tab 6. Target State and Gap Analysis.

Tab 6. Target State and Gap Analysis

Activity

• Whiteboarding exercise
• Excel-based assessment
• Team debrief and discussion

Input

• Data strategies
• Current environment
• Data management strategy and enabler results

Output

Target assessment results

Materials

• Data Management Assessment and Planning Tool
• Tab 6. Target State and Gap Analysis
• Tab 7. Gap Analysis Results
• Data Management Assessment Printout

Review how the proliferation of cloud services will impact your organization’s environment

~ 30 minutes

1/3 Amount of data created that will live in or pass through the Cloud in 2020. (Source: CSC)

Chris Chiancone, Info-Tech Senior Director, holds a more aggressive estimate and believes that that number will, in fact, be closer to 70%

Objective

Determine the implications of your organization’s current usage of cloud services and how its plans for the future will impact the organization’s data management capabilities and plans.

Instructions

1. Discuss how your organization currently uses cloud services. Also discuss plans for increasing the organization’s placement of applications and data in the cloud over the coming years.
2. Brainstorm how the organization’s plans for the cloud will impact the data management practice. Pay particular attention to components of data management that are heavily impacted by Cloud decisions.
• Data Risk Management
• Corporate Enterprise Integration

Questions to Consider

• How are you protecting data in-flight in cloud integration scenarios?
• Is highly confidential and sensitive data being stored in cloud-based applications or services?
3. Identify key DM requirements that should be considered and applied to current and future plans of the organization.
4. Document results and identify next steps related to data integration and security requirements for related infrastructure and applications teams.

Input

• Application and infrastructure analysis
• Cloud strategy

Output

DM requirements for cloud practices

Participants

• Project manager
• Project team and related DM and SME professionals

Additional Research

Implement and Manage a Cloud Strategy

Ensure Cloud Ensure Security in a SaaS Environment

Ensure Cloud Security in IaaS and PaaS Environments

STEP 2

Develop Alignment Strategies and Improvement Plans

2.2 Develop alignment strategies and improvement plans

Step Objectives

• Planned and documented data management initiatives

Step Activities

2.2.1 Use the results of your assessment to guide you in designing your data management initiatives.

2.2.2 Evaluate organizational, environmental, and technology factors that will impact the planning and execution of initiatives.

2.2.3 Formalize capability and strategy planning into data management initiatives.

Outcomes

Documented data management initiatives

Research Support

Data Management Assessment and Planning Tool

Initiative Definition Tool

Participants in This Step

• Project Manager
• Project Sponsor
• Project Team
• Oversight Body

2.2.1 Use the results of your assessment to guide you in designing your data management initiatives

Analyze Gap Analysis results

Brainstorm Alignment Strategies

Document Data Management Initiatives

Instructions

2.2.1a Analyze Gap Analysis Results

1. As a group, discuss the high-level results on tab. 7 Gap Analysis Results. Discuss the implications of the gaps identified.
2. Do a line item review of the gaps between current and target levels for each assessed capability by using Tab 6. Target State and Gap Analysis.

2.2.1b Brainstorm Alignment Strategies

3. Brainstorm what effort and activities will be necessary to support the practice in building its capabilities to the desired target level. Use the detailed guidance on slide 2.2.1b (next slide) for more guidance

2.2.1c Document Data Management Initiatives

4. Bucket alignment strategies and like activities into like initiatives.
5. Continue to evaluate the assessment results in order to create a comprehensive set of data management initiatives that supports your practice in building capabilities across all the relevant sub-practices.
6. Document initiatives in the Initiative Definition Tool.


Tab 6. Gap Analysis Results



Tab 6. Target State and Gap Analysis

Activity

• Team discussion and whiteboarding exercise

Input

• Target state and gap analysis results
• Strategy planning outcomes

Output

• Preliminary initiative plans

Materials

• Data Management Assessment and Planning Tool
• Initiative Definition Tool

Participants

• Project manager
• Project team and related DM professionals

2.2.1b Brainstorm alignment strategies; use Info-Tech’s building blocks to help create comprehensive and detailed plans

60-180 Minutes

Instructions

1. Use tape to divide a table into swim lanes that will be used to document initiatives. Label swim lanes based on your organization’s own needs and preferences.
• You can keep them high level at the component level, or go granular, breaking up component or adding specific initiatives such as staffing and hiring.
2. Run through all the building blocks. Identify if a building block is needed, already present, or not needed.
3. Place selected building blocks in the relevant initiative swim lane.
4. Use the information on the building blocks as well as your completed assessment results to guide you in organizing and designing your initiatives.
5. Use the following prompts to continue to refine your initiatives:
• What activities must occur to enable this capability?
• What actions must be taken to make this building block be in place (formal, informal, or otherwise)?
• What changes/additions to resources, process, technology, business involvement, and communication must occur?

Activity

Collaborative working session with project team

Input

• Target state and gap analysis results
• Data strategies

Output

Data management initiatives

Materials

Data Management Building Block Resource

Participants

• Project manager
• Project team and related DM professionals

2.2.1 Use this sample initiative plan for a segment of an organization’s assessment to guide your own planning stage

Sample Activity Outcome

Using the organization’s assessment results as a starting point, a team framed their initiative planning discussion on what activities would be necessary to move their maturity gauge to a managed level (4).

Considering their capability goals, they built the following initiatives:

Perform a Data Audit

Key Activities

• Perform an inventory and audit of current data quality issues in the business environment
• Prioritize documented issues and address high-priority issues
• Perform root cause analysis and build a plan for addressing systemic causes for chronic data quality issues

Establish a Data Quality Program

Key Activities

• Train identified data stewards on data quality management principles and techniques
• Set SLAs for data quality
• Create a schedule for performing bi-annual audits of data

Build Automated Profiling and Cleansing Capabilities

Key Activities

• Purchase a profiling tool that will cleanse data before its entry into key systems
• Improve the cleansing and transformation capabilities of ETL practices
• Train data stewards on new technology

2.2.2 Evaluate organizational, environmental, and technology factors that will impact the planning and execution of initiatives

~ 30-45 minutes

Prompt

What environmental factors and capabilities must be considered as you plan changes to business and IT processes and identify investment needs.

Input

• Organizational structure and culture
• Business environment
• Project assessment and strategy planning results

Output

• Additional initiative activities and objectives
• Initiative implementation considerations

Materials

Initiative documentation

Instructions

1. As a team, evaluate the data management capabilities across the different sub-practices of data management that the organization is looking to develop. Identify the additional organizational, environmental, and technology factors that should also be addressed. (Use the elements and components of DAMA and Cobit 5’s frameworks on the following slide to help provide additional considerations).
2. Consider the following prompts to help guide your discussion and planning:
• What has caused past data management initiatives to fail?
• How will culture and personnel/stakeholders interests impact or perceive this initiative?
• What must be done to prepare the business?
• What must be done to prepare for changes to IT processes, capabilities, or investments?
3. Identify additional activities that should be included in the team’s planned initiatives that will assist in making preliminary initiative plans more comprehensive and have an improved likelihood for approval and eventual success during implementation.
4. Update and add additional initiatives to the planning documentation in the Initiative Definition Tool.

People

Roles and responsibilities

Process

Business and IT processes

Practices

IT and data management considerations

Politics

Organizational culture and business satisfaction with DM and IT

2.2.2 Use DAMA and Cobit 5 frameworks to help strengthen your data management initiative plans

Info-Tech Insight

Maintain buy-In for your roadmap initiatives and your data management practice by proactively addressing common factors that undermine data projects and past initiatives. Data management is a complex topic with definitions and implementation methods varying depending on data use cases, business environments, and organizational size. Frame your data management practice, its structure, and position in a way that has longevity and generates the most value to the business.

2.2.3 Formalize capability and strategy planning into data management initiatives

Instructions

1. Refine the initiatives planned for data management planning. Continue to build out the documentation related to each initiative for data management. Add details to further identify the level of effort, cost, and accompanying requirements and capabilities. (These will help to guide sequencing and ultimately support the implementation of the initiatives.)
2. Review the initiatives against the expressed goals of the organization’s data strategies. Validate if these data strategies are supported by the planned initiatives. (Are the data management strategies and the enablers associated with them included in this planning?)

Note: This planning will also be included in the data management roadmap and will best reflect the level of due diligence and consideration to implementation and follow through that occurred with this project.

The Initiative Definition Tool enables documentation of up to 10 initiatives, but can easily be expanded by copying and adding tabs.

Input

• Future state and gap analysis results
• Initiative planning

Output

Data management initiatives

Materials

Initiative Definition Tool

Participants

• Project manager
• Project team and related DM professionals

STEP 3

Create a Roadmap and Plan of Action for Data Management

2.3 Create a roadmap and plan of action for data management

Step Objectives

• Translate planned data management initiatives into a strategic roadmap
• Present and gain approval for the project’s findings and deliverables

Step Activities

2.3.1a Evaluate initiative planning against considerations of dependencies, feasibility, and business priorities

2.3.1b Use effort/transition mapping techniques

2.3.3 Build a data management roadmap

2.3.4 Create a communication plan for sharing the plans for the data management practice

2.3.5 Organize your project results and roadmap plans into a final project deliverable

2.3.6 Leverage early success stories and opportunities as means to express how the program could generate immediate returns

2.3.7 Establish metrics for identifying the value of your data management and its adherence to its roadmap plans

2.3.8 Submit your roadmap to your Data Governance Steering Committee or project oversight body

Outcomes

An approved Data Management Roadmap

Research Support

• Data Management Assessment and Planning Tool
• Initiative Definition Tool
• Data Management Roadmap Template

Participants in This Step

Project Manager

Project Sponsor

Project Team

Oversight Body

2.3.1a Evaluate initiative planning against considerations of dependencies, feasibility, and business priorities

Instructions

1. Before proceeding to effort mapping for each initiative consider the following key questions:
• What preceding activities/initiatives must be in place before this initiative can be implemented?
• Is this initiative feasible considering the organization’s environment and the resourcing, scope, and capabilities of the data management practice?
• How does this initiative line up to business priorities? Does it directly correspond and support the achievement of a data strategy and the business’s broader strategic goals?
2. Use the results of this analysis to help frame your roadmap plans and your sequencing of data management initiatives.

Activity

• Brainstorming session

Input

• Business priorities
• Practice investments and capabilities
• Initiative plans

Output

Analysis of initiatives

Materials

Initiative Definition Tool

Participants

• Project manager
• Project team and related DM professionals

2.3.1b Use effort/transition mapping techniques to support you in prioritizing and sequencing initiatives

~ 30-90 minutes

Overview

Unfortunately, most organizations don’t have unlimited resources and time to dedicate to improving their data architecture. As a result, prioritizing and sequencing initiative projects based on feasibility and benefits can help to create immediate value and help your team in maintaining momentum as they implement longer running initiatives.

Instructions

1. Evaluate the different initiatives planned by the team to address identified performance gaps.
2. Consider the constraints, effort, and benefits associated with each project. Using these considerations, plot the initiatives on an Effort Map. (Customize how this activity is performed if necessary. If it better suits project planning, change the axis to reflect risk or time required.)
3. Use the findings of this map to help sequence and stage your initiatives on a timeline that best aligns with business priorities and implementation capabilities.

Example: Initiative Mapping Exercise

Materials

Data Management Assessment and Planning Tool

Tab 10. Effort Mapping

Input

Initiative planning

Output

Effort and feasibility analysis

2.3.3 Create a data management roadmap

~ 90 minutes

Instructions

1. Use the findings from your effort mapping and initiative planning to sequence, plot, and build your organization’s data management roadmap. Document key information into Tab 8. Initiative Planning (e.g. timelines, initiative ownership, etc.).
2. Document and review the total roadmap findings in Tab 9. Roadmap.

Tab 8. Initiative Planning (Columns I-M)

Tab 9. Roadmap

Activity

• Brainstorming and planning session(s)

Input

• DM Initiatives (Initiative Definition Tool)
• Effort mapping results
• Dependency results

Output

Roadmap

Materials

Data Management Assessment and Planning Tool

Tab 9. Roadmap

Participants

• Project manager
• Project team and related DM professionals

2.3.4 Create a communication plan for sharing the project outcomes and the next steps for the data management practice

~ 30 minutes

Overview

Prepare to communicate the project’s findings and plans for your data management practice with a formalized plan.

Instructions

1. Create a communication plan that addresses the multitude of executive/high level stakeholders as well as larger groups that will need to be updated on the changes and the implications.
Use the template below to help you identify the different individuals and groups that will need to be communicated with and determine the specific messaging you will use.

Info-Tech Insight

Consider the general data management literacy of the stakeholders as you craft your messaging. Take the time to define the principles and share the vision and goals for these changes and investments.

2.3.5 Organize your project results and roadmap plans into a final project deliverable

Instructions

1. Organize the outcomes of the project’s strategy planning, assessment, roadmap, and initiative planning activities into a consolidated and presentable document.
2. Review the document and ensure its messaging and recommendations align with the original objectives of the project, and outline a clear and persuasive plan to the project’s stakeholders and sponsors.

Prerequisites

Completion of planned initiatives and roadmap

Materials

Data Management Roadmap Template

Participants

• Project manager
• Relevant project members

Info-Tech Insight

Maintain Business Alignment

As you present your project findings and gain approval for your initiative roadmap, make sure you articulate the value of the data management initiatives and envisioned practice based on business priorities and definitions of value.

2.3.6 Leverage early success stories and opportunities as a means to express how the program could generate immediate returns

~ 15 minutes

As you propose the plans for your data management practice, take the opportunity to not only propose your vision and plans, but also articulate immediate and already existing methods to which the practice has delivered real value and support to the business.

Draw from the potential findings and results from Activity 1.2.3c

What business issue or opportunity was uncovered during the project’s analysis and interview steps?

What steps by the practice and its members were taken to support the business with this item?

What was the benefit and outcome?

• Cost savings
• New insight opportunities
• Hours of labor saved

Potential Opportunities

• Providing access to data sources that previously was not available
• Addressing a root cause for a systemic data quality issue
• Addressing a functionality limitation within a critical system
• New report of data collection capabilities

2.3.7 Establish metrics for identifying the value of your data management and its adherence to its roadmap plans

Overview

Set metrics that will not only showcase business value from improved data management, but also the improvements and changes to the data management practice itself.

Instructions

1. As a group, discuss and brainstorm metrics that can be used to show the performance of the data management practice.
2. Select metrics that can be currently measured. Set a target for the selected metric(s).
3. Take a baseline of selected metrics and set a schedule for measuring the metrics.

Sample Practice Level Metrics

Adherence to Practice Plans

• Number of initiatives completed with their targeted time
• Number of initiatives that delivered on their stated objectives
• Number of data strategies addressed/answered by the practice’s implemented plans
• Number of changes implemented without following standard change/release management principles

Data Quality and Fitness of the Data Management Environment

• Quality levels for in scope data
• E.g. number of duplicate data records in the CRM
• E.g. Number of records with empty fields
• Number of databases outages or minutes of downtime
• Number of violations determined during the data/security audit(s)

Activity

• Brainstorming and planning session(s)

Input

• Practice vision
• Practice plans and roadmap

Output

Practice metrics

Participants

• Project manager
• Project team

2.3.8 Submit your roadmap to your Data Governance Steering Committee or project review team

Objective

Translate project findings and outcomes into a consolidated document that can easily be presented and shared with project sponsors and stakeholders.

Instructions

1. Take your prepared roadmap presentation and present it to the stakeholders responsible for approving the roadmap and investing in the entire data management practice, and who will support the ongoing planning and approval of the included initiatives.
2. As you present the proposed plans for the practice, showcase both the high-level vision and purpose, as well as the strategic plans related to the roadmap.

Tips for a Successful Proposal

• Be consistent with your messaging; frame how the outlined practice plans align to your expressed vision and mission and ultimately work to support the business in using data as a key enabler to realizing its strategic goals.

Come Prepared

Be ready to answer these common questions:

• Why should the business care about data management now?
• What benefits should be expected from investments in this program?
• How is this plan different from others in the past that quickly lost momentum and failed to deliver?
• How much effort and cost will be associated with this?

Materials

Data Management Roadmap Template

Next Steps

Following the approval of your roadmap, begin to plan the implementation of your first initiatives.

Tackle your first initiatives for data management

Leverage Info-Tech’s additional data management research as you begin to actualize the plans in your data management roadmap.

Use these blueprints and additional research artifacts as a means to kick-start your roadmap initiatives.

Establish Effective Data Governance

Modernize Data Architecture for Measurable Business Results

Develop a Winning BI Strategy

Optimize the Organization’s Data Integration Practices

Develop a Master Data Management Strategy and Roadmap

If you wan additional support, have our analysts guide you through this phase as part of an Info-Tech workshop

Book a workshop with our Info-Tech analysts:

Assess the current maturities and envisioned target state for each applicable data management component

The facilitator will guide participants in completing the data management assessment using a printout version of the tool statements. Depending on workshop size, the assessment can be filled out as a group or with each individual completing their own and achieving consensus through debate.

Analyze the implications of your data management assessment

The facilitator will lead the group in reviewing the performance gaps for each data management component and honing in on critical capability gaps. Using this analysis, the group will begin to envision initiatives and places to build up critical capabilities.

Develop data management initiatives

Using the assessment performance gaps and data strategies as critical context and inputs, the facilitator will guide the team in identifying initiatives within and across the components of data management. Focusing on the critical question – what activities will be necessary to get your capabilities from A-B (current to target)? – the facilitator will support the team in identifying and solidifying specific initiatives that will generate tangible and high-impact outcomes.

Sequence your initiatives into a roadmap

Using effort, feasibility, priority, and dependency considerations, the facilitator will support your team in analyzing their identified initiatives and mapping out the initiatives and timelines for capability fulfilment across a 3-5 year roadmap.

Plan your project messaging and next steps

To close out the engagement, the facilitator will guide the team in building a communication plan for specific individuals and groups of the organization that will be impacted and engaged by these changes. As a final step, the facilitator will debrief with the participants and talk about immediate next steps that they will undertake to complete or carry out the workshop’s activities and plans.

Appendix B

CDO Job Description, Research Contributors, Bibliography

Chief Data Officer

Job Description Template

A CDO is not a necessity for a successful data management process. Consider the following while determining the fit of a CDO for your business.

These resistance arguments are being used less and less

A January 2014 survey by TechTarget found that over 1/3 of respondents identified BI and DM projects as top priority items on the business agenda.

Research contributors

Internal Contributors

• Joel McLean, Founder
• Scott Bohannon, CEO
• Gord Harrison, SVP, IT Research and former CIO
• Nurith Rochon, former Senior Director of Information Management
• Chris Chiancone, Senior Director of Security and Risk Management
• Bernie McGilles, Executive Advisor
• Andy Wozybun, Executive Advisor
• Jeff Caughell, VP, Marketing & Sales Operations
• Daniel Ko, Research Manager
• Ryan Smith, Senior Consultant

External Contributors

• Michael Blaha, Consultant and Author, Patterns of Data Modeling
• David Birmingham, Senior Solutions Architect, BrightLight Consulting
• Mario Cantin, Chief Data Strategist, Prodago
• Nancy Couture, Delivery Enablement Lead, Datasource Consulting
• Mehrdad Novin, Senior Data Architect/Modeler Consultant
• Martin Sykora, Director, NexJ Analytics
• Kirti Shetty, Data Quality Business Delivery Manager, TD Canada Trust
• Anne Marie Smith, Ph.D, Board of Directors, DAMA International
• John Talburt, Chief Data Scientist, BlackOak Analytics

+ 2 anonymous contributors

Research contributors and experts

Michael Blaha, Consultant

Author, Patterns of Data Modeling

Michael Blaha is a consultant and trainer who specializes in conceiving, architecting, modeling, designing, and tuning databases. He has worked with dozens of organizations around the world. Blaha has authored seven U.S. patents, seven books, many articles, and two video courses. He received his doctorate from Washington University in St. Louis and is an alumnus of GE Global Research in Schenectady, New York. He is a member of the IEEE Computer Society and Chicago DAMA. You can find out more about him at superdataguy.com.

Mario Cantin

Chief Data Strategist, Prodago

Mario Cantin has been a Data Quality Strategist for over 20 years. He is the founder and Chief Data Strategist of Prodago, a firm implementing Lean Data Governance in organizations. A sought after speaker and a passionate communicator, Mario has been sharing his views on data governance, quality and value with multiple audiences in the past years.

Anne Marie Smith, Ph.D, Board of Directors

DAMA International

Anne Marie Smith, Ph.D. is an Information Management professional and consultant with broad experience across industries. She is a certified data management professional (CDMP), and is a frequent speaker and an author on data management topics. Anne Marie is a primary author of several sections of the DAMA-Data Management Body of Knowledge (DAMA-DMBOK). Anne Marie received the DAMA International Professional Achievement Award in 2015.

Anne Marie's consulting areas include: enterprise information management assessment and program development, data governance, data warehousing, business requirements gathering and analysis, master data management, data quality management, data architecture, and information systems planning. She has taught numerous workshops and courses in her areas of expertise.

Anne Marie holds the degrees of Bachelor of Arts and Master's of Business Administration in Management Information Systems and Risk Management from La Salle University; she earned a Ph.D. in MIS at Northcentral University.

Martin Sykora, Director

NexJ Analytics

Martin currently manages the direction and architecture of NexJ Customer Data Management solutions, and serves as subject matter expert in data management, algorithms, and decision analytics. He is also responsible for identifying emerging technology opportunities, competitive research, and contributing to prototypes. In addition to managing the product roadmap, he ensures best practices for the implementation of NexJ’s Big Data and Analytics projects. He is also responsible for product marketing activities, including messaging and positioning of products within the market to generate interest in NexJ Customer Data Management solutions, and to drive sales. Through his research and thought leadership in Analytics, Data Sciences, Business Intelligence, and Data Warehousing, Martin is the analytics subject matter expert and works with customers, prospects, and analysts.

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