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Build a Robust and Comprehensive Data Strategy

Key to building and fostering a data-driven culture.

  • The volume and variety of data that organizations have been collecting and producing have been growing exponentially and show no sign of slowing down.
  • At the same time, business landscapes and models are evolving, and users and stakeholders are becoming more and more data centric, with maturing expectations and demands.

Our Advice

Critical Insight

  • As the CDO or equivalent data leader in your organization, a robust and comprehensive data strategy is the number one tool in your toolkit for delivering on your mandate of creating measurable business value from data.
  • A data strategy should never be formulated disjointed from the business. Ensure the data strategy aligns with the business strategy and supports the business architecture.
  • Building and fostering a data-driven culture will accelerate and sustain adoption of, appetite for, and appreciation for data and hence drive the ROI on your various data investments.

Impact and Result

  • Formulate a data strategy that stitches all of the pieces together to better position you to unlock the value in your data:
    • Establish the business context and value: Identify key business drivers for executing on an optimized data strategy, build compelling and relevant use cases, understand your organization’s culture and appetite for data, and ensure you have well-articulated vision, principles, and goals for your data strategy
    • Ensure you have a solid data foundation: Understand your current data environment, data management enablers, people, skill sets, roles, and structure. Know your strengths and weakness so you can optimize appropriately.
    • Formulate a sustainable data strategy: Round off your strategy with effective change management and communication for building and fostering a data-driven culture.

Build a Robust and Comprehensive Data Strategy

1. Data Strategy Research – A step-by-step document to facilitate the formulation of a data strategy that brings together the business context, data management foundation, people, and culture.

Data should be at the foundation of your organization’s evolution. The transformational insights that executives and decision makers are constantly seeking to leverage can be unlocked with a data strategy that makes high-quality, trusted, and relevant data readily available to the users who need it.

2. Data Strategy Stakeholder Interview Guide and Findings – A template to support you in your meetings or interviews with key stakeholders as you work on understanding the value of data within the various lines of business.

This template will help you gather insights around stakeholder business goals and objectives, current data consumption practices, the types or domains of data that are important to them in supporting their business capabilities and initiatives, the challenges they face, and opportunities for data from their perspective.

3. Data Strategy Use Case Template – An exemplar template to demonstrate the business value of your data strategy.

Data strategy optimization anchored in a value proposition will ensure that the data strategy focuses on driving the most valuable and critical outcomes in support of the organization’s enterprise strategy. The template will help you facilitate deep-dive sessions with key stakeholders for building use cases that are of demonstrable value not only to their relevant lines of business but also to the wider organization.

4. Chief Data Officer – A job description template that includes a detailed explication of the responsibilities and expectations of a CDO.

Bring data to the C-suite by creating the Chief Data Officer role. This position is designed to bridge the gap between the business and IT by serving as a representative for the organization's data management practices and identifying how the organization can leverage data as a competitive advantage or corporate asset.

5. Data Strategy Document Template – A structured template to plan and document your data strategy outputs.

Use this template to document and formulate your data strategy. Follow along with the sections of the blueprint Build a Robust and Comprehensive Data Strategy and complete the template as you progress.


Member Testimonials

After each Info-Tech experience, we ask our members to quantify the real-time savings, monetary impact, and project improvements our research helped them achieve. See our top member experiences for this blueprint and what our clients have to say.

9.2/10


Overall Impact

$39,037


Average $ Saved

21


Average Days Saved

Client

Experience

Impact

$ Saved

Days Saved

Toronto Police Service

Guided Implementation

8/10

$1,600

2

Suncoast Credit Union

Guided Implementation

9/10

N/A

N/A

State Information Technology Agency SOC Limited

Guided Implementation

8/10

$40,000

50

Dar Al Handasah Consultants Shair & Partners Holdings Ltd.

Guided Implementation

10/10

$123K

32

LION

Guided Implementation

9/10

$11,159

10

Medcan

Workshop

10/10

$200K

32

University of Prince Edward Island

Guided Implementation

10/10

$3,000

10

Multnomah County

Workshop

10/10

N/A

35

Liquid Environmental Solutions

Guided Implementation

9/10

$30,999

20

Midland Credit Management, Inc.

Workshop

8/10

N/A

20

Hensel Phelps

Guided Implementation

10/10

N/A

5

University of Western Cape

Guided Implementation

10/10

$105K

95

University of Prince Edward Island

Guided Implementation

10/10

$5,000

10

University of Johannesburg

Guided Implementation

8/10

$61,999

50

Tenneco

Workshop

10/10

N/A

N/A

Omaha Public Power District

Guided Implementation

9/10

$12,399

5

YMCA of Central Florida

Guided Implementation

10/10

$2,479

2

New Mexico Department Of Health

Guided Implementation

10/10

$1,983

20

Barnardos Australia

Guided Implementation

10/10

$2,000

5

Endeavour Foundation

Workshop

9/10

$92,999

18

Colorado Judicial ITS

Guided Implementation

10/10

N/A

9

MCAP Service Corporation

Workshop

9/10

$25,000

35

Wabtec Corp

Workshop

9/10

$30,999

10

Mott MacDonald LLC

Guided Implementation

10/10

$17,100

10

Seven Seventeen Credit Union, Inc.

Guided Implementation

9/10

$12,399

5

The Research Institute of the MUHC

Guided Implementation

8/10

N/A

5

Agriculture Financial Services Corporation

Guided Implementation

8/10

$50,000

32

Bath Iron Works Corporation

Guided Implementation

10/10

$61,999

10

Legal Practitioners Fidelity Fund

Guided Implementation

10/10

$14,240

35

Wabtec Corp

Guided Implementation

10/10

N/A

10


Onsite Workshop: Build a Robust and Comprehensive Data Strategy

Onsite workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost onsite delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.

Module 1: Establish Business Context

The Purpose

  • Establish the business context for the business strategy.

Key Benefits Achieved

  • Substantiates the “why” of the data strategy.
  • Highlights the organization’s goals, objectives, and strategic direction the data must align with.

Activities

Outputs

1.1

Advisory kick-off session with the data strategy sponsor (such as the Chief Data Officer [CDO], Chief Architect, Digital Transformation Leader, CIO).

  • Business context and strategic drivers
1.2

Executive and senior business stakeholder interviews, one-on-one and small groups, to understand stakeholders’ strategic priorities and the alignment with data, discuss the vision, mission, goals, and principles of the data strategy, and understand the organization’s data culture.

  • Defined vision and mission
  • Defined principles and goals
  • Data Culture Survey diagnostic results analysis

Module 2: Ensure You Have a Solid Data Foundation

The Purpose

  • Build use cases of demonstrable value and understand the current environment.

Key Benefits Achieved

  • An understanding of the current maturity level of key capabilities.
  • Use cases that represent areas of concern and/or high value and therefore need to be addressed.

Activities

Outputs

2.1

Build use cases of demonstrable value: Drivers, challenges, and opportunities.

  • High-value use cases
2.2

Understand the current data environment: Data management enablers.

  • High-level evaluation of the current environment
2.3

Understand the current data environment: People and organizational structure – key roles and skill sets.

Module 3: Build Your Future State Plan and Initialize the Roadmap

The Purpose

  • Build out a future state plan that is aimed at filling prioritized gaps and that informs a scalable roadmap for moving forward on treating data as an asset.

Key Benefits Achieved

  • A target state plan, formulated with input from key stakeholders, for addressing gaps and for maturing capabilities necessary to strategically manage data.

Activities

Outputs

3.1

Target state plotting: Gap analysis and roadmap planning.

  • Target state plan – high-level roadmap
3.2

People and organizational structure planning: Key roles, and skill sets.

  • High-level RACI for key functional areas

Module 4: Formulate Your Data Strategy

The Purpose

  • Consolidate business and data needs with consideration of external factors as well as internal barriers and enablers to the success of the data strategy. Bring all the outputs together for crafting a robust and comprehensive data strategy.

Key Benefits Achieved

  • A consolidated view of business and data needs and the environment in which the data strategy will be operationalized.
  • An analysis of the feasibility and potential risks to the success of the data strategy.

Activities

Outputs

4.1

Build your business-data-needs model.

  • Business-data-needs model
4.2

Risk and feasibility analysis: Conduct a SWOT analysis.

  • SWOT analysis
4.3

Initialize the organization’s data strategy.

  • Initialized data strategy document

Build a Robust and Comprehensive Data Strategy

Key to building and fostering a data-driven culture.

ANALYST PERSPECTIVE

Data Strategy: Key to helping drive organizational innovation and transformation

"In the dynamic environment in which we operate today, where we are constantly juggling disruptive forces, a well-formulated data strategy will prove to be a key asset in supporting business growth and sustainability, innovation, and transformation.

Your data strategy must align with the organization’s business strategy, and it is foundational to building and fostering an enterprise-wide data-driven culture."

Crystal Singh,

Director – Research and Advisory

Info-Tech Research Group

Our understanding of the problem

This Research is Designed For:

  • Chief data officers (CDOs), chief architects, VPs, and digital transformation directors and CIOs who are accountable for ensuring data can be leveraged as a strategic asset of the organization.

This Research Will Help You:

  • Put a strategy in place to ensure data is available, accessible, well integrated, secured, of acceptable quality, and suitably visualized to fuel decision making by the organizations’ executives.
  • Align data management plans and investments with business requirements and the organization’s strategic plans.
  • Define the relevant roles for operationalizing your data strategy.

This Research Will Also Assist:

  • Data architects and enterprise architects who have been tasked with supporting the formulation or optimization of the organization’s data strategy.
  • Business leaders creating 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:

  • Get a handle on the current situation of data within the organization.
  • Understand how the data strategy and its resulting initiatives will affect the operations, integration, and provisioning of data within the enterprise.

Executive Summary

Situation

  • The volume and variety of data that organizations have been collecting and producing have been growing exponentially and show no sign of slowing down. At the same time, business landscapes and models are evolving, and users and stakeholders are becoming more and more data centric, with maturing and demanding expectations.

Complication

  • As organizations pivot in response to industry disruptions and changing landscapes, a reactive and piecemeal approach leads to data architectures and designs that fail to deliver real and measurable value to the business.
  • Despite the growing focus on data, many organizations struggle to develop a cohesive business-driven strategy for effectively managing and leveraging their data assets.

Resolution

Formulate a data strategy that stitches all of the pieces together to better position you to unlock the value in your data:

  • Establish the business context and value: Identify key business drivers for executing on an optimized data strategy, build compelling and relevant use cases, understand your organization’s culture and appetite for data, and ensure you have well-articulated vision, principles, and goals for your data strategy.
  • Ensure you have a solid data foundation: Understand your current data environment, data management enablers, people, skill sets, roles, and structure. Know your strengths and weakness so you can optimize appropriately.
  • Formulate a sustainable data strategy: Round off your strategy with effective change management and communication for building and fostering a data-driven culture.

Info-Tech Insight

  1. As the CDO or equivalent data leader in your organization, a robust and comprehensive data strategy is the number one tool in your toolkit for delivering on your mandate of creating measurable business value from data.
  2. A data strategy should never be formulated disjointed from the business. Ensure the data strategy aligns with the business strategy and supports the business architecture.
  3. Building and fostering a data-driven culture will accelerate and sustain adoption of, appetite for, and appreciation for data and hence drive the ROI on your various data investments.

Why do you need a data strategy?

Your data strategy is the vehicle for ensuring data is poised to support your organization’s strategic objectives.

The dynamic marketplace of today requires organizations to be responsive in order to gain or maintain their competitive edge and place in their industry.

Organizations need to have that 360-degree view of what’s going on and what’s likely to happen.

Disruptive forces often lead to changes in business models and require organizations to have a level of adaptability to remain relevant.

To respond, organizations need to make decisions and should be able to turn to their data to gain insights for informing their decisions.

A well-formulated and robust data strategy will ensure that your data investments bring you the returns by meeting your organization’s strategic objectives.

Organizations need to be in a position where they know what’s going on with their stakeholders and anticipate what their stakeholders’ needs are going to be.

A data strategy is needed and is relevant across industries

Data and its associated data strategy is not only relevant for profit-generating industries.

The bottom line: data is valuable to everyone.

Regardless of the industry in which you operate, data is still the key to informed decision making. So whether you’re a nonprofit, a government entity, or in any other industry, data still empowers you to make better decisions.

The data strategy will serve as the mechanism for making good-quality and well-governed data readily available and accessible to deliver on your organizational mandate.

Whether your key stakeholders are customers, students, patients, donors, residents, or citizens, an effective data strategy will ensure you have the right data to make the right decisions in the interests of your stakeholder groups.

“Data have the power to enable the government to make better decisions, design better programs and deliver more effective services.”

“But for this to occur— and for us to share data in a way that allows other governments, businesses, researchers and the not-for-profit sector to also extract value from data—we need to refresh our approach.”– Privy Council Office, Government of Canada

Data cannot be fully leveraged without a cohesive strategy

Most organizations today will likely have some form of data management in place, supported by some of the common roles such as DBAs and data analysts.

Most will likely have a data architecture that supports some form of reporting.

Some may even have a chief data officer (CDO), a senior executive who has a seat at the C-suite table.

These are all great assets as a starting point BUT without a cohesive data strategy that stitches the pieces together and:

  • Effectively leverages these existing assets
  • Augments them with additional and relevant key roles and skills sets
  • Optimizes and fills in the gaps around your current data management enablers and capabilities for the growing volume and variety of data you’re collecting
  • Fully caters to real, high-value strategic organizational business needs

you’re missing the mark – you are not fully leveraging the incredible value of your data.

Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions

And, less than 1% of its unstructured data is analyzed or used at all. Furthermore, 80% of analysts' time is spent simply discovering and preparing, data with over 70% of employees having access to data they should not. Source: HBR, 2017

Organizational drivers for a data strategy

Your data strategy needs to align with your organizational strategy.

Main Organizational Strategic Drivers:

  1. Stakeholder Engagement/Service Excellence
  2. Product and Service Innovations
  3. Operational Excellence
  4. Privacy, Risk, and Compliance Management

“The companies who will survive and thrive in the future are the ones who will outlearn and out-innovate everyone else. It is no longer ‘survival of the fittest’ but ‘survival of the smartest.’ Data is the element that both inspires and enables this new form of rapid innovation.– Joel Semeniuk, 2016

A sound data strategy is the key to unlocking the value in your organization’s data.

Data should be at the foundation of your organization’s evolution.

The transformational insights that executives are constantly seeking to leverage can be unlocked with a data strategy that makes high-quality, well-integrated, trustworthy, relevant data readily available to the business users who need it.

Whether hoping to gain a better understanding of your business, trying to become an innovator in your industry, or having a compliance and regulatory mandate that needs to be met, any organization can get value from its data through a well-formulated, robust, and cohesive data strategy.

According to a leading North American bank, “More than one petabyte of new data, equivalent to about 1 million gigabytes” is entering the bank’s systems every month. – The Wall Street Journal, 2019

“Although businesses are at many different stages in unlocking the power of data, they share a common conviction that it can make or break an enterprise.”– Jim Love, ITWC CIO and Chief Digital Officer, IT World Canada, 2018

Data is a strategic organizational asset and should be treated as such

The expression “Data is an asset” or any other similar sentiment has long been heard.

With such hype, you would have expected data to have gotten more attention in the boardrooms. You would have expected to see its value reflected on financial statements as a result of its impact in driving things like acquisition, retention, product and service development and innovation, market growth, stakeholder satisfaction, relationships with partners, and overall strategic success of the organization.

The time has surely come for data to be treated as the asset it is.

“Paradoxically, “data” appear everywhere but on the balance sheet and income statement.”– HBR, 2018

“… data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset.”– “5 Essential Components of a Data Strategy,” SAS

According to Anil Chakravarthy, who is the CEO of Informatica and has a strong vantage point on how companies across industries leverage data for better business decisions, “what distinguishes the most successful businesses … is that they have developed the ability to manage data as an asset across the whole enterprise.”– McKinsey & Company, 2019

How data is perceived in today’s marketplace

Data is being touted as the oil of the digital era…

But just like oil, if left unrefined, it cannot really be used.

"Data is the new oil." – Clive Humby, Chief Data Scientist

Source: Joel Semeniuk, 2016

Enter your data strategy.

Data is being perceived as that key strategic asset in your organization for fueling innovation and transformation.

Your data strategy is what allows you to effectively mine, refine, and use this resource.

“The world’s most valuable resource is no longer oil, but data.”– The Economist, 2017

“Modern innovation is now dependent upon this data.”– Joel Semeniuk, 2016

“The better the data, the better the resulting innovation and impact.”– Joel Semeniuk, 2016

What is it in it for you? What opportunities can data help you leverage?

GOVERNMENT

Leveraging data as a strategic asset for the benefit of citizens.

  • The strategic use of data can enable governments to provide higher-quality services.
  • Direct resources appropriately and harness opportunities to improve impact.
  • Make better evidence-informed decisions and better understand the impact of programs so that funds can be directed to where they are most likely to deliver the best results.
  • Maintain legitimacy and credibility in an increasingly complex society.
  • Help workers adapt and be competitive in a changing labor market.
  • A data strategy would help protect citizens from the misuse of their data.

Source: Privy Council Office, Government of Canada, 2018

What is it in it for you? What opportunities can data help you leverage?

FINANCIAL

Leveraging data to boost traditional profit and loss levers, find new sources of growth, and deliver the digital bank.

  • One bank used credit card transactional data (from its own terminals and those of other banks) to develop offers that gave customers incentives to make regular purchases from one of the bank’s merchants. This boosted the bank’s commissions, added revenue for its merchants, and provided more value to the customer (McKinsey & Company, 2017).
  • In terms of enhancing productivity, a bank used “new algorithms to predict the cash required at each of its ATMs across the country and then combined this with route-optimization techniques to save money” (McKinsey & Company, 2017).

A European bank “turned to machine-learning algorithms that predict which currently active customers are likely to reduce their business with the bank.” The resulting understanding “gave rise to a targeted campaign that reduced churn by 15 percent” (McKinsey & Company, 2017).

A leading Canadian bank has built a marketplace around their data – they have launched a data marketplace where they have productized the bank’s data. They are providing data – as a product – to other units within the bank. These other business units essentially represent internal customers who are leveraging the product, which is data.

Through the use of data and advanced analytics, “a top bank in Asia discovered unsuspected similarities that allowed it to define 15,000 microsegments in its customer base. It then built a next-product-to-buy model that increased the likelihood to buy three times over.” Several sets of big data were explored, including “customer demographics and key characteristics, products held, credit-card statements, transaction and point-of-sale data, online and mobile transfers and payments, and credit-bureau data” (McKinsey & Company, 2017).

What is it in it for you? What opportunities can data help you leverage?

HEALTHCARE

Leveraging data and analytics to prevent deadly infections

The fifth-largest health system in the US and the largest hospital provider in California uses a big data and advanced analytics platform to predict potential sepsis cases at the earliest stages, when intervention is most helpful.

Using the Sepsis Bio-Surveillance Program, this hospital provider monitors 120,000 lives per month in 34 hospitals and manages 7,500 patients with potential sepsis per month.

Collecting data from the electronic medical records of all patients in its facilities, the solution uses natural language processing (NLP) and a rules engine to continually monitor factors that could indicate a sepsis infection. In high-probability cases, the system sends an alarm to the primary nurse or physician.

Since implementing the big data and predictive analytics system, this hospital provider has seen a significant improvement in the mortality and the length of stay in ICU for sepsis patients.

At 28 of the hospitals which have been on the program, sepsis mortality rates have dropped an average of 5%.

With patients spending less time in the ICU, cost savings were also realized. This is significant, as sepsis is the costliest condition billed to Medicare, the second costliest billed to Medicaid and the uninsured, and the fourth costliest billed to private insurance.

Source: SAS, 2019

What is it in it for you? What opportunities can data help you leverage?

RETAIL

Leveraging data to better understand customer preferences, predict purchasing, drive customer experience, and optimize supply and demand planning.

Netflix is an example of a big brand that uses big data analytics for targeted advertising. With over 100 million subscribers, the company collects large amounts of data. If you are a subscriber, you are likely familiar with their suggestions messages of the next series or movie you should catch up on. These suggestions are based on your past search data and watch data. This data provides Netflix with insights into your interests and preferences for viewing (Mentionlytics, 2018).

“For the retail industry, big data means a greater understanding of consumer shopping habits and how to attract new customers.”– Ron Barasch, Envestnet | Yodlee, 2019

The business case for data – moving from platitudes to practicality

When building your business case, consider the following:

  • What is the most effective way to communicate the business case to executives?
  • How can CDOs and other data leaders use data to advance their organizations’ corporate strategy?
  • What does your data estate look like? Are you looking to leverage and drive value from your semi-structured and unstructured data assets?
  • Does your current organizational culture support a data-driven one? Does the organization have a history of managing change effectively?
  • How do changing privacy and security expectations alter the way businesses harvest, save, use, and exchange data?

“We’re the converted … We see the value in data. The battle is getting executive teams to see it our way.”– Ted Maulucci, President of SmartONE Solutions Inc. IT World Canada, 2018

Where do you stack up? What is your current data management maturity?

Info-Tech’s IT Maturity Ladder denotes the different levels of maturity for an IT department and its different functions. What is the current state of your data management capability?

Innovator - Transforms the Business. Business Partner - Expands the Business. Trusted Operator - Optimizes the Business. Firefighter - Supports the Business. Unstable - Struggles to Support.

Info-Tech Insight

You are best positioned to successfully execute on a data strategy if you are currently at or above the Trusted Operator level. If you find yourself still at the Unstable or Firefighter stage, your efforts are best spent on ensuring you can fulfill your day-to-day data and data management demands. Improving this capability will help build a strong data management foundation.

Guiding principles of a data strategy

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 strategy, helping to shape initiative plans and day-to-day behaviors related to the use and treatment of the organization’s data assets.

“Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes.”– McKinsey, 2018

Build a Robust and Comprehensive Data Strategy

Business Strategy and Current Environment connect with the Data Strategy. Data Strategy includes: Organizational Drivers and Data Value, Data Strategy Objectives and Guiding Principles, Data Strategy Vision and Mission, Data Strategy Roadmap, People: Roles and Organizational Structure, Data Culture and Data Literacy, Data Management and Tools, Risk and Feasibility.

Follow Info-Tech’s methodology for effectively leveraging the value out of your data

Some say it’s the new oil. Or the currency of the new business landscape. Others describe it as the fuel of the digital economy. But we don’t need platitudes — we need real ways to extract the value from our data. – Jim Love, CIO and Chief Digital Officer, IT World Canada, 2018

1. Business Context. 2. Data and Resources Foundation. 3. Effective Data Strategy

Our practical step-by-step approach helps you to formulate a data strategy that delivers business value.

  1. Establish Business Context and Value: In this phase, you will determine and substantiate the business drivers for optimizing the data strategy. You will identify the business drivers that necessitate the data strategy optimization and examine your current organizational data culture. This will be key to ensuring the fruits of your optimization efforts are being used. You will also define the vision, mission, and guiding principles and build high-value use cases for the data strategy.
  2. Ensure You Have a Solid Data and Resources Foundation: This phase will help you ensure you have a solid data and resources foundation for operationalizing your data strategy. You will gain an understanding of your current environment in terms of data management enablers and the required resources portfolio of key people, roles, and skill sets.
  3. Formulate a Sustainable Data Strategy: In this phase, you will bring the pieces together for formulating an effective data strategy. You will evaluate and prioritize the use cases built in Phase 1, which summarize the alignment of organizational goals with data needs. You will also create your strategic plan, considering change management and communication.

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 keep 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.

Build a robust and comprehensive data strategy – project overview

Project Overview. Lists the 3 phases and the steps included.

Workshop Overview

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

Workshop Overview. It consists of a four day workshop with various activities.

Phase 1

Establish Business Context and Data Value

Phase 1 will help you to establish the business context and value of your data strategy

In this phase, you will determine and substantiate the business drivers for optimizing the data strategy.

  1. Identify the business drivers that necessitate or support the case for data strategy optimization.
  2. Understand your current organizational data culture, as this will be key to ensuring the fruits of your optimization efforts are being used.
  3. Establish the vision and mission and define the principles and goals of your organization’s data strategy.
  4. Build high-value use cases for informing the business case for the data strategy.

"To stay competitive, we need to become more data driven. Compliance pressures are becoming more demanding. We need to add a new functionality."

Leverage the Data Strategy Stakeholder Interview Guide Template for gathering business context findings from key stakeholders.

Phase 1, Step 1: Identify Your Business Drivers for the Data Strategy

This step will walk you through the following activities:

  • Understand common organizational business drivers
  • Identify the business drivers, goals, and objectives that necessitate optimizing data in your organization

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Outcomes of this step

  • An understanding of the business’ use of data in day-to-day functions
  • An understanding of the business’ vision, goals, objectives, KPIs, and strategic priorities as well as the business drivers that will necessitate optimizing the data strategy

Data strategy optimization should be driven by business goals

Data is an enabler of the business. Data strategy optimization therefore needs to be driven by business goals and objectives.

Common Business Drivers

  1. Stakeholder Experience/Service Excellence
  2. Product and Service Innovations
  3. Operational Excellence and Efficiency
  4. Risk and Compliance Management

Deconstructing common business drivers

In the face of continuously changing business models, organizations of today are looking at their data as a source of competitive advantage.

1. Stakeholder Experience/Service Excellence

As an organization, your current focus is on improving your stakeholder experience and striving for service excellence, whether by offering highly tailored products or services, upselling, cross-selling, sending targeted communication, or building customer/client loyalty levels.

For every organization, stakeholders are going to be different.

For instance, in retail, key stakeholders include customers, suppliers, partners, and employees.

In healthcare, they include patients, physicians, nurses, staff, suppliers, pharmaceutical firms, insurance providers, and the government.

In education, they include students, faculty, staff, researchers, the community, and boards.

In government, they include residents, citizens, the community, unions, and governing bodies.

2. Product and Service Innovations

In order to maintain or establish your competitive edge, your organization is looking to become innovative in the product(s) and service(s) that you offer.

As an organization, you’re seeking to differentiate through product or service innovation. You’re inventing and adapting to keep pace and/or get ahead of changing customer and stakeholder preferences by understanding purchasing habits, consumption, behaviors, more varied and larger data sets, IoT, and other disruptive forces.

3. Operational Excellence and Efficiency

As an organization, you’re focused on optimizing your operational excellence and efficiency to ensure you are delivering high-quality products or services in the most cost-effective manner.

This may mean your focus is on optimizing your ordering, production, and fulfillment processes. Or you may be working on the efficiency of your operations, making them leaner, reducing waste, and optimizing resource utilization, all of which can contribute to lower costs and higher profit margins.

4. Risk and Compliance Management

As an organization, you’re operating in a highly regulated industry or you may be a government entity (federal, state, provincial, municipal) and mandated to meet certain regulatory requirements. Your data strategy optimization may therefore be in response to changes in the existing regulatory/compliance landscape.

Risk mitigation is also another driver for formalizing or optimizing a data strategy. Your current practices and environment may be outdated, leading to potential exposure to risk.

Begin your data strategy planning 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 data strategy planning and the resulting outcomes around data directly align with business needs and value and are in support of key strategic priorities.

Key Items to Consider:

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

  1. Stakeholder Experience/Service Excellence
  2. Product and Service Innovations
  3. Operational Excellence and Efficiency
  4. Risk and Compliance Management

Questions for your business stakeholders:

  1. Is the organization’s business model changing? (Are we an organization that grows by acquisitions, are we expanding operations in to a new jurisdiction?)
  2. Are business operations evolving and changing?
  3. Are regulations causing the organization to re-evaluate how data is used and managed by the business?

Determine and document the business context of the data strategy

1.1

Estimated Time: 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 to understand the data environment, ensure you understand the business’ vision, goals, objectives, KPIs, strategies, priorities, and key drivers. (Leverage your project or executive 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 embark on your data strategy initiative.
  3. Record findings in the Data Strategy Stakeholder Interview Guide and Findings document.

OUTPUT

Use the findings of your analysis of the business to identify:

  • Organizational vision, goals, objectives, KPIs
  • Strategic priorities
  • Business drivers

Recommended Techniques

Interview business stakeholders. (See Activity 1.1a for support.)

Review key business artifacts. Typical review inputs include:

  • Business vision and mandate
  • Three-to-five-year roadmaps, department-level business plans, audit reports

Info-Tech Insight

Prevent the interviews from becoming a platform for the business to complain about data. Open the discussion by having them share about their current use of data, then switch gears to finding out what they would like to do with data and how they see data supporting their strategic plans.

Interview business stakeholders

1.1a

Estimated Time: Average of 1 hour per interview session

Objective

Increase the team’s understanding of organizational strategic plans and how the organization would benefit from data as a strategic enabler.

Activity: Individual and small-group interviews with business stakeholders

Instructions:

  1. Identify members of the business to interview to understand their current and desired data usage. (Try to interview as many lines of business as possible to create a more comprehensive picture.)
  2. Leverage the interview questions in the Data Strategy Stakeholder Interview Guide and Findings template as a starting point.
  3. Interview the identified members of the business.
  4. Debrief and document results in the Data Strategy Stakeholder Interview Guide and Findings document.

Data strategy optimization must be driven by business need.

A screenshot of Info-Tech's Data Strategy Stakeholder Interview Guide and Findings.

Data Strategy Stakeholder Interview Guide and Findings

INPUT

  • Data Strategy Stakeholder Interview Guide, business stakeholders

OUTPUT

  • Identified business drivers that apply to your organization.

Materials

  • Data Strategy Stakeholder Interview Guide and Findings Template

Participants

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Phase 1, Step 2: Understand Your Organization’s Data Culture

This step will walk you through the following activities:

  • Understand the organizational data culture
  • Understand users’ appetite for data
  • Understand appreciation of data in terms of governance, quality, accessibility, ownership, and stewardship

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders (data owners, data stewards)

Outcomes of this step:

  • An understanding of the current culture as it relates to the use and consumption of data
  • An understanding of whether data is currently perceived to be an asset at the organization

Conduct Data Culture Survey

1.2

Estimated Time: Average of 1 hour per respondent

Objective

  • Increase the team’s understanding of the organizational data culture, users’ appetite for data, and their appreciation of data in terms of governance, quality, accessibility, ownership and stewardship

Activity: Stakeholder survey

Instructions:

  1. Identify members of the data user base, data consumers, and other key stakeholders for surveying.
  2. Conduct an information session to introduce the Data Culture Survey diagnostic. Explain the objective and importance of the survey and its role in helping to understand the current data culture and inform the improvement of that culture.
  3. Roll out the Info-Tech Data Culture Survey diagnostic to the identified users and stakeholders.
  4. Debrief and document results/scorecard in the Data Strategy Stakeholder Interview Guide and Findings document.

A data-driven culture is an indicator of data being treated as an asset.

A screenshot of Info-Tech's Data Culture Scorecard. Contact your account manager for more information on the Data Culture Survey.

“Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes.” – McKinsey, 2018

INPUT

  • Data Culture Survey diagnostic, business stakeholders

OUTPUT

  • Understanding of current data culture

Materials

  • Data Culture Survey
  • Data Strategy Stakeholder Interview Guide and Findings Template

Participants

  • Data architect
  • Enterprise architect
  • Business analyst
  • Senior leaders and business stakeholders

Phase 1, Step 3: Vision, Mission, and Guiding Principles of Your Organization’s Data Strategy

This step will walk you through the following activities:

  • Establish the vision, mission, and guiding principles of your organization’s data strategy

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Outcomes of this step

  • Vision and mission of your organization’s data strategy
  • Defined principles and goals of your organization’s data strategy

Create compelling vision and mission statements for the organization’s data strategy

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 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 statements

1.3.1

Estimated Time: 2 hours

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 strategy.

Instructions

Get the conversation started:

Ask everyone to complete the following sentence:

  • Five years from now, our data landscape 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 organization with an effective high-value data strategy look like?
      • How will our organization benefit and grow from an improved data strategy?
      • 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 findings and document the results.

INPUT

  • Business context as derived from stakeholder interview session and input

OUTPUT

  • The vision and mission statements for the data strategy

Materials

  • Whiteboard

Participants

  • Data architect
  • Enterprise architect
  • Business analyst
  • Senior leaders and business stakeholders

Guiding principles for your organization’s data strategy

The 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.

Examples:

Principle #1

The organization’s data supports fact-based decision making.

Principle #2

Data is comprehensively integrated.

Principle #3

Data is appropriately accessible and available to support timely consumption and insight generation.

Principle #4

Quality of data will be measured, maintained, and managed.

Principle #5

Data definitions are consistent and are maintained and managed to support data users.

Principle #6

Data owners and data stewards are accountable and responsible for their domains.

Principle #7

Data is managed (curated, retained, archived, disposed) across its lifecycle.

Principle #8

Data is appropriately secured across its lifecycle.

Principle #9

Data is governed.

Info-Tech Insight

Take the time to craft your guiding principles. They are shared, long-lasting beliefs that will guide your treatment of, investment in, and decisions related to data and data management. Devise a set of guiding principles that speak to your organization’s data strategy and your current or desired culture.

Create guiding principles for your organization’s data strategy

1.3.1

Estimated Time: 2 hours

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 strategy and the data management practice. These will help you develop the practice’s ultimate principles. Attempt to brainstorm between five and ten values.
  2. What is the business looking to get from its data? How is the data strategy going to deliver the business needs?

    Quality data?

    Accessible data?

    Timely data?

    Integrate varied data sets for improved decision making? Create a scalable environment to accommodate growth? Create a culture of accountability and stewardship?
  3. Divide into small groups, with each group taking one of the values. For each value, determine:
    • Why is it important to the data strategy?
    • How will it help to improve the delivery, use, quality, or management of data?
    • Complete one of the following sentences:
      • Data is…
      • The business is able to…
      • The data strategy…
  4. 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?
  5. Finalize and document your guiding principles.

INPUT

  • Business context as derived from stakeholder interview session and input

OUTPUT

  • Guiding principles

Materials

  • Whiteboard

Participants

  • Data architect
  • Enterprise architect
  • Business analyst
  • Senior leaders and business stakeholders

Phase 1, Step 4: Build High-Value Use Cases for Informing the Business Case for Data Strategy

This step will walk you through the following activities:

  • Brainstorm top data challenges, risks, and opportunities
  • Formulate problem statements that articulate business needs
  • Examine how the need is filled today, if at all, and what the future state looks like
  • Identify relevant metrics and KPIs for measuring success
  • Build data flow of the current state

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders (data owners and data stewards)

Outcomes of this step

  • Use cases that highlight business needs and/or opportunities

Build high-value use cases for informing the business case for data strategy

Rounding out the business context for the data strategy is the very important exercise of creating high-value use cases.

Hold focused conversations and conduct deep-dive sessions with business stakeholders to identify high-priority initiatives for your organization’s data strategy. Leverage Info-Tech’s data requirements and mapping methodology for creating use cases.

Bring data owners, data stewards, business subject matter experts, and their IT partners or data custodians together to discuss and create use cases that represent the top business needs and priorities. If addressed, these will deliver value and support the strategic direction of the organization.

Include in these conversations current challenges, risks, and opportunities associated with the use of data across lines of business. Also explore which other stakeholder groups/lines of business will be impacted and how you will measure success.

Info-Tech Insight

Coming out of these sessions with use cases identified by your data owners, data stewards, and business SMEs will highlight the areas or processes top-of-mind for these stakeholders and their units. If addressed, these will deliver value to the organization as a whole.

Info-Tech’s framework on developing use cases for the data strategy

Objective: Business-needs gathering activity to highlight and create relevant use cases around data-related problems or opportunities that are clear and contained and if addressed will deliver value to the organization.

Info-Tech’s Data Requirements and Mapping Methodology for Creating Use Cases

Breakout Session #1

  1. What is a number-one risk you need to alleviate?
  2. What is a number-one opportunity you wish to see happen?
  3. What is a number-one pain you have when working with data?

Once you identify a data-related business activity/process, define a problem statement.

Breakout Session #2

  1. What are your challenges in performing the activity today?
  2. What does “amazing” look like if we solve this perfectly?
  3. What other business unit activities/processes will be impacted/improved if we solve this?
  4. What compliance/regulatory/policy concerns do we need to consider in any solution?
  5. What measures of success/change should we use to prove the value of the effort (KPIs/ROI)?

Breakout #3

  1. What are the steps in the process/activity today?
  2. What are the applications/systems used at each step?
  3. What data elements (domains) are involved, created, used, or transformed at each step

The resulting use cases are to be prioritized and leveraged for informing the business case for the data strategy.

Build high-value use cases for informing the business case for data strategy

1.4

Estimated Time: Average of 2 hours per use case

Objective

Business-needs gathering activity to highlight and create relevant use cases around data-related problems or opportunities that are clear and contained and if addressed will deliver value to the organization.

Activity: Use-case activity with business stakeholders and data custodian (IT partner)

Instructions:

  1. Bring together key business stakeholders (data owner, stewards, SMEs) from a particular line of business as well the data custodian to build cases for their units.
  2. Leverage Info-Tech’s Data Requirements and Mapping Methodology for Creating Use Cases.
  3. Have the stakeholders move through each breakout session, using flip charts to brainstorm and to document thoughts.
  4. Debrief and document results in the Data Strategy Use Case Template.

Repeat this exercise with as many lines of business as possible.

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

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

A screenshot of Info-Tech's Data Strategy Use Case Template

Data Strategy Use Case Template

INPUT

  • Business stakeholders’ subject area expertise; data custodian systems, integration, and data knowledge

OUTPUT

  • Identified business drivers that apply to your organization.

Materials

  • Info-Tech’s Data Requirements and Mapping Methodology for Creating Use Cases

Participants

  • Data architect
  • Enterprise architect
  • Business analyst
  • Data owners, data stewards, data custodians

Phase 2

Ensure You Have a Solid Data and Resources Foundation

Phase 2 will help you ensure you have a solid data and resources foundation for your data strategy

In this phase, you will focus on understanding and evaluating the foundational components of the data environment, resources and skills.

  1. Understand your current data environment: Data management enablers – data governance, data architecture, data operations, data quality management, data risk management
  2. Understand the required resources portfolio: Key people and skill sets
  3. Put things in perspective: determine your strengths, weaknesses, opportunities, and threats through a SWOT analysis.

"To stay competitive, we need to become more data-driven. Compliance pressures are becoming more demanding. We need to add a new functionality."

Phase 2, Step 1: Understand Your Current Data Environment

This step will walk you through the following activities:

  • Understanding your current data environment: Data management enablers including data governance, data architecture, data operations, data quality management and data risk management

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Outcomes of this step:

  • An understanding of the foundational data management enablers that are key to operationalizing your data strategy

Optimized data management enablers means a strong foundation for the data strategy

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.

Data Management Enablers:

  • Data Governance
  • Data Architecture Management
  • Data Operations Management
  • Data Risk Management
  • Data Quality Management
  • Practice Evolution
Data Management Enablers. A table is shown. Data Management Enablers include: Govern & Direct, Align & Plan, Build, Acquire, Operate, Deliver, & Support, Monitor & Improve.

Ensure you have a data management practice with strong process capabilities

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).

Data Management Enablers. A table is shown. Data Management Enablers include: Govern & Direct, Align & Plan, Build, Acquire, Operate, Deliver, & Support, Monitor & Improve.

Govern and Direct

  • Ensures data management practices and processes follow the standards and policies outlined for them
  • Manages the executive oversight of the broader practice

Align and Plan

  • Aligns data management plans to the business’ data requirements
  • Creates the plans to guide the design and execution of data management components

Build, Acquire, Operate, Deliver, and Support

  • Executes the operations that manage data as it flows through the business environment
  • Manages the business’ risks in relation to its data assets and the level of security and access required

Monitor and Improve

  • Analyzes the performance of data management components and the quality of business data
  • Creates and executes plans to improve performance of the practice and quality and use of data assets

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.

Three tables are shown on top of one another. They are labeled: Data Management Enablers, Information Dimensions, and Business Information.

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

Many components are included under the umbrella of data management, all working together 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 management enabler: Data governance

Governance is the structure for making decisions. (CIO Magazine)

Data governance is:

  • An enabling framework of decision rights and accountabilities for information-related processes.
  • Agreed-upon models that describe who can take what actions with what information, when, and using what methods. (The Data Governance Institute, 2014)
  • True business-IT collaboration that will lead to increased consistency and confidence in decision making, which in turn increases innovation and growth.

If done correctly, data governance is not:

  • An annoying, finger-waving roadblock in the way of getting things done.
  • Meant to solve all data-related business or IT problems in an organization.
  • A sweeping project that will clean up your data “once and for all.” (Accenture, 2011)
  • A multiyear process that will cost a lot and have uncertain benefits.

Data governance is the central component of the data management framework

This research is created with reference to the Data Asset Management Association’s Data Management Book of Knowledge, Version 2 (DAMA DMBOK2).

The Dama-DMBOK2 Guide Knowledge Area Wheel

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, 2017).

In other words, getting the right information to the right people at the right time.

The research in this blueprint will focus on data governance, the central idea of data management, without which the surrounding data management initiatives would have no structure.

Data governance directly complements all ten data management initiatives.

See Info-Tech’s Enable Shared Insights With an Effective Data Governance Engine blueprint for more information on data governance.

Data governance enables the business and IT to climb the data value chain together

After starting the data governance engine with the organization’s fuel – its data, information, and needs – the real power from the engine emerges when the program is implemented. Using Info-Tech’s methodology and extensive resources will help you get the power that you need out of your data governance engine. Together, this will help drive your organization up the data value chain to gain knowledge and shared insight from the business.

Info-Tech Methodology of Implementation includes: Policies &Procedures, and People. The data value chain is to the right and explains the knowledge, information and data that leads to shared insight.

Data management enabler: Data architecture is an integral aspect of data management

The set of rules, policies, standards, and models that govern and define the type of data collected and how it is used, stored, managed, and integrated within the organization and its database systems.

In general, the primary objective of data architecture is the standardization of data for the benefit of the organization.

Myth

Data architecture is purely a model of the technical requirements of your data systems.

Reality

Data architecture is largely dependent on a human element. It can be viewed as “the bridge between defining strategy and its implementation.” - Erwin, 2016

Functions

A strong data architecture should:

  • Define, visualize, and communicate data strategy to various stakeholders.
  • Craft a data delivery environment.
  • Ensure high data quality.
  • Provide a roadmap for continuous improvement.

Business Value

A strong data architecture will help you:

  • Align data processes with business strategy and the overall holistic enterprise architecture.
  • Enable efficient flow of data with a stronger focus on quality and accessibility.
  • Reduce the total cost of data ownership.

Data architecture is not a standalone concept; it fits into the more holistic design of enterprise architecture

Data Architecture in Alignment

Data architecture cannot be designed to simply address the focus of data specialists or even the IT department.

It must act as a key component in the all-encompassing enterprise architecture and reflect the strategy and design of the entire business.

Data architecture collaborates with application architecture in the delivery of effective information systems and informs technology architecture on data-related infrastructure requirements/considerations.

Please refer to the following blueprints to see the full picture of enterprise architecture:

Flowchart of the Enterprise Architecture. Business Architecture is on top, two arrows lead from it to data architecture and application architecture. Arrows lead from those two to Infrastructure Architecture and Security Architecture.

Adapted from TOGAF

Refer to Phase C of TOGAF and Bizbok for references to the components of business architecture that are used in data architecture

Data architecture involves planning, communicating, and understanding technology

Data Architecture

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, 2011).

The subject area of data management that defines the data needs of the enterprise and designs the master blueprints to meet those needs (DAMA DMBOK, 2009).

IBM (2007) defines data architecture as the design of systems and applications that facilitate data availability and distribution across the enterprise.

Definitions vary slightly across major architecture and management frameworks.

However, there is a general consensus that data architecture provides organizations with:

  • Alignment
  • Planning
  • Road Mapping
  • Change Management
  • A guide for the organization’s data management program

Data architecture must be based on business goals and objectives and developed within the technical strategies, constraints, and opportunities of the organization in support of providing a foundation for data management.

A box on the left is labeled current data management. An arrow is beside it that has text: alignment, planning, and road mapping in list form. A box is beside the arrow on the right labeled goal for data management.

Info-Tech Insight

Data architecture is not just data models. Data architects must understand the needs of the business, as well as the existing people and processes that already exist in the organization, to effectively perform their job.

Capitalize on trends in data architecture before you determine the tactics that apply to you

Stop here. Before you begin to plan for optimization of the organization’s data environment, get a sense of the sustainability and scalability of the direction of the organization’s data architecture evolution.

Practically any trend in data architecture is driven by an attempt to solve one or more or the common challenges of today’s tumultuous data landscape, otherwise known as “big data.” Data is being produced in vast amounts, at very high speeds, and in a growing number of types and structures.

To meet these demands, which are not slowing down, you must keep ahead of the curve. Consider the internal and external catalysts that might fuel your organization’s need to modernize its data architecture:

Big Data Data Storage Advanced Analytics Unstructured Data Integration

Hadoop ecosystem

The discussion about big data is no longer about what it is but about how businesses of all types can operationalize it.

Is your organization currently capturing and leveraging big data? Are you looking to do so in the near future?

The cloud

The cloud offers economical solutions to many aspects of data architecture.

Have you dealt with issues of lack of storage space or difficulties with scalability? Do you need remote access to data and tools?

Real-time architecture

Advanced analytics (machine learning, natural language processing) often require data in real-time. Consider Lambda and Kappa architectures.

Has your data flow prevented you from automation, advanced analytics, or embracing the world of IoT?

Graph databases

Self-service data access allows more than just technical users to participate in analytics. NoSQL can uncover buried relationships in your data.

Has your organization struggled to make sense of different types of unstructured data?

Is ETL enough?

What SQL is to NoSQL, ETL is to NoETL. Integration techniques are being created to address the high variety and velocity of data.

Have your data scientists wasted too much time and resources in the ETL stage?

The Five-Tier Data Architecture Model helps to understand your data and unlock true business insight

The framework below is a reference of a typical corporate data architecture, adapted from the DAMA Data Management Body of Knowledge (DMBOK), highlighting the role of business intelligence.

Use this as a guide moving forward.

Screenshot of the Five-Tier Data Architecture Model.

Tiers 4 and 5 are where the true magic happens. This is where BI tools are essential for analyzing, visualizing, and making sense of your data. Without BI tools, the knowledge and insight locked in your data would remain hidden.

Data sources are any data structures that support the line of business applications. They can reside on many different platforms and can contain structured as well as unstructured data.

Downward facing arrow labeled: Movement and transformation of data.

Data from siloed sources needs to come together in one place. Every database shares its information with a centralized location called a data warehouse. This is where different information from different departments gets linked together.

Data management enabler: 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 to archiving and purging (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.
A screenshot of the Data Lifecycle.

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 management enabler: Data risk management

Guiding Principles and Value

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

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 in.

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 from 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. PII 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 on technology usage and data access take into account security principles and incorporate security controls during their planning and implementation.
  • The organization proactively tests its environment for security threats/weaknesses.

Data management enabler: Data quality management

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 (DAMA, 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 are 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, trustworthy data is available to the business and its processes.
  • Analyze and recommend improvements to data management components 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.

Poor data quality develops due to multiple root causes

After you get to know the properties of good-quality data, understand the underlying causes of why a lack of those indicators can point to poor-quality data.

If you notice that the relevance, accuracy, timeliness, or usability of the organization’s data is suffering, one or more of the following root causes are likely plaguing your data:

A screenshot of the common root causes of poor data quality, through the lens of Info-Tech's Five-Tier Data Architecture.

Common root causes of poor data quality, through the lens of Info-Tech’s Five-Tier Data Architecture:

These root causes of poor data quality are difficult to avoid, not only because they are often generated at an organization’s beginning stages but also because change can be difficult. This means that the root causes are often propagated through stale or outdated business processes.

Consider the core IT capabilities and competencies of an effective data quality practice

Before addressing data quality issues in the organization, make sure that the competencies and capabilities necessary for properly fixing those issues are present in the organization’s IT department.

Quick Tip: Competencies vs. Capabilities

Being capable is different than being competent, and it is worth differentiating the two despite them often being used interchangeably.

A competency is a skill that you are functionally adequate at. When we evaluate competency, we are asking, “Who knows how to do [action], and how well do they know how to do it?”

A capability is a feature or faculty that can be developed and improved. When we evaluate capability, we are asking “Can we access and apply the competencies we need to get [action] done?”

– Innovation Management Services, 2008

A flowchart on Organizational Data Structure.

Competencies and Capabilities of IT

The core competencies and capabilities of the IT department must be assessed and, if necessary, improved before addressing specific business data quality improvements. These include:

Competencies: IT competencies related to data quality include the ability of IT to functionally carry out data quality improvement techniques in databases and systems. These include data cleansing and data validation.

Capabilities: Related to the ability of IT to fulfill the needs of the business. This includes having business-informed data-quality-related policies and procedures, related roles and structure, the appropriate oversight and communication, and the appropriate technologies to measure and fix data quality issues.

Data quality management must be sustained for ongoing improvements to the organization’s data

Data quality management is a long-term commitment that shifts how an organization views, manages, and uses its corporate data assets. Long-term buy-in from all involved is critical.

Data quality is a program that requires continual care.
  • Data quality is never truly complete; it is a set of ongoing processes and disciplines that requires a permanent plan for monitoring practices, reviewing processes, and maintaining consistent data standards.
  • Set the expectation for stakeholders that a long-term commitment is required to maintain quality data within the organization. This is critical to the success of the program.
  • A data quality maintenance program will continually revise and fine-tune ongoing practices, processes, and procedures employed for organizational data management.

Data management enabler: 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.

Phase 2, Step 2: Key Resources – People and Skill Sets

This step will walk you through the following activities:

  • Understand the typical roles and responsibilities for effective management of data
  • Understand the key roles and responsibilities as they relate to the governance of data

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business stakeholders

Outcomes of this step

  • An understanding of the key resources – people and skill sets – critical for operationalizing the data strategy and its associated initiatives

Key resources for your data strategy: People and skill sets

The right people and skill sets are another key component for an effective data strategy.

Some of the key roles for executing on your data strategy: The Management of Data

  • Data Architects
  • Database Administrators
  • Data Engineers
  • Data Integrators – ETL, ESB
  • Data Analysts – Data quality tools, data rules
  • Data Scientists
  • Data Risk Officers
  • Data Privacy Officers
  • Data Security Officers
  • Chief Data Officer

Some of the key roles for executing on your data strategy: The Governance of Data

  • Data Steward
  • Data Custodian
  • Data Owner
  • Data Governance Working Group
  • Data Governance Steering Committee
  • Data Governance Council
  • Executive Sponsor

Key roles for operationalizing the data strategy

Data Engineer

Works with and analyzes data to generate reports to support business decisions.

  • An individual or group of individuals who work with data to integrate, optimize, and support data with deep technical knowledge of underlying technologies.
  • Could also be called database administrator (DBA), big data engineer, big data architect, etc., depending on the technology and platforms.
  • Typically the first to experience data integration pains through slow or inaccurate data.

Business Analyst

Communicates with business to identify requirements and satisfaction. Demonstrates competencies for stakeholder management, analytical techniques, and the ability to “speak the language” of both the business and IT.

  • Role that functions as the crucial link between the business and the IT roles responsible for designing, developing, and implementing data changes.
  • The designated business analyst(s) for the project has responsibility for end-to-end requirements management.
  • Works collaboratively with their counterparts in the business and IT (e.g. developer teams or procurement professionals) to ensure that the approved requirements are met in a timely and cost-effective manner.

Data Architect

Understands the data environment in a holistic manner and designs solutions. Has a greater knowledge of operational and analytical data use cases.

  • Reviews project solution architectures and identifies cross impacts across the data lifecycle.
  • Is a hands-on expert in data management and warehousing technologies.
  • Facilitates the creation of the data strategy.
  • Manages the enterprise data model.
  • Has a greater knowledge of operational and analytical data use cases.

The data architect is one of the most important roles for operationalizing the data strategy

The data architect:

  • Acts as a “translator” between the business and data workers to communicate data and technology requirements.
  • Facilitates the creation of the data strategy.
  • Manages the enterprise data model.
  • Has a greater knowledge of operational and analytical data use cases.
  • Recommends data management policies and standards and maintains data management artifacts.
  • Reviews project solution architectures and identifies cross impacts across the data lifecycle.
  • Is a hands-on expert in data management and warehousing technologies.
  • Is not necessarily its own designated position but a role that can be completed by a variety of IT professionals.

Data architects bridge the gap between strategic and technical requirements.

There is a flowchart shown. Data workers is in a box connected by an arrow to data architect. Business is also in a box connected to data architect. Below is data and applications that are also connected to the box data architect by an arrow.

The data architect must maintain a comprehensive view of the organization’s rapidly proliferating data.

As a data architect, you must maintain balance between the technical and the business requirements

The data architect role is integral to connecting the long-term goals of the business with how the organization plans to manage its data for optimal use.

Data architects need to have a deep experience in data management, data warehousing, and analytics technologies. At a high level, the data architect plans and implements an organization’s data, reporting, and analytics roadmap.

Some of the role’s primary duties and responsibilities include:

  • Data modeling
  • Reviewing existing data architecture
  • Benchmarking and improving database performance
  • Fine-tuning database and SQL queries
  • Leading on ETL activities
  • Validating data integrity across all platforms
  • Managing underlying framework for data presentation layer
  • Ensuring compliance with proper reporting to bureaus and partners
  • Advising management on data solutions

There is a flowchart shown. Data workers is in a box connected by an arrow to data architect. Business is also in a box connected to data architect. Below is data and applications that are also connected to the box data architect by an arrow.

Info-Tech Insight

The data architect role is not always clear cut. Many organizations do not have a dedicated data architect resource and may not need one. However, the duties and responsibilities of the data architect must be carried out to some degree by a combination of resources as appropriate to the organization’s size and environment.

Critical roles and responsibilities for data governance

Data Governance (DG) Council

Generally:

  • Senior executive representatives operating at the organization’s strategic level
  • Aligns organizational strategies and goals to data strategies and data management plans
  • Supports top-down approach to DG, helps to champion/socialize DG, and supports adoption

Data Owners

Traditionally, data owners:

  • Are ultimately accountable for all issues related to the data assets under their purview
  • Are organizational leaders whose teams are heavy users of the data assets
  • Review the permissions of user groups for different data sets
  • Accountable for the quality of the data and whether it enables employees to perform their jobs efficiently
  • Determine the institutional impact of changing permission statuses
  • Understand the lifecycle of the data

Data Governance Steering Committee

Generally:

  • Cross-functional body responsible for creating tactical plans
  • Manages data- and practice-related issues
  • Monitors and guides data and data governance initiatives
  • Oversees performance and management of working groups

Critical roles and responsibilities for data governance

Data Governance Working Groups

  • Working groups are the cross-functional teams that deliver on data governance projects, initiatives, and ad hoc review committees

Data Custodians:

Traditionally, data custodians:

  • Serve on an operational level addressing issues related to data and database administration
  • Support the management of access, quality, escalating issues, etc.
  • Are subject matter experts from IT and database administration

Data Stewards

Traditionally, data stewards:

  • Serve on an operational level addressing issues related to adherence to standards/procedures, monitoring data quality, raising issues identified, etc.
  • Responsible for managing access, quality, escalating issues, etc.

Traditional data governance organizational structure: committees and roles that span across strategic, tactical, and operational duties

There is no one-size-fits-all data governance structure. However, most organizations follow a similar pattern when establishing committees, councils, and cross-functional groups. Most organizations strive to identify roles and responsibilities at a strategic, tactical, and operational level. Several factors will influence the structure of the program, such as the focus of the data governance project as well as the maturity and size of the organization.

There is a triangle shown that has a data governance structure. Operational is the bottom, Tactical is the middle, and Strategic is in the top section of the triangle. Each tier is labeled with the involved parties and has additional text that explains each tier.

Data stewards ensure controls exist to protect the data assets within your organization

Choose the right data stewards – they apply data governance on a daily basis.

  • Organizations have begun to understand the significant function this role plays in the pursuit of dependable data and have begun to delegate responsibility to data stewards for the creation and management of the data.
  • Data stewards are typically mid-level managers in the organization and should be relatively tech-savvy with a vested interest in improving data quality.
  • The main thrust of the data steward role is to work with the data owners and steering committee to approve naming standards, develop consistent data definitions, determine data aliases, document rules, monitor data quality, and potentially define security requirements.
  • Stewards are tasked with keeping a particular data domain/silo clean and free of errors as well as protecting the organization against data loss in the midst of changes, disruptions, mergers between on-premises and cloud applications, etc.

Use the Data Governance Implementation Plan Template to identify and document data stewards. Clearly define their role within the organization and their responsibilities in terms of data.

Data Stewards Are Found, Not Made

  • Whenever possible, appoint people who are already interested and involved in quality of data.
  • They should have some background experience familiarizing them with data management.
  • Data stewards must be provided with the authority and accountability to make final decisions on which data definitions, formats, and standard processes are acceptable for their data set.

For a job description of your typical data steward, look here: Data Steward Job Description

Data owners bring accountability to the data of their departments

Organizations often have trouble determining and defining who owns the data. In order to create accountability, identify representatives from different departments to step into the data owner role.

One approach to creating effective data owners:

  1. Identify different data sets/domains within your organization and group them according to different departments.
  2. Assign an owner for each data set or domain.
    1. The data owner should be senior-level business personnel or department heads and come from the business rather than technical teams. This allows them to better represent the organization’s needs.
  3. Create formal job descriptions that highlight responsibilities and expectations. This will help data owners accept ownership and accountability. The data owner should ultimately decide who has access to enterprise data. Other responsibilities could include:
    1. Review the permissions of user groups to different data sets.
    2. Assess the quality of the data and whether it enables employees to perform their jobs efficiently.
    3. Determine the impact of changing permission statuses.
  4. Ensure the data owner role is clearly defined, repeatable, and effective.

Refer to the Data Governance Implementation Plan Template to identify and document data owners within the organization. Clearly define their roles and responsibilities.

Info-Tech Insight

When there are decision points that determine if someone should be granted permission to access data, a data owner must exist. If the enterprise fails to identify a data owner, this duty will get offloaded to members of IT who have limited understanding of the organizational or business context of the data they are assigned to manage.

Put a data governance steering committee in place to enforce policies and procedures

Data Governance Steering Committee

  • The steering committee must consist of organization-wide representatives. Ensure that they understand the data needs of the organization and the technological constraints, if any, of the existing infrastructure and applications.
  • The steering committee must be given the necessary political authority to make decisions on how the organization’s data is maintained, what the data contains, and how long the data is kept in the system according to regulatory constraints.
  • The steering committee will be the governing body and final voice regarding organizational data processes, policies, and standards used throughout the organization. Any changes surrounding the manipulation of data must be authorized by the steering committee before implementation.
  • An effective data governance steering committee depends on the size and complexity of the organization. The team needs to be big enough to represent key stakeholders but small enough to ensure that tasks can be carried out efficiently.

Core objectives of a data governance steering committee:

  1. Strengthen data-driven decisions by acting as the in-between for the data governance council and the working groups.
  2. Ensure information is consistently defined and well understood.
  3. Create trusted data as an enterprise asset.
  4. Improve the consistency of data use across an enterprise.

The following categories of key stakeholders should make up the group:

  1. Data owners
  2. Departmental subject matter experts
  3. Departmental process owners
  4. Data stewards

Refer to the Data Governance Implementation Plan Template. Document the members and responsibilities of the data steering committee. Determine how frequently the committee will meet.

A data governance council acts as the ultimate level of authority in the data governance program

The data governance council:

For smaller organizations, this function may only be one person, but for larger organizations, it can be several people. Ideally, the governance council should have a seat for an executive sponsor.

Responsibilities include:

  • Assist in setting direction for future data initiative
  • Provide guidelines around new data policies, procedures, and standards.
  • Authorize changes to existing policies and approve the implementation of new policies.
  • Resolve issues escalated by the steering committee.

A data governance council should include some of the following:

  • CEO, CIO, CFO, CDO, and senior management.

Involving C-suite executives will help solidify their buy-in for data-related initiatives.

Refer to section 2.4 of the Data Governance Implementation Plan Template:

  • Document council members.
  • Determine the frequency of meetings.
  • Identify their responsibilities.

Info-Tech Insight

Taking a cross-functional approach and including inputs from senior managers and C-suite executives will help ensure the data governance program is aligned with organizational needs.

Key resources for your data strategy: The CDO

The Dawn of the Chief Data Officer (CDO):

Where once in the past at your typical organization, a combination of executives such the chief marketing officer (CMO), chief financial officer (CFO), and chief information officer (CIO) had responsibility of the data, now we are seeing more and more a transition to a single executive role focused on data – the chief data officer.

Three small circles are on the right. One is labeled: CMO, another is labeled: CIO, another is labeled: CFO. There is an arrowing pointing to the right with a big circle beside it, labeled Chief Data Officer

“Digital transformation remains a central business initiative that relies on data. Initially, data functions were focused on regulatory compliance, however, many executive teams now want to see continued innovation and results from the Chief Data Officer that generate value and growth for the company.”– Guillaume Le Galiard, Collibra, 2019

Some key considerations for the CDO

  • How much of the enterprise data has been given a "value" through a standardized process?
  • What is the quality of data in your landscape?
    • Relevance
    • Accuracy
    • Timeliness
    • Availability
  • How much data from the corporate landscape has been published as open data?
  • How much of the data landscape has been classified appropriately?

Some measures of success for the CDO

Some measures of success for the CDO:

  • How has your data capability landscape matured?
  • How have you increased the volume and variety of data accessible to your data consumers and stakeholders?
  • What does the enterprise data roadmap look like, and how is that tied to the data strategy?
  • What is the mechanism in place to change the culture such that data is recognized as a corporate asset?
  • What is the health of your data landscape?
  • Has the business noticed a positive impact in data accessibility and availability?

A high-level sample RACI view CIO vs CDO vs CTO

CIO CDO CTO
Data Ethics Consulted Accountable Informed
Data Security and Privacy Consulted Accountable Consulted
Data Collection Consulted Accountable Consulted
Data Exploitation Consulted Accountable Consulted
Application Development Accountable Consulted Consulted
Technology Training Accountable Informed Informed
Vendor Management Accountable Informed Informed
Technology Deployment Consulted Informed Accountable
Architecture Change Consulted Consulted Accountable

See Info-Tech’s Chief Data Officer job description for further context.

Phase 2, Step 3: Put Things Into Perspective – SWOT Analysis

This step will walk you through the following activities:

  • Conduct a SWOT analysis, examining your data management enablers as well as key resources and skill sets

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Outcomes of this step

The SWOT analysis will provide an opportunity to self-assess your data foundation: the data management enablers and key resources and skill sets to help round out the data strategy in terms of risk and feasibility.

A SWOT analysis will help identify both the internal and external factors impacting the data environment

A SWOT analysis will provide an opportunity to self-assess your data foundation: the data management enablers and key resources and skill sets.

SWOT stands for strengths, weaknesses, opportunities, and threats. Each word is a category of internal and external factors that could impact the data strategy and must be taken into consideration.

An example of a SWOT analysis.

Review these questions to help you conduct your SWOT analysis on your current data management

Strengths (Internal)

  • How does the Data Management department succeed at supporting the business?
  • Which data management services are known for being the most effectively delivered?
  • Which data management capabilities and enablers are the most mature?
  • Are the data management department’s people professional, knowledgeable, and talented?
  • Is the Data Management department good at innovating?

Weaknesses (Internal)

  • What areas of your Data Management department require improvement?
  • If your end users were to provide you with constructive criticism, what would it be about?
  • Is the IT/Data Management budget sufficient? (This can also be a strength.)
  • Are the data management processes well documented and monitored? Are people trained on performing the processes?
  • How strong is the IT-business communication and alignment? (This can also be a strength.)

Opportunities (External)

  • Are there any vendors or external partners that can help the Data Management department deliver better solutions?
  • Do cloud solutions provide any opportunities?
  • Do we plan on taking advantage of technology trends such as the internet of things, AI, and ML?
  • Are there any business trends in your organization’s industry that would need data management support?
  • Are there any technology trends that your competitors are implementing or thinking of implementing?

Threats (External)

  • Are there any obstacles external to the IT and Data Management department that will impact your ability to achieve success?
  • Has there been an increase in the frequency of security breaches in your industry?
  • Is shadow IT/shadow BI/rogue reporting prevalent at your organization?
  • Have there been regulatory changes likely to drastically change the way data solutions/IT solutions are delivered (e.g. GDPR)?

Conduct a SWOT analysis for the data strategy

2.3

Estimated Time: 2 Hours

Instructions:

  1. Break the group into two teams:
    • Assign team A internal strengths and weaknesses.
    • Assign team B external opportunities and threats.
  2. Have the teams brainstorm items that fit in their assigned grids. Use the prompt questions on the previous slide as guidance.
  3. Pick someone from each group to fill in the SWOT grid.
  4. Conduct a group discussion about the items on the list; identify implications for the data strategy and opportunities to innovate.
  5. Input the results into the Data Strategy Document Template.

Blank SWOT analysis chart.

INPUT

  • Data strategy creation team expertise

OUTPUT

  • Analysis of internal and external factors impacting the data strategy

Materials

  • Whiteboard and markers

Participants

  • Enterprise architect
  • Data architect
  • Senior IT personnel
  • Data governance representatives

Phase 3

Formulate a Sustainable Data Strategy

Phase 3 will help you to bring the pieces together for formulating an effective data strategy

In this phase, you will focus on putting the output pieces of the previous phases together to formulate an effective data strategy.

  1. Evaluate and prioritize the use cases built in Phase 1.
  2. Formulate your business-needs model, which summarizes the alignment of organizational goals with data needs, your internal enablers and barriers, factors in the external environment, and the technology trends landscape.
  3. Understand the role of change management and communication as you implement the various initiatives of your data strategy.
  4. Create your strategic plan

"To stay competitive, we need to become more data-driven. Compliance pressures are becoming more demanding. We need to add a new functionality."

Download the Data Strategy Document Template.

Phase 3, Step 1: Prioritize Your High-Value Use Cases

This step will walk you through the following activities:

  • Use case evaluation and prioritization

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Outcomes of this step

  • Evaluation and prioritization of the use cases built in Phase 1

Conduct a risk/value quadrant assessment to assess and prioritize your use cases

3.1

Instructions:

  1. Recall that in Phase 1, the business stakeholders worked on crafting use cases that represent high-value areas that need to be addressed for their teams or functions.
  2. Based on your answers to the following questions, place the use-case topics on the quadrant according to where they lie along the risk and value axes:
    • What do you do?
    • Challenges?
    • Activities/Systems?
    • Value?
    • What is the risk associated with this data asset if mishandled, stolen, or lost?
  3. An example of creating a risk value quadrant assessment
  4. Use cases in the top right (value and risk driven), top left (value driven), and bottom right (risk driven) quadrants should be considered for building out into a business case.

INPUT

  • Department answers to questions targeting key data needs

OUTPUT

  • Assessed and prioritized use cases

Materials

  • Note-taking materials
  • Whiteboard or flip chart, markers, sticky notes

Phase 3, Step 2: Build Your Business- and Data-Needs Model

This step will walk you through the following activities:

  • Business-data-needs modeling

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Outcomes of this step

  • Business- and data-needs model with organizational goals, data needs, internal enablers and barriers, factors in the external environment, and the technology trends landscape

Business and data needs must align for your data strategy

Info-Tech’s Business- and Data-Needs Model

  • Identifying the organizational goals and demonstrating how data supports those goals is key to a successful data strategy.
  • Augmenting the business-needs model with your internal enablers (those key pieces or things that you do well or have in place internally that would help drive the strategy) and barriers (things you need to be cognizant of, as they could potentially derail or inhibit the initiative, and need to plan mitigations for) makes for a realistic and feasible representation.
  • Rounding out the model with factors in the external environment and examining the technology market and trends landscape creates a holistic view of your data strategy.
A screenshot of Info-Tech's Business- and Data-Needs Model

Business- and Data-Needs Model (Template)

Identifying organizational goals and how data can support those goals is the first step to a successful data management strategy. Rounding out the business model with technology drivers, environmental factors, and internal barriers and enablers creates a holistic view of data strategy within the context of the organization as a whole. Through business interviews, the following holistic model can be created to articulate the alignment of the organizational goals and data needs in an all-encompassing context.

A screenshot of Info-Tech's Business-and Data-Needs Model blank template

Sample business- and data-needs modeling for data strategy

A screenshot of a Sample of Info-Tech's Business- and Data-Needs Model template

Phase 3, Step 3: Understand the Role of Communication and Change Management

This step will walk you through the following activities:

  • Understanding the role of communication and change management

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Outcomes of this step

  • An understanding of the role of change management and communication in your data strategy

Optimizing data strategy relies on communication between the business and IT/data management

Communication across all levels of the organization is key for starting to build a data-driven culture.

You will have your technical audience, your business users, and data consumers, and you will have your senior leaders.

Communication at all stages is also going to be key. It is very important that you demonstrate and share your wins.

Remember: Roles such as data architects and business relationship managers bridge the gap between strategic and technical requirements of data.

There is a flowchart shown. Data workers is in a box connected by an arrow to data architect. Business is also in a box connected to data architect. Below is data and applications that are also connected to the box data architect by an arrow.

Therefore, as you plan the data strategy optimization and its interactions and impact, it is imperative that you communicate the plan and its implications to the business and other stakeholders.

Create strong communication for the data strategy

Effective communication will aid in building and sustaining a healthy data culture.

Some key messages to convey to the business and the wider organization:

Business Value: How the data strategy aligns with the organization’s strategic priorities and business goals, whether those are around Client Intimacy, Service Excellence, Product and Service Innovation, Operational Excellence, and/or Risk Management

The Data Strategy Objectives, Guiding Principles, Vision and Mission

Building a Data Culture Through Learning and Collaboration: Data literacy and improving the organization’s data IQ

Data Roles and Responsibilities: Data is a corporate asset and hence requires a partnership across the business and IT to ensure it is treated as an asset.

Key characteristics of the communication:

Consistent, permeates all levels of the organization, frequent, open and honest, two-way, creative, and relevant

Expected outcomes:

Drives adoption, helps manage change, fosters relationships, reduces resistance, builds credibility, gives visibility, and helps drive support and buy-in across the enterprise

Vehicles for communicating the messages out to the organization and for maintaining the visibility of data:

Lunch and learns, information sessions, roadshows, show-and-tell events, and data IQ tournaments

Phase 3, Step 4: Create Your Strategic Plan

This step will walk you through the following activities:

  • Pull all of the pieces together into your strategic plan

This step involves the following participants:

  • Data architect
  • Enterprise architect
  • Business analyst
  • Business stakeholders

Outcomes of this step

  • Your strategic plan for going forward
  • Leverage the Data Strategy Document Template to pull together the output of all preceding activities and start building a data strategy document.
  • See the table of contents of the Data Strategy Document Template to get a firm understanding of the minimum components required for crafting a robust and comprehensive data strategy.

To implement your data strategy changes, you must plan to accommodate the issues that come with change

Once you have a plan in place, one the most challenging aspects of improving an organization is yet to come … overcoming change!

Flowchart. Top box labeled Create roadmap, connected to box below labeled Communicate roadmap. Followed by a box labeled implement roadmap, and the last box is labeled change management.

Change management improves core benefits to the business: the four Cs

Most organizations have at least some form of change control in place, but formalizing change management leads to the four Cs of business benefits:

  1. Control: Change management brings daily control over the data environment, allowing you to review every relatively new change, eliminate changes that would have likely failed, and review all changes to improve the data environment.
  2. Consistency: Request-for-change templates and a structured process shape implementation, test, and backout plans to be more consistent. Implementing processes for preapproved changes also ensures these frequent changes are executed consistently and efficiently.
  3. Collaboration: Change management planning brings increased communication and collaboration across groups by coordinating changes with business activities. The Change Advisory Board (CAB) also brings a more formalized and centralized communication method for IT and the data management group.
  4. Confidence: Change management processes will give your organization more confidence through more accurate planning, improved execution of changes with less failure, and control over the data environment. This also leads to greater protection against audits.

Info-Tech Best Practice

Some organizations will not be able to assign a dedicated change manager, but they must still task an individual with change review authority and with ownership over the risk assessment and other key parts of the process.

Avoid the pains of poor change management

An ineffective change management process will lead to lost productivity, service disruptions, and slow deployments.

A screenshot of pains of poor management. Deployments, incidents and end users are the main categories reviewed and how they can have poor change management.

“With no controls in place, IT gets the blame for embarrassing outages. Too much control, and IT is seen as a roadblock to innovation.” – VP IT, Federal Credit Union

There are several common misconceptions about change management

  1. It’s just a small change; this will only take five minutes to do.
  2. Reality: Even a small change can cause a business outage. That small fix could impact a large system connected to the one being fixed.

  3. Ad hoc is faster; too many processes slow things down.
  4. Reality: Ad hoc might be faster in some cases, but it carries far greater risk. Following defined processes keeps systems stable and risk-averse.

  5. Change management is all about speed.
  6. Reality: Change management is about managing risk. It gives the illusion of speed by reducing downtime and unplanned work.

  7. Change management will limit our capacity to change.
  8. Reality: Change management allows for a better alignment of process (release management) with governance (change management).

Leverage Info-Tech’s resources to smooth change management

As changes to the data environment occur, the data strategy team must stay ahead of the curve and plan the change management considerations that come with major architectural, systems, operations, role definition and assignment, and governance decisions.

See Info-Tech’s resources on change management to smooth changes:

Screenshot of Info-Tech's Optimize Change Management. Screenshot of Info-Tech's Optimize Change Management blueprint

Optimize Change Management

Screenshot of Info-Tech's Change Management Roadmap Tool

Change Management Roadmap Tool

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:

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  • To accelerate this project, engage your 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:

Screenshot of Step 1.1.1a from the blueprint

Build use cases that necessitate or support the case for data strategy optimization.

In this activity, the facilitator will guide the team of stakeholders through deep-dive sessions for identifying high-priority initiatives for your organization’s data strategy through Info-Tech’s data requirements and mapping methodology.

Screenshot of Phase 2, Step 1 from the blueprint

Understand the current data environment and build an initialized future state roadmap for operationalizing the data strategy.

In this activity, the facilitator will work with the team of stakeholders to understand the current environment across data management enablers, information dimensions, people, culture, and skill sets and will analyze gaps and initialize a corresponding roadmap for the data strategy. This artifact will be a key component of the resulting data strategy document.

Research contributors and experts

Internal Contributors

  • David Wallace, Vice President, Research – Industry Practice
  • Michael Fahey, Executive Counselor – Executive Services
  • Valence Howden, Principal Research Director – CIO
  • Dirk Coetsee, Research Director – Data and Analytics
  • Reddy Doddipalli, Senior Workshop Director – Data and Analytics
  • Igor Ikonnikov, Research Advisor – Data and Analytics
  • Andrea Malick, Research Director – Data and Analytics
  • Rajesh Parab, Research Director – Data and Analytics

External Contributors

  • Lisa Bobo, CIO, City of Rochester
  • Stephen Burt, Assistant Deputy Minister, Data, Innovation, and Analytics, and Chief Data Officer, Department of National Defence, Government of Canada
  • Dr. Irshad Siddiqui, Chief Health Information Officer (CHIO), Blessing Health System
  • Head of Enterprise Information Management at an African central bank

Related Info-Tech research

A screenshot of Info-Tech's Create a Plan for Establishing a Business-Aligned Data Management Practice blueprint

Ensure you have a strong data practice and foundation. Optimize your data management capabilities, guided by Info-Tech’s Create a Plan for Establishing a Business-Aligned Data Management Practice blueprint.

A screenshot of Info-Tech's Enable Shared Insights with an Effective Data Governance Engine blueprint.

Define an effective and sustainable data governance program by leveraging Info-Tech’s Enable Shared Insights With an Effective Data Governance Engine blueprint.

A screenshot of Info-Tech's Build a Business-Aligned Data Architecture Optimization Strategy

The data architecture needs to evolve to keep pace with changing business, data and user landscapes. Info-Tech’s Build a Business-Aligned Data Architecture Optimization Strategy blueprint provides you with a phased approach to building a modernized data architecture for meeting changes landscapes.

A screenshot of Info-Tech's Restore Trust in your Data Using a Business-Aligned Data Quality Management Approach blueprint

The step-by-step approach outlined in Info-Tech’s Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach blueprint helps you to improve the organization’s data quality by finding the root causes, remediating and fixing the issues, and building a practice to ensure that improved quality is sustained.

Bibliography

“The 5 Essential Components of a Data Strategy.” SAS, n.d. Web.

Barasch, Ron. “The Power of Big Data in Retail.” Envestnet | Yodlee, 14 Jan. 2019. Web.

Beall, Anne-Lindsay. “Big data in health care: How three organizations are using big data to improve patient care and more.” SAS, 2019. Web.

CanadianCIO. “Meeting The Data Challenge: Insights from IT Leaders in Four Sectors.” IT World Canada, 2018. Web.

Castellanos, Sara. “Morgan Stanley Center of Excellence Readies Bank for AI’s Data Demands.” The Wall Street Journal, 22 April 2019. Web.

Cole, Zak. “The Economic Value of Enterprise Architecture and How to Show It.” erwin, 25 Aug. 2016. Web.

Cooper, Christopher. “Five governance opportunities for healthcare organizations.” Collibra, 7 Feb. 2019. Web.

DalleMule, Leandro, and Thomas H. Davenport. “What’s Your Data Strategy?” HBR, May-June 2017. Accessed 2019.

DAMA International. DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK Guide). DAMA International, 2009. Accessed April 2014.

DAMA International. DAMA Data Management Body of Knowledge. 2nd ed. Technics Publications, 2017.

Díaz, Alejandro, et al. “Why data culture matters.” McKinsey & Company, Sept. 2018. Web.

Garg, Amit, et al. “Analytics in banking: Time to realize the value.” McKinsey & Company, 11 April 2017. Web.

Hannagan, Tim. Management Concepts and Practices. 3rd ed. Harlow: Prentice-Hall, 2002.

“IT World Canada.” IT World Canada, n.d. Web.

Kopanakis, John. “5 Real-World Examples of How Brands are Using Big Data Analytics.” Mentionlytics, 14 June 2018. Web.

Le Galiard, Guillaume. “Data Governance and Business Transformation.” Collibra, 25 Jan. 2019. Web.

Le Galiard, Guillaume. “Retours sur le séminaire Collibra «Gouvernance des données et transformation des entreprises»” LinkedIn, 3 Jan. 2019. Web.

“A Model for Data Governance: Does Your Organization Really Have One?” Accenture, 2011. Web.

Redman, Thomas C. “5 Ways Your Data Strategy Can Fail.” HBR, 11 Oct. 2018. Web.

“Report to the Clerk of the Privy Council: A Data Strategy Roadmap for the Federal Public Service.” Government of Canada, 2018. Web.

Roberts, Roger, and Anil Chakravarthy. “Managing data as an asset: An interview with the CEO of Informatica.” McKinsey & Company, 1 May 2019. Web.

Semeniuk, Joel. “Data Is the New Oil.” Fusion by Fresco Capital, Medium. 15 Dec. 2016. Web. Senge, Peter M., et al. The Fifth Discipline Fieldbook: Strategies and Tools for Building a Learning Organization. Currency, 1994.

Thomas, Gwen. “The DGI Data Governance Framework.” The Data Governance Institute, 2014. Web.

TOGAF© 9.1. The Open Group, 2011. Web.

Vincent, Lanny. “Differentiating Competence, Capability and Capacity.” Innovation Management Services, June 2008. Web.

Williams, Steve. Business Intelligence Strategy and Big Data Analytics. Morgan Kaufmann, 2016.

“The world’s most valuable resource is no longer oil, but data.” The Economist, 6 May 2017. Web.

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Guided Implementation #1 - Establish Business Context and Value
  • Call #1 - Understand what a data strategy is and why it needs to be aligned with the organizational strategy.
  • Call #2 - Identify the business drivers that necessitate optimizing the data strategy.
  • Call #3 - Create a tactical plan to optimize data architecture across Info-Tech’s five-tier logical data architecture model.

Guided Implementation #2 - Ensure You Have a Solid Data and Resources Foundation
  • Call #1 - Understand the key enablers of data management as well as the required resources portfolio: people and skill sets.
  • Call #2 - Determine the current state of your environment: data management enablers, people and data organizational structure, and data culture.
  • Call #3 - Understand the risk and feasibility as they relate to the data strategy.

Guided Implementation #3 - Formulate a Sustainable Data Strategy
  • Call #1 - Determine the target state and initialize the corresponding roadmap for the data strategy.
  • Call #2 - Understand the role of effective change management and communication in operationalizing the data strategy.
  • Call #3 - Consolidate and refine all findings – formulate the data strategy document for senior leadership consumption.

Author(s)

Crystal Singh

Contributors

  • Lisa Bobo, CIO, City of Rochester
  • Stephen Burt, Assistant Deputy Minister, Data, Innovation, and Analytics, and Chief Data Officer, Department of National Defence, Government of Canada
  • Dr. Irshad Siddiqui, Chief Health Information Officer (CHIO), Blessing Health System
  • Head of Enterprise Information Management at an African central bank

Search Code: 93277
Published: July 7, 2020
Last Revised: July 7, 2020

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