Rajesh Parab
Director, Research & Advisory, Data & Analytics Practice
Info-Tech Research Group

Crystal Singh
Director, Research & Advisory, Data & Analytics Practice
Info-Tech Research Group

• Lack of data centric leadership results in downstream issues such as integration, quality, and accessibility.
• The complex nature of the data and lack of understanding leads to de-scoping delivery of data services that does not meet business needs or add value.
• Poorly designed practice and siloed platforms result in an initiative that is lengthy, costly, fizzles out, does not deliver business value, and ends up being considered a failure.

• Data problem: When the data problem is diagnosed, the organization adopts a tactical approach.
• Confirmation bias: Subject matter experts (SME) are hired to resolve the poorly defined problem, but the success of the SME is impacted by lack of architecture, technology, and organizational alignment.
• Still no value: The selected tactical approach does not provide a solid foundation or solve your data problem.
• Strategy for sake of strategy: Implementing a strategic approach for the sake of being strategic but this becomes overwhelming.
• Fall back to tactical and operational: The data services are now potentially exposed and vulnerable, which strains business continuity and increases data debt.
• Increased complexity and risk: Data silos, poor understanding, and high complexity results in an unmanageable data environment.

• Requirements: Define and align your data requirement to business.
• Capabilities: Discover data, identify data capabilities, and map your requirements.
• Practices: Design and select fit-for-purpose data practices.
• Platform: Optimize your data platform investments though sound architecture.

• Lack of data-centric leadership results in downstream issues: integration, quality, accessibility
• The organization’s data is too complex to manage without a cohesive plan.
• The complex nature of the data and a lack of understanding leads to de-scoping delivery of data services that does not meet business needs or add value.
• Poorly designed practice and siloed platforms result in an initiative that is lengthy, costly, fizzles out, does not deliver business value, and ends up being considered a failure.

• Data problem: When the data problem is diagnosed the organization adopts a tactical approach.
• Confirmation bias: Subject matter experts (SME) are hired to resolve the poorly defined problem, but the success of the SME is impacted by lack of architecture, technology, and organizational alignment.
• Still no value: the selected tactical approach does not provide a solid foundation or solve your data problem.
• Strategy for sake of strategy: Implementing a strategic approach for sake of being strategic but this becomes overwhelming.
• Fall back to tactical and operational: The data services are now potentially exposed and vulnerable, which strains business continuity and increases data debt.
• Increased complexity and risk: Data silos, poor understanding, and high complexity result in an unmanageable data environment.

Build Your Data Practice and Platform

Bring Your Data Strategy to Life

Attempting to Solve Your Data Problems

DATA SYMPTOM EXHIBITED Mismatch report, data quality issue, or similar symptom of a data problem.	DATA PROBLEM DIAGNOSED Data expert identifies it as a data problem.	COMPLEX STRATEGIC APPROACH ATTEMPTED Recognized need to attempt it strategically, but don't have capacity or culture to execute.
SOLUTION FAILS The tactical solution fails to solve the root cause of the data problem, and the data symptoms persist.	TACTICAL SOLUTION FALLBACK A quick and dirty solution is attempted in order to fix the data problem.	THE COMPLEX APPROACH FIZZLES OUT Attempted strategic approach takes too long, fizzles out.

Solving Your Data Problems

1. DEFINE YOUR DATA REQUIREMENTS Incorporate a Business to Data Approach by utilizing Info-Tech's business capability templates for identifying data needs. BUSINESS-ALIGNED DATA REQUIREMENTS
2. CONDUCT YOUR DATA DISCOVERY Understand the data behind your business problem. Identify the required data capabilities and domains as required by your business processes. RECOMMENDED DATA CAPABILITIES
3. DESIGN YOUR DATA PRACTICES Build your custom data practices based on the predefined reusable models. CUSTOMIZED DATA PRACTICE
4. ARCHITECT YOUR DATA PLATFORM Build your custom data platform based on the redefined reusable architecture patterns. CUSTOMIZED DATA PLATFORM

Develop a roadmap to establish the practice and implement the architecture as designed. Ensure continuous alignment of the practice and architecture with the business landscape.

• Phase 1: Step 1 – Define Your Data Requirement
• Phase 1: Step 2 – Conduct Your Data Discovery
• Phase 2 – Design Your Data Practice
• Phase 3 – Architect Your Data Platform

Our diagnostics are the simplest way to collect the data you need, turn it into actionable insights, and communicate with stakeholders across the organization.

Use our low-effort surveys to get the data you need from stakeholders across the organization.

Our diagnostic engine does all the heavy lifting and analysis, turning your data into usable information.

Wow your executives with the incredible insights you've uncovered. Then, get to action: make IT better.

(Source: Info-Tech’s Data Culture Diagnostic, 53 Organizations, 3138 Responses)

Build a Data-Driven Strategy Using Info-Tech Diagnostic Programs

The first step is to align business strategy with data strategy and then start building your data practice and data platform

DIY Toolkit

Guided Implementation

Workshop

Consulting

Diagnostics and consistent frameworks used throughout all four options

1. Identify your top initiatives
2. Map your data initiatives to data capabilities

1. Understand the practices value statement
2. Review the Info-Tech practice pattern
3. Initiate your practice design and setup

1. Identify your data component
2. Refine your data platform architecture
3. Design your data platform
4. Identify your new components and capabilities
5. Initiative platform build and rollout

Workshop Overview

Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

"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."

Data Needs and Discovery

Data Practice Design

Data Platform Design

Workshop 1:
Data Needs and Discovery

Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

Establish Business Context and Value

Identify Your Top Initiatives

Analyze Data Challenges

Map Data Capability

1.1 Understand/confirm your organization’s strategic goals

1.2 Classify the strategic goals and map to business drivers

1.3 Identify the business capabilities that the strategy focus is on

1.4 Identify the business processes realizing the strategy

2.1 Highlight data-related outcomes /goals to realize to fulfill the business goal

2.2 Map business data initiatives to the business strategic goals

2.3 Prioritize Data initiatives

3.1 Understand data management capabilities and framework

3.2 Classify business data requirements using Info-Tech’s classification approach

3.3 Highlight data challenges in your current environment

4.1 Map data challenges to Info-Tech data challenges

4.2 Review Info-Tech data capabilities based on prioritized initiative

4.3 Discuss Data Platform and Practice Next Steps

• Business context and strategic drivers
• Prioritized business capabilities and processes
• Data Culture Survey results analysis

• High-value business-aligned data initiative

• List of data challenges preventing data maturation with the organization

• Required data capabilities
• Data platform and practice – plan
• Initialized data management RACI

Workshop 2:
Data Practice Design

Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

Plan Your Data Practices

Design Your Data Practices 1

Design Your Data Practices 2

Design Your Data Practices 3

Prerequisite: Business context, business data requirement, and data capabilities

1.1 Understand data practice framework

1.2 Define your practice implementation approach

1.3 Review and update data management RACI

2.1 Understand Info-Tech data practice patterns for each prioritized practice

2.2 Define your practice setup for each prioritized practice

2.3 Highlight critical processes for each practice

3.1 Understand Info-Tech data practice patterns for each prioritized practice

3.2 Define your practice setup for each prioritized practice

3.3 Highlight critical processes for each practice

4.1 Understand Info-Tech data practice patterns for each prioritized practice

4.2 Define your practice setup for each prioritized practice

4.3 Highlight critical processes for each practice

4.4 Discuss data platform and practice next steps

• Data practice implementation approach
• Data management RACI

• Data practice setup pattern for your organization
• Data practice process pattern for your organization

• Data practice setup pattern for your organization
• Data practice process pattern for your organization

• Data practice setup pattern for your organization
• Data practice process pattern for your organization
• Data platform and practice – plan

Workshop 3:
Data Platform Design

Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

Data Platform Overview

Update Data Platform Reference Architecture

Design Your Data Platform

Design Your Data Practices 4

Prerequisite: Business context, business data requirement, and data capabilities

1.1 Understand data platform framework and data capabilities

1.2 Understand key data architecture principles and best practices

1.3 Shortlist data platform patterns

2.1 Map and identify data capabilities to data platform components

2.2 Build data platform architecture using Info-Tech data platform reference architecture

2.3 Highlight critical processes for each practice

3.1 Design your target data platform using Info-Tech’s data platform template

3.2 Identify new capabilities and components in your platform design

4.1 Identify new capabilities and component in your platform design

4.2 Discuss data platform initiatives

• Shortlisted data platform patterns

• Data platform reference architecture for your organization

• Data platform design for your organization

• Data platform plan

• Strategic goals and objectives are the outcomes that the organization is looking to achieve. These are typically articulated by senior leadership to the organization and are communicated in the business strategy or strategic plan.
• Business capabilities define what a business does to enable value creation in value streams. (Value streams can be thought of as the interconnected activities that support strategic objectives.)
• Business capabilities represent stable business functions and typically will have a defined business outcome. Business capabilities can be thought of as business terms defined using descriptive nouns such as “marketing” or “research and development.”
• By working with the right stakeholders, you can develop a business capability map that speaks a common language and accurately depicts your business.
• This depiction of the business will be vital for building a data practice and platform that delivers value by enabling business processes and capabilities and ultimately, the organization’s business objectives.

Info-Tech Insight:

A business capability map can be thought of as a visual representation of your organization’s business capabilities and hence represents a view of what your data practice and data platform need to support.

For more information, refer to Info-Tech’s Document Your Business Architecture.

Validate your business capability map with the right stakeholders, including your executive team, business unit leaders, and/or other key stakeholders.

Info-Tech Tip: Leverage your business capability map verification session with these key stakeholders as a prime opportunity to share and explain the role of data, its practice and platform, in supporting the value realization capabilities under discussion. This will help to build understanding, awareness, and visibility of the data practice and platform program.

For this business capability map, download Info-Tech’s Industry Reference Architecture for Higher Education.

• Map business strategic goals to the organization’s data goals.
• Brainstorm the organization’s ideal state and data environment with key stakeholders.

• Obtain confirmation of the organization’s strategic goals, objectives, priorities, mandates, and drivers.
• Identify the business capabilities and associated business processes pertinent to realizing the organization’s strategic goals, objectives, priorities, mandates, and drivers.
• Identify the data related outcomes, and goals required to fulfill the identified business goals.
• Define the ideal desired data environment needed to deliver and support the business context as defined above.

• Determine the data capabilities required to address challenges and deliver on data outcomes.

• Identify your ideal data environment to achieve your data goals.
• Map these to Info-Tech’s ideal/amazing classification.
• Identify and highlight the data challenges faced in achieving this desired outcome.
• Map the data challenges to the data capabilities required to realize the desired data outcome.

• Define the roles, accountabilities, and responsibilities for the lead of the different data practices.
• Assign a lead for each of the data management practice areas. Consider: who will lead your data architecture practice, your data quality practice, etc.?
• Smaller organizations will likely see an overlap in the assignment of these roles. As the leader of data in your organization, here are other areas you should have under consideration as you design, build, and formalize your data practice(s):
  • Resource planning
  • Role assignments
  • Effective exposure or advertising of services to the organization
  • Engagement model

• Define your practice implementation approach
• Data Management RACI
• Data Governance Practice
• Data Architecture Practice
• Data Quality Management Practice
• Metadata Management Practice
• Master Data Management Practice
• Data Integration Practice
• Data Warehouse/ Data Lake Practice
• Reporting and Analytics Practice

1. Incremental
2. Prioritized Groups
3. Parallel

General Approach to Setting Up Data Practices

Guidelines for designing and establishing your various data practices.

Data Management Data Practice Setup

• Define the practice lead’s accountabilities/responsibilities
• Assign the practice lead
• Design the practice, defining the details of the practice (including the core capabilities, accountabilities, processes, essential roles, the elements that provide oversight or governance of the practice, the practice’s services and deliverables or output for the organization).
  
  
  • Define services and accountabilities:
    1. Define deployment and engagement model
    2. Define practice governance and metrics
    3. Define processes and deliverables
    4. Summarize capabilities
    5. Activity slide to assign the skills to role

1. Define the practice lead accountabilities /responsibilities for data practice: who will lead your data architecture and data quality practice?
2. Assign a practice lead for each practice area.
   • Resource planning
   • Role assignment
   • Service exposure
   • Engagement model
   • Embed service

Data Governance

Design and Establish Your Data Governance Practice

Info-Tech Insight:

Your organization’s value streams and the associated business capabilities require effectively governed data. Without this, you face elevated operating costs, missed opportunities, eroded stakeholder satisfaction, and increased business risk.

Data Governance Is a Foundational Data Management Capability

• Defined accountability and responsibility for data across the organization.
• Standardization, knowledge, and common understanding of data assets.
• Increased trust and confidence in traceable data.
• Improved data ROI and reduced data debt.
• A solid foundation for advanced uses of data (advanced analytics, AI, ML).
• Increased transparency and ethical use and handling of data in a culture of data excellence.
• Enhanced data operations including brokered expansion of the data ecosystem, bringing in external data and new sources, sharing data with partners, and operating in data marketplaces.

• Establishing and maintaining common business data language.
• Data quality management that is aligned with business priorities.
• Enterprise projects data advisory.

See Data Governance Practice – Value Statement

• Data governance organizational structure model
• Data governance roles and responsibilities model
• Data governance roadmap
• Data governance guiding principles
• Business data glossary
• Data catalog and classification
• Data policy standards and guidelines catalog
• Data governance communication

• Understand the Data Governance Value Statement
• Understand the Data Governance Practice Pattern
• Understand the Data Governance Organization Structure Pattern
• Obtain Approval to Design and Establish the Data Governance Practice
• Define Data Governance Leadership

Output: Senior leaders in the organization understand the value of data governance, the need for well-defined and established practice, and have given the approval or appointed a data (governance) leader.

• Form Initial Steering Committee
• Define Data Governance Practice: capabilities, roles, accountabilities, processes, services, and deliverables
• Build Your Data Governance Organizational Structure Based on Info-Tech’s Models
• Define and Assign Data Governance Roles: Data owner, steward, custodians, and working groups as needed.

Data Architecture

Design and Establish Your Data Architecture Practice

Info-Tech Insight:

Data architecture is not just about models. Viewing data architecture as just technical data modeling can lead to a data environment that does not aptly serve or support the business. Identify your business’s priorities and adapt your data architecture to those needs.

Data Architecture Is a Core Data Management Capability

• Bringing strategic agility to the organization.
• Translating business needs and goals into data.
• Facilitating the alignment between business and IT.
• Acting as a change agent.

• Data architecture development
• Data modeling
• Data integration architecture
• Analytical model
• Data lineage and data flow provisions

• Data model artefacts for development
• Enterprise data integration architecture
• DDL for DBA’s
• Data pipeline architecture
• Published data models and metadata
• Business capability to conceptual data model mapping
• Data platform architecture
• Master data architecture

The accountabilities and responsibilities for the data architecture practice have been established and assigned to a practice lead (example: data architecture lead may reside under existing enterprise architecture practice or data management practice).

1. Download and review Data Architecture Practice Pattern (Level 1 – Data Architecture Practice Pattern). Download the Build Your Data Practice and Platform Models ArchiMate file
2. Download and review Data Modeling Practice Pattern (Level 1 – Data Modeling Practice Pattern). Download the Build Your Data Practice and Platform Models ArchiMate file
3. Highlight importance of data modeling practice within or alongside your data architecture practice

Download and Update

• Data Architecture Operating Model
• Data Modeling Practice Pattern
• DBA Practice Pattern
• Data Architecture Organization Pattern

1. Ensure CIO or head of IT/Data is ready to initiate data architecture practice.
2. Align enterprise architecture, software development, and data management roles with stakeholder.
3. Download and review Data Architecture Practice Pattern (Level 1 – Data Architecture Practice Pattern).

Download the Build Your Data Practice and Platform Models ArchiMate file

4. Start establishing your data architecture practice to:
   • 4.1 Define services and accountabilities
   • 4.2 Define processes and deliverables by stakeholder
   • 4.3 Design practice operating model
   • 4.4 Perform skills inventory and design roles
   • 4.5 Determine practice governance and metrics
   • 4.6 Summarize practice capabilities

Data Quality Management

Design and Establish Your Data Quality Management Practice

Info-Tech Insight:

Data quality suffers most at the point of entry. The resulting domino effect of error propagation makes it one of the most costly forms of data quality errors. Fix data ingestion, whether through improving your application and database design or improving your data ingestion policy, and you will fix a majority of data quality issues.

Data Quality Management Is a Core Data Management Capability

• Proactive data quality management
• Improving data usage and trust
• Reducing cost and inefficiencies
• Becoming data-driven

• Data quality assessment
• Data quality process management
• Intake of data quality issues
• Data problem service desk
• Data quality dashboard
• Root-cause analysis
• Develop preventative and corrective actions

• Data quality certificate
• Data quality governance report
• Data quality root-cause analysis and recommendation
• Data quality standard, procedures, and guidelines
• Data quality strategy and framework
• Data quality SLA’s
• Data quality issue list and status
• Publish preventative and corrective action list

The accountabilities and responsibilities for the data quality practice have been established and assigned to a practice lead (example: data quality lead may reside under existing quality management practice or data governance practice).

1. Download and review Data Quality Practice Pattern (Level 1 – Data Quality Practice Pattern) in ArchiMate file. Download the Build Your Data Practice and Platform Models ArchiMate file
2. Review and update data quality processes for your organization.

1. Ensure data governance committees are established.
2. Align data quality practice lead responsibilities with data governance working committee.
3. Download and review Data Quality Practice Setup (Level 1 – Data Quality Practice Setup).

Download the Build Your Data Practice and Platform Models ArchiMate file

4. Start establishing your Data Quality Practice
   • 4.1 Define services and accountabilities
   • 4.2 Define processes and deliverables by stakeholder
   • 4.3 Design practice operating model
   • 4.4 Perform skills inventory and design roles
   • 4.5 Determine practice governance and metrics
   • 4.6 Summarize practice capabilities

Download and Update

• Process Template: Data Quality Process

Metadata Management

Design and Establish Your Metadata Management Practice

Info-Tech Insight:

Metadata should be pliable. It needs to grow with the changing cultural and corporate vernacular and knowledge and adapt to changing needs.

Metadata Management Is a Core Data Management Capability

• Increased Confidence in Data
• Reduced Data-Orientation Research Time
• Provide Organizational Understanding of Increased Value of Master Data
• Reduction of Out-of-Date or Incorrect Data
• Collect and Manage Metadata from Diverse Sources
• Improved Time-to-Market - Reduction in ADLC
• Increased Data Quality and Timeliness
• Provide Standardized Metadata
• Reduction in Training Cost
• Support Regulatory Compliance
• Ensure Metadata Quality and Security

• Business term definition management
• Metadata publishing service
• Business term definition
• Metadata repository management
• Metadata ingestion
• Data lineage service

• Business data glossary
• Data dictionary
• Data catalog
• Report catalog
• Data lineage
• Metadata standards
• Metamodel
• Taxonomy

The accountabilities and responsibilities for the metadata management practice have been established and assigned to a practice lead (example: data quality lead may reside under existing metadata management practice or data governance practice).

1. Download and review Metadata Practice Pattern (Level 1 – Metadata Management Practice Pattern). Download the Build Your Data Practice and Platform Models ArchiMate file
2. Review and update the metadata capabilities for your organization

1. Ensure data governance committees are established.
2. Align metadata working group responsibilities with data governance committee.
3. Download and review Metadata Practice Setup (Level 1 – Metadata Practice Setup)

Download the Build Your Data Practice and Platform Models ArchiMate file

4. Start establishing your metadata practice
   • 4.1 Define services and accountabilities
   • 4.2 Define processes and deliverables by stakeholder
   • 4.3 Design practice operating model
   • 4.4 Perform skills inventory and design roles
   • 4.5 Determine practice governance and metrics
   • 4.6 Summarize practice capabilities
5. Define key metadata deliverables and processes

Download and Update

• Maintain Business Glossary Process

Master Data Management

Design and Establish Your Master Data Management Practice

Info-Tech Insight:

Successful MDM requires a comprehensive approach. To be successfully planned, implemented, and maintained it must include effective capabilities in the critical processes and subpractices of data management.

Master Data Management Is a Core Data Management Capability

• Establishing single source of master data
• Establishing single source of reference data

• Master data service
• Reference data service
• Master data integration services
• Master data ontology
• Master data API’s
• Reference data API’s

• Master data services
• Master data catalog
• Integrated master store
• Master data taxonomy
• Master data strategy
• Master data platform architecture
• Master data catalog
• Reference data catalog
• Reference data mapping catalog
• Master data architecture
• Master data policies and procedures

Master data management practice pattern includes key services and outputs that must be delivered by establishing core capabilities, accountabilities, roles, and governance for the practice.

Assumption:

The accountabilities and responsibilities for the master data management practice have been established and assigned to a practice lead (example: metadata management lead may reside under existing content management practice or data governance practice).

1. Download and review Master Data Management Practice Pattern (Level 1 – Master Data Management Practice Pattern). Download the Build Your Data Practice and Platform Models ArchiMate file
2. Review and update master data management processes

Info-Tech Insight

An organization with heavy merger and acquisition activity poses a significant master data challenge. Prioritize your master data practice based on your organization’s ability to locate and maintain a single source of master data.

1. Ensure data governance committees are established.
2. Align master data management working group responsibilities with data governance committee.
3. Download and review Master Data Management Practice Setup (Level 1 – Master Data Management Practice Setup).

Download the Build Your Data Practice and Platform Models ArchiMate file

4. Start establishing your master data practice
   • 4.1 Define services and accountabilities
   • 4.2 Define processes and deliverables by stakeholder
   • 4.3 Design practice operating model
   • 4.4 Perform skills inventory and design roles
   • 4.5 Determine practice governance and metrics
   • 4.6 Summarize practice capabilities
5. Define key master data management deliverable and processes.

Download and Update

• Process Template: MDM Conflict Resolution

Data Integration

Design and Establish Your Data Integration Practice

Info-Tech Insight:

Every IT project requires data integration. Any change in the application and database ecosystem requires you to solve a data integration problem.

Data Integration Is a Core Data Management Capability

A loose coupling integration strategy helps mitigate the challenges of point-to-point integration and realize the benefits of well-connected data.

• Data migration
• Data delivery service
• Data replication service
• ETL service
• Data streaming service
• Data interoperability solution

• Data interface
• Source to target mapping
• Data lineage
• Common message model

Data integration practice pattern includes key services and output that must be delivered by establishing core capabilities, accountabilities, roles, and governance for the practice.

Assumption:

The accountabilities and responsibilities for the data integration practice have been established and assigned to a practice lead (example: Data integration lead may reside under existing application or data development team).

1. Download and review Data Integration Management Practice Pattern (Level 1 – Data Integration Practice Pattern). Download the Build Your Data Practice and Platform Models ArchiMate file
2. Review and update integration processes for your organization.

Info-Tech Insight

Mid- to large-size organizations may benefit from establishing integration COE to effectively manage the flow of data within the organization and avoid spaghettification of the architecture.

1. Ensure data integration working group/COE is established.
2. Align data integration working group responsibilities with CIO expectation.
3. Download and review Data Integration Practice Setup (Level 1 – Data Integration Practice Setup).

Download the Build Your Data Practice and Platform Models ArchiMate file

4. Start establishing your data integration practice
   • 4.1 Define services and accountabilities
   • 4.2 Define processes and deliverables by stakeholder
   • 4.3 Design practice operating model
   • 4.4 Perform skills inventory and design roles
   • 4.5 Determine practice governance and metrics
   • 4.6 Summarize practice capabilities
5. Define key data integration deliverables and processes

Download and Update

• Process Template: Data Integration Process Template

Data Warehouse

Design and Establish Your Data Warehouse Practice

Info-Tech Insight:

Explore the art-of-the-possible. Don’t get stuck trying to gather technical requirements from business users who don’t know what they don’t know.

Data Warehouse/Data Store Is a Core Data Management Capability

As a centralized point of storage for current and historical data, the data warehouse has become synonymous with creating a “single source of truth.”

It has provided organizations with a method to consolidate disparate databases and data stores to:

• Create a more comprehensive view of corporate data.
• Facilitate reliable analyses that support data-driven decision making.

• Intake of new data sources for analysis
• Delivery of historical data for analysis
• Aggregation of the data for analysis
• Performance management to enable faster time to analysis or reporting

• Populated historical data
• Data warehouse model
• Data lineage
• Data aggregation and engineering designs

Data warehouse pattern includes key services and outputs that must be delivered by establishing core capabilities, accountabilities, roles, and governance for the practice.

Assumption:

The accountabilities and responsibilities for the data warehouse practice have been established and assigned to a practice lead (example: data warehouse lead may reside under existing IT application or data development team).

1. Download and review Data Warehouse Management Practice Pattern (Level 1 – Data Warehouse Practice Pattern). Download the Build Your Data Practice and Platform Models ArchiMate file
2. Review and update data warehouse capabilities for your organization.

Info-Tech Insight

Data warehouse and data lake are data repositories. The need for one over other or both needs to be carefully evaluated based on organization maturity and requirement. Many organization have prematurely embarked on more complex data lake implementation.

1. Ensure your data warehouse development group is established.
2. Align data warehouse development group responsibilities with CIO expectations.
3. Download and review Data Warehouse Practice Setup (Level 1 – Data Warehouse Practice Setup).

Download the Build Your Data Practice and Platform Models ArchiMate file

4. Start establishing your data warehouse practice
   • 4.1 Define services and accountabilities
   • 4.2 Define processes and deliverables by stakeholder
   • 4.3 Design practice operating model
   • 4.4 Perform skills inventory and design roles
   • 4.5 Determine practice governance and metrics
   • 4.6 Summarize practice capabilities
5. Define key data warehouse deliverables and processes

Download and Update

• Process Template: Data Warehouse Development Process Template

Reporting and Analytics

Design and Establish Your Reporting and Analytics Practice

Info-Tech Insight:

Formulating an enterprise reporting and analytics strategy requires the business vision and strategies to first be substantiated. Any optimization to the data warehouse, integration, and source layer is in turn driven by the enterprise reporting and analytics strategy.

Reporting and Analytics Is a Vital Data Management Capability

The core purpose of reporting and analytics is to provide the right data to the right users at the right time and in a format that is easily consumable and actionable. It bridges the gap between an organization’s data assets and consumable information that facilitates insight generation and informed decision making.

• Report and analytics development services
• Data analytics service
• Data service
• Data query and support service
• Data audit and quality service
• Data discovery service
• Self service support service

• Report catalog
• Business user reports
• Support center
• Self service strategy
• Data sourcing plan
• Reporting and analytics standards and guideline

Reporting and Analytics pattern includes key services and output that must be delivered by establishing core capabilities, accountabilities, roles, and governance for the practice.

Assumption:

The accountabilities and responsibilities for the reporting and analytics practice have been established and assigned to a practice lead (example: data warehouse lead may reside under existing IT application or data development team).

1. Download and review Reporting Analytics Practice Pattern (Level 1 – Reporting and Analytics Practice Pattern). Download the Build Your Data Practice and Platform Models ArchiMate file
2. Review and update reporting and analytics capabilities for your organization.

Info-Tech Insight

The demand for advanced analytics has grown over the years. However, many organizations continue to struggle with basic reporting and analytics implementation. The maturation of reporting and analytics needs to happen before embarking on the advanced analytics initiative.

1. Ensure reporting and analytics working group is established.
2. Align reporting and analytics responsibilities with data governance.
3. Download and review Reporting and Analytics Practice Setup (Level 1 – Reporting and Analytics Practice Setup).

Download the Build Your Data Practice and Platform Models ArchiMate file

4. Start establishing your reporting and analytics practice
   • 4.1 Define services and accountabilities
   • 4.2 Define processes and deliverables by stakeholder
   • 4.3 Design practice operating model
   • 4.4 Perform skills inventory and design roles
   • 4.5 Determine practice governance and metrics
   • 4.6 Summarize practice capabilities
5. Define key reporting and analytics deliverables and processes.

Download and Update

• Process Template: Data Warehouse Development Process Template

54% of participants do not feel they have all the necessary tools to process the data in their area.

52% Of participants do no have access to the reporting when they need it via an easy solution.

(Source: Info-Tech’s Data Culture Diagnostic, 53 organizations, 3138 responses)

“A data platform is an integrated set of technologies that collectively meets an organization’s end-to-end data needs. It enables the acquisition, storage, preparation, delivery, and governance of your data, as well as a security layer for users and applications” (MongoDB)

Info-Tech Insight

Build your conceptual data architecture based on well-thought-out formulated patterns that align with your organization’s needs and environment.

• Identify Required Data Component
• Refine Your Data Architecture Pattern
• Design Your Target Data Platform
• Compare Current State With Target Stage Design
• Develop Data Platform Implementation Roadmap

• Data Modeling
• Master and Reference Data
• Data Integration Approach
• Historical Data Storage
• Analytics and Data Science
• Reporting Component

• Remove optional component MDM and ODS service unless absolutely needed.
• Remove advance analyst component unless absolutely needed in near future.

Download the Build Your Data Practice and Platform Models ArchiMate file

Download the Build Your Data Practice and Platform Models ArchiMate file

• Populate the template with agreed upon components
• Populate the template with software components

• Highlight new capabilities and components in your data platform design.
• Highlight capabilities and components in your data platform design that require significant modernization or redesign.

• Plan new component rollout
• Plan software vendor selection
• Plan platform modernization efforts
• Assign project manager/implementation coordinator

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

Streamline your data management program with our simplified framework.

Establish data trust and accountability with strong governance.

Gauge your organization’s current data culture. Contact your account representative for details.

Enhance data literacy in your organization to build bridges between the business leads who own the data and IT, who is its custodian.

Data needs to be good, but truly spectacular data may go unnoticed. Provide the right level of data quality, with the appropriate effort, for the correct usage. This blueprint will help you determine what “the right level of data quality” means and create a plan to achieve that goal for the business.

Integrate your data or disintegrate your business.

Make sure your most important data is accurate and accessible across your business units to ensure optimal decision support and to monetize your data assets.

Optimizing data architecture requires a plan, not just a data model.

Enable the business to achieve operational excellence, client intimacy, and product leadership with an innovative, agile, and fit-for-purpose data architecture practice.

Business insights come from both structured and unstructured data.

Deliver actionable business insights by creating a business-aligned reporting and analytics strategy.

Accelerate data-driven decision making.

Use data architecture best practices to prepare data for reporting and analytics.

Establish a well-architected core model with just enough oversight and governance.

Arm yourself with a data strategy that puts your data to work for you. This Info-Tech Concierge Service offers expert review of your data strategy and guidance on how to improve it.

Be the voice of data in a time of transformation. This Info-Tech Concierge Service helps you devise a 100-day plan to realize early value and set yourself up for long-term success.

Fast-track your AI explorations by learning from early adopters.

Build your target-state architecture from predefined best-practice building blocks.

Control machine bias to prevent discriminating against your consumers and damaging your organization.