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Build Your Data Quality Program

Quality data drives quality business decisions.

  • Experiencing the pitfalls of poor data quality and failing to benefit from good data quality, including:
    • Unreliable data and unfavorable output.
    • Inefficiencies and costly remedies.
    • Dissatisfied stakeholders.
  • The chances of successful decision-making capabilities are hindered with poor data quality.

Our Advice

Critical Insight

  • Address the root causes of your data quality issues and form a viable data quality program.
    • Be familiar with your organization’s data environment and business landscape.
    • Prioritize business use cases for data quality fixes.
    • Fix data quality issues at the root cause to ensure proper foundation for your data to flow.
  • It is important to sustain best practices and grow your data quality program.

Impact and Result

  • Implement a set of data quality initiatives that are aligned with overall business objectives and aimed at addressing data practices and the data itself.
  • Develop a prioritized data quality improvement project roadmap and long-term improvement strategy.
  • Build related practices such as artificial intelligence and analytics with more confidence and less risk after achieving an appropriate level of data quality.

Build Your Data Quality Program

Start here – read the Executive Brief

Read our concise Executive Brief to find out why you should establish a data quality program, review Info-Tech’s methodology, and understand the four ways we can support you in completing this project.

1. Define your organization’s data environment and business landscape

Learn about what causes data quality issues, how to measure data quality, what makes a good data quality practice in relation to your data and business environments.

2. Analyze your priorities for data quality fixes

Determine your business unit priorities to create data quality improvement projects.

3. Establish your organization’s data quality program

Revisit the root causes of data quality issues and identify the relevant root causes to the highest priority business unit, then determine a strategy for fixing those issues.

4. Grow and sustain your data quality practices

Identify strategies for continuously monitoring and improving data quality at the organization.


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.

8.8/10


Overall Impact

$44,733


Average $ Saved

36


Average Days Saved

Client

Experience

Impact

$ Saved

Days Saved

Oregon Department of Employment

Workshop

10/10

$123K

120

Elara Caring

Guided Implementation

10/10

N/A

20

MHI Canada Aerospace, Inc.

Guided Implementation

9/10

N/A

2

Atlantic Canada Opportunities Agencies

Guided Implementation

6/10

$10,000

2

University of Pittsburgh Medical Center

Workshop

9/10

$247K

50

Transport Canada

Workshop

8/10

N/A

N/A

Arizona Department of Environmental Quality

Guided Implementation

9/10

$7,439

5

Central Arizona Project

Guided Implementation

9/10

N/A

20

Libro Credit Union

Guided Implementation

9/10

N/A

N/A

TriServe Tech

Guided Implementation

10/10

$12,733

5


Data Quality

Please note that the Academy content will be updated in Winter 2021.

A manifesto for strategic data quality improvement.
This course makes up part of the Data & BI Certificate.

Now Playing: Academy: Data Quality | Executive Brief

An active membership is required to access Info-Tech Academy
  • Course Modules: 5
  • Estimated Completion Time: 2-2.5 hours
  • Featured Analysts:
  • Crystal Singh, Research Director, Applications
  • David Piazza, VP of Research & Advisory, Applications Practice

Onsite Workshop: Build Your Data Quality Program

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: Define Your Organization’s Data Environment and Business Landscape

The Purpose

  • Evaluate the maturity of the existing data quality practice and activities.
  • Assess how data quality is embedded into related data management practices.
  • Envision a target state for the data quality practice.

Key Benefits Achieved

  • Understanding of the current data quality landscape
  • Gaps, inefficiencies, and opportunities in the data quality practice are identified
  • Target state for the data quality practice is defined

Activities

Outputs

1.1

Explain approach and value proposition

  • Data Quality Management Primer
1.2

Detail business vision, objectives, and drivers

  • Business Capability Map Template
1.3

Discuss data quality barriers, needs, and principles

  • Data Culture Diagnostic
1.4

Assess current enterprise-wide data quality capabilities

  • Data Quality Diagnostic
1.5

Identify data quality practice future state

  • Data Quality Problem Statement Template
1.6

Analyze gaps in data quality practice

Module 2: Create a Strategy for Data Quality Project 1

The Purpose

  • Define improvement initiatives
  • Define a data quality improvement strategy and roadmap

Key Benefits Achieved

  • Improvement initiatives are defined
  • Improvement initiatives are evaluated and prioritized to develop an improvement strategy
  • A roadmap is defined to depict when and how to tackle the improvement initiatives

Activities

Outputs

2.1

Create business unit prioritization roadmap

  • Business Unit Prioritization Roadmap
2.2

Develop subject areas project scope

  • Subject area scope
2.3

By subject area 1 data lineage analysis, root cause analysis, impact assessment, and business analysis

  • Data Lineage Diagram

Module 3: Create a Strategy for Data Quality Project 2

The Purpose

  • Define improvement initiatives
  • Define a data quality improvement strategy and roadmap

Key Benefits Achieved

  • Improvement initiatives are defined
  • Improvement initiatives are evaluated and prioritized to develop an improvement strategy
  • A roadmap is defined to depict when and how to tackle the improvement initiatives

Activities

Outputs

3.1

Understand how data quality management fits in with the organization’s data governance and data management programs

3.2

By subject area 2 data lineage analysis, root cause analysis, impact assessment, and business analysis

  • Data Lineage Diagram
  • Root Cause Analysis
  • Impact Analysis

Module 4: Create a Strategy for Data Quality Project 3

The Purpose

Determine a strategy for fixing data quality issues for the highest priority business unit

Key Benefits Achieved

Strategy defined for fixing data quality issues for highest priority business unit

Activities

Outputs

4.1

Formulate strategies and actions to achieve data quality practice future state

4.2

Formulate a data quality resolution plan for the defined subject area

  • Data Quality Improvement Plan
4.3

By subject area 3 data lineage analysis, root cause analysis, impact assessment, and business analysis

  • Data Lineage Diagram

Module 5: Create a Plan for Sustaining Data Quality

The Purpose

  • Plan for continuous improvement in data quality
  • Incorporate data quality management into the organization’s existing data management and governance programs

Key Benefits Achieved

  • Sustained and communicated data quality program

Activities

Outputs

5.1

Formulate metrics for continuous tracking of data quality and monitoring the success of the data quality improvement initiative

  • Data Quality Practice Improvement Roadmap
5.2

Workshop Debrief with Project Sponsor

  • Data Quality Improvement Plan (for defined subject areas)
5.3

Meet with project sponsor/manager to discuss results and action items

5.4

Wrap up outstanding items from the workshop, deliverables expectations, GIs

About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

Member Rating

8.8/10
Overall Impact

$44,733
Average $ Saved

36
Average Days Saved

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.

Read what our members are saying

What Is a Blueprint?

A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.

Quality data drives quality business decisions.

Need Extra Help?
Try Our Guided Implementations

Get the help you need in this 4-phase advisory process. You'll receive 9 touchpoints with our researchers, all included in your membership.

Guided Implementation #1 - Define your organization’s data environment and business landscape
  • Call #1 - Learn about the concepts of data quality and the common root causes of poor data quality.

Guided Implementation #2 - Analyze your priorities for data quality fixes
  • Call #1 - Identify the core capabilities of IT for improving data quality on an enterprise scale.
  • Call #2 - Determine which business units use data and require data quality remediation.

Guided Implementation #3 - Establish your organization’s data quality program
  • Call #1 - Create a plan for addressing business unit data quality issues according to priority of the business units based on value and impact of data.
  • Call #2 - Revisit the root causes of data quality issues and identify the relevant root causes to the highest priority business unit.
  • Call #3 - Determine a strategy for fixing data quality issues for the highest priority business unit.

Guided Implementation #4 - Grow and sustain your data quality practices
  • Call #1 - Identify strategies for continuously monitoring and improving data quality at the organization.
  • Call #2 - Learn how to incorporate data quality practices in the organization’s larger data management and data governance frameworks.
  • Call #3 - Summarize results and plan next steps on how to evolve your data landscape.

Author(s)

Crystal Singh

Contributors

  • Izabela Edmunds, Information Architect, Mott MacDonald
  • Sujay Deb, Director of Data Analytics Technology and Platforms, Export Development Canada
  • Akin Akinwumi, Manager of Data Governance, Startech.com
  • Asif Mumtaz, Data & Solution Architect, Blue Cross Blue Shield Association
  • Diraj Goel, Growth Advisor, BC Tech
  • Patrick Bossey, Manager of Business Intelligence, Crawford and Company
  • Anonymous contributors
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