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Build Your Data Quality Program

Quality data drives quality decisions.

In today’s data-centric world, your organization’s decision-making process is becoming increasingly reliant on data. However, this shift has brought to light several critical challenges:

  • Data Accuracy & Expectations: Decisions are only as good as the data they’re based on. Decisions fall short due to data that doesn’t meet the expectations set forth.
  • Data Ownership & Validation: Without clear ownership and robust validation mechanisms, data can become a liability rather than an asset. Many organizations lack the necessary frameworks to ensure their data is consistently reliable.
  • Process Efficiency & Complexity: The tools and processes designed to drive data-driven decisions are often riddled with inefficiencies and complexities. This not only hampers productivity but also affects the overall agility of your organization.

Our Advice

Critical Insight

Quality data begins with a clear understanding of your data consumer’s needs and expectations. Organizational success is unattainable without unlocking the true value of data.

  • Decision-makers often grapple with the enigma of low data value and want to understand how to close the gap.
  • Problems with data appear too daunting to tackle within existing organizational constraints.
  • Complex operating structures create data siloes, making it challenging to organize resources and initiate value-driven projects.

Impact and Result

The caliber of your decisions and outcomes is intrinsically linked to the quality of the data that informs them. Unlock data’s potential by establishing a robust data quality program to harness the full value of your data.

  • Pinpoint the data quality issues that impede your strategic goals. Focus on problems that, when solved, will drive significant value.
  • Develop a data quality improvement plan that addresses the root causes, ensuring lasting and effective resolutions.
  • Position your data management capabilities to support your data quality initiatives, fostering a culture of continuous improvement and sustained success.

Build Your Data Quality Program Research & Tools

1. Build Your Data Quality Program Deck – A step-by-step document that walks you through how to properly align with the business and achieve data quality excellence.

This blueprint will help you establish a comprehensive approach for your data quality program by following our four-phase methodology:

  • Assess the scope of data quality.
  • Identify the root causes of data quality issues.
  • Build your data quality improvement plan.
  • Scale your data quality practice.
Build Your Data Quality Program Phases 1-4

2. Data Quality Supporting Workbook – Capture the outcomes from activities in the Build Your Data Quality Program blueprint.

The Data Quality Supporting Workbook will help you define the scope for your data quality program, analyze root cause issues, and build an improvement plan to sustainably enhance data quality across the organization.

Data Quality Supporting Workbook

3. Data Quality Playbook – A template to help you build a clear and compelling case for a data quality program for executive management and sponsors.

The Data Quality Playbook Presentation Template will document critical results and information for your data quality program.

Present this presentation to executive management and sponsors and ensure your data quality program is endorsed. Organization stakeholders can also learn about how data quality empowers them and how they can help in the data quality journey.

Data Quality Playbook
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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.

9.3/10

Overall Impact

$21,124

Average $ Saved

14

Average Days Saved

Client

Experience

Impact

$ Saved

Days Saved

Geidea

Guided Implementation

9/10

N/A

N/A

Clear explanations from Wayne, perfect planning for building data quality program as MVP as starting point, and interesting session and templates... Read More

South African Reserve Bank

Guided Implementation

9/10

$30,549

18

The reinforcement of the SARB's approach and the advice given was the best part of the experience.

FirstRand Bank Ltd.

Guided Implementation

10/10

$11,699

9

Oregon Department of Employment

Workshop

10/10

$125K

120

The facilitator was excellent. Reddy was prepared with all the materials and knowledge from our prior Data Governance workshop, so the experience w... Read More

City Of Chesapeake

Workshop

10/10

$62,999

60

This workshop helped our team dedicate time over a fixed week instead of this effort being spread over a few months. This gave the team a kick sta... Read More

Elara Caring

Guided Implementation

10/10

N/A

20

Tailored advice by the experts certainly has been the best part.

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

Workshop exceeded expectations. Excellent blend of data quality aligning to our business. The metrics, critical data elements, workflows were extre... Read More

Transport Canada

Workshop

8/10

N/A

N/A

The workshop was well delivered and the documents reflect what was discussed during the workshop. It would be a good idea to have a real life exam... Read More

Arizona Department of Environmental Quality

Guided Implementation

9/10

$7,439

5

Unknowns at this time. Depends on implementation and resources required and then measured gains. I was late to the call today. Apologies for bei... Read More

Central Arizona Project

Guided Implementation

9/10

N/A

20

Libro Credit Union

Guided Implementation

9/10

N/A

N/A

This was just an introduction so unfortunately i can not quantify cost or dollar savings yet.

TriServe Tech

Guided Implementation

10/10

$12,733

5

Best => Give me an idea how to start with a Data Quality project Worst => N/A

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Data Quality

A manifesto for strategic data quality improvement.

This course makes up part of the Data & BI Certificate.

  • 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

Now Playing:
Academy: Data Quality | Executive Brief

An active membership is required to access Info-Tech Academy

Workshop: Build Your Data Quality Program

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 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: Assess the Scope of Data Quality

The Purpose

Identification of key data quality problems that when resolved, will improve the state of strategic priorities.

Key Benefits Achieved

Data quality program scope defined for data profiling, improvement, and monitoring.

Activities

Outputs

1.1

Identify symptoms of data quality problems.

  • Data quality problem statements.
1.2

Develop data quality problem statements.

1.3

Identify critical data elements involved.

  • Critical data elements identified.
1.4

Determine the value and impact drivers of those data elements.

  • Value and impact drivers of those data elements.

Module 2: Identify the Root Causes of Data Quality Issues

The Purpose

Profile data quality issues impacting strategic priorities.

Key Benefits Achieved

Identification of root cause issues hindering data value.

Activities

Outputs

2.1

Define the data quality program scope.

  • Data quality program scope.
2.2

Perform scenario-based data lineage.

  • Data lineage scenario diagram.
2.3

Conduct fishbone root cause analyses.

  • Root cause issue identification.

Module 3: Build Your Data Quality Improvement Plan

The Purpose

Definition of data quality improvement initiatives.

Key Benefits Achieved

Development of data quality improvement plan with assignment of resources and improvement roadmap .

Activities

Outputs

3.1

Identify data quality improvement opportunities.

  • Initial data quality improvement opportunities.
3.2

Define data quality improvement working groups.

  • Initial data quality improvement working groups.
3.3

Develop the data quality improvement roadmap.

  • Data quality improvement initiative roadmap.

Module 4: Scale Your Data Quality Practice

The Purpose

Identification of data management capabilities that make data quality improvement sustainable.

Key Benefits Achieved

Alignment of most applicable data quality dimensions with data management capabilities as mechanisms for sustained improvement.

Activities

Outputs

4.1

Identify the most applicable data quality dimensions

  • Data management capabilities for sustained improvement.
4.2

Identify data management capabilities for sustained data quality improvement.

Unlock This Blueprint

Build Your Data Quality Program

Quality data drives quality decisions

Analyst perspective

You can’t be a data-driven organization with bad data.

All organizations need to make decisions at every capacity. Many are turning to data to drive decision-making with key insights. Data is an asset that is used to realize value at the organization level and across each user’s output.

However, as soon as data is created and ingested into organizations’ environments, the quality of that data can degrade in many ways. This means that value and effectiveness of decision-making is limited and is only as good as the data fueling these efforts.

Data quality is something that everyone feels the impact of, and most data owners agree that managing it is a challenge. It can kickstart the need to have, or see the benefits of having, effective data strategy and governance programs.

Data quality is not a binary state. It needs to be sufficient to help realize organizational goals and objectives. A balanced investment in people, process, and technology will enable organizations to unlock the true potential of data.

Photo of Ibrahim Abdel-Kader, Research Analyst, Data, Analytics, and Enterprise Architecture, Info-Tech Research Group.

Ibrahim Abdel-Kader
Research Analyst, Data, Analytics, and Enterprise Architecture
Info-Tech Research Group

Executive Summary

Your Challenge

Your organization is increasing their reliance on data as input to key decision-making. You are noticing several challenges:

  • Poor decisions are made because the data is not accurate enough or meeting the expectations set.
  • Ownership of data and the mechanisms needed to validate your data are not present nor effective enough.
  • Your data-driven processes and tools are inefficient, complex, and that is having an impact on productivity.

Common Obstacle

Organizational success is hindered without unlocking the true value of data.

  • Decision-makers don’t know why value from data is low and what needs to change.
  • Problems with data are perceived to be too big and overwhelming to properly rectify given organizational constraints.
  • Data siloes stemming from complex operating structures, and the considerable scale of challenges make it difficult to align resources to launch initiatives.

Info-Tech’s Approach

The quality of decisions and outcomes is as good as the quality of data used to drive them.

Unlock the true value of data by forming a data quality program that you can execute on.

  • Define the data quality problems impacting strategic priorities that are worth solving.
  • Design an improvement plan that resolves root cause issues effectively.
  • Position data management capabilities in alignment with the data quality program for sustained success.

Info-Tech Insight

Data quality starts with understanding what your data consumer needs. You will not succeed without understanding their expectations.

Data quality is slowing the pace of value from being realized

70% who work with data say that data quality is their biggest issue and is the #1 challenge in these areas:

  • 60% of those professionals agree that data quality issues impact data integration projects.
  • 50% say data quality is the biggest challenge to data integrity.
  • 41% struggle to effectively use location data (it is not fit for purpose, verified, and standardized).
    • (Source: Drexel University’s LeBow College of Business, “2023 Data Integrity Trends and Insights Report,” 2023)

Executives and professionals are aligned on data quality being a huge impediment to their work in many ways.

Pie chart titled '75% of executives claim that data is limiting business opportunities' with four sections including 'Data limits operational efficiency' at 50% and 'Data affects productivity and agility' at 27%.
(Source: Solutions Review, “Expert Reveals Data Quality Trends to be Aware of in 2023,” 2023)

Common barriers making it difficult to achieve high quality data

BAr chart titled 'Top Data Quality Issues' with 'Unstandardized data' being the largest at 48%.
(Source: Validity, “Today’s Top 7 Data Management Challenges,” 2024)

Commitment is needed to improve data quality

  • 77% Of data leaders say data-driven decision-making is a leading goals for their data programs.
  • 60% Improvements in data quality, analytics, and insights are realized in organizations with a data governance program.
    • (Source: Precisely, “Data Quality Trends for 2024,” 2024)

Infographic titled 'Build Your Data Quality Program - Your oragnization has current and upcoming initiatives that involve critical data elements. Ensure success by building an effective data quality program.' There is a list of 'Major problems that occur due to poor data quality' and what they lead to, and below a list of 'Measurement dimensions of data quality'. The main graphic is two coinciding rows of 'Elements of an effective data program' and 'Steps on an effective data program' that connect to each other in sequence. The Insight at the bottom reads 'Data quality starts with understanding what your data consumer needs. You will not succeed without understanding the quality expectation'.

What is data quality?

Definition

A method to measure the condition of data based on dimensions such as accuracy, completeness, consistency, reliability, and whether it’s up to date. It measures how well suited a data set is to serve the purpose it was intended for. (Adapted Source: Heavy.AI, 2024)

Diagram starting with 'Data Quality' surrounded by adjectives that describe it, followed by an arrow labelled 'drives' pointing to 'The purpose data is set to serve'.
(Source: DAMA-DMBOK2.0, 2024 )

How can data quality be applied?

Application of business rules to assess whether the quality of the data is fit for the purpose it is intended to be used for. If data quality standards are consistently adhered to throughout the data flow, then more value is achieved.

This means that the organization needs to align the following to sustain high quality data:

  1. Arrange to have the appropriate data roles executing on the necessary responsibilities.
  2. Optimize process and standards associated with organizational priorities and data.
  3. Make the most out of technology capabilities to effectively address data quality at scale.

High-Level Reference Architecture for Data Analytics With Data Quality Management

Diagram of a high-level reference architecture with the column headers sequentially 'Data Creation & Ingestion', 'Data Persistence', 'Data Engineering', and 'Data Delivery & Consumption', each with other steps and components broken down beneath them. They all point to 'Data Quality Dimensions'.

Common root cause areas

Root cause issues typically exist due to the ineffectiveness or lack of the following:

Roles and Process:
human error

  • Roles
    • Data owner
    • Data steward
    • Data custodian
  • Performance actions
    • Data entry process
    • Training and awareness
    • Execution of SOPs
    • Quality assurance monitoring
    • Issue escalation

Data Design:
modeling error

  • Data capabilities
    • Data architecture
    • Data modeling
    • Data integration
    • User interface design
  • Data actions
    • Enterprise conceptual model
    • Physical models derived from logical and conceptual models
    • Consistency and completeness of data records
    • Specification of data formats and attributes

Computational Consistency:
relationship error

  • Data capabilities
    • Master and reference data management
    • Data engineering
    • BI and analytics solution design
  • Data actions
    • Record deduplication
    • Data aggregation
    • Data augmentation
    • Application of computational rules
    • Data structure optimization

Key Roles and Responsibilities

Data Steward

  • Works as SME.
  • Ensures that data quality is aligned with business priorities.
  • Ensures common business data language is adopted.
  • Provides leadership and decision-making authority.

Data Owner

  • Supports and enforces data policies.
  • Assigns data security.
  • Resolves data quality escalations.
  • Communicates data standards.
  • Initiates data projects.

Data Custodian

  • Works as IT/data management expert.
  • Responsible for performance optimization and platform modernization.
  • Applies data retention policies.

“Data stewards ideally should be positioned as the key folks to serve the business and business strategy.”

(Frédéric Fourquet, Senior Product Marketing Manager, MEGA International)

Insight summary

Understand the quality expectations of strategic priorities

Data quality starts with understanding what your data consumer needs.
You will not succeed without understanding the quality expectation.

Determine the data quality problems worth solving in key strategic priorities

Data quality is in the eyes of the beholder, which is the data owner. There is a lack of trust in data because it is not fit for purpose.

Get a comprehensive view of source issues and opportunities to improve data quality

Data lineage makes hidden data visible. It is imperative to understand the flow of critical data elements from data creation to consumption for effective data quality improvement.

Execute the data quality improvement plan with the right tools for scaled impact

An effective data quality improvement plan isn’t just about fixing root cause issues. It is also about enabling the organization with sustainable monitoring and issue prevention mechanisms.

Manage data quality in tandem with foundational data capabilities

Data management capabilities (such as master data management and data architecture) are required mechanisms to sustainably improve and maintain acceptable data quality across the organization.

Present the data quality value case to get organization-wide buy-in

Explaining the value proposition of data effectively is more likely to gain the necessary leadership commitment in investing in a data quality program. The value proposition becomes clearer when you align it to a key project (such as migration or ERP).

Blueprint deliverables

Each step of this blueprint is accompanied by supporting deliverables to help you accomplish your goals:

Key deliverable:

Data Quality Playbook

Use this template to help you build a clear and compelling case for a data quality program for executive management and sponsors.

Sample of the 'Data Quality Playbook'.

Data Quality Supporting Workbook

Use this tool to help support the completion of exercises associated with the Build Your Data Quality Program blueprint and Playbook.

Sample of 'Data Quality Supporting Workbook'.

Info-Tech’s methodology for data quality

1. Assess the Scope of Data Quality

2. Identify the Root Causes of Data Quality Issues

3. Build Your Data Quality Improvement Plan

4. Scale Your Data Quality Practice

Phase Steps
  1. Identify strategic priorities where data quality causes most problems.
  2. Define the scope for your data quality program.
  1. Profile data quality issues.
  1. Design improvement initiatives to resolve root cause issues.
  2. Finalize the improvement plan.
  1. Identify data management capabilities that make data quality improvement sustainable.

Phase Outcomes
  • Identification of key data quality problems that when resolved, will improve the state of strategic priorities.
  • Data quality program scope defined for data profiling, improvement, and monitoring.
  • Identification of root cause issues hindering data value.
  • Definition of data quality improvement initiatives.
  • Development of data quality improvement plan with assignment of resources and improvement roadmap.
  • Alignment of the most applicable data quality dimensions with data management capabilities as mechanisms for sustained improvement.

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

DIY Toolkit

Guided Implementation

Workshop

Executive & Technical Counseling

Consulting
“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.” “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.” “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.” “Our team and processes are maturing; however, to expediate the journey we'll need a seasoned practitioner to coach and validate approaches, deliverables, and opportunities.” “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 five options.

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 8 to 12 calls over the course of 4 to 6 months.

What does a typical GI on this topic look like?

Phase 1

Phase 2

Phase 3

Phase 4
Call #1:
Identify strategic priorities where data quality causes most problems.

Call #2:
Define the scope for your data quality program.

Call #3:
Perform scenario-based data lineage.

Call #4:
Conduct fishbone root cause analyses.

Call #5:
Design improvement initiatives to resolve root cause issues.

Call #6:
Finalize the improvement plan.

Call #7:
Identify data management capabilities that make data quality improvement sustainable.

Call #8:
Populate the Data Quality Playbook.

Workshop Overview

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

Pre-workshop

Session 1

Session 2

Session 3

Session 4

Post-workshop

Context Prerequisites

Assess the Scope of Data Quality

Identify the Root Causes of Data Quality Issues

Build Your Data Quality Improvement Plan

Scale Your Data Quality Practice

Next Steps and Wrap-Up (Offsite)

Activities
  1. Identify strategic priorities for scope consideration.
  2. Conduct data quality diagnostic (optional).
  1. Identify symptoms of data quality problems.
  2. Develop data quality problem statements.
  3. Identify critical data elements involved.
  4. Determine the value and impact drivers of those data elements.
  1. Define the data quality program scope.
  2. Perform scenario-based data lineage.
  3. Conduct fishbone root cause analyses.
  1. Identify data quality improvement opportunities.
  2. Define data quality improvement working groups.
  3. Develop the data quality improvement roadmap.
  1. Identify the most applicable data quality dimensions.
  2. Identify data management capabilities.
  1. Develop final workshop report.
  2. Workshop debrief with project sponsor.
    • Meet with project sponsor/manager to discuss results and action items.
    • Complete in-progress items from the workshop.

Deliverables
  1. List of key strategic priorities for program scope consideration.
  2. Data quality diagnostic results to be used as input for first three activities of session 1.
  1. Data quality problem statements.
  2. Critical data elements identified.
  3. Value and impact drivers for data quality scope.
  1. Data quality program scope
  2. Data lineage scenario diagram
  3. Root cause issue identification
  1. Initial data quality improvement opportunities
  2. Initial data quality improvement working groups
  3. Data quality improvement initiative roadmap
  1. Data management capabilities for sustained improvement
  1. Data Quality Supporting Workbook
  2. Data Quality Playbook Draft

Prerequisite Checklist/ Assumptions

Before applying Info-Tech’s data quality methodology, you should have addressed the following criteria:

  • Have a data strategy promoting a data-driven culture.
  • Have competent levels of data literacy.
  • Have a data governance program enabling organizational strategies.
  • Select the right data quality tools.

To get a complete view of the field you want to explore, please refer to the following Info-Tech resources:

Build Your Data Quality Program

Phase 1

Assess the Scope of Data Quality

Phase 1

Phase 2

Phase 3

Phase 4

1.1 Identify strategic priorities for areas that data quality causes most problems.

1.2 Define the scope for your data quality program.

2.1 Profile data quality issues.

3.1 Design improvement initiatives to resolve root cause issues.

3.2 Finalize the improvement plan.

4.1 Identify data management capabilities that make data quality improvement sustainable.

This phase will walk you through the following activities:

  • Identify the data quality symptoms and problems of strategic priorities
  • Determine the value and impact of critical data elements involved in strategic priorities
  • Define the scope for your data quality program

This phase involves the following participants:

  • Strategic priority owners
  • Data owners
  • Data stewards
  • Business and IT representation

Step 1.1

Identify strategic priorities for areas that data quality causes most problems.

Activities

  • 1.1.1 Identify strategic priorities for scope consideration
  • 1.1.2 Identify symptoms of data quality problems
  • 1.1.3 Develop data quality problem statements

This step involves the following participants:

  • Strategic priority owners
  • Data owners
  • Data stewards
  • Business representation

Outcomes of this step

Identification of key data quality problems that when resolved, will improve the state of strategic priorities.

Assess the Scope of Data Quality

Step 1.1 Step 1.2

Organizational Context & Data Quality

“When you think about a data quality program, the biggest contention that anyone has from an operating and engineering perspective in communicating to leadership is they don't talk the organization’s language.” (Diraj Goel, Founder, GetFresh Ventures)

  • To ensure the data improvement strategy is organization driven, start your data quality project evaluation by understanding the organization context. You will then determine which strategic priorities use data and create a roadmap for data quality improvement.
  • Your organization context is represented by your corporate organization vision, mission, goals and objectives, differentiators, and drivers. Collectively, they provide essential information on what is important to your organization and some hints on how to achieve that. In this step, you will gather important information about your organization view and interpret the organization view to establish a data view.

  • Organization Vision
  • Organization Drivers
  • Organization Goals
  • Organization Differentiators

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On Demand

Webinar

Build Your Data Quality Program

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Speakers


Ibrahim Abdel-Kader

Research Analyst


Reddy Doddipalli

Principal Director


Andrea Malick

Advisory Director

Build Your Data Quality Program visualization

Quality data drives quality decisions.

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

9.3/10
Overall Impact

$21,124
Average $ Saved

14
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.

Need Extra Help?
Speak With An Analyst

Get the help you need in this 4-phase advisory process. You'll receive 8 touchpoints with our researchers, all included in your membership.

Guided Implementation 1: Assess the Scope of Data Quality
  • Call 1: Identify strategic priorities where data quality causes most problems.
  • Call 2: Define the scope for your data quality program.

Guided Implementation 2: Identify the Root Causes of Data Quality Issues
  • Call 1: Perform scenario-based data lineage.
  • Call 2: Conduct fishbone root cause analyses.

Guided Implementation 3: Build Your Data Quality Improvement Plan
  • Call 1: Design improvement initiatives to resolve root cause issues.
  • Call 2: Finalize the improvement plan.

Guided Implementation 4: Scale Your Data Quality Practice
  • Call 1: Identify data management capabilities that make data quality improvement sustainable.
  • Call 2: Populate the Data Quality Playbook.

Author

Ibrahim Abdel-Kader

Contributors

  • Diraj Goel, Founder, GetFresh Ventures
  • Saad Ahmed, Senior Consultant - Data Modernization, Deloitte
  • Ajitesh Shukla, Director of Data & Technology Products - Clinical Analytics, Novartis
  • David Kolinek, VP of Go-To-Market, Ataccama
  • Lenka Studnicna, Data Quality Product Manager, Ataccama
  • Haritha Allaparthi, Data Science Engineer, PiLog Group
  • 10+ anonymous contributors

Related Content: Data Management

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