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.

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.

(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

(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)

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)

(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

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.

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.

Info-Tech’s methodology for data quality

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

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?

Workshop Overview

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

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 a Robust and Comprehensive Data Strategy
• Foster Data-Driven Culture With Data Literacy
• Establish Data Governance
• Data Quality (DQ) – SoftwareReviews Category

Build Your Data Quality Program

Phase 1

Assess the Scope of Data Quality

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

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