Setting Up Your Data Quality Program.

A Data Quality Assessment and Strategy Approach

Your Situation and Challenges

Your organization is experiencing the pitfalls of poor data quality, including:

  • Unreliable data and unfavorable output.

  • Inefficiencies and costly remedies.

  • Dissatisfied stakeholders.

Poor data quality hinders successful decision-making.

Not understanding the purpose and execution of data quality causes some disorientation with your data.

  • Failure to realize the importance/value of data quality.

  • Unsure of where to start with data quality.

  • Lack of investment in data quality.

Organizations tend to adopt a project mentality when it comes to data quality instead of taking the strategic approach that would be all-around more beneficial in the long term.

Address the root causes of your data quality issues by forming a viable data quality program.

  • Be familiar with your organization’s data environment and business landscape.

  • Prioritize business use cases for data quality fixes.

  • Fixing data quality issues at the root cause to ensure a proper foundation for your data to flow.

It is important to sustain best practices and grow your data quality program.

Challenges

Common Obstacles

Insight

Building a Data Quality Program

Our pragmatic approach is rooted in delivering value quickly while having a solid, best-practice approach to build on. We ensure data culture, technology, processes, organization, and real business problems are integrated into a program that works. Even more critical, ensure you have a proactive program that focuses on facilitating data quality improvement.

How Incept Can Help

Data quality suffers most at the point of entry. This is one of the causes of the domino effect of data quality – and can be one of the most costly forms of data quality errors due to error propagation. In other words, fix data ingestion, whether through improving your application and database design or improving your data ingestion policy, and you will fix a large majority of data quality issues.

In a nutshell, data debt can hinder ROI, lack of trust leads to low usage, bad data can risk compliance and increase costs, poor data quality hinders the adoption of data-driven technology, and running on bad data can harm the customer experience.

Based on the leading professional frameworks like DAMA (DMBOK), our approach assumes that data quality management exists within each of the data practices, information dimensions, business resources, and subject areas that comprise the larger data management framework, which has data governance at the center.

Here is how we help:

  • This step identifies the foundational understanding of your data and business landscape, the essential concepts around data quality, as well as the core capabilities and competencies that IT needs to effectively improve data quality.

  • To begin addressing specific, business-driven data quality projects, you must identify and prioritize the data-driven business units. This will ensure that data improvement initiatives are aligned to business goals and priorities.

  • After determining whose data is going to be fixed based on priority, determine the specific problems that they are facing with data quality, and implement an improvement plan to fix it.

  • Item descriptionNow that you have put an improvement plan into action, make sure that the data quality issues don’t keep cropping up. Integrate data quality management with data governance practices into your organization and look to grow your organization’s overall data maturity.

Deliverables

  • Before attempting to identify solutions to the data quality issues in the business unit identified, you must define the problem. To define the problem, we ask the members of the business unit the questions from this template to identify the symptoms of poor data quality. Nothing like good documentation.

  • This document helps evaluate current and target capabilities for your data quality practice and related sub-practices in conjunction with the overall Data Quality Program. We use this tool to build an understanding of your current environment and plan how to achieve target-level performance through the creation of initiatives that address recommended action items and performance gaps.

    In addition, this worksheet serves as a resource for planning and roadmapping data quality improvement projects based on business unit priority.

  • We use data lineage diagrams to create a comprehensive view of the applications, databases, and data that exist within a business unit.

    Based on your project’s parameters and oversight, we can either use it as an internal document to centralize the work or adapt it into a presentation document for educating project stakeholders on progress and findings.

  • The Data Quality Improvement Plan Template is a valuable tool that documents critical project results and information as you transition through the various phases of your data quality improvement initiative. This deliverable helps you identify and address data issues and barriers, brainstorm objectives, and document important notes and observations that can serve as inputs to your initiative and roadmap development.

    The template provides a structured approach to gathering and documenting the business context of your organization, analyzing how data quality is impacting your business attributes, assessing and prioritizing subject areas, identifying root causes of data issues, and proposing solutions to fix them.

    Additionally, the template includes a testing approach section that helps you validate the effectiveness of your proposed solutions. By using this template, you can revisit critical information on an ongoing basis, build your own final presentations and executive documents, and ensure that your data quality initiative is aligned with your business objectives.

Clarity is important. Having a firm grasp on what’s expected when you engage us, including objectives and deadlines, is crucial to your success. We like to make things clear so you know what you’re getting.

Data Quality Improvement Plan

For a data asset, a group of data assets, a data domain, or a critical data entity.

As an option or subsequent engagement, Incept, as a data quality consulting firm, can support an organization in creating a tangible plan to fix specific data quality problems in a structured and systematic manner. Incept's approach to solving data quality problems starts by first understanding the business context and objectives of a specific domain or data asset to focus on.

Once a domain or data asset has been selected, Incept conducts an assessment to identify data issues and barriers, and to uncover the root causes of these problems. This assessment involves reviewing and summarizing the goals defined to achieve the vision and the mission, including relevant objectives for each goal. Incept may work with senior management to develop business objectives if none are present.

Next, Incept identifies the non-functional requirements and stakeholders' drivers for the focus area. Incept then creates a data quality statement for the selected domain or data asset, documenting any final observations and insights uncovered when performing the assessment.

After identifying the root causes of the data issues, Incept proposes solutions to fix these problems while keeping the business context in mind. Incept documents the proposed solutions to the data quality issues fleshed out above during the root cause analysis exercise. If the data is crucial to the organization's functions, the proposed solution will likely need to be more aggressive than if the data is not used for essential purposes.

Incept also helps organizations test their approach to fixing data quality problems using methods such as rerunning BI reports, unit testing, integration testing, and user interface testing.

In essence, we combine our expertise in the processes and technologies for data quality and fill out the Data Quality Improvement Plan Template mentioned in the Deliverables section.

Contact us.

Avoid the pitfalls of a Data Quality framework that is not in harmony with your culture and technology and leverage the learning from the many client experiences we benefit from. Team Incept’s experience will help you to make it right the first time.