May 07, 2019

Data Integrity Series: Understanding Data Quality

Bobby Pennington
Felix Gallardo

Bobby Pennington and Felix Gallardo

Data Integrity Series: Understanding Data Quality

Enterprise data is one of the most critical assets a company owns. Yet many organizations treat data as an afterthought, rather than a strategic advantage that can create a close-knit relationship between technology, business users, and customer experiences. For companies that look to leverage data as a competitive advantage, focusing on data integrity is often the first step in actualizing value from this critical asset.

In this four-part blog series on data integrity, we will start by looking at the importance of data quality before moving on to how data integration, data governance, and advanced analytics can help maintain a high level of data integrity and provide meaningful insights. The goal for this series is to communicate how your data can be an asset, while also starting to set in motion the necessary steps to have your data become a strategic and competitive advantage for your company’s growth. At Credera, it is common for us to help our clients navigate this data journey, from envisioning ways to leverage data to implementing the technical steps to reach true business objectives.

What is Data Quality?


Oftentimes, data is seen as an IT problem, an afterthought, or ambiguous pieces of information that cannot be effectively used. Bypassing these stigmas often starts with data quality. At Credera, we define data quality as “data that fits your business’ purpose and that you trust enough to help you make key decisions across your organization.

Data quality is typically grouped into six focus areas that aim to organize and build structure around how data can be managed:

  1. Accuracy – Does my data have the correct values formatted in acceptable standards?

  2. Completeness – Does my data tell the whole story?

  3. Integrity – Is my data reliable and consistent for its entire lifecycle?

  4. Timeliness – Are data insights available when I need them?

  5. Uniqueness – Is my data unique and not contradictive to my business?

  6. Validity – Has my data been tested to ensure it meets agreed upon standard values?

While this list isn’t all-inclusive, it’s a great starting point to assess the level of effort needed to improve your data. Understanding your data quality helps move you from running an organization based on emotion and politics to a more data-driven culture that focuses on objectivity to make decisions. Regardless of your business area (IT, marketing, accounting, etc.), data quality can enable better decision making that drives revenue for your organization.

Results of No Data Quality


The colloquial phrase “garbage in, garbage out” is an apt one for poor data quality. As data grows through the influx of information from business applications, product vendors, customers, and employees, it becomes more difficult to maintain quality data. Translating data into a usable format is an increasing challenge for organizations of all sizes, with many companies resorting to manually-intensive activities to move and store data. The result of this challenge can lead to an inability to successfully institute advanced analytics projects.

The truth is that companies don’t need to have perfect data to unlock and leverage their data’s potential, but using poor data will not lead to good decisions. Poor decisions can often be directly tied to poor data quality. Garbage in, garbage out.

Business Case for Improving Data Quality

As companies move away from viewing data quality as a technical problem, it is important to create a business case that describes the value associated with any data quality initiative. Data quality is both a business and technical problem that needs the right team and the right sponsorship in place to have a successful, executable data plan. A meaningful and complete business case is key to achieving this goal. Without a strong business case, data quality projects fail to deliver—or simply put—they become a waste of time and effort for everyone involved.

When developing a business case, it is important to understand the groups involved, as well as key factors that are critical for a successful data quality initiative.

Organizational Groups Involved
  • Technology Teams**: Data silos, application architectures, and key data information initiatives need to be aligned across technology groups. The right data tools should be assessed as part of a longer-term vision that supports automation, data integration, and advanced analytics. Ultimately, these tools should support and enable your business goals.

  • Functional Business Teams**: Data owners, data stewards, and functional stakeholders should help tie data quality to a set of key business metrics that show progress and capture the business value of improving data quality.

Factors for a Successful Business Case:
  • Obtain buy-in from business, technology, and executive

  • Work with business stakeholders to understand and align business priorities.

  • Ensure stakeholders and executive sponsors select the proper metrics to measure performance.

  • Leverage a data quality approach to provide:

    • Current state assessment as a starting benchmark.

    • Future state assessment with a strategic data quality vision.

    • Tool selection that supports your data quality vision, if applicable.

  • Create a business value scorecard to associate the anticipated business value against results being completed by data quality efforts.

  • Kickoff a data quality implementation effort along with monitoring of data quality improvements over time.

The work doesn’t stop here. Data quality is not a one-time effort. Part of this growth for companies means a continued commitment to the philosophy of data as an asset. This should translate to ongoing data governance that yields higher quality data over time.

Talk to Credera to Help You on Your Data Quality Journey

Credera’s extensive experience with data integration across process and technical landscapes and our deep technical skills across multiple industries uniquely positions us as a valuable partner for your data needs.

Our Analytics and Business Intelligence Practice can help you improve existing data quality pain points and create a clear strategy to unlock your potential data and business value. If you’re interested in learning more, please reach out to us at

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