Data Talks

How to ensure data quality through governance

Guest Author
Guest Author

Data governance is a fundamental pillar of modern digital businesses. It refers to a framework of processes and guidelines that companies use to ensure all enterprise data assets are managed and utilized appropriately.

Even if an organization has large investments in data infrastructure and teams, without a structured data governance framework, organizations will struggle to harness the full value of their data.

A strong framework provides a clear set of guidelines for all employees who access and consume data in downstream applications.

It also contributes to greater trust in the authenticity and quality of data and allows data stakeholders to focus on core data tasks instead of worrying about whether the data was created, processed, stored accurately, and in compliance with national or domain-related legislations like GDPR, HIPAA, CCPA, and data localization laws.

Given recent data breaches, the importance of a structured data governance framework cannot be emphasized enough.

In this article, you’ll learn how to ensure data quality through better data governance mechanisms, leading to an increase in data informed decision-making. You’ll also learn how a clear data governance framework contributes to improved data quality and value creation across the entire organization.

Why do you need data governance?

The digital revolution is founded on data and the idea that data can generate insights that are critical for decision-making and long-term planning. With the emergence of cloud technologies, it’s easier for businesses to see the importance of data and store it in a more accessible, scalable, and secure way.

A data governance framework is a set of rules and processes for collecting, storing, and using data. This diagram shows a simplified outline for how to think about building a data governance framework for your organization.

However, collecting and storing data is just the tip of the iceberg. Without a clear and robust governance framework, you can’t fully understand the value of your data. High-quality data will help you make the best possible decision for your company.

A data governance framework consists of several layers, stakeholders, business goals, and structured processes with a focus on information and project management. This accountability means organizations can build high-quality data products with confidence.

This is evident in the case of top technology companies like Google and Amazon that have invested early and massively in data and data-driven technologies. They benefited from investing and enforcing a data governance framework that lowers the organizational threshold, velocity, and efficiency with which businesses can adapt to change.

**So, why is data governance important?**

Investing in data governance leads to many benefits including:

  • Empowering data-driven business decision-making.
  • Increasing awareness, access, and utilization of data to achieve key business goals.
  • Facilitating compliance with local, national, or domain-specific regulations.
  • Promoting good practices among the various stakeholders, processes, and teams.
  • Influencing people and builds a culture of acceptance and trust in organizational data.
  • Reducing the threshold for adoption of new tools and technologies to help enforce data governance.
  • Increasing ownership and accountability of maintaining data quality across the organization.

Even though the process of defining and operating a framework can seem daunting, the proven benefits will make a lasting impact on your business.

Ensure data quality through governance

A major outcome of a solid data governance framework, if carried out properly, is improved data quality. When organizations follow these guidelines, it leads to a clearer understanding of their data assets and increases accountability.

First, think about your data lineage. Record the source of each data set and the date/time that it is accessed. It’s also critical to understand the teams that are accessing the data including the applications they’re using.This ensures compliance and prevents data breaches.

You can test data quality by asking different stakeholder teams to provide the value for a common business metric. More often than not, different teams will have conflicting answers for the same metric. This can be the result of a flaw in your data governance strategy, fuzzy guidelines, or scattered metrics logic across downstream tools.

Create policies that ensure data accuracy

Maintaining accurate data across the organization is difficult but rewarding. Once a new data asset is created, either internal or external, it needs to be systematically logged and entered into the appropriate databases.

Consistently using data governance best practices for  completeness, relevance, reliability, and lifecycle can lead to better data quality and accuracy.

Develop practices to test data completeness

Data completeness refers to the wholeness of the data. Data is complete when there are no missing values, records, or duplicates. Basic automated checks to validate the number of rows and columns, dimensionality, missing and null values, and data format mismatch can help identify missing elements.

Adopt technologies to check data relevance

Data relevance refers to the utility of data in providing critical insights. It’s important to remember that not all data is useful or relevant to particular business problems, and identifying the right set of input data can help focus subsequent analytics and modeling efforts.

Track relevance with data reliability

Data reliability is an indicator of how useful and relevant it is over time. It builds upon the concepts of completeness and relevance, and is more likely to be used and reused by teams for their work. This lays the foundation for multiple use cases and business insights.

Stay compliant with data depreciation and lifecycle

Data timeliness and lifecycle management provides clear timelines for the validity and deprecation of data, ensuring that it’s used only when relevant and compliant with privacy laws. This regulates the lifecycle before it is depreciated or deleted permanently.

Standardizing metrics as part of your data governance strategy

Let’s take a look at how you can standardize your metrics through metrics catalogs and policies and build into a data governance strategy that ensures data quality.

Catalog metrics in a metrics store

Standard metrics like annual recurring revenue (ARR), gross merchandise value (GMV), customer acquisition cost (CAC), customer lifetime value (LTV), and net promoter score (NPS) are common. Once you've defined your metrics, these metrics can be stored in a metrics catalog for greater ease of access, use, and re-use across the organization.

A metrics catalog has several advantages. It reduces valuable organizational time and effort to reproduce the underlying analysis, and it creates a centralized metrics store that facilitates better understanding and decision-making.

As depicted in the figure below, a metrics store is a centralized and governed place for organizations to store key metrics, creating a repository for stakeholders to access key metrics in a repeatable way, regardless of where people access their data.

How a metrics store can be incorporated into the enterprise data ecosystem

Policies and practices for sign-off

Before creating a metric, there needs to be a clear policy on the steps that people use to analyze and validate their business metrics. Data quality policies should not be treated as an administrative exercise but regarded as an important milestone in this stage of data transformation.

In addition to assigning an owner for each of your critical metrics, you should also think about executive sponsorship for the organization’s most important, “north-star” metrics. A stamp of approval from the C-suite or an executive sponsor conveys the importance of the data policy framework to the entire organization but can also be used to negotiate and expedite resolutions when conflicts arise.


In this article, you’ve learned about data quality as an index that can be used for many attributes of data in an organization. A data governance framework creates a set of best practices that improve data accuracy and relevance.

A data governance framework also makes it possible to distribute high-quality data to your teams in the most efficient way possible.  Building a metrics store is a critical part of this process because metrics are the language that you use to express whether you achieved your organizational goals. A metrics store, like the Transform Metrics Store, centralizes all of this knowledge in one place for easy access and collaboration.

To learn more about the metrics catalog and other solutions, visit

This post is guest authored by Dr. Sundeep Teki. Dr. Sundeep Teki is a leader in AI and neuroscience with professional experience in the US, UK, India, and France. He has published 40+ papers; built and deployed AI for consumer tech products like Amazon Alexa; advises and consults tech startups on AI/ML, product, and strategy; and coaches data and AI professionals and executives.

*Thumbnail image Courtesy of Andrew Ridley via Unsplash