The Cost of Bad Data Governance: Causes & Remedies

Updated March 28th, 2024

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Investopedia estimates that non-compliance with data regulations costs businesses $15 million a year. Organizations need data governance systems to stay in line with privacy and security policies. Doing nothing about this will obviously cost you money. But doing it poorly is even worse. The cost of bad data governance could end up amounting to more than doing nothing at all.

In this article, we’ll look at the obstacles to good data governance, what it costs when it goes wrong, and how you can avoid those scenarios.

Table of Contents #

  1. What is data governance & why does it matter
  2. The cost of bad data governance
  3. How to solve data governance problems
  4. Data governance that doesn’t lose money
  5. Related reads

What is data governance & why does it matter #

Gartner defines data governance as a way to “specify decision rights and accountability to ensure appropriate behavior as organizations seek to value, create, consume, and control their data, analytics, and information assets.”

Data governance can be organizational policy, administrative roles, or software systems. Nowadays, most organizations use a mix of the three. Without an effective data governance system, an organization loses significant value. If your data isn’t readily accessible - or, even worse, you don’t know where it is - your business can spin its wheels waiting for answers.

For example, a growing pet care business was losing substantial business due to its call centers. When they assigned analysts to find the issue, they had to wait over six months for results due to poor data discoverability.

Data governance is also critical for adhering to governmental data regulations. IN 2023, TikTok was fined $379 million in violation of the EU’s GDPR regulations.

The cost of bad data governance #

Poor data governance has a broad impact on an organization’s entire structure. It can hinder business outcomes, delay data insights, compromise security and privacy, and undermine the value of the data stack.

The business costs of low data trust #


Driver of cost: Delay or cancellation of projects due to lack of trustworthy data

A lack of data governance leads to a lack of trust and coordination. Projects stall due to questions of data usability or conflicts between competing definitions. Without trust, organizations can’t achieve the collaboration necessary for business initiatives.

For example, a leading global computer technology company wanted to build over a thousand AI use cases. However, they couldn’t trust the underlying datasets without a governance system in place.

Data governance also keeps everyone on the same page. One Fortune 500 CPG company had different departments using different metrics. Data consumers didn’t know which metrics to use for decision-making, leading to material loss and impacting costs.

Insights delayed by inaccessible data #


Driver of cost: Inability to find data delays shipment of new reports and related data products that drive decision-making

Poor data cataloging makes data difficult to access and understand. If you don’t have a single source of truth for your data, data consumers may not know how to find what they need to drive decision-making. All data requests fall onto experts with the experience required to navigate the fragmented data environment.

For example, one leading digital media platform took over three weeks to deliver insight reports to their marketing agency customers. Without an intuitive data catalog, account managers receiving the requests didn’t have the skills to field report requests independently. They then turned to business insight teams, who had to rely on data engineering teams to wrangle the data.

When data is hard to find, data teams end up overwhelmed with requests from the rest of the organization. One revenue management platform serving over four thousand customers had a data team caught in the “data service” trap, receiving more than 350 data requests per quarter. The data team struggled to meet SLAs and attract new talent. That’s because their job had become “fulfilling tickets” instead of more value-added work, such as making their company’s data estate easier to manage.

Lack of management tools delays compliance #


Driver of cost: Absence of automation means tasks require manual processing, which takes longer

Ineffective data governance makes regulation compliance challenging. Without the right tools to find and manage data, many commonplace requests may take days worth of mind-numbing manual labor to satisfy.

Consider a so-called “right to erasure” request, where a consumer petitions your company to remove any record of their personally identifiable information (PII). Typically, this information doesn’t exist in a single database. It’s spread through multiple data stores within a company as a customer uses multiple products or different teams process their data.

Companies with a strong data governance system in place have set up tools that enable deleting such information with a few button clicks. Without these tools, someone has to search and delete records from dozens of databases by hand.

A leading UK fintech firm spent almost 50 days on GDPR compliance because the production team had to find and delete consumer personal information from their back-end data stores manually. These struggles only expanded as their tech and data stacks grew larger and more complex.

Laborious compliance efforts can be an obstacle to expansion. One leading B2B e-commerce platform in Latin America struggled to expand into South America because they couldn’t efficiently tag and mask PII in compliance with the regulations of the new market they were entering.

Lack of data awareness undermines data stack value #


Driver of cost: Unfamiliarity with tools leads to low user uptake, abandonment

Without effective governance, a huge portion of a data stack’s value is lost. If data consumers can’t find the data they need, they will likely ignore the expensive data stack their company built and do things “like we’ve always done them”. Poor data discoverability leads to low adoption rates that might doom your entire investment.

Poor quality data - particularly metadata - can also lead to low adoption. If data consumers don’t know how to use the data they find, they’re likely to ignore it.

One eight-billion-dollar investment firm found that their 8-9 figure data stack wasn’t producing full value. On inspection, they discovered that users didn’t know what data assets existed or what they could be used for.

Poor data governance means poor monitoring, which can lead to wasteful spending. A global data infrastructure SaaS firm serving more than 38 thousand customers was building assets in Fivetran. The ease of replicating data allowed engineers to create many data assets.

However, the data governance team couldn’t monitor usage. They didn’t realize that many of these assets were sitting in storage as dark data, not creating any business value.

How to solve data governance problems #

Effective data governance avoids these pitfalls. It supports business projects, provides data insights, streamlines security, and compliance, and enhances data stack value.

Business impacts of increasing trust #


With a modern data catalog, everyone is on the same page. Aligned, globally available, and understandable metrics cuts through confusion in business decision-making. Automated data quality checks improve trust in data, making sure that business teams can move forward with confidence that their data is accurate and useful.

Accelerated data insights with discovery tools #


Good data governance makes data discovery easy via intuitive natural language search functions. When users can self-serve data, data teams don’t have to spend all their time fielding requests. Instead, they can work on projects that improve efficiency, develop meaningful datasets, and build informative products.

Streamlined compliance with metadata #


Data governance systems provide organization-wide role-based access controls, making it simple to ensure that data privacy and security comply with regulations. Data tracking and automated data tagging via lineage make it easy to identify data that needs to be hidden or deleted in accordance with the law.

Governance integration to support stack value #


Search tools make it easy for users to find data assets that have already been built, preventing redundant work and maintaining the value of past development efforts. Visible data lineage makes it easy to understand the impact of updates or migrations so that downtime can be minimized. Embedded collaboration tools add data discovery and sharing to existing systems, enhancing their value.

Data governance that doesn’t lose money #

It can lead to costly fines, damaging delays, and confusion among teams. By contrast, effective data governance supports development, data discovery, and regulatory compliance.

Atlan data governance systems deliver simplified search, automatic data tagging, visual data lineage, and embedded collaboration tools, bolstering the value of the data stack and avoiding the costs of poor data governance. Request a personalized demo today, and see what Atlan can do for you.

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