Data governance is a set of practices and principles that ensure data is accurate, available, usable and secure. In the age of generative AI, it’s never been more important.
Gartner researchers found that poor-quality data costs companies $12.9 million annually on average. IBM has estimated that bad data costs a total of $3.1 trillion a year in the U.S. alone. These losses stem from bad decisions, operational inefficiencies and poor customer experiences.
Organizations are collecting more and more data, but if that data is unreliable, it does more harm than good. The harm is amplified if that same data is plugged into generative AI tools. AI tools have no way of knowing if data is inaccurate. They simply look for patterns and try to predict the right answers. If they’re fed poor-quality data, they will generate poor-quality results.
Before they can use AI strategically, organizations need to reexamine their data governance strategies. Data governance has traditionally been used to aid in regulatory compliance. The emphasis was on establishing ownership and accountability for quality standards for various data sets. From a technical perspective, IT teams focused on optimizing data storage and ensuring security and privacy.
These elements are still important, but organizations should now implement procedures for improving data quality, management and usage. Here’s how that aligns with generative AI.
Make Data Quality a Priority
In Precisely’s 2023 Data Integrity Trends and Insights Report, only 46 percent of survey respondents said they have “high” or “very high” confidence in their data. Of those who lack trust in their data, 70 percent said poor data quality is the biggest problem. Fifty percent of respondents said poor data quality is their No. 1 data integrity challenge.
Many data quality problems stem from the speed at which large volumes of data are collected and the number of forms that data takes. Organizations should start with the basics by normalizing data fields, deduplicating data sets and detecting anomalies. They should then integrate multiple related data sources to create a single source of truth. These efforts should extend beyond structured data sources to documents and collaboration platforms.
Make Data Accessible and Usable
Traditionally, users had to rely on data analysts to generate charts and reports. Generative AI puts that power into users’ hands — but only if they can access the right data.
Data governance strategies should enable users to leverage data to become more efficient and effective. This starts by making it easier to access data without having to extract it from multiple sources. Cataloging and documenting data are also critical, so users understand what’s available.
In addition, users need to trust the data that they’re utilizing. Organizations should identify common use cases and curate known good data sets. Of course, these strategies should be implemented with an eye toward regulatory requirements for data storage and sovereignty.
Develop AI-Oriented Policies and Guidance
Organizations should develop or update data usage policies for generative AI tools. The first step is to have data owners determine which data sets are appropriate for use in AI. The data governance team should then ensure that there are adequate controls to maintain security, privacy and regulatory compliance. Users should be given guidance on what data sets are appropriate for use in AI and which are not.
These policies should extend to third-party data sources. Organizations need to understand their responsibilities with regard to customer and partner data and its use in AI.
How Technologent Can Help
Technologent’s data management experts have developed a six-pronged framework for helping organizations develop a data governance strategy. Let us help you maximize the value of generative AI tools through data quality, accessibility, security, privacy and compliance.
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