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TDWI Upside - Where Data Means Business

Why Data Quality Will Rise to the Top of Enterprise Priorities in 2024

Data is the foundation of modern organizations, so be sure you have a sound data strategy.

A lot can change in a year; just look at the impact ChatGPT and other generative AI large language models (LLMs) have had. By August, ChatGPT saw over 180 million unique visitors. More than three of every four consumers currently use an AI-powered device. OpenAI’s first developer event just introduced new APIs and chatbots -- including a no-code option -- to make generative AI even easier to work with.

For Further Reading:

Succeeding with Data Governance and Data

Q&A: Why Is Data Quality So Elusive?

The Fundamentals of Data Quality

However, without a well-thought-out data strategy, jumping into generative AI could cost organizations time and money -- and could end in outright failure. Data is the foundation of modern organizations, so whether they want to dive into generative AI or not, they’ll still need to prioritize a sound data strategy.

With that in mind, here are three predictions for data in 2024.

Prediction #1: Increased adoption of generative AI will drive the need for clean data

The foundation of generative AI is data. However, that data also needs to be clean. Cleansing your data will help you get rid of duplicates, converge different data sets, and correct any data that is incorrect, incomplete, irrelevant, or improperly formatted. Deduplication is particularly vital, and it can save a great deal of money and other resources in the long run. 

Regardless of where you’re pulling the data from -- whether you’re using modeling or a warehouse of your choice, for example -- quality data will be essential. Bad data can lead to bad recommendations, inaccuracies, and bias, among other problems. Having a strong data governance strategy will become more important as more organizations seek to leverage the power of generative AI in their organization. Ensuring your data stewards can access and control this data will also be key.  

A previous study by HFS Research found that 56% of businesses lack centralized governance. That’s why the aims of centralized data management systems are not always in sync with consistent governance methods. It makes sense, then, that -- as the study also found -- there’s a disconnect between data usability and data trust. You need to keep this in mind as you evaluate individuals’ opinions about what constitutes “good” or usable data.

Prediction #2: The shift to the data fabric will accelerate thanks to AI

When I surveyed the landscape at the end of 2022, I anticipated that more organizations would move from a data mesh approach to a data fabric to help break down information silos and make data available to business users more quickly. We haven’t quite seen as fast a transition as I’d thought, but this trend is certainly accelerating, and in 2024, this trajectory will be driven largely by increased adoption of AI and other self-discovering technologies. There’s been a lot of discussion about data fabric in recent years, but it will become a bigger goal for organizations thanks to the emergence of more advanced AI. 

Prediction #3: Data quality will start to become an executive-level topic 

Data quality is essential. Even if you have found the data sets that will be appropriate for training an AI model or digging for insights, your results will be poor if the quality of your data is poor.

Ownership of data and data quality are core to business success but are still too often ignored or overlooked by the executives and board of directors of most organizations. We can see this in the disconnects between perception and reality. Over 80% of executives that HFS surveyed think they trust their data, but the reality is that there are still many people doing a lot of work to get data quality to a level where the data can be consumed and relied upon. As data quality takes on greater importance, it will escalate to an executive-level conversation.   

Enterprises will need to start collecting and documenting data, metadata, processes, and business rules as they pursue data quality. Without these basic elements, AI models won’t be able to produce insightful and exact results. If you haven’t yet, you’ll need to invest in initiatives to improve your data quality so you can build a strong foundation for AI use.

Predict to Prepare

Some predictions are by their nature tentative, but it’s clear that data will play an increasingly important role in the life of businesses in the coming year. The purpose of predicting what lies ahead is to prepare for it and position your company for success. It’s my sincere hope that these three data predictions will help you do just that.

About the Author

Rex Ahlstrom is the CTO and EVP of innovation and growth at Syniti.

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