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TDWI Upside - Where Data Means Business

From Peak Hype to Reality: Deriving Actionable Insights and ROI from AI

AI can be highly effective at automating the data life cycle and managing data efficiently. It may just be the catalyst enterprises need to increase their focus on data classification, sustainability, and governance next year.

Very few people would dispute that AI -- and more specifically generative AI tools (such as ChatGPT, Claude, Bard, and many others) -- were some of the most compelling and interesting innovations of 2023. You could even say 2023 was the Year of GenAI.

For Further Reading:

Powering a Greener Future: How Data Centers Can Slash Emissions

How to Deploy Generative AI Effectively in 2024

Classification, Clustering, and ML Challenges

Looking ahead, 2024 will be the year when companies begin to assess how they can both leverage AI for a competitive edge and use it to protect corporate networks and data from its inevitable misuse by bad actors.

It will also be the year that businesses realize they are collecting and storing too much data. They’ll connect the dots and come to the realization that they must prioritize data classification and data life cycle best practices to deliver the AI-driven business intelligence that will help them achieve top line revenue goals.

With that, here are the three most important data-related trends I predict we will see in 2024.

Trend #1: Companies will establish guardrails to manage generative AI use

Generative AI seemed to come out of the blue, dominating both the media and mindshare. According to Gartner, generative AI has already reached the peak of inflated expectations. Whether it will make a quick descent to the “trough of disillusionment” remains to be seen. Although AI and generative AI have the potential to make a tremendous impact on how businesses manage daily operations (reducing costs and increasing efficiencies), the effectiveness of the insights provided by AI-driven business intelligence is only as good as the data the AI engine is fed.

To increase the ROI of AI, large language models (LLMs) must ingest clean and high-quality data for accurate, meaningful insights. This is only possible by investing in data discovery and data classification solutions and processes.

Organizations will also face growing AI-related security challenges in 2024. This will (hopefully) lead them to set up guardrails that protect corporate and customer data. Businesses must also consider that company-specific or proprietary data ingested by LLMs could put organizations at risk if company financials or other private information are replicated to a public AI engine and exposed.

This will lead many organizations to opt for a secure, privately hosted version of ChatGPT, such as an enterprise offering from IBM or Google. Data governance policies will also need to be updated to mitigate potential threats and data leakage. On the flip side, AI can be an effective tool to manage data privacy and compliance because it can assist in identifying and managing personally identifiable information (PII) and ensuring compliance with data protection regulations such as GDPR or HIPAA.

Trend #2: AI will trigger a data classification revolution

Despite overhype, I believe AI is one of the most transformational technologies since the invention of the internet. The challenge for organizations right now, beyond how to employ AI safely and securely, is how to make it actionable and effective. My hope is that AI will spark a data management renaissance in 2024, with companies moving to automatically classify and tag data based on its content, risk profile, and sensitivity (among other categories) which is essential for data governance and compliance. This will make the data clean and relevant for their AI engine and make it easier to manage and secure data throughout its life cycle.

Another challenge is the amount of data that companies are storing on site and in the cloud. For example, we recently did a review of an organization’s data and found that the average age of their data was nine years and more than half of it was seven years or older. It’s nearly impossible to gain actionable insights from old and outdated data. There are many opportunities for businesses to benefit from AI; however, there also needs to be a rapid evolution of data classification and data life cycle management before businesses will be able to derive the value they expect from AI. This is especially important if companies are trying to justify ROI from their AI investment.

Trend #3: AI will help organizations meet new sustainability reporting requirements

Sustainability took a back seat during the pandemic and long after the worst of it passed, as organizations made major adjustments to operations and tried to find their new (or old) normal. Sustainability will make it to the top of the business objectives list again in 2024 for one big reason: new global ESG rules and regulations rolling out and simply the fact that climate change isn’t going away.

As AI matures, one big problem it may be able to solve is automating sustainability reporting, a huge asset especially when it comes to Scope 3M emissions-reporting requirements, which are complex. Laws such as California’s Climate Corporate Accountability Act, which is similar to the EU’s Corporate Sustainability Directive (CSRC), in addition to the SEC’s proposed climate rules, will put pressure on both public and private companies to get their sustainability reporting processes in order. Sustainability is reported in two ways: greenhouse gas emissions (whether it happens to be carbon, methane, or anything else) as well as energy usage. This may sound simple, but understanding the entire carbon footprint, and the accounting required to determine it, is an enormous and time-consuming task.

There is a direct correlation to the amount of data organizations are storing and their sustainability footprint due to energy use. Once organizations determine that half of their data is redundant, obsolete, or trivial (ROT) and/or older than seven years, they can perform data sanitization on the data that’s no longer needed, decreasing the energy required to store unnecessary data. As companies realize they’re spending money to store data they don’t need, data center operators will follow suit by looking for ways to become more sustainably minded and energy efficient in 2024.


AI can be highly effective at automating the data life cycle from ingestion and processing to storage and archiving. Beyond that, AI can ensure that data is managed efficiently and according to compliance and retention policies. For these reasons, among others, AI may just be the catalyst needed for an increased focus on data classification, sustainability, and governance in 2024.

About the Author

Russ Ernst joined Blancco in 2016 as executive vice president of products and technology. and in September 2022 was named chief technology officer. He is responsible for defining, driving, and executing the product strategy across both the data erasure and mobile diagnostics product suites. Critical parts of his role include developing a strong team of product owners and cultivating an organizational product culture based on continuous testing and learning. You can connect with the author on LinkedIn.

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