Unsure about your generative AI plans? Start with your data

Commissioned: Call it an overnight success years in the making. Generative AI (GenAI) has exploded on the scene as one of the most critical areas organizations need to invest in. Decades of AI innovation paired with a natural language interface created the magic that has captured our collective attention. This is a wave for the ages. In oceanography there’s a term for these rare and unusually large phenomena: “rogue wave,” an outlier where the height is greater than twice the significant wave height. It’s an apt term for what we’re seeing with GenAI, and the challenge with these waves is they often materialize out of nowhere. Who had GenAI as the most important investment opportunity for CIOs in 2023 on their bingo card?

Catching the wave versus being caught under it

A boat can float on the water; it can also sink in it. Technology leaders everywhere, as captains of their respective boats, are rushing to stay ahead of this potentially crushing wave. The challenge is where to start? A decade ago, IT leaders worried about shadow IT projects springing up outside of governance; today they have a new concern: shadow AI.

At the same time, they’re also seeing opportunities. Forward looking and agile organizations are eagerly piloting GenAI solutions, often in the software development space where things like GitHub Copilot have really captured the mindshare of developers. What’s interesting is that while 70 percent of developers surveyed by Stack Overflow say they are already using or plan to use GenAI in coding, only 42 percent trust the accuracy of the output in their tools. This tells us we’re still relatively early in the GenAI cycle, but the rate of acceleration might keep some leaders up at night.

While there’s a broader conversation to be had on developing a winning GenAI strategy and who should be leading the charge, today we’ll focus on the role data will play and why it’s critical to your AI strategy. The good news? You can take steps to address it now.

Data’s value is only increasing with GenAI

As organizations become increasingly digital, every activity in their business is effectively exhausting data. Now not all data is equal; some data is inherently more valuable. For example, data that’s proprietary or hard to acquire, private or confidential data and, of course, data that’s useful to the business. This is where it gets tricky, though, because today very little of this data is meaningfully used. For instance, only 26 percent of ITDMs say all innovation efforts are based on data insights. This is because historically the volume of data has greatly surpassed our ability to analyze it.

Enter GenAI. Now imagine for a second that same corpus of data can be parsed with AI in minutes, if not seconds (depending on how much power you’re putting behind it). Suddenly, the long tail of data which was previously incomprehensible, has now been unlocked. This puts us in a quandary because existing logic on data retention and the understanding of how data can be used, or even its useful longevity, has now completely changed.

Getting your data house in order

As you start your GenAI journey, you must start first by getting your data house in order. Because you might not know what questions to ask your data today, but at some point you will – and if you haven’t retained that data, those answers will always elude you.  You can begin building the strategy and experimenting, but getting a handle on your data now is key. With that in mind, here are four areas to consider:

• Collection: digital end points or data creation areas are currently generating data? Are they connected? Is the data just living on an edge device somewhere never being utilized?

• Curation: Do you have a way to tag and classify data so its value and usage restrictions are known? Do you know who would benefit from having access to this data, and have you also appropriately applied labeling to understand privacy, governance, and intellectual property protections?

• Storage: Is there a process for centralizing this data and ensuring access? Have you looked into connecting different IT environments, potentially clouds? Have you established an effective data tiering strategy to align with the data’s value and lifecycle?

• Protection: Can you protect against a data loss or ransomware event? Can you ensure you’re meeting your governance and data sovereignty requirements? Do you understand the risks with various data and have you built an approach to mitigate them and secure access?

Walk before you run

I know it’s an incredibly exciting time and the way GenAI has captured our mindset has been very consumer oriented. There’s a lot of work for IT to do to catch up. In many ways it’s like in the “bring your own device” wave of innovation; it was very easy for consumers to move quickly. There’s no existing legacy infrastructure or data to consider; simply buy a smartphone and you’re up and running. But as we saw in that time period, the backend requirements to enable work from anywhere in a secure manner took years to catch up. Avoid the temptation to barrel right into something without first setting the stage for your success. While you build the strategy for the next decade, be sure to triage your existing environment as well.

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