I’ve spent some time in the last few months thinking about the rise of AI and what it means not just to the workforce as a whole, but specifically to the world of data and the various careers it encompasses.

I foresee considerable changes coming. Not necessarily of the “AI is coming for your job!!!” variety, but mostly in the kind of value future data professionals will be required to bring to the table.

AI: The great equalizer

On one hand, I see a continued rise in the importance of analytics engineers – technically savvy with a strong business understanding. The quantity and complexity of data will continue to rise, and making sure that quality, semantics and governance follow will continue to be a mission-critical job.

On the other hand, AI – assuming it continues to evolve in the same lightning-speed that it has in the past year – will become a “great equalizer.” 

AI holds the potential to increase productivity and automate many of the tasks that we now spend time on because we need to, not because it directly drives the bottom line. The ability to ask intelligent questions from a place of profound understanding of the business, the users and the product, will lead to increasingly stronger outcomes.

Whereas innovation in AI is happening at a dazzling speed, adoption of these new technologies is not without its challenges. 

The future of data is… contextual

Some leaders were quick to shift their organization to be AI-led, but most are taking the (justified) time to make sure that this doesn’t become a security nightmare and that they understand the actual value that will come out of it; others still are finding it hard to understand where to even begin.

Even so, AI adoption is not uniform across professions. While the necessary context already exists across the web, or in their code repository, many professionals (data practitioners in particular) do not have native and intuitive ways of making their context open and democratized. In fact, data teams have been struggling with this for years (with varying levels of success) long before AI was on their mind. With AI, the problem just became much more pressing.

The secret sauce

One emerging solution is the Semantic Layer: give the AI your business definitions and voila! In reality, this view is too narrow to succeed at scale, as it ignores the fact that in most organizations there are multiple metrics with similar names and definitions, and finding the right one isn’t always easy even for humans.

But there is one element that already captures all you need to know in order to make informed decisions: Metadata! 

Take your Semantic Layer, add a cup of Usage, a splash of Lineage and decorate with Certification, and you have all the pieces of information needed to make the right decision. Luckily, all of these ingredients are already in your data cupboard, to keep the analogy going, and it’s simply a matter of collecting and stitching it together.

AI changed the game, and now data teams need new rules. Is metadata the secret sauce? Time will tell, but I certainly believe so!