Welcome to the latest edition of Mind the Gap, a monthly column exploring practical approaches for improving data understanding and data utilization (and whatever else seems interesting enough to share). Last month, we explored analytics architecture stuck in the 1990s. This month, we’ll look at the rise of the data product.
It wasn’t so long ago that data products were the next big thing, and many of us were very hopeful. After all, data products are more than just the data. They require the information management activities that we’ve been advocating for a long time. Even better, the business was driving IT to do it!
Maybe it wasn’t data curation in the way
We had imagined, but it was a huge step in the right direction.
Vendors developed data product marketplace software, or added marketplace features to existing products. Consultancies offered data product implementation services. Several companies took the paradigm and ran with it, creating their own marketplace interfaces and workflows. It was at the top of everybody’s to-do list.
Until it wasn’t.
In the wake of the pandemic, data products seem to israel whatsapp number data have dropped off the radar – partly because to do data products right you have to do all the data curation stuff that most everyone has been resisting all along, and partly because data products got blown out of the water by the next, next big thing: generative AI.
But an interesting thing is now happening
Companies are beginning to recognize that AI increase returning customers with retention email marketing requires high-quality, well-understood data.
Oftentimes this is because they’re experiencing the negative impact of low-quality or poorly understood data on their models. As a result, data products are making a comeback. I was atb directory excited to see data products introduced as a new entrant on the Innovation Trigger portion of the Gartner Hype Cycle for Data Management in 2023. After all, if somebody else can understand and certify the data for me, then I can use it confidently. This demand is the engine that propels data curation efforts.
