Big data is evolving fast. It used to be about collecting as much data as possible and analyzing it to see what value we can get from it. Now that we’ve reached 2018, this is no longer the case.
Today, big data is much more nuanced. We’re starting to look at our data from a predictive analytics perspective as opposed to simply, “what can we do with our data today?”
We’ve realized the power of data and are moving towards effectively using it to predict future results and performance.
Where is Big Data Headed?
I recently spoke with Judith Hurwitz, CEO of Hurwitz & Associates, at a Strata conference to get her thoughts on big data and where the industry is going.
The Need for Data Integration
Big data integration is key to this movement. Companies are not taking data from a single source anymore. They’re collecting useful data in a variety of places and they need a central repository to analyze all of this data effectively. To get the full picture, companies need to understand how all of the individual data sources work together.
These streams must populate in real-time to be effective. In order for teams to think ahead and consistently improve, they need a constant, reliable flow of data in place – especially when it comes to AI and machine learning.
On a surface level, we don’t need massive amounts of data for AI and machine learning to be effective. We just need the right data, in the right place, at the right time, so your applications can teach themselves the best possible solutions.