The amount of new data generated each data continues to rise. To cope with that, organisations have invested in cloud computing, analytics and AI. But is this really giving them the results that they want? Enterprise Times sat down with Barath Gowda, VP Product Marketing, Databricks to ask what is the real challenge organisations face? Is it a need for better analytics? Is it a more effective AI that can explain its results?
Gowda told us that: “The biggest problem is not the analytics but the data management side.” He points to the huge amount of data inside and outside the enterprise and the challenge of creating a usable data set. Without that, he says, the data: “is not ready for any meaningful analytics.”
This is not just about the integration of often very disparate data sets. After all, we’ve been doing data import, ETL and other processes for decades. The problem is that once the data has been ingested by the organisation, it has to be surfaced to the right analytics team. It is a problem that most organisations are not addressing. Why? That’s simple. They are too busy trying not to drown in the data flood.
One of Databricks founders, Matei Zaharia, was the creator of Apache Spark. According to Gowda, the Spark team realised that there were two problems they weren’t solving. One was the quality of the data, the other was the need for queries on very large data sets to run efficiently. This is what Databricks is focused on.
To hear what else Gowda had to say listen to the podcast.
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