When thousands of data scientists and business analysts converge on New York next week for the year’s main data industry event, Strata, they're going to try to find answers to the chaos in the business and technological scheme.

The current generation has seen tectonic shifts in the data and analytics area in 2019, fueled by seemingly endless wave of acquisitions, a number of that were really epic in proportion: Salesforce acquired Tableau for $15.7 billion, and Google acquired Looker for $2.6 billion, any downstream in valuation however no slighter, CommVault purchased Hedvig for $225 million, Cloudera bought geographical region information, Logi Analytics bought Zoomdata, Sisense bought optical instrument data, and Alteryx bought clerestory for $20 million. That’s over $18 billion price of acquisitions — and we’re only in three-quarters of the method through the year.

At a similar time, Hadoop’s precipitous decline any redoubled this year with Cloudera’s stock value dropping in June and MapR’s worth plunging. With most enterprise information tangled inside Hadoop, its decline raises tons of uncertainty.

Here are the four predictions which are going to be most mentioned on the show floor, over low, and in the after-parties.

The acquisitions have just begun

The wave of 2019 acquisitions are simply the start. That said, we’re not gazing a basic shift within the business intelligence landscape. Rather, we’re seeing a natural progression as businesses request to maximise their investments in business intelligence (BI) — gaining quicker, simpler, and a lot of correct insights. BI is moving out of the hands of data scientists and into the hands of call manufacturers – that is strictly why we’re getting to see a lot of consolidation on the interpretation facet of metallic element and fewer on the presentation facet.

Keep an eye on AI

It is a secret in the entrepreneurial community: if you don’t have “AI” in your tagline, you get less funding. Enterprises are moving forward in their huge knowledge life cycle. The section of building data discipline – i.e. achieving the potential of not discard any data— is behind USA. Now, corporations are at the stage of seeking unjust and accessible insights from their knowledge. And only AI-powered machine learning apps will be of such huge data sets.

Massive investment in apps for the cloud

The on-prem process power that might be required for real time period BI over massive investments sets is prohibitively expensive. On-prem technical school was designed for execution. This is often why knowledge migration to the cloud has gone into overdrive. Currently it’s time for corporations to reap the fruits of this move with cloud-specific versions of the apps that run their businesses. New app comes begin with cloud-native versions. Nevertheless 90 percent of enterprise apps were designed to run on prem – and these got to be massively custom or utterly remodeled to figure on the cloud.

Security and privacy: not here

In the outside world, there's nearly no discussion of data that doesn’t touch on privacy and security. At Strata this year, however, people expect privacy and security problems to be raised less in the context of knowledge itself. This is often fascinating as a result of in Hadoop’s period of time, there have been endless discussions of obfuscation, knowledge lineages, and alternative security issues. Today, due to the shift to the cloud, knowledge security is perceived in the data and analytics community as a priority for large school and infrastructure corporations — not their direct responsibility and not a pressing conference issue.