2021 Predictions on Pervasive Intelligence

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We would have never given importance to the thought that what would really happen when advanced analytics become pervasive. The path of pervasive intelligence for the year 2021 is clear.

According to Luke Han, co-founder and CEO at Kyligence and co-creator and Project Management Committee Chair of the Apache Kylin project three millstones pushing towards pervasive intelligence in the year 2021 are:

Milestone 1: Embedded Analytics in SaaS

A SaaS company operates on tons of data with tremendous potential value. The benefits of analytics are becoming the need of an hour for enterprise customers. As more and more companies turn to SaaS solutions, the SaaS vendors will be the ultimate beneficiaries of this pervasive analytics. The SaaS vendors can offer their existing customers with potential new revenue stream as they offer useful insights at every turn. The new analytics promises the life and value of their core product.

This will increase the customer stick with the new wealth of easy-to-consume insights. These insights come at a cost. Pervasive analytics capabilities create some familiar requirements for SaaS vendors: Greater concurrent, Data freshness and accuracy, and Data privacy.      

Milestone 2: Unified Data and Semantics

The gap between data scientists and business analysts will continue to blur. Business analysts conduct more advanced data science research using enterprise-grade machine learning software, which demands a more powerful data service layer that needs a consolidated view of data across the companies. A Business analyst needs to be fast and light to interact with their data sets, today we don’t have information workers who are this fast. So here we need a data service layer along with machine learning algorithms that can deliver us the insights at the speed of the machine.

Today’s data service architecture does not only offer this, we need a consolidated layer of data science that serves human analytics and also machine learning works with unified data and semantics, at the speed of 10x or 100x.

Milestone 3: Analytics everywhere and anywhere

Most companies are using multi-cloud as a result of data gravity. The common multi-cloud approach seen is the choice of clouds for different applications as the multi-cloud offers a mix of private, public, and on-premise architecture. This approach does possess a challenge, here we need to have a new set of data silos with different supporting cloud infrastructure. A cloud-neutral multi-cloud friendly data service layer can prove helpful to enable pervasive analytics.

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