4 Big Data Trends To Watch In 2018

by Mahesh Lalwani    Dec 05, 2017

big data

We saw the rise of big data in 2017 and the trend will continue to gain speed. Accessing and preserving big data is now a common practice at almost all organizations and we see following trends emerging from data-driven initiatives.

1. No Data Silos

The most important asset - Data is not in silos anymore. Plethora of systems of records now feed into big data lakes which then make it available to anyone within the organization with right credentials and use case. Thanks to hundreds of connectors made available by companies such as Talend and Spark new datasets can be added in hours instead of days and weeks.

2. Utilize Legacy & Future-Ready

Most organizations are not in a rush to replace legacy (transaction) systems but they are building new parallel systems which are future ready and can utilize data from legacy systems. These new systems are taking processor-intensive tasks away from legacy system and in the process helping them make more efficient. The new systems will also add intelligence to the data by mining and deep learning with tools such as Google TensorFlow, R, and Python etc.

3. Micro Subscription Models

New systems are beginning to create micro product/service offerings for each customer in real-time and a micro subscription pricing to go along with it, e.g. a travel insurance policy provided to a customer at the time of boarding a plane and applicable only for the duration of the flight. Real-time processing at scale is made possible by Kafka, Cassandra etc. with Python and Google TensorFlow models executing on new data.

4. AI Powered Self-Service & Problem Resolution

More organizations are using artificial intelligence to help customer find answers to their product, billing or technical question. Deeper integration with Apple Siri, Amazon Echo and Google Home will accelerate this trend and soon we will be able to buy and sell products/services and locate and resolve problems with just our voice.

[The author is VP, Head of Data & Cognitive Analytics at Mphasis]