Experts talk Big Data and Analytics Trends for 2020

From data lakes and the cloud, to machine learning and artificial intelligence, the world of big data and analytics continues to evolve.

At the same time, the need for improved governance and security practices is also intensifying as data privacy concerns and new regulations like GDPR and CCPA require new approaches for responsible use of data.

In response, leading organizations are turning to innovative new technologies and strategies to reinforce their analytics ecosystems with greater speed, flexibility and scalability, as well as smarter automation and collaboration.

DBTA recently held a webinar featuring Mike Distler, senior director, product marketing, Qlik; David Ronald, director of product marketing, TigerGraph; and Rajesh K. Parthasarathy, founder, president and chief executive officer, MENTIS, who discussed what’s ahead for big data and analytics for 2020.

The world is getting more fragmented, Distler explained, it’s time for analysis and synthesis. Data formats now more varied and fragmented or “wide.” Companies need new ways to deal with data that’s not only big, but wide.

DataOps plus self-service is the new agile, he said. DataOps is an emerging discipline to build and manage efficient data pipelines.

Another solution on the rise is graphing technology, Ronald said. Graph succeeds where RDBMS and NoSQL struggle and complements existing data management investments.

TigerGraph provides:

  • advanced analytics in graph
  • Foundational for ML, AI features and applications
  • Built on the only scalable graph database
  • Designed for OLAP and OLTP workloads in same database
  • SQL-like querying for fast user adoption

In the midst of these emerging solutions is the constant debate over privacy, according to Parthasarathy. According to GDPR, anonymized information refers to “information which does not relate to an identified or identifiable natural person or personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.” Anonymized data should preserve privacy without hampering the analytical value.

Businesses possess large reserves of data and should exercise responsible use of personal data, Parthasarathy said.

 Analytics can be performed without compromising data privacy. Anonymized analytics consists of an approach where privacy is preserved without hampering the analytical value of data.

An archived on-demand replay of this webinar is available here.