Connecting the dots: the hybrid data management arena

Data acts as the foundation of organisations across the globe, but as more data is produced and customer demands grow, how that data is managed, analysed and used becomes more complex.

A hybrid data management system is required to make sense of the ever-increasing influx of data from various sources within organisations.
Actian is one company that makes it possible to analyse data from multiple data sources – hybrid data management.
It spun out of Ingres years ago, and now is a $100m turnover/300 employee database management, data integration and analytics vendor – focused at the very big end of big data management, integration and analytics.

Actian also helps companies to take ‘hybrid’ data spread across multiple cloud and on-premise systems and keep it synchronised for the many hundreds of cloud and on-premise applications and analytics platforms that today’s modern, large businesses run. Attaining this holistic view of an organisation is a major challenge.

Following a somewhat turbulent management during 2016, the then CEO and some of the executive team at Actian left. A new CEO – Rohit De Souza – and a largely new team is leading Actian and its customers in a new direction.
In an interview with Information Age, De Souza and members of his executive team – Jeff Veis, SVP, Marketing and John Bard, Senior Director of Marketing – discussed this transition and the launch of Actian X – the first combined big data platform that combines both an operational/transaction processing database, with a fast analytics engine – all on one platform.

The name of the game is speed, and this platform provides real-time analytics for applications like fraud detection in real-time transactional data streams (banks) and real time personalised offers to customers in stores.

In the interview, the Actian executive team ran through a number of case studies, where hybrid data management helped run and improve operations. For example, the Irish revenue is tracking tax-return fraud through analysis of data, and Oxford University uses it for huge analytics of genetic data on ½ million people to hunt for genetic causes of diseases and early death to help save lives.

Is hybrid data management now a necessity?
De Souza: Absolutely. If you think about applications today, whether they be in manufacturing trying to do predictive analytics on the supply chain or optimise supply chains, or whether you’re talking about fraud detection in finance or insurance, or real-time dynamic pricing – these things don’t just need information from your typical transaction systems. They’re reliant on social interactions from open source systems, machine observations from all sorts of real-time systems and this includes extraction or processing of information from traditional enterprise systems.

All these things are taken together to provide real-time inside-driven applications. The phenomenon here that we see is businesses trying to drive these applications – these pieces of data is now flattened across the organisation – no longer present in one single large repository deep in the enterprise, but they come from a number of different places really spread across the enterprise.

It’s now incumbent on the company/companies that claim that want to profit from this information to be able to extract it, process it and analyse it from these multiple sources in multiple formats to really drive some of these insights.

Can you talk about what exactly is ‘real-time’?
De Souza: A lot has been made of real-time. Real-time is individual based on different needs. The issue is that the information is available to you when you need to activate it and when you need to act on it. So, you can act on the information in the time horizon that you need to produce a different result. Not everything needs to be ‘real-time’.