Bridging the Gap between Insight and Action

In the past few years, IT leaders and C-level executives have associated ”big data” with looking at historical information with a fresh set of eyes. The concept has been to delve further into transactions retrospectively to identify trends, and draw heretofore unrecognized actionable insights, based on previous events.

The approach has been largely effective, but only scratches the surface of what analytics can offer to businesses, leaving a critical data set outside of what’s being studied; we’re ignoring the most relevant data — data that’s being created in the here and now.

Many organizations have attempted to alleviate this gap using in-memory data processing frameworks like Apache Spark. Entire teams built around the idea of generating real time insights are being constructed within various industries. However, these groups are often segmented from teams building transactional applications. Organizations are heavily investing in accumulating data and analyzing, rather than adopting the right tools to act on it in the moment.

In some instances, this approach has been necessary. Data is pouring into today’s enterprises from more sources than ever before; mobile inputs, IoT connected devices, beacons and social media are just a handful of new data sources, and the sheer volume of information being created and analyzed requires some level of separating the systems running the business (OLTP) from the ones managing it (OLAP, Data Warehouses).

Earlier this month, GigaSpaces took a new approach with our high performance data grid, decoupling and open sourcing the core of our XAP platform. From this decision, which enables developers to more readily incorporate a decentralized and distributed approach to identifying data insights, we’ve learned a great deal about how to most effectively bridge the gap between real-time transactions and various analytics workloads. We’re enabling clients to process analytic workloads at the data source, or network edge, which is dramatically cutting latency and delivering on the promise of closed-loop analytics and fast data.

By way of example, the early returns of our approach have enabled:

• A financial services firm to build a smarter approach to identifying fraud
• A retailer to offer shoppers better product recommendations as they browse a website
• A telecom provider to remind customers of roaming charges for international travel before the customer was charged.

The early returns on our new approach have been extremely encouraging, effectively replacing the legacy methodology of separating transactions from analytics. Companies that have incorporated GigaSpaces In-Memory Computing products have made the transition from big data to an insight-driven business. Bridging the gap between insight and action is finally enabling enterprises to deliver on the promises we’ve all heard about becoming a digital insight-driven business.