Theoretically, in this world of big data, analytics and mobility, your employees and customers should be able to access the right information, at the right time, on their device of choice. I call this delivering right-time experiences. Other firms call it contextual services or moments. Yet, as we’ve acquired more data, we’ve moved farther, not closer to creating contextual services that deliver the right information at the point of need.
Companies have more information than ever before with mobile, sensor and social data. More information represents both opportunities and challenges. Companies need to store and process large volumes of heterogeneous information, but IT also needso deliver actionable insights to business users from this wealth of data.
While we have the technologies to store and process data (frequently referred to as big data tools), there’s no single easy way to do this across the various data sources. Hadoop was once considered the holy grail for this, but after several years we’ve seen the need for advanced solutions such as Apache Spark, an open source big data processing framework built around speed, ease of use, and sophisticated analytics.
To deal with the challenge of analyzing distributed data (that includes systems of record and engagement as well as data from other sources such as social sentiment, sensor data etc.), companies are now moving vast amounts of data back to specific locations that are frequently called data lakes — soon to become archipelagos. What’s needed is a way for companies to effectively and quickly combine and analyze information from various sources.
It is within this context that the President of SAP platforms, Steve Lucas, announced SAP HANA Vora. It’s a new in-memory query engine that leverages and extends the Apache Spark execution framework to provide enriched interactive analytics on Hadoop. The goal is to apply in-memory computing to distributed data with OLAP-like analytics and a business semantic understanding of data in and around the Hadoop ecosystem.
What does that mean in plain English? Consider linking your corporate data such as customer records, with social sentiment data from various social networks and weather. You can analyze all of these various data sources to understand how say weather patterns and current social sentiment will impact the sales of your company’s latest line of outerwear. Other use cases that SAP provided include:
- Mitigating risk and fraud by detecting new anomalies in financial transactions and customer history data
- Optimizing telecommunication bandwidth by analyzing traffic patterns to help avoid network bottlenecks and improve network quality of service (QoS).
- Delivering preventive maintenance and improve product re-call process by analyzing bill-of-material, services records and sensor data together
In my article on VMworld, I mentioned the trend of big vendors going big on open source. Yesterday’s, SAP announcement was unexpected, but it highlights how large companies are integrating with open source to remain relevant in the new economy. Businesses needed federated data query across different data platforms to provide relevant context on why a certain event is happening. There will be many approaches to this problem. It’s interesting to see SAP offering one that integrates with the open source community. While the margins will not be the same as its traditional business, it’s good to see SAP thinking creatively on how to expand its market.
This article was written by Maribel Lopez from Forbes and was legally licensed through the NewsCred publisher network.