When you step away from the phrase “Big Data,” you’ll find that most enterprises agree that real time data management will become indispensable to their business by making live or critical data available instantly to employees, partners, and customers.
Several drivers are emerging, and the ones we hear most are as follows:
- Making the impossible achievable – Real time data is being applied in a variety of scenarios to allow organizations to be proactive rather than reactive. Use cases already include fraud detection, customer sentiment analysis, real-time stock inventory control, scale-out transactions, and real-time bus and train tracking. Many of these would be impossible without a real-time data management (RTDM) framework in place.
- The quest for fresh insight – Organizations are looking at ways to use analytics to acquire and retain customers. But without customer intelligence, integrated data, or predictive analytics, it’s extremely challenging to deliver new insights. If a customer stops in front of the cornflakes for 45 seconds and doesn’t buy, why is that?
- Data volume – The integration of heterogeneous data sources is also proving a key challenge for many enterprises, especially those with thousands of applications, databases, and data repositories. Data volume growth continues to put pressure on IT. According to an April 2013 Forrester Consulting survey of several hundred IT professionals, commissioned by SAP, data grows 50% annually on average for critical applications. And it’s not unusual to hear IT leaders talking in terms of exabytes these days. Enterprises continue to raise the bar on how much data is stored, mostly driven by compliance requirements and new uses such as composite and machine-to-machine applications, RFID, and geo-location–driven apps. All of these exacerbate the problems of data management, especially when supporting data in real time.
- Data variety – Business users and consumers alike want to take advantage of all types of data. Traditionally, enterprises focused primarily on structured data, which was sufficient to support basic reporting and queries. However, to gain in-depth insights via advanced analytics, and predictive analytics, support for other unstructured data such as XML, logs, video, audio, and documents has become vital.
- A lack of tools – New insights are often deferred due to the lack of a comprehensive data management platform. We find that traditional data management platforms are lagging when it comes to delivering new business requirements such as predictive analytics, real-time insights, and extreme scalable transactions.
It’s no wonder that we have heard so much buzz about real-time data management. But how do you get started? And how do you avoid the obstacles many organizations come up against when developing a data integration service layer? We’ll take a look at this topic in our next blog. But if you would like to skip to the solution now, why not visit www.sap.com/realtime_data or watch our short video.