In-memory Analytics
In-memory analytics is a process that allows for queries to be done on data that resides on a computer’s RAM (Random Access Memory). This results in very short query times, which allows firms to support business process decisions with real-time data. The impact of in-memory analytics will change how firms interact with their data. No longer will companies need to extract data overnight into analytical databases, which causes data to be at least 24 hours behind real-time. Firms now, with in-memory analytics, can immediately perform data analysis on up to date data sets. In-memory analytics may also in the future render data marts only places were data is stored that is not queried frequently. The need to no longer aggregate data will lead to a decrease in IT costs for most firms. In 2012 SAP introduced their new HANA (or SAP High-Performance Analytic Appliance) in-memory cloud system which is a combination of database, data processing, and application platform capabilities in a single in-memory platform. Businesses that implement HANA get access to all of SAP’s state of the art datacenters that provide the in-memory processing power. HANA provides firms with a platform and a service that allows for expandable real-time big data analytics as well as real-time predictive analytics. In 2014 the luxury clothing company Burberry implemented HANA and began to use in-memory analytics. Their application now analyzes data from SAP e-commerce, Salesforce.com, and social feeds, and it serves up customer insight to in-store salespeople through an HTML5 interface on iPads. This real-time sales data now allows for Burberry’s sales force to be much more affective. In-memory analytic is the future for business intelligence and its speed and efficiency are sounding the death toll for the old Extract, Transform, and Load method of data aggregation
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