![]() ![]() ![]() According to Bill Powell, one of the owners of the firm, “ There was a sea of information coming in and it could take up to two days to pull it together, which affected our service levels…In-memory HANA means we can answer questions in seconds. Using data compression, real-time information is stored in RAM, and fleet managers can access this data within minutes. According to Aberdeen Group, companies using in-memory analytics can process 3 times more data at a speed 100 times faster than their competition.Ĭase in point: SAP HANA has been to create real-time, in-memory analytics for a large trucking operation with fleets in the U.S., Canada, and, most recently the UK. As the data is read into memory, you can access it at higher speed. The infrastructure changes brought in by this approach to analytics eliminate the need to constantly shuffle the data resigning on the disk. Using in-memory analytics and self-service BI, companies can maximize their data access speed and “dig up” deeper insights that can be immediately used for decision making. Additionally, 59% of leaders claim that their legacy data storage systems require too much processing to be in-line with the current business requirements. In some cases, they require a week or even more time. 37% of executives admit that they need at least one day to access the sources for analytics, per Attivio. Traditional analytics tools flop when it comes to analyzing ever-increasing volumes of data, at a speed sufficient to make timely decisions. Source The Business Value of In-Memory Analyticsĭata is pouring in at exponential levels, but IT departments and business leaders still struggle to understand how this data can impact their business goals. This eliminates the standard I/O processing that is responsible for the slow performance of traditional BI processing. In-memory computing, as the name suggests, enables data storage in computer’s RAM. ![]() What is in-memory computing? Traditional business intelligence works by processing data that is held in a relational database on external hardware. With in-memory computing, the analytical timeframe changes significantly. This circumstance results in less than full analysis and lack of detailed information that should be gathered to make the best business decisions. Alternatively, rather than processing data in a way to get truly detailed reports, businesses most often settle for aggregate reports, due to the processing time and resources required. Analytical processes must often run overnight, so as not to cause system contention. The rest of immense volume remains siloed in proprietary software as with traditional BI tool, operationalizing large batches of data is something that can take hours. The problem lies in making that data useful for analytical purposes – only 0.5% of all available data is ever processed by companies. Businesses now have access to more data than ever before. ![]()
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