The importance of data to business decisions and operations cannot be understated. On a daily basis, 2.5 quintillion bytes of data is generated. Managing that much data so the necessary intelligence is easily accessible or to make sure insights are not missed, can be incredibly challenging. Luckily, technology does exist that can help to significantly ease the strain of this deluge of data: in-memory analytics.
Initially adopted by the finance industry—where complex, on-the-fly decision-making is required–in-memory analytics is incredibly adept at handling the analysis of substantial amounts of data very quickly. And as data more ubiquitous, and the development of public cloud services has made specialized hardware affordable to companies of all sizes, in-memory analytics technology is being applied in a variety of verticals from retail, to supply chain to healthcare. In addition, given in-memory computing enables a range of analytics features that are very useful to businesses of all kinds and extremely difficult to achieve with disk-based platforms, the technology is really poised to expand rapidly.
Slice and Dice Any Which Way
One of the hardest parts of being a data professional today is leveraging all the data at your disposal. Unfortunately, business intelligence (BI) pros are often constrained in terms of what portion of the company’s data they can and cannot analyze depending on pre-defined data queries and the limitations of their BI tools.
In-memory analytics, however, enables analysts to explore data without any of those limitations. This creates an experience of train-of-thought analysis that helps businesses make deeper analyses and uncover valuable new insights into their market and their own performance. This means retailers for example, can have a global, precise view of the market and cross-reference data from sales numbers, client receipts and loyalty programs, to develop a better understanding of what customers expect from them.
Figuring Out “What If?”
Due to the vast amount of data involved, and the limitations of physical storage discs, the calculation of the impact of any business decision on complex KPIs can take a long time to resolve.
By utilizing in-memory analytics BI pros can continuously compare and contrast “What If” scenarios—such as running stress tests for financial institutions or setting up disruption test cases for supply chain businesses—with a clear, immediate view of the potential outcomes, so they can be as prepared for the future as possible. This means businesses are able to limit the amount of times they are caught off guard or mitigate the impacts of any unexpected turns.
With how rapidly the world moves today, having data that is as current as possible is pivotal. Static datasets only updated once or twice a day can lead to significant, costly misjudgments in some cases. In-memory analytics on the other hand can support a continuous stream of data from multiple sources so analysts always work with the most relevant set of data. This means supply chain businesses, for example, can have real-time oversight over operations to better cope with any disruption that occurs and ultimately save millions of dollars in potential losses.
The boom of data science has opened a new field of use cases for in-memory analytics, ranging from the validation of new algorithms derived from ML/AI to industrializing the building of new analytics views and dashboards. If you have invested heavily in data science, in-memory analytics could be the pivotal cog in generating the maximum output from each piece of your organization’s data infrastructure.