In this special guest feature, Antoine Chambille, Global Head of Research & Development at ActiveViam, discusses how in-memory analytics is helping businesses solve their data challenges. Since 2005, Antoine has been leading the team in charge of designing, developing and supporting ActiveViam’s in-memory analytics solutions. Before joining ActiveViam, Antoine worked several years for a consulting firm in the financial sector. He graduated from Ecole Polytechnique and Telecom Paris.
Data is at the center of virtually every business decision today. But with over 2 quintillion bytes of data on a daily basis alone, managing and leveraging data properly is much easier said than done. More and more companies hit an “analytical wall” where the volume of data generated becomes too much to handle and cannot be analyzed fast enough to provide actionable insights.
Some of them however have found a solution, already widely used by industries like finance who faced their own data revolution some decades earlier than the others: in-memory analytics.
In-memory analytics has quietly become a go-to solution for many businesses today where real-time decision-making is the norm — such as finance, retail or healthcare. Three main reasons have made in-memory analytics an increasingly popular solution across industries.
Access to Real-Time Data
Today’s business world moves incredibly quickly, so having static data-sets that are updated on a daily basis or even less frequently can have serious ramifications on a company’s ability to identify a cutting edge opportunity. In-memory analytics technology however, is capable of working with an ongoing stream of data so that business decisions can be made based on the most current data possible. This allows businesses to adapt to real-time situations and either capitalize on an opportunity or mitigate losses due to an unforeseen issue.To this end they can set up automated alerts and practice “exception management” where the system brings to the forefront all situations that require their attention, and only those.
Enabling “Train-of-Thought” Analysis
With so much data at their disposal, trying to find the best way to utilize all of the data that is available is one of the most challenging aspects of a business professionals job today. Moreover, business intelligence pros also face challenges in that they are often limited in terms of what data they have access to based on pre-determined data queries or the limitations of their own BI tools.
In-memory analytics removes these confines and enables business intelligence professionals to be able to perform “train-of-thought” analysis whereby they can pursue any avenue of analysis without any limitations. This allows businesses to be able to dig deeper and widers to find key insights and uncover hidden value from any data source — such as sales figures or customer engagement.
Forecasting Decision Impacts
Business intelligence is not just about making a split second decision to achieve success in a given moment. Rather it is often about what is the best decision for the immediate, short and long-term. Unfortunately, too often BI professionals are unable to forecast out to see how a decision may impact KPIs because with the data volumes involved, calculations simply take too long. .
With in-memory analytics however, businesses can continuously run “What If” tests to see the fallout of a particular decision on KPIs instantly. This is incredibly important for industries such as finance or manufacturing where stress tests scenarios need to be run routinely to anticipate and plan for any potential problem..
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The growth of data in the business world has been incredibly exciting. In addition, it has also uncovered that we have really only scratched the surface on the potential of in-memory analytics technology. From validating algorithms derived from ML/AI to streamlining analytics dashboard, in-memory analytics is set to be a vital ally for businesses in the years ahead.
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