Transform Data Into Insights With In-Memory Data Grids

With the advent of big data, the power to make informed business decisions based on historical data is now possible. Given the progress made in data processing technology and the increasing adoption rates of cloud computing, smart devices, and in-memory data grids (IMDG’s), now is as good a time as ever to leverage big data and analytics tools to understand the behavior patterns of customers, stakeholders, and competitors.

An IMDG allows you to use prescriptive analytics in making recommendations on next steps and mitigating potential business risks. Previously, organizations resorted to aggregate reports to save time from the creation of detailed reports, but this led to inefficiencies that ultimately resulted in bad business decisions. An IMDG decreases data processing times significantly while being easily scalable, making it a cost-effective solution for every business looking to make a seamless digital transformation.

Big data isn’t going anywhere, and it can only grow bigger and more complex through the years. It can be a challenge to sift through all gathered data and make them useful; among all the data gathered, a mere 0.5% is ever analyzed. Operationalizing large amounts of data takes several hours, and businesses have employed a host of proprietary software just to address speed and performance challenges. Keeping data siloed in proprietary software is inefficient and can lead to sharing and permission issues in the future. An IMDG can help democratize data processing by acting as a platform where data can be processed and analyzed.

Big Data and Prescriptive Analytics

Big data has reached a level where it can be leveraged by businesses to make decisions that affect market and competitive effectiveness. When it comes to the question of improving business decisions and outcomes, the usual answer is big data. Big data, however, needs advanced tools to become useful, and this is where IMDG-powered prescriptive analytics comes in. In recent years, it has become a vital part of every organization’s strategy in enhancing operations, mitigating risk, and managing performance.

From describing who your customers are, prescriptive analytics goes one step further and provides insights into what is likely to happen in the next few days, months, or years. It also recommends specific actions to take so you can create predictable business outcomes. It shifts focus from what has happened and what is likely to happen to prescribed actions based on an understanding of why and how they result in the desired outcomes.

Aside from machine learning and the use of algorithms, there are a variety of methods or modeling techniques used in prescriptive analytics, including the following:

●      Spreadsheet Modelling
This technique uses spreadsheets to perform complex mathematical computations that help businesses determine measures of success based on past and current operations data. These spreadsheets are designed to allow for updates in input data and automatic recalculations whenever there are any changes. This ensures that results are always conclusive and updated even if input data regularly changes. It’s used as a metric for determining what is likely to happen and what steps or actions to take to get the desired outcomes.

●      Statistical Analysis
By collecting and analyzing large datasets, statistical analysis can help to highlight trends that predict what events are likely to occur in the future. It can be used for gathering statistical data and leveraging a “big picture” approach to identifying and interpreting past patterns and their implications for the future.

●      Quantitative Analysis
Compared to statistical analysis, this technique takes a more specific and targeted approach. It dives deep into the data and provides an understanding of why and how specific events occurred. While being just as important as statistical analysis, it’s important to differentiate the two and know when it’s best to use one or the other.

●      Simulation
This method simulates the situation by creating a model of the system where the problem exists. Simulation is based on the relationship of events and other factors, using a cause-and-effect algorithm to analyze data and make projections of the future. In sales, models are created to simulate customer experience regarding product quality, sales staff, and a variety of market factors to see how they relate to each other. This analysis is then added to the simulation model together with the data to determine possible cause-and-effect scenarios and discover opportunities.

●      Optimization
This method is used to find ways of maximizing existing data to achieve desired outcomes. It involves data assembly, model building and evaluation, and presentation of results. It’s a technique typically used in fields where accuracy and efficiency is expected, including production, inventory, and supply chain systems.

An Innovative Duo

Prescriptive analytics may be a relatively new field, but with the help of an IMDG, it can push businesses into new heights through high-speed data processing, high availability, and intuitive scaling and elasticity. It eliminates the guesswork from business decisions and instead relies on more precise mathematical calculations to determine next steps in multiple business scenarios. The biggest thing about prescriptive analytics is that it not only aims to improve outcomes but also allows decision makers to quantify consequences even before decisions are made.

Author’s Bio

Edward Huskin is a freelance data and analytics consultant. He specializes in finding the best technical solution for companies to manage their data and produce meaningful insights. You can reach him via his LinkedIn profile.