Many companies understand that data are important asset. Therefore, many companies have invested at business intelligence software.

As data dynamically grow in size and variety, business analysts and corporate management face a new challenge to analyze large data sets from different sources. Furthermore, demand for real-time analytics has become imperative for nowadays business operations.

The Case for Big Data Analytics

Big data analytics are created to blend data from different sources in answering user’s queries. Enhanced with data visualization technology, analysts and corporate management can get a big picture of what is happening at the business.


Generating insights from past data and current data can only reveal factual situation of what is happening now. A smarter solution with higher value for business is to have a “prescriptive analytics” tool that enables users to predict what will happen in the future on what circumstances it will happen, so that proper decisions can be made.

In nowadays business operations, there are many parameters being considered in making a decision. Meanwhile, the advances of computing system enable users to ingest heterogeneous data from different sizes in real-time manner. With such conditions, investment in prescriptive analytics software will greatly benefit for operational efficiency. To win business competition in the future, it will depend on how the business makes use of data and anticipate changes.

Why Predictive Analytics?

Using predictive analytics, it can provide insights what will happen, why it will happen, and how it will happen based on your enterprise data (financial data, sales/marketing data, manufacturing data, procurement data, inventory data, etc). It does not only help corporate management to make strategic and operational decisions, but also enables the company to better anticipate future changes.

Sample use-cases of prescriptive analytics in real world are:

  • Demand optimization to maximize revenue, profit, and sales volume at minimum production cost.
  • Optimal resource allocation based on capital constraints and production capability.
  • Optimizing manufacturer-retailer supply chain based on different parameters.
  • Price optimization to match dynamic demand and supply in generating maximum profit.