Driving Real-Time Decision Making with Business Analytics

How is analytics converting data into action?


We are increasingly placing a premium on time. The average customer is being trained to expect things instantaneously, and data analytics is increasingly able to facilitate this desire. In many cases, if solutions to a customer’s need are not provided straight away then the opportunity is lost.

Companies are now moving their focus away from using Big Data to find insights, and trying instead to convert that data into action as quickly as possible. They are doing this by unifying the databases used for operational applications with those used by analysts, leveraging insights directly from their data warehouse. Doing this allows for models to be continuously updated, and for business functions to be adjusted as things are happening, rather than reacting to events.

As-it-happens information is now essential in a number of areas. One of these is targeted marketing. Targeted marketer Urban Airship, for example, sends out 180 billion text messages every month, many of which are sent to sports fans, to whom a delay in information makes it practically irrelevant, particularly in gambling circles. They also provide services for Starbucks, so when a customer is in store they will receive the best possible experiences by drawing them to offers.

One example of the as-it-happens business principle in practice is in retail. Take the shopper who leaves a major retailer’s website without buying the item in his cart. Thanks to the real-time processing of data, they then receive an email from the retailer almost immediately, offering the product at an unadvertised low price which balances profitability while still enticing the customer into a bricks and mortar store to make the purchase and browse for things they hadn’t been looking for before. This tactic helped one retailer boost conversions by 50% last Cyber Monday.

Most firms are still reacting to data. They are missing out on the lower costs that can be achieved by shortening the data-to-action cycle. There are many implications for analytics of this type, particularly for healthcare. The challenge rests in how to handle the data - how to get the data in and scale it to deliver real-time and actionable analytics needed. Firms who are succeeding in doing this are not necessarily those who are uprooting all of their technology, but it is those who are taking a dynamic approach and changing the way they think about their existing technology and their business processes themselves. 


Read next:

Social TV: Cross-Channel Insights on the ShareThis Platform