Key considerations for optimizing predictive analytics initiatives

Predictive analytics continue to offer benefits in making organizations more decisive and positioned toward the future. Merely understanding what customers want isn't enough - businesses have to effectively project the direction and trajectory of future consumer actions. They have to then be able to leverage these insights in strategies that can help guide the customer to a conclusion that both parties have, on some level, foreseen. On an internal level, the predictive process works much the same way - business end users can engage in self-service analytics to make smart forecasts about resources, project management and other decisions.

Of course, predictive analytics can't launch successfully without effective applications. There are a multitude of considerations that must factor into the process of designing an effective predictive analytics program. The approach that a developer takes to designing and deploying the application goes a long way into determining how high its ceiling will be, as well as the depth of insights end users can expect to extract from it. So where should a programmer concentrate his or her attention?

Design for decision-making
Giving an end user the opportunity to engage with a big data application and visualize information is important, but it's only part of the puzzle. The successful app enables the end-user reporter to make meaningful decisions based on predictive modeling. Given the time-sensitive nature of most operations, it's critical that predictive analytics apps facilitate efficient conclusions. This means that developers have to understand the principles of decision management and ensure that tools to expedite this process are baked into the app user environment.

As decisioning technologies expert James Taylor recently observed, decision management effectively represents the implementation and outcomes of predictive analytics. Many employees are often in situations in which pressure is on them to make a decision - yet they may have received little training or instruction, given the fast-paced nature of most corporate environments. This is especially true in the case of front-line employees in the sales and marketing departments, who play a crucial role in actually using predictive analytics but may not receive intensive IT or software application usage training.

"[Employees are] not in a position to know what decision they should be making," Taylor said, according to SiliconANGLE. "So, if you don't know what decision you're trying to embed the analytics into, you can't do a good job with the analytics."

Unstructured data, while harder to extract and make sense of, is critical to leveraging real value from predictive analytics applications. Software programmers must incorporate the means to synthesize unstructured data streams into visualizations and insights that end users can quickly act on.

Structuring the unstructured
Establishing effective mechanisms for self-service end users to wade through previously unstructured and siloed data can be a tall order. For developers, this may mean some modifications to their database management principles. Specifically, instead of focusing on database querying, it may be more effective to hone in on data discovery, said business analytics expert Vasant Dhar, according to The Wall Street Journal. This enables the end user to transition from using data to explain something to using data to make predictions.

"Unlike database querying, which asks[:] What data satisfies this pattern (query)? discovery asks[:] What patterns satisfy this data?," Dhar stated. "Specifically, our concern is finding interesting and robust patterns that satisfy the data, where interesting is usually something unexpected and actionable and robust is a pattern expected to occur in the future."

A program like ActiveAnalysis provides the platform to design applications with data prediction in mind. An interactive, drag-and-drop user interface, limitless data binding and functionality with Windows Forms, ASP.NET and Silverlight enable developers to concentrate on helping end users make meaningful decisions.