Predictive analysis has a growing role in government

Raj Nathan is chief marketing officer and executive vice president of Worldwide Marketing and Business Solutions Operations for Sybase.
Joydeep Das is director of Analytics Product Management for Sybase.

We live in an uncertain and ever-changing world. Events that cause these uncertainties and changes can be viewed in three categories across a spectrum.

First, there are events that are essentially unpredictable, such as certain types of natural disasters. Second are events that can be predicted with a high degree of certainty, such as the likely year your child will graduate high school and the likelihood that you will incur college expenses that year.

In the middle of the spectrum is the third type -- events that can be predicted with a degree of certainty that allows preparation ahead of time but not with exact precision, such as the potential impact of broad technology and business trends.

In the real world, not much can be done or needs to be done in the context of the first and second categories of events. In the third category, however, prudent people — both in business and personal contexts — will plan and act with a high degree of scenario planning and analysis. The challenge we face is how to anticipate this third category of events and plan for likely scenarios. In the business world, at least, there are tools, technologies, products and solutions that can help us address this challenge.

Advanced analytics systems have been a fixture in data-intensive industries such as financial services, telecommunications and marketing for years. They have enabled organizations to track trends, inventories and other factors in real time in order to make the most effective business decisions.

As organizations have realized the tangible benefits of advanced analytics, they have pursued even more innovative uses of analytics. The result is that analytics technology has continued to advance, providing the ability to explore and analyze data in ways that previously have not been possible.

Predictive Analytics: Insight and Foresight

Lately, there has been a great deal of interest in and excitement about predictive analytics. This is an advanced analytics activity that differs from conventional analytics and business intelligence in that it focuses not on what happened or how it happened, but what might happen or could happen. It’s not simply about uncovering insights, but gaining foresight.

If there is one capability government agencies — federal civilian, defense and intelligence agencies, as well as state and local government agencies — would like to have in these challenging and complex times, it would be the ability to examine data through a future-facing lens in order to predict future trends, behavior patterns, external forces and other factors so they can act proactively to protect and serve their citizen constituencies.

It is not surprising therefore, that many government agencies are using predictive analytics in their core, day-to-day activities.

Some of the areas in which predictive analytics is being used by government include:

National health organizations. During the H1N1 pandemic, organizations such as the Centers for Disease Control and Prevention and the National Institutes of Health relied on predictive analytics to track and predict the spread and virulence of the virus in order to provide the basis for their public health advisories and activities.

Federal and state revenue departments. Many tax agencies employ predictive analytics to identify patterns that warrant further investigation and to prioritize legal action to collect unpaid taxes. Additionally, they use predictive analytics to understand the likely impact of policies on revenue generation.

Defense and intelligence. Defense and intelligence organizations in many countries often turn to predictive analytics as one of the tools they use to assemble, cross reference and analyze large volumes of data to determine what actions to take to fine-tune their strategies and tactics.

Public safety departments. Police, fire and rescue organizations face a constant struggle to serve the public with limited resources. Predictive analytics is used by police departments to uncover patterns of criminal behavior to help them determine the most effective deployments of their officers. Likewise, fire and rescue departments can use predictive analytics to forecast the greatest demand for fire and rescue assets to deploy their resources most effectively.

Integrating predictive analytics into existing enterprises

Predictive analytics is based on the concept of modeling business problems using mathematical and statistical algorithms. These algorithms use a set of input variables from a given data set to predict target variables that will support decision-making.

From a process perspective, predictive analytics involves a number of key steps:

• Understanding the problem.
• Tying prediction variables to the problem.
• Selecting appropriate statistical models relevant to the problem.
• Preparing the input data for application of the models.
• Validating the models with test data.
• Applying the models to production data, observing the accuracy over time and making adjustments as necessary.

Choosing the right methodology, tools and technologies is critical

The backbone for most predictive analytics applications is the data management repository for the data set(s) used to build and score the predictive analytics models. These tend to be high-performance database servers. Increasingly, columnar databases, designed specifically for analytics, are being employed as opposed to traditional relational databases.

In addition to data management systems, enterprises require data movement and data quality software to ensure accurate representation of the input data.

A key requirement for predictive analytics projects is a predictive analytics tooling platform to build and score the predictive analytics models. These platforms provide a comprehensive set of design, development, algorithmic and other aids essential for predictive analytics operations.

A fairly recent innovation — in-database analytics, wherein application logic is embedded within the analytics database server to increase performance, reduce latency and improve security — is emerging as a very popular and useful technology for optimizing predictive analytics applications.

As predictive analytics is successfully applied to a wide variety of problems, especially as they relate to events that can benefit from proactive analysis and actions, it will become an increasingly essential capability for government and private-sector organizations.

Many organizations have already embraced and deployed predictive analytics systems and realized substantial business benefits from doing so. More will undoubtedly follow suit. The good news is that there are many examples of successful predictive analytics implementations, and there are experienced technology partners are available to help implement these systems.