According to market research firm IDC the Business Analytics market is poised to reach $50.7B by 2016. With this type of growth, business analytics are now a significant line item on senior executives’ agendas.
In a recent article, Dan Vasset, program VP for IDC’s Business Analytics Solutions unit, stated, “Driven by the attention-grabbing headlines for big data, and more than three decades of evolutionary and revolutionary developments in technology…the business analytics software market has crossed the chasm into the mainstream mass market.”
Business Analytic software is divided into three primary segments:
- Data Warehousing Platform Software, which in 2011 grew the fastest at 15.2%
- Analytic Applications, which grew at 13.3%;
- And, Business Intelligence (BI) and Analytic Tools, which grew at 13.2%.
This growth has caught the eye of many businesses that were previously uninterested what the technology could offer. One type of BI and Analytic tool is Predictive Analytics. Predictive Analytics, according to PredictiveAnalyticsWorld.com is, “a BI technology that produces a predictive score for each customer or other organizational unit.”
According to Anthony Perez, director of Business Strategy for the Orlando Magic NBA team said that using predictive analytic models to predict games that would oversell or undersell the box office was able to adjust prices to maximize attendance and profits.
“This season we had the largest ticket revenue in the history of our franchise, and we played on 34 games of the 45-game season due to the lockout,” Perez said.
Predictive analytics can play an essential piece of a company’s success, like it did for the Orlando Magic basketball team; however, analytics are not something to take on blithely. Before you cannonball on in, read about some best practices:
Make a (Small) Business Case
- Identify a clear business problem you are trying to solve. Start small here, by choosing a low risk business process to optimize. Some good places to start are customer support/retention and user experience.
- These may seem insignificant to the bottom line, however with analytics they have the potential to be impactful.
Your Very Own Business Champion
- The support of a business-decision maker or executive sponsor is fundamental to the success of the project.
An Easy Win
- Find a business process analytics can demonstrate value by producing measurable results. These results build credibility; you don’t want to lose the battle before you begin the war.
- Hire a trained analyst who has the expertise to develop and apply a model to a business problem, while determining the right data feeds. Their expertise will not only provide a confident plan, but also prevent the project from failing.
Know Your Data
- Ensure you have not only enough data, this means enough historical data and enough granularities in the data to feed your planned model.
Prepare Your Data
- Data prep issues can quickly put a project off track. Everyone is ashamed of their data state, but the data that matters is usually in okay shape. So don’t wait for your data to become picture perfect, clean up what’s important and the rest will follow.
- Once you get the first model right, the next is significantly less expensive to model – assuming you’re using the same data. If you aren’t don’t worry too much – with experience comes knowledge and the more you build the quicker the process becomes.
Talk the Business Talk
- Data visualization will be the best way to display your results to your audience.
- Take for example Brian Jones, Director of Countermeasures and Performance Evaluations at the Office of Inspector General within the U.S. Post Office. He wanted to use analytics to determine fraudulent healthcare claims. On his first attempt, Jones sent out a spreadsheet – which was mostly ignored by the investigators. Next, Jones hired a consultant to assist with modeling the data. Using a web based interactive heat map, the GUI represented contracts as circles denoting the biggest costs and highest fraud risk. It caught on and investigators deemed Jones’ information as credible and valuable to their roles.
Predictive analytics are initially costly, requiring a substantial investment up front; however, even small projects can produce a significant return-on-investment. They also can cause concern that the model is designed to take over decision making, but these predictions must be viewed as judgment support integrated in the process how the end user decides.
When your business does decide to “join the hype” be sure to follow the aforementioned best practices to reduce risk. “The risks are high, but so are the rewards. Take it to the end. Be successful and act on what you learn,” says Anne Robinson from Informs.