For hundreds, if not thousands of years, people have been seeking a better understanding of who they are and a roadmap for their future. Using everything from crystal balls to tea leaves, psychics make you believe they have the divine power to forecast the future.
Now, predicting the future has come out of the shadows and into the mainstream. And predictive analytics are replacing the crystal ball with real-time, actionable data to predict the probability of what lies ahead.
Data mining created a fertile foundation for predictive analytics. Technology advances and massive amounts of available data turned basic BI into high-tech fortune-telling. By exploiting data patterns, businesses from health care to retail to law enforcement can predict both risks and opportunities.
Unlike traditional fortune-telling, there is a method and process to predicting business outcomes:
- Defining the project—Define business outcomes and identify which datasets will be used.
- Data collection—Data mining for predictive analytics prepares the data from multiple sources for analysis.
- Data analysis—The process of cleansing, transforming and modeling data to arrive at conclusions.
- Statistics and validation—Statistical analysis allows you to validate assumptions.
- Modeling—Predictive modeling provides the ability to automatically create accurate predictive models about the future. It also can be used to select the best solution from a multi-model evaluation.
- Deployment—Predictive model deployment provides the option to deploy the analytical results into everyday decision making.
Businesses Line Up
Predictive analytics is not only being used by numerous verticals, but also by a variety of roles within an organization. This expands the customer opportunity and creates the need for a holistic, solution-based approach. Here are a few examples:
- The CFO of a UK restaurant chain uses predictive analytics to predict demand for food and drink that could potentially knock 14% off EAT’s food wastage, as well help to manage staffing requirements more effectively.
- Marketers use predictive mobile analytics to map potential customers, identify their spend range for targeted promotions, and predict which product you may purchase next based on your past behavior. If you've ever seen an ad on social media and wondered "How did they know this about me?" — The answer: predictive analytics.
- A large manufacturer of technology for the transportation industry employs data scientists who use predictive modeling for manufacturing, supply chain and logistics, engineering, and Internet of Things and Services.
Predictive analytics ranks high on the value/difficulty scale of analytics solutions, according to Gartner. Engaging in predictive analytics is not a tool selection, but a holistic solution that looks at available structured and unstructured data, how it is served, stored, and instantly made available to draw conclusions.
You don’t need tea leaves to understand how large the market is. According to a June 10, 2015 report from The Business Insider, the analytics domain is likely to open up enormous job opportunities as big data and predictive analytics are set to dominate the recruitment industry in the next six months.
Because of the popularity of predictive analytics, there are a lot of players in this field. IBM and Ingram Micro have made significant investments to support this rapidly growing business demand. From planning to training, and ultimately to better business outcomes, partnering with Ingram Micro and IBM will give you and your customers the power to predict the future.
By Nina Buik, Ingram Micro Global Training
*This post originally appeared on IBM's Global Training Blog.