Operating a modern enterprise is complex, with many moving parts and a great deal of data to analyze. In fact, the amount of data collected is ever-increasing (hence the term “big data”), with not only traditionally structured data but also information collected from IoT devices, video, text messages, and more. It can be difficult, even for experts, to determine exactly what information matters for things like managing resources and forecasting inventory, let alone how to use that data to improve performance in a meaningful way.
To assist in understanding all this data, predictive analytics uses tools such as data mining, statistical analysis, machine learning, and artificial intelligence to gather large amounts of data and analyze that data for actionable outcomes. The typical predictive analytics process includes the following steps:
Predictive analytics can be used for many different purposes across a wide variety of fields, such as:
- Identifying potential risks and opportunities in the market and in your business – For example, enterprises can determine which customers are most likely to default on payments, reduce employee turnover and fill unseen gaps in talent, manage logistics to avoid downtime, or identify unmet opportunities for additional sales.
- Forecasting inventory and manage resources – For example, healthcare organizations can optimize inventory, place equipment where and when it is needed, and determine staffing needs more accurately, while medical personnel can improve diagnostic accuracy and improve patient outcomes.
- Planning equipment maintenance and purchases – For example, a manufacturer can determine, based on historical patterns and current measurements, when a particular piece of machinery is likely to break down, and can therefore plan maintenance schedules more effectively, or even plan a budget to replace the machinery before a breakdown occurs.
- Analyzing customer behavior and setting pricing – For example, marketing and sales departments can gain insight using detailed information about buying patterns, where potential customers are spending time online and offline, and the impact of various channels on sales. Using this information, they can more effectively target marketing messages, identify good prospects and dissatisfied customers, and determine how best to interact with customers and set pricing for increased sales.
While all these things can be accomplished on some level with standard data analysis, predictive analytics offers more effective ways to look at big data more deeply and draw useful inferences from that data, with recommendations or even automated adjustments based on the results. Predictive analytics can accomplish what might otherwise take a sizable team of experts a great deal of time to figure out, saving your business time and money.