In recent years, few enterprise buzzwords have garnered as much attention as big data. This style of analysis promises considerable benefits, including the potential to reap important insights about a company’s customers, business processes, partnerships, and beyond. But in order to achieve these advantages, organizational leaders must first understand the specific analytics strategies their business will use to get there.
Two top analytics processes include applied predictive analytics and preemptive, or prescriptive, analytics. But what exactly do these strategies entail, and what is the difference between them?
Three pieces of a puzzle
Lithium Technologies Chief Scientist Michael Wu told Business News Daily that business analytics is made up of three main parts, including descriptive analytics, predictive analytics and prescriptive analytics. Descriptive analytics, one of the simplest forms of analysis according to Wu, is a summary of raw data. Predictive and prescriptive analytics build on these insights and help create more actionable results.
“Predictive analytics forecasts what will happen in the future,” noted IT expert Immanuel Lee. “Prescriptive analytics can help companies alter the future.”
A closer look
But what does this really mean? Let’s take a look at each of these terms to better understand the differences:
- Predictive analytics: This type of advanced analytics involves making predictions about future events, and can include strategies like modeling, machine learning and artificial intelligence. Analytics results provide data-backed prognostication that can help business leaders better understand unknown, future occurrences. Predictive analytics help answer questions including what happened and why, what is currently happening, and what will happen in the future.
- Prescriptive analytics: Also known as preemptive analytics, this style of analysis examines data with other, specific questions in mind, including what should be done, and what can the organization do to make a specific outcome happen. According to Gartner, prescriptive or preemptive analytics includes processes like graph analysis, simulation, event processing, machine learning and heuristics.
Bringing the pieces together
In this way, predictive analytics can lay the groundwork, where prescriptive analytics can provide the path to a certain desired outcome or goal.
“Prescriptive analytics builds on [predictive] by informing decision-makers about different decision choices with their anticipated impact on specific key performance indicators,” Zoomph product officer Thomas Mathew told Business News Daily.
Analytics in action
Mathew noted that one real-world example of these styles of analytics involves traffic navigation apps. These programs analyze an array of different data and factors according to the user’s chosen destination. From here, the app is able to provide route choices alongside predicted estimated time of arrival.
Another example emerges in the retail sector. A sporting goods store, for instance, may use predictive analytics to discover insights about upcoming popular items. Through analysis, the store forecasts that a specific shoe will be in high demand with customers, especially as warmer weather approaches. As a result, the store increases its inventory of this popular shoe, based on its predictive analytics results.
At the same time, however, this heightened demand may not take place at the same rate throughout every store location. Instead, more sales may follow geographical weather patterns. Cancelling inventory orders would present a massive challenge for the sporting goods organization. As opposed to going this route, the company can leverage prescriptive analytics to leverage other third-party data sources in its data analysis, including weather and climate information. This allows the business to get ahead of seasonal demand patterns and adjust its supply chain to ensure that stores aren’t under- or overstocked on the popular shoe.
Integrating the right data
Both predictive and prescriptive analytics hinge upon the inclusion of the right data sets to support successful, forward-looking and actionable results. Tying in incomplete or unnecessary information could skew results and may not point businesses to the right path.
That is why it can be helpful to have an expert on the side of the business to help ensure that data is cataloged and prepared correctly. To find out more, contact Unifi today.