In this data-centric era we live in, it is very easy to get confused by all the terms related to analytics. And with the current hype around big data and analytics, it can be challenging for organizations to sift through all the buzzwords and marketing noise. Looking at all the analytic options can be a daunting task.
However, luckily these analytic options can be categorized at a high level into three distinct types. No one type of analytic is better than another, and in fact, they co-exist with – and complement – each other. In order for a business to have a holistic view of the market, and how a company competes efficiently within that market, requires a robust analytic environment which should include:
- Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer: “What has happened?”
- Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer: “What could happen?”
- Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”
Descriptive Analytics: Insight into the past
Descriptive analysis or statistics does exactly what the name implies – they “describe” or summarize raw data and make it something that is interpretable by humans. Descriptive analytics are useful because they allow us to learn from past behaviors and understand how they might influence future outcomes.
A business learns from past behaviors to understand how they will impact future outcomes. Descriptive analytics is leveraged when a business needs to understand the overall performance of the company at an aggregate level and describe the various aspects. Descriptive statistics are useful to show things like, total stock in inventory, average dollars spent per customer and year-over-year change in sales.
Predictive Analytics: Understanding the future
Predictive analytics has its roots in the ability to “predict” what might happen. These analytics are about understanding the future. Predictive analytics provides companies with actionable insights based on data. Predictive analytics provide estimates about the likelihood of a future outcome.
It is important to remember that no statistical algorithm can “predict” the future with 100% certainty. Companies use these statistics to forecast what might happen in the future. This is because the foundation of predictive analytics is based on probabilities.
Examples of Predictive Analytics are:
- Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
- Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
- Improving operations. Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
Prescriptive Analytics: Advise on possible outcomes
The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. In a nut-shell, these analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.
Prescriptive analytics is all about application. This is typically where data science and math comes into play. Prescriptive goes past predictive analytics by prescribing the actions that you and your team should make to achieve your predictive outcomes. Predictive analytics helps you find where your sales team can improve.
Prescriptive analytics boils down to prescribing different actions that can be taken and how each of those actions will help in those improvements. Prescriptive analytics partially predicts possible outcomes in addition to why that outcome will happen. Prescriptive analytics helps you take advantage of the results from predictive analytics.
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