Different types of Analytics!
Descriptive Analytics(What has happened in 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. They are analytics that describes the past. The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.
When to use Descriptive Analytics?
Use Descriptive Analytics when you need to understand at an aggregate level what is going on in your company, and when you want to summarize and describe different aspects of your business.
Examples: Reporting, dashboards, visualization, etc etc etc ……..
The Goal is to turn data into information, and information into insight~
Diagnostic Analytics(why something happened in the past?)
Diagnostic analytics takes descriptive data a step further and provides deeper analysis to answer the question: Why did this happen? Often, diagnostic analysis is referred to as root cause analysis. This includes using processes such as data discovery, data mining, and drill down and drill through.
Here is an example of diagnostic analytics “Revenue is up in the business and the likely reason is the increase in investment on targeted marketing approach, closure of a major competitor in the area.” Take note that descriptive analytics cannot provide an answer to important questions such as “How can we avoid this problem” or “How can we duplicate this solution?” These are covered by diagnostic analytics.
When to use Diagnostic Analytics?
Use Diagnostic Analytics when you need to understand the RCA of anything that happened in the past or willing to know why my sales are down and how we can control it. Diagnostic analytics helps in decision making so when you need to take any decision after exploring past data then it plays a vital role.
Summer-induced stupidity. That was the diagnosis…
Predictive Analytics(What will happen in the future?)
Predictive analytics takes historical data and feeds it into a machine learning model that considers key trends and patterns. The model is then applied to current data to predict what will happen next.
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 provides 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.
Here is an example of predictive analytics to produce a credit score. These scores are used by financial services to determine the probability of customers making future credit payments on time.
When to use Predictive Analytics?
Use Predictive Analytics any time you need to know something about the future or fill in the information that you do not have.
“The only way you can predict the future is to build it.”
Prescriptive Analytics(How can we make it happen?)
The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions and guide them towards a solution. In a nutshell, these analytics are all about providing advice. Prescriptive analytics attempts 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.
Suppose you are the CEO of an airline and you want to maximize your company’s profits. Prescriptive analytics can help you do this by automatically adjusting ticket prices and availability based on numerous factors, including customer demand, weather, and gasoline prices. When the algorithm identifies that this year’s pre-Christmas ticket sales from Los Angeles to New York are lagging last year’s, for example, it can automatically lower prices, while making sure not to drop them too low in light of this year’s higher oil prices.
When to use Prescriptive Analytics?
Use Prescriptive Analytics any time you need to provide users with advice on what action to take.
I believe in prescription drugs. I believe in feeling better.
