As data science students, all of us have heard the term predictive analysis which is basically forecasting or predetermining data for the future based on the trends and patterns of the past few years.As decision makers, stakeholders want to know what next lies in store for them in terms of the company’s future. Data visualizations on company’s performance, market value, stock prices are all indicators of what could happen next.
But there are two other dimensions to the analysis of any data or visualizations. Both are seldom heard and mentioned in market, and yet happen to have an immutable importance in the field of data analytics. These are descriptive and prescriptive analytics.
Descriptive analytics uses data aggregation and data mining to dig into the past data and understand “what exactly happened”. Prescriptive analytics on the other hand, use simulation to find alternatives and possible outcomes and answer “what can we do”.
Most companies conduct descriptive or predictive analytics on their data, mostly because they are trying to figure out what went wrong and what will be the future effects of it. They also hire professional experts to do the job of suggesting different recovery strategies, recommendations, and providing them with the best advice. However, the field of prescriptive analytics is relatively new and slowly getting its long-due attention. We rely on professionals and years of their knowledge and experience to prescribe what’s the best move for our companies. Predictive models, computational modeling and algorithms however are getting their long-due share of recognition as a more reliable and congenial way of approaching a business problem solution. They say to err is human and it has been proven right time to time! With all the amazing progress in data analytics, it is time now to move over human expertise and use prescriptive analysis.
References:https://channels.theinnovationenterprise.com/articles/data-analytics-top-trends-in-2017