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Descriptive, Predictive & Prescriptive Analytics will fail to Help You Understand Your Business


It has been a practice followed religiously by companies and organizations to analyze how they have performed over a period of time. It is mandatory for them to do so; just that some do it to survive and some do it to thrive in concurrent market dynamics. If we look at the history of big data, it has been a common practice for all; trying to understand how the world and various businesses, completely resting on the analysis of first hand data available. This is to an extent that people in the business define their careers progression as BBD and ABD; before big data and after big data.

Few years back, it was like looking at the crystal ball the wizard would describe what all happened in the past with your business. Today it is called descriptive analysis. With advent in data technology and understanding about BIG data walking in, brought along answers to questions like what is going to happen to my business in future, called the predictive analysis.

The times have changed and so have changed the market dynamics and world economies, which are more colorful than rainbows. This has compelled organizations across the globe to resort to the final stage of analytics, the prescriptive analytics which is all about “so what”, “why not”, etc. All other analytics were able to provide insights to businesses about what their customers buy, when and where. But with Prescriptive analytics is what makes them understand “why”.

Today every business is subjected to new expectations, competitors, channels, threats and opportunities. Organizational leaders and C-suites across the globe, from largest companies; have clearly understood that in order to boost revenues, increase profitability and build customer loyalty, they are required to make decisions with an understanding of customer actions, attitudes and opinions. The required complete insight is something that descriptive, predictive & prescriptive analytics can help them with, if adapted and adopted while taking to important aspects into considerations.

One of them is that all these three types of analytics should co-exist; as one is not better than and without the other. If you are aware of the basics of data analytics; you would agree that though all three of them are more consecutive, they contribute to the objective of improved decision making. The second but more important an aspect, which needs to be paid utmost attention to, is something that we will discuss towards the end of this article.

Know the past of your business through descriptive analytics

The term past here refers to timeline from 1 minute to a certain number of years back then. Such analytics mainly assist in comprehending the relationship between your products and your customers; where the main purpose of this assessment is to finalize the futuristic approach. It is kind of learning from past behavior to influence futuristic outcomes.

To be candid, this type of analytic should not come to you as a surprise. You and others in your capacity have been using it more or less in form of reports that give you better insight into finances, operations, customer preferences and sales etc. Descriptive analysis has played a vital role in determining what to do next, by transforming data to information to conclude the future outcome of acts and events.

Know the future of your business through predictive analytics

This type of analytic is effective and useful in providing businesses with actionable insights based on data; yes remember this. Data mining, data modeling, game theory, machine learning etc. are put at work to obtain estimation regarding outcomes in the future. Simply put, predictive analysis is to identify potential risks and opportunities – if any. 3 components of predictive analytics are predictive modeling, decision analysis and optimization, & transaction profiling. Useful across a wide plethora of departments in your organization, they help in forecasting demand for operations or determining risk profiles for finance team, to predicting customer behavior in sales & marketing. Determining risk profiles needs a lot of data, both public and social.

Predictive analytics are also useful when it comes to forecasting product or service demand for a particular geography or taking a segment approach for customer services; and adjusting manpower and production, accordingly. Data sets put at task for performing this analytics include data from weather, example, sales data, social media data etc.  The usage of historical and transactional data needs a special mention here. They are used to identify patterns, whereas statistical models and algorithms are utilized to assess the relationship between several data sets. With the advent in Big Data, predictive analytics has taken a really big leap. The more data that you have on hand in an organized manner means more accurate predictions.

Intelligent insights derived from descriptive & predictive analytics, is Prescriptive Analytics

Though in existence for a few less years in a decade; it will not be wrong to say that prescriptive analytics is taking baby steps. May be this is the reason why Gartner considers it to be a “Innovative Trigger” that will take 5-10 years to get fully functional and productive. All this said and done, the best part about prescriptive analytics is that it not only tells what will happen and when, but also why will it happen. Along with this, it also suggests how to get in action to reap appropriate benefits of predictions made.

A fine blend of business rule algorithms, computational modelling techniques and machine learning is used for prescriptive analytics; along with a wide plethora of historical, transactional, public and social data sets. The beauty of it is in how prescriptive analytics foresees what would be the effect of a particular decision taken, and the suggestions that it has to make to adjust the decisions that are actually made; ultimately enhancing the decision making process and the bottom line of course. But as mentioned earlier, due to its recent existence; very few companies utilize this technique, and that too with humongous amount of errors. The best example to it is self-driving google cars that are required to decide based on predictions and future outcomes.

Prescriptive analytics has the potential to leave a gigantic impact on your business, making them operationally efficient and effective as against competition, by optimizing scheduling, production, inventory management and supply chain design of your business.

Final phase of analytics

So now, we come to that second & more important aspect, which needs to be paid utmost attention to; is data collection or data extraction followed with data cleansing and data processing. If these processes are not in place; though descriptive, predictive and prescriptive analytics are known for making people understand their businesses, you are set to fail miserably. Better informed decisions for future outcomes will become a fantasy, as you will be struggling with managing your day to day operations as well. Prescriptive analytics as per IBM is the “final phase of analytics”; but in absence of accurate and well managed data sets – your business could reach the final phase of liquidity or bankruptcy.