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The amazing analytics journey

The three-stage process going from descriptive to predictive to prescriptive/pre-emptive analytics … and three final steps for implementation.

By Shashikiran PB

The evolution of analytics integration in day-to-day business is widely considered a three-stage process, from descriptive analytics to predictive analytics to prescriptive analytics/pre-emptive analytics. Let’s review each stage.

Descriptive analytics is the science and art of summarizing data that is logically grouped and structured in layers to create a seamless flow of insights that can be drilled down into, as well as be presented in a visually appealing way. It involves creating aggregates by establishing data linkages that provide a visual presentation to make it intuitive and easy to understand and use. The objective of descriptive analytics is to allow a business user to see the status of business as it really is, in the simplest manner possible, and then do a post-mortem to determine what drove the business to its current state. Most companies are already deploying this type of analytics across their business functions. There is still a long way to go before the job is complete, but the importance of implementation is clearly understood and there are no debates on its necessity and urgency.

The future looks bright for companies that complete the analytics journey.

The future looks bright for companies that complete the analytics journey.
Source. ThinkStock

Predictive analytics is the next stage of this evolution. Simply put, this is all about saying what is going to happen in the future. Based on data from the past present, this field of analytics deals with making predictions about future events, as well as specifies the probability of the prediction turning out true. It involves statistical modeling and advanced analytical methods such as machine learning and artificial intelligence. It relies on data to determine the causality of past events and uses these underlying factors to determine where the future is headed. This is where data scientists revel in using applied mathematics and technology to provide prophecies that can be logically constructed and debated.

Businesses, however, need more than just insights. It is not enough to know why the past turned out as it did or to know what is likely to happen in the future based on the present. Businesses need more actionability, an urge to shape the future by knowing what factors are driving it, by being able to determine the effort in influencing these driving factors and the risks that lie underneath. Decision-makers want to simulate the future by playing out scenarios and then choosing a course of action that looks the most desirable to them. Welcome to what I call pre-emptive analytics, more widely known as prescriptive analytics.

Tale of Three Concepts

To better understand the three concepts, consider an illustrative scenario:

  • A hotel chain wants to understand the state of its business and what factors are driving its business for better or worse. The hotel chain will need answers to the following questions:
  • What is the revenue by hotel type, by customer category, by season and by a dozen other groupings?
  • What is the composition of revenue from existing versus new customers?
  • Why did a new customer come to my hotel?
  • What made an old customer churn?

These and more are answered by providing a structured grouping of past and present data through graphs and charts. That is descriptive analytics, often called basic business intelligence.

As time passes, the hotel chain wants to determine answers regarding the future, such as:

  • Given the customer demographics and trends affecting the business, what are the likely revenues in the next five years?
  • How many new customers are likely to stay in our hotels in the next holiday season?
  • What might be the occupancy rates at various price points?

Such questions are answered by having the ability to see the patterns that have determined the past and present, and are likely to have a similar effect in the future. The process involves determining possible factors influencing business outcomes based on hypothesis and intuition first, and then testing these hypotheses through available data to determine which input affects what output and by how much. This requires people with advanced skill sets in statistics, creation of algorithms and using technology on large data sets to find answers faster and more accurately. This is what we call the field of predictive analytics.

The story only gets better from here. Now that the hotel chain knows the past and the present, as well as how the future may turn out if things grow in an organic manner, it wants answers to more nuanced questions:

  • Given an investment capability of $1 million, where should we deploy it to get the best return on investment?
  • Which customers should be sent discount coupons to most improve those customers’ lifetime value to the hotel chain?
  • Which promotions should the hotel chain run for the next holiday season?

These questions are more open-ended. The managers are seeking options to determine the future, as well as recommendations regarding which options may work better than others. The answers involve optimizing under business constraints such as investments, time availability, irreversible choices or, in some cases, the need to make only a set of reversible choices under risk.

In this instance, the development of a solution requires an analyst to understand the business scenario in great depth and codify its DNA to link inputs to potential outputs. The next step is to simulate output scenarios and place the power of an informed, data-driven choice in the hands of the decision-maker. Furthermore, it may be necessary to optimize by automating the process of choice itself so that human fallacies are removed from the decision-making.

This is where the world of business analytics is headed. When Google Maps tells us which route to take among the seemingly infinite choices to reach the airport on time, or, when Amazon makes the product recommendations when you login to your home page, or when your bank calls you to offer you a customized loan term, it is pre-emptive/prescriptive analytics at play. Every industry needs to exploit the power of pre-emptive analytics to improve decision-making, make fewer mistakes and grow faster.

Three Steps to Implementation

How does a company implement prescriptive analytics in real-world practice?

The first and most crucial step is to determine what the business truly needs – what are the key performance indicators (KPIs) that the decision-makers want to improve? This process is not as simple as it sounds. Many businesses struggle to define the set of KPIs that matter the most to them. While every company wants to make more money and wants to do so faster and with less investment, the process of choosing KPIs requires a deeper look to determine what is truly needed and what is just “nice to have.”

Given the industry and the competitive scenario, what are the KPIs that matter the most in making the company more successful in the face of constraints it is subject to? As mentioned earlier, this requires analysts and senior business executives to work hand in hand, think deeply, break norms and define the future. Then it is the job of analysts to crystallize the definition of KPIs and the mechanism through which these KPIs are published.

The second step is to create data points that connect the inputs (decisions) to outputs (KPIs). This step is heavy on maths and technology. There are no shortcuts here. An incomplete data set, a wrong algorithm or limited options to optimize decisions may lead businesses to make wrong choices that are costly to correct later. A company that has a solid foundation in predictive analytics would be able to accomplish this task without many hassles.

The third step is to connect the result of analytics to a real-time solution implementation. This is where the analyst needs to wear the hat of a product designer. The questions to ask at this step are:

  • How do I make it convenient for the results of analytics to be deployed in business decisions in a quick and scalable manner?
  • Who are the decision makers, how often do they decide, and what is the accuracy required in decision making?
  • Can I automate the process of decision-making?
  • Can I create a feedback loop that learns from the decisions made, both right and wrong, and improves on it in a continuous manner?

This step requires the greatest amount of patience from the analyst as well as the business user, keen attention to detail and a tight implementation plan. A seasoned analyst will also leave enough room for flexibility so that the business is free to respond to changing external scenarios.

As daunting as the process may seem, it is all about getting the basics right. Top down, the priorities are to get the goals right and determine what needs to be evaluated to meet the goals. Bottom up, it is all about clean data, data in one place and systems that talks to each other in real time. In the middle is the layer of algorithms and statistical models that connects the data below to decisions above.

The whole process of implementing analytics to address business needs requires a united effort from analysts and business decision-makers, as well as a coordinated effort between technology, mathematics and pragmatic decision-making.

Shashikiran PB is principal of analytics delivery at Tredence, an analytics services and solutions company. Shashikiran has 15 years of experience in consulting, operations, analytics and product management. He has worked across industries from retail to automotive and more in India, the Middle East and the United States.

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