The Analytics Revolution

How to Improve Your Business By Making Analytics Operational In The Big Data Era

Bill Franks

• Publisher: Wiley
• Year: 2014
• Print ISBN:
  9781118873670
• eBook ISBN:
  9781118976760
• Edition: 1
• Topic: Commerce
• Subtopic: Commerce Général

Lead your organization into the industrial revolution of analytics with The Analytics Revolution

The topics of big data and analytics continue to be among the most discussed and pursued in the business world today. While a decade ago many people still questioned whether or not data and analytics would help improve their businesses, today virtually no one questions the value that analytics brings to the table. The Analytics Revolution focuses on how this evolution has come to pass and explores the next wave of evolution that is underway. Making analytics operational involves automating and embedding analytics directly into business processes and allowing the analytics to prescribe and make decisions. It is already occurring all around us whether we know it or not.

The Analytics Revolution delves into the requirements for laying a solid technical and organizational foundation that is capable of supporting operational analytics at scale, and covers factors to consider if an organization is to succeed in making analytics operational. Along the way, you'll learn how changes in technology and the business environment have led to the necessity of both incorporating big data into analytic processes and making them operational. The book cuts straight through the considerable marketplace hype and focuses on what is really important. The book includes:

• An overview of what operational analytics are and what trends lead us to them
• Tips on structuring technology infrastructure and analytics organizations to succeed
• A discussion of how to change corporate culture to enable both faster discovery of important new analytics and quicker implementation cycles of what is discovered
• Guidance on how to justify, implement, and govern operational analytics

The Analytics Revolution gives you everything you need to implement operational analytic processes with big data.

PART I
THE REVOLUTION HAS BEGUN

CHAPTER 1
Understanding Operational Analytics

Yes, the revolution has begun. Operational analytics are leading the charge in the industrial revolution of analytics and are already starting to push the boundaries of what companies do with analytics. Operational analytics will, over time, vastly increase the number of analytics processes that must be built and the speed with which those analytics must execute. As we'll discuss later, new concepts such as decision time and time to insight will become primary drivers of how to invest and where to focus effort.

Operational analytics require a disciplined and organized approach across an organization and a lot of technological, process, and cultural change as well. People are not initially comfortable turning over many day-to-day decisions to machines and analytics processes. However, time will prove that if organizations build the right operational analytics, the results will be well worth the effort.

Yes, the revolution has begun! Before that statement can be understood, it is necessary to explain exactly what it means. This chapter lays the groundwork that the rest of the book builds on. We define what operational analytics are. We also discuss some market trends that are supporting the push for operational analytics. Last, we reinforce several important themes that are worth remembering as an organization moves toward operational analytics.

Defining Operational Analytics

This book is about operational analytics. But what are operational analytics? We need to define the term if it is to be the focus of this book. After first doing that, this section walks through what differentiates operational analytics from traditional analytics and makes operational analytics unique.

What Are Operational Analytics?

The term “operational analytics” describes a situation where analytics¹ have become an inherent part of the individual decisions made and the individual actions taken within a business. Operational analytics don't support big or strategic decisions but rather the many small and tactical decisions that happen from moment to moment every day. More important, when an analytics process is operationalized, the process actually drives what happens directly. An operational analytics process does not simply recommend an action but directly causes an action to take place. The prior facts are the heart of what defines operational analytics. By directly driving decisions and actions without human intervention, operational analytics takes analytics integration and impact to a whole new level.

Most traditional analytics processes generate results that inform a decision or feed into a decision process. However, a person usually interjects human judgment into that decision process and then approves the action. When analytics are operationalized, an analytics process is run and actions are taken immediately as a result of that analysis. There is no human intervention at the point of decision or action.

Of course, it takes human intervention to decide that an operational analytics process is needed and to build the process. However, once the process is turned on, the process accesses data, performs analysis, makes decisions, and then actually causes actions to occur. The process may be executed thousands or millions of times per day. Once people within an organization realize that they're able to have analytics embedded at this level, they often want more. The result is demand for ever more analytics and an ever higher level of sophistication. Having automated operational analytics in place also leads to the need for careful monitoring of the processes. We cover that topic in Chapter 6.

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Get Prescriptive!

A defining feature of operational analytics is to go beyond being descriptive or even predictive. Operational analytics are prescriptive. This means that operational analytics are embedded within a business process to directly make decisions and cause actions to happen based on algorithms . . . all without human intervention.

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There has been a lot of focus over the last decade on the shift from descriptive analytics to predictive analytics. Within a classic business intelligence environment, the focus is on summarizing what happened from a descriptive perspective. This might entail determining how many sales each region had, how many deliveries were on time, or other important metrics. With predictive analytics, in contrast, the goal is to predict what will happen in the future. How can on-time delivery rates be influenced moving forward? Which customers are most likely to respond to an upcoming marketing offer? Operational analytics take things a step further and make analytics prescriptive. An operational analytics process starts by identifying what actions will influence delivery times or increase response rates and then makes the analytics prescriptive by automatically causing the actions to occur. Table 1.1 summarizes these differences.

Table 1.1 Descriptive versus Predictive versus Prescriptive Analytics

Descriptive analytics	Summarize and describe what happened in the past
Predictive analytics	Predict what will happen in the future
Prescriptive analytics	Determine actions to take to make the future happen

Differentiating Operational Analytics

Differentiating operational analytics from an operational application of analytics is very important. At first that distinction might sound like a semantic game, but I assure you it's not. After we go through some examples, the distinction will be very clear.

Analytics have been applied to operational problems for many years. That's going to continue to be true, and the operational applications of analytics will remain important. Operational analytics take things further than past efforts, however. It would be ideal if a term existed that cleanly separated operational analytics from operational applications of traditional analytics, but I do not know of one. That is unfortunate because the similarity of the phrases can cause confusion, and the phrases certainly sound awkward when spoken together. When I was leading a discussion on this topic at a conference, I had an attendee jokingly suggest that I coin the term “Franks-izing” analytics, which is clearly too self-serving even if it wasn't a joke. So, I'll focus on the distinction between the two approaches rather than the labels applied to them.

The distinction between an operational application of analytics and operational analytics makes it easy to see why operational analytics are both important and complex. Operational analytics processes are often as sophisticated as any analytics process an organization has built before, but the process has to be automated, scaled massively, and executed lightning quick. There's a lot of power in such a process, but there's also a lot of complexity and hard work. Let's look at some examples that will further clarify the distinction.

One important differentiator is that with operational analytics, the analytics are executed in what might be called “decision time” in an automated and embedded fashion. Decision time means an analysis is executed at the speed required to enable a decision. In some cases, decision time is real time (or very close to it). In other cases, decision time can involve minutes, hours, or even days of latency. Knowing the decision time is critical to success because an analytics process has to be available and executed within that window in order to be used for the decision.

Historically, many organizations have customized websites by identifying key things about customers' buying habits and then allocating specific offers or customizations to be shown when each customer returns. Web customization has been proven very powerful and is almost ubiquitous today. Processing what is known about a customer tonight to precompute and make ready customizations for the customer to see in the morning is an operational application of analytics. Precomputing customizations is not an example of operational analytics. Precomputing customizations before a customer visits the site is simply applying traditional batch analytics in an operational environment.

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Don't Just Apply Analytics to Operations

Analytics processes have been applied to operational problems for many years. However, operational analytics go beyond using the results of a traditional batch analytics process for operational purposes. Operational analytics become embedded and are executed in decision time for each individual decision.

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Operational analytics require customizing a customer's next page after the “next” button is clicked and prior to serving the next page. The process must use not only the customer's historical information but also information up to and including what the customer has just done while on the website. Altering how a web page is presented in that short time between clicks is operational analytics. Note that this analysis isn't happening for just one customer but for all customers visiting the site, which leads to millions of microdecisions being made based on the analytics. Even if the customers do not perceive the difference between the batch and operational approaches when navigating the site, there is a real difference underneath the hood.

Another example of the distinction, which we dive into more deeply later in the book, comes from the manufacturing space. Engine sensor data is allowing manufacturers to derive much better maintenance schedules. Having det...

Table of contents

• Cover
• Additional praise for The Analytics Revolution
• Title Page
• Copyright
• Dedication
• Foreword
• Preface
• Acknowledgments
• Part I: The Revolution Has Begun
• Part II: Laying the Foundation
• Part III: Making Analytics Operational
• Conclusion: Join the Revolution!
• About the Author
• Index
• End User License Agreement