Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data Into Profitable Insight

"Behind Every Good Decision" is a common-sense, clear guide to the world of business analytics, and how it can help you solve many of the problems in your organization.

This is a subject that, in the wrong hands, could easily get so boring and complex that most readers would put the book down by the end of the first chapter. But this doesn't happen with "Behind Every Good Decision."

This book was written for those of us without an advanced degree in math or statistics. It's analytics in layperson's terms, which means it's straightforward and easy to understand.

The most useful aspect of the book is the authors' BADIR process, a five-step system that will help you convert data into a decision or insight. After learning these steps you'll be able to start using analytics in your organization to drive change and make improvements.

Piyanka Jain is president and CEO of Aryng, a management consulting company focused on analytics. She's considered an industry thought leader on the subject, and is a keynote speaker at many industry conferences and organizations.

Puneet Sharma is vice president of analytics, growth hacking and user research at Move, Inc. He has 15 years' experience working in analytics leadership roles in Fortune 500 companies.

So, stay tuned to learn two simple approaches for analyzing data, how to ask the right questions to get the best data, and how to make recommendations after your analysis that will make sense to your stakeholders.

"Behind Every Good Decision" is divided into four sections.

Section one gives you a thorough overview of analytics. Here, you'll learn what analytics is, why it's important, and some of the common methodologies used in this discipline.

Section two dives into the authors' five-step process for using analytics to get results in an organization. This is called BADIR.

Section three will be most useful for business and analytics leaders who want to make their organization more data driven. The authors go over several methodologies you can use to set up an analytics agenda, avoid common pitfalls, and increase the impact of your analytics.

The last section contains several case studies that show how some organizations have used analytics to drive growth and overcome obstacles.

Let's start with the basics, and look at what analytics really means.

According to the authors, analytics is the science of applying a structured method to solve a business problem using data and analysis to drive impact.

There's also hypothesis-driven analytics, which is what can really help organizations drive growth and stay relevant. Put simply, you develop a hypothesis based on intuition, stakeholder experience, or your understanding of the business environment. You ask, "What's happening?" and "Why is it happening?"

You then use data to prove or disprove your hypotheses, and answer those two simple questions. This, in turn, gives you reliable information that helps you decide what's best to do in a given situation.

Here's a simple example. Imagine your organization has a problem with customer engagement. You and your team come up with four possible reasons – four hypotheses – that could explain why. You conduct business testing and experiments, analyze the data you find, and discover that two of those hypotheses could be true. Another round of testing and learning generates a good solution, which you and your team implement.

Analytics is as simple as that. It's a problem-solving tool that helps you make smarter, more informed decisions. And that's useful for any manager or leader, working in any organization. When it's put that way, analytics doesn't seem quite so frightening.

So, how do you actually use analytics? The authors go into several methodologies in chapters two and three.

One of these is called aggregate analysis. This is the simplest and most commonly used methodology. You use aggregate analysis to describe a population or segment, or to compare two segments.

Here's an example from the book of what aggregate analysis looks like.

You own a small winery that also hosts weddings, and you want to increase the efficiency of your marketing. One way to do this is to find out more about the people who book your winery for weddings. Who are they?

When you look over your wedding bookings in the past three years, you see that out of 300 bookings, 85 percent of them were made by women, with an average age of 33. Sixty percent of these women live in Oregon in the United States.

It sounds almost too simple, but this straightforward analysis provides some great information you can use to target your marketing efforts and get a better return.

You can also use aggregate analysis to answer other questions like "How are my customers different in one geographical region versus another?" or "Do younger people access our digital product through tablets more than older people?"

Another useful methodology is correlation. The authors say that correlation looks for the relationship between two or more things, with the prospect of being able to explain or drive one with the other.

To see what this methodology looks like, let's go back to our winery example.

You're looking for good leads for your winery. And you have a few hypotheses.

First, you suspect that Google paid search and some wine guides produce better leads. You also think that certain locations produce good leads, and that people who visit your website's pricing page are bad leads.

So you start gathering data. You check your web statistics and look at everyone who fills out an inquiry form. Specifically, you're looking at where those people came from.

Your research proves that two of your hypotheses are true, and two aren't. By comparing all this data, you realize that you're spending money in some places that aren't producing good leads, and not enough on other sites that are producing good leads.

There are five more methodologies covered in this chapter. These give a good overview of how analytics can be applied in different ways to solve common business problems.

This is all well and good. But how do you actually go about using analytics to solve problems?

This question is answered in section two, where the authors outline their five-step framework that helps you go from raw data to good decisions. This process is called BADIR. That's an acronym that stands for: business questions, analysis plan, data collection, insights, and recommendations.

Let's look at the first step in the BADIR framework: business questions.

The authors say it's essential that you start this process by asking the right questions.
To do this, put the problem into context. Ask questions like "What happened?", "Why are you interested?" and "What's the problem or opportunity?"

Next, look at who or what is impacted. Ask questions like "When did it take place?", "Where did it happen?", and "Who's impacted?"

Last, narrow down the reasons by asking "What might have caused this?" and "What do you think drives this?"

You also need to understand your timeline, and identify any stakeholders involved. Questions like "What decisions need to be taken?" and "When by?" will help you figure out how much time you have. Questions like "Who's asking for this analysis?" or "Who will be impacted by the analysis?" can help you identify your key stakeholders.

Once you've put the problem or issue into context with some good questions, it's time to move on to the next step and create an analysis plan.

In this step, you're going to align your analysis methodology with your business need. Planning also forces you to think through a timeline, and identify the resources you'll need to get started. A good plan will also help you get buy-in from others.

Your analysis plan has five building blocks. These are analysis goals, hypotheses, methodology, data specification, and project plan.

Let's look at the first building block, your analysis goal. Basically, this is a SMART goal that answers the key business question or problem you identified in the last step.

The authors go into the other four building blocks in great detail, so by the time you're finished you have all the elements you need to create a solid plan for analysis.

The third step in the BADIR framework is data collection. The authors say that a lot of times, this is where people want to start. But that's a mistake. You need the previous two steps to make sure you're on the right track, and that you have a plan in place to move forward.

Your analysis is only as good as the quality of data that you have, so it's really important to take your time here.

The authors say that you need to validate your data as you go about collecting it, and they outline several tips to help you do this.

Once you have some reliable data, it's time to look for insights. This is the fourth step in the framework.

To find the meaning in your data, start by looking for patterns. Next, look at your hypotheses one at a time. Examine the relevant data that's needed to prove or disprove each one. Hopefully, this will eliminate some of your hypotheses and allow you to focus on the ones that really need your attention.

There are several ways you can go about examining your data for insights, and the authors go over four methodologies that are particularly useful for this.

This chapter is fairly in-depth, and the steps involved in these four methodologies are too complex for us to go into here. This is one section of the book that you might have to read through a couple of times to fully understand. However, it is the exception. For the most part, the authors do a good job of keeping their explanation and instructions very clear and approachable.

The last step in the BADIR framework is recommendations. The authors say that while every step is important, they believe that recommendations is the most vital.

This is because your goal is to solve a specific problem. And all the steps leading up to this one help you do that. This is your opportunity to use your analysis to recommend a specific course of action.

First, make sure your recommendations are insightful and concise. You want to be clear, but you don't want to bog down your stakeholders in too many details, at least not early on.

Put together an executive summary that's short and compelling. Make sure this summary answers the question "What's in it for me?" It also needs to contain seven other components.

One of these is the objective. You need to explain the business problem, or goal of the analysis. You also need to include some background. This helps put the analysis in context so everyone can understand it.

Next, explain the scope of your analysis. This is what's included, and what's not included, in the analysis.

There are four more elements that need to be present in your executive summary, and we think all of them are spot on. In fact, we feel this list would be useful when writing any kind of master summary.

So, what's our last word on "Behind Every Good Decision"?

This is a practical, hands-on guide to learning the basics of business analytics. The authors stress time and time again that analytics doesn't have to be complex to be useful, and they prove this to be true in the book.

While it's not the most exciting book you'll ever read, it is practical and approachable. The authors do a great job of keeping the book focused on the needs and knowledge levels of the common reader. By the time you're finished you'll know why analytics is important, how some simple methodologies can help solve problems and, more importantly, you'll have a clear and actionable framework to help you get started.

"Behind Every Good Decision" by Piyanka Jain and Puneet Sharma is published by AMACOM.

That's the end of this episode of Book Insights. Thanks for listening.