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Book Review: Good decisions aren’t just about Big Data

Behind Every Good Decision: How Anyone Can Use Business Analytics To Turn Data Into Profitable Insight. By Piyanka Jain and Puneet Sharma. Amacom. 256 pp. Behind Every Good Decision: How Anyone Can Use Business Analytics To Turn Data Into Profitable Insight. By Piyanka Jain and Puneet Sharma. Amacom. 256 pp.

“Nothing is rocket science except, well, rocket science. The corollary is true: Analytics is not rocket science. Sure, it is a specialized subject involving megaloads of information in every conceivable format, and we spend millions trying to find patterns through analysis—with some human insight, simple math, and complex statistics. Okay, that sounds hard, but just as with any subject you begin to understand, there are fundamentals, methods, and simple tricks to master it and use it for your own benefit. Albeit with a complex component, analytics—business analytics—is actually a simple problem-solving tool…” — Piyanka Jain and Puneet Sharma

What if I told you there was a simple tool you could use to enhance or solve 80% of your business decisions at the bank? Would you want to know what it was and how it worked? Of course you would.

The authors of this book claim they can deliver on that promise, using solely the data you already have within the bank, all by applying simple analytics. Not only do they claim they can help all business CEOs make better decisions, they claim all employees can use these simple techniques to enhance their decisions as well, using only an Excel spreadsheet.

And deliver they do.

A book for decision makers at all levels

This book is written for everyone who wants to know more about analytics—and how to benefit from their use. You should begin by reading Sections I: “Hello Analytics”;  III: “Leadership Toolkit”; and IV: “Analytics At Work: Ten Case Studies.”

If, after reading those sections, you have a desire for learning the more in-depth hands-on analytics you should add Section II: “Diving Deep” to your reading. The authors have taken a mathematical process generally perceived to be complicated, and broken it down into an understandable and systematic approach to decision making.

Ever since banking progressed from paper recordkeeping to computerized recordkeeping, the opportunity to use customer data or “big data,” as it is called today, has been present. But we bankers haven’t always known how to extract and perform analytics on the data.

As a bank president, I have been easily caught up in the daily activity of running the shop and forget what a powerful tool all of the customer information can be when making decisions. It isn’t that I’ve been unaware of the information surrounding me as much as I’ve not been absolutely sure how to select and process the relevant information to aid me in making decisions.

“Analysis paralysis” is what the authors call this lack of understanding of how to use all of the data around us, coupled with the fear of analysis as being too complex a process to use in decision making.

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The authors counter such disconnect and fear with a common sense, five-step approach to fighting analysis paralysis. They reduce this to the acronym B.A.D.I.R., standing for Business question, Analysis plan, Data collection, Insights, and Recommendations. Using this process will take you from raw data to decisions.

Getting into the details

First let’s define business analytics. It is the use of simple analytics methodologies on past data. There are seven most common analytics methodologies defined in the book, but only four are suggested for simple analytics.

The first method outlined by the authors is aggregate analysis.

This method is the simplest and most commonly used analytics methodology.

Such analysis is used to describe a population or segment or to compare two segments. Examples of possible banking uses would be gathering information to see what age group is using your mobile banking platform most frequently. This information could be used to provide more targeted advertising to that particular age group to encourage more to sign up.

Another example would be comparing the success rate of online account opening between a short-form application and a longer application asking for more customer information. The goal would be to see if the collection of more information discourages enrollments.

The second method is correlation analysis.

Correlation looks for the relationship between two or more factors with the prospect of being able to explain the connection or to determine which factors drive which.

A possible banking application would be comparing the various sources for loan leads to see which is the most productive. You could compare leads from existing loan customers, the local Chamber of Commerce, and a list of business names within ten miles of the bank that you purchase from a vendor to see which source provided you with the most loan closings.

Bankers can easily develop gut beliefs regarding which sources we think may be the best, but an analysis of the actual data may prove us wrong.

The third method is trend analysis.

This is simply an analysis of trends over time. I suspect this is the most widely used analytics method in banking.

Banks seem to focus very well on the financial trends in their banks. However, trend analysis could also be used to plot the average age of the deposit customer over time or the average size of deposit accounts.

The fourth method is sizing and estimation.

This really isn’t a true analytical methodology, because it doesn’t use historical data. However, the authors included it because it is a method often used to drive the decision for the introduction of new products.

I could see this method being used by a bank to determine if management wanted to enter the mobile banking space. Questions a bank could ask would include how many smart phones have been sold in our community and how many of those do we think we could sign up to use our mobile banking product if developed?

Analytics: a tool, not a panacea

There are nine pitfalls leaders can fall into when using analytics, the authors warn.

These include not knowing how to measure success in your organization. Most banks use financial drivers to measure success within the bank, but there could be other measures related to customer and employee satisfaction that are just as important to the future success of the bank. And such factors can be measured with analytics.

One of the other pitfalls of a leader is engaging in gut-based decision making where decisions are made solely on someone’s intuition or in the case of banks, another competitor bank’s products. I have been guilty of this type of decision making, but hope to do better after applying the methods taught in this book.

Where leaders err with analytics

The authors point out that a common mistake is trying to outsource analytics. You know your customer base better than anyone.

The authors—who sometimes serve as outside consultants themselves—don’t recommend hiring consultants as a long-term solution because they believe it is difficult for an outsider to have the business context in which your company does business. They advise that consultants can offer good advice on how to establish the process of making your bank a data-driven institution, but they maintain that analysis of the data itself is best done from the inside by employees who understand the business.

Leaders also fail to understand the power of analytics, which can help you make informed decisions that improve the bottom line of the organization. With bank profit margins being compressed due to interest rates, additional regulation, and increased competition it is important to make effective and knowledgeable decisions that you know will add to the bank’s profitability.

Applying the lessons to your business

So, how do you begin to use data to make better decisions within your bank? 

The book includes a step-by-step guide you can use to begin. Maybe you don’t think the entire guide applies to you. Some of it may not. But I bet you will find helpful procedures no matter what your level of expertise at data analytics.

As I mentioned earlier, banks have been using analytics in tracking their financial data for many years. However, I think using analytics to analyze customer behavior is something fairly new to community banking.

This is due in part to a preconceived notion that you had to have complicated software to perform analytics. The authors demonstrate this to be incorrect and have given all businesses and individuals a simple yet effective formula for analyzing data to make a better and more informed decision.

I am amazed at how many reports our core processor provides to us on a daily basis that we don’t use. I’m sure this is the case with most banks, so the challenge for bankers is to determine which data can be helpful in the decision-making process.

“Analytics” is a word that may scare off some potential readers. I found this book was much more interesting to read than I thought it would be when I began reading the introduction.

The authors have taken what is perceived to be a very complicated process and broken it down into a simpler way to employ analytics to solve problems.

I found the section at the end of the book describing ten real-life examples of cases where analytics were used to help solve problems helpful. The cases range from fighting crime in Memphis to helping a bank improve anti-money laundering reporting.

There is something in this book for everyone no matter what your analytics skill level.

Jane Haskin

Jane Haskin is president & CEO, First Bethany Bank, Okla. Haskin, is a member of the Banking Exchange Editorial Advisory Board and a former member of ABA's Community Bankers Council. She is a frequent reviewer for www.bankingexchange.com

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