April 29, 2017

Business Analytics: What’s The Story

By Claire Cameron
Co-Founder, Full Spectrum Leadership Inc.

 

Business Analytics: What is it?

 

AnalyticsBusiness Analytics refers to skills, technologies, applications, methodologies and practices that are used in a “continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.” (Beller, Michael J.; Alan Barnett (2009-06-18). Next Generation Business Analytics). This definition implies that Business Analytics are embedded into an organization’s business and operational processes. In some situations this is true. However, there are also many situations where Business Analytics are used in a more ad hoc, one off application to address a specific circumstance or decision facing the organization. The objective of gaining insight and driving business planning remains.

Business Analytics is typically a data driven process including elements of one or all of the following: analysis, statistics, predictive modeling, optimization and quantitative investigation. The objective of Business Analytics is to provide decision support to organizations. In other words, to provide additional information and perspectives in the decision making process. And even to provide the decision making criteria themselves.

 

I see references to Business Analytics everywhere…

is it just another management fad?

Looking at the business press, journals and conferences, one might think that business analytics has just been discovered and is the latest and greatest in terms of management, organizational and decision making tools. However, the concept of Business Analytics has been around since production line manufacturing began. Recently though, the advent of computers and the access to digitized data has greatly impacted the accessibility and sophistication of Business Analytics.

In today’s digital world, organizations can gain access to Business Analytics at a speed and level of sophistication that was impossible even ten years ago.

Whilst Business Analytics as we know it today has evolved in recent years, the discipline has been around for decades. For some it may seem like the latest fad, but I would disagree. In fact, I have staked my career on it. It is now over 30 years that I have been working with Business Analytics in various forms in a wide variety of industries. Business Analytics are a crucial part of the decision making process in organizations and will become increasingly so as technology continues to develop and the last drop of efficiency is squeezed from operations.

Business Analytics, applied intelligently, provides perspective that leads to a more informed and conscious decision making process.

What kind of questions or issues do Business Analytics address?

In their 2007 book from the Harvard Business School Press, Competing on analytics : the new science of winning, Thomas Davenport and Jeanne Harris determine that Business Analytics can answer questions like why is this happening, what if these trends continue, what will happen next (that is, predict) and what is the best that can happen (that is, optimize)? These are very broad questions that can apply to many different aspects of an organization’s operations and to organizations in any industry. It basically comes down to using Business Analytics to uncover what is the story.

Business Analytics: Is it Science or Art?

Most people would assume that, given the quantitative and analytical nature of Business Analytics, it is a scientific discipline. However, there are many aspects of Business Analytics that rely on the artistic nature of judgment, creativity, interpretation of data, the ability to see patterns and imagine the story behind the numbers. It is about figuring out the story that the numbers are telling about what is going on and then looking at where that may ultimately lead, what potential changes in outcome are possible through changes in strategy, processes, resource allocation and so on.

Predictive Analytics

Garbage in, garbage out

The old maxim “garbage in, garbage out” definitely holds true when it comes to Business Analytics. It is a data driven process. The quality of data used as the foundation for the analysis is a key driver in the quality of the analytical input to the decision making process. The access to masses of digitized data has dramatically changed the scope of Business Analytics when I think of how we worked 30 years ago. The disadvantage of such a mass of data is that it can be difficult to distinguish the quality of the data and the usefulness of the data to the issue at hand. Turning the overload of data into useful information is a big part of what Business Analytics has become as computers and digitized data has evolved.

However, the idea goes beyond just the quality of the data. Particularly in modeling processes, there are many judgments that have to be made along the way (the art part of analytics). Judgments about which data points constitute outliers. Judgments about what are the best proxy data for a variable for which complete, accurate and consistent data is not available. Judgments about which variables to consider as potential explanatory variables.

Then there is the quality of the presentation and interpretation of the results. It doesn’t matter how sophisticated or revealing the analysis is, if the results are not presented, interpreted and communicated in a way that is easily understood by decision makers and in a way that is relevant to the decision under consideration then the analysis is of little value as a decision support tool.

The advent of spreadsheet software on everyone’s desk leads to the possibility of Business Analytics being undertaken by anyone – with or without any statistical, quantitative or modeling training from both the art and the science angles of Business Analytics. It takes skill and experience to create analytics and models that are integral and sound and to interpret the data and output from the analysis in ways that are not misleading and provide insight into the decision at hand… to find the story and interpret the story for decision making.

Let’s get real: Some examples

The applications for Business Analytics are as varied as the questions that businesses have to answer day by day in their on-going operations and in their longer term planning, strategy development and implementation. If there is a decision to be made in a business, chances are that Business Analytics can add a useful dimension to the decision making process.

When the applications are so broad, how to focus in on a few examples? Perhaps the easiest way is to give you some of the types of questions I personally have worked on over the past 30 plus years.

At the beginning of my career I worked in a small consulting firm on the outskirts of London, England specializing in using what would now be called Business Analytics to drive profit improvement strategies for major retailers in the UK and Europe. We were pioneers in the field. The first computer was installed at the firm while I worked there. Until then we did everything on calculators… in fact one of the senior consultants was most comfortable using a slide rule. Computers and digitized data have transformed what is possible in terms of the speed, depth and sophistication of Business Analytics. However, the questions we were addressing back then are the same questions that today’s retailers address with today’s Business Analytics.

  • Where should stores be located?
  • What design features should be incorporated into a store to attract the appropriate customers?
  • How big should a store be?
  • What market segments should be targeted?
  • How should a certain market segment be targeted in terms of products, styles, price points, store environment, location?
  • Who are our actual customers?
  • What are the gaps in the local market that a store could fill?
  • How should space in the store be allocated to the various product groups and price points for maximum profitability?
  • What is the biggest source of high frequency/low severity customer claims such as slip and falls and clothing rips on fixtures?
  • What mitigation strategies are effective and cost efficient – for example changes to packaging, fixtures, placement of product?
  • What is the best customer loyalty program for our customers?
  • What is the optimal staffing level on the sales floor?
  • How many checkouts are optimal in a store?
  • Would acquisition of a particular store brand improve profitability?
  • Would divestment of a particular store brand improve profitability?
  • Would creation of a new store brand improve profitability?

The list is seemingly endless. And with today’s point of sale digitized sales data, there are many additional questions about inventory levels, what sells and at what price, seasonal sales forecasting and so on that can so easily be addressed that were minefields 30 years ago.

Look at that list of issues that we were addressing all those years ago with Business Analytics and that was just one aspect (target customer marketing) of one industry (retailing)!

Let’s move forward in my career… I spent some time working in the Marketing Econometrics department of the Post Office. There we used econometric modeling, statistical and quantitative analysis to look at all aspects of the Post Office’s business – which is surprisingly complex when you recognize the wide variety of transactions that can be undertaken in a Post Office, as well as the complexity of collecting, sorting and delivering all the mail.

Risk ManagementThen there is the time I have spent using Business Analytics as a decision support tool in the insurance risk management arena. I can’t think of an industry that isn’t subjected to analysis of its risks when purchasing insurance. Usually, the analysis is done by the underwriters from their perspective. That creates an imbalance in the information during the negotiations. Put an independent analysis of the risks into the client’s, and its advisors, hands and now the negotiations are more balanced from the point of view of the analytics.

From the client’s point of view, the types of questions that Business Analytics can address in the risk financing and risk management fields are:

  • How much insurance to buy.
  • What should the deductible be?
  • What is a reasonable premium to pay for the insurance?
  • How much should the organization expect to pay for claims within its deductible?
  • What mechanism is the most appropriate to manage the deductible part of the risk?
  • What are the financial implications for the organization of the various risk financing options?
  • What are the insurance-type risks associated with a new direction in the company? For example a new product line, opening a new international market, an acquisition, a divestiture, a new building, a new manufacturing process, a new distribution system, new legislation, new case law and so on.

Clearly, there isn’t a change in a company’s operations or environment that doesn’t have some implication for risk financing and risk management. Some may be insignificant, but some change the risk profile drastically and therefore need to be taken into consideration as part of the evaluation of that strategy. Business Analytics plays a crucial role in that evaluation.

Let’s close this section of examples of the application of Business Analytics with my experience setting up a team to provide analytics for the management of a portfolio of loans. The objective was to provide analytics that could predict the performance of the portfolio in terms of late payments on the loans, missed payments on the loans and defaults on the loans. We used predictive behavioural modeling to determine the characteristics of a borrower that indicated a loan was in trouble or heading for trouble. This enabled the collections department to mount proactive telephone campaigns to preempt the loan defaults through renegotiating terms for example.

The list is endless. Whenever a decision is being made whether that involves products or services, distribution, resource allocation, new markets, human capital, financing, or any other aspect of business, whenever future strategies are being explored, whenever there is a need to predict or optimize, whenever there is a story to be teased out, then Business Analytics can, and some would say should, play a role.

The future of Business Analytics

Customer Relationship Management
Whilst analysis has been an important component of the benefits and human resource administration part of Human Resource Management, it is only since the beginning of the 21st Century that we are witnessing the emergence of the application of Business Analytics in the Human Capital and Human Dynamics side of the human resource function. Questions lending themselves to analysis include:

  • What would be the impact on the organization of losing key individuals?
  • What is the impact on the organization of lost time or lost productivity due to stress or injuries?
  • What would be the impact on the organization of a rogue employee who engages violence, sabotage, or fraud, for example?
  • What are the gaps in expertise and leadership in the organization?
  • How can those gaps be filled internally or externally?
  • What are the wide organizational implications of changing compensation structures, incentive mechanisms or motivational strategies?
  • Are the right people making decisions at the right level of the company?
  • Do the organization’s leadership and communication styles fit its employees?

As computers continue to evolve, as more and more data becomes accessible, as organizations need to squeeze more and more efficiencies, as the operational environment becomes more complex and interrelated, the need for focused, relevant and comprehendible Business Analytics will continue to grow.

The key will be to ensure that those providing the analysis are sufficiently trained and experienced in both the science and the art of Business Analytics and are able to grasp the practical realities of the business situation. The analysts must be able to communicate the assumptions and approach underlying their work in such a way that the decision makers in the organization can say, “OK that makes sense.” And they must be able to interpret and communicate the results of their analysis in a manner that facilitates decision makers integrating the perspective into their decision making process along with all the other factors.

It is my personal belief that there are only a few instances where Business Analytics should be the only driver of a decision, in most organizational decisions the analytics plays an important role, but not the only role. Decision makers are abdicating their responsibility if they rely solely on the results of analysis without bringing their in depth operational knowledge to bear in creating a context for the analytical results. Multidisciplinary teams are becoming more important as the complexities and interrelatedness of business grow.

And isn’t that the whole point?

Business Transformation