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Ready to Jump on the Analytics Bandwagon?


Posted on February 18, 2016

It seems everywhere we turn these days data is all around. We are constantly capturing it, sharing it and being bombarded by it—but are we really using it? In the world of business leaders, internal auditors, and really anyone else involved in the financial reporting process, this question keeps people up at night.

“We know we have data, and lots of it, but are we doing enough with it? How can we get it to do more? How are our competitors using it to their advantage?”

In enters analytics–full of promise and potential. But can it deliver? 

Analytics is an interesting term that seems to mean different things to different people. To some, it is an intimidating word that  is very complex, and it can be—but it can  also be quite simple.

Analytics is the science and art of discovering and analyzing patterns, identifying anomalies, and extracting other useful information in data.[1] 

As finance people, we use data as part of our regular work lives on a daily basis. Typically, we have a massive spreadsheet somewhere with some crazy formulas that no one else understands. And one wrong hit of the delete key and this delicate structure will be completely destroyed. 

Now imagine that same information in your spreadsheet, but instead of columns or tabs that go on for days, picture it summed up in one graph or chart. In one view you would be able to see the information that is important to you without having to dig deep into the entire spreadsheet. Analytics—and in this case data visualizations (graphs or charts in the form of pictures)—allow you to easily see patterns and relationships in the data and you can see the data broken down the way you want.

Of course analytics are not just visualizations; they can be used to interrogate data to extract useful pieces of information that are relevant to the question at hand and can still take the form of a spreadsheet. And there are a wide variety of techniques that can be used, from trend analysis, regression analysis and machine-learning, to achieve results ranging from descriptive analytics (i.e., what happened?) to prescriptive (how can we make it happen?)[2]

But the key to analytics, no matter the format or technique being used, is having the right data available to you when you need it. Let’s face it, there’s more data out there than we can possibly interpret so analytics allows us to more easily understand and process the data and make it relevant. 

It also allows us to review information more clearly by being able to pick out unusual items, or unexpected patterns or trends in the data that may be cause for concern (like errors or fraud or operational problems) that would be more difficult to uncover in a spreadsheet.

And those are just the basics…Some of the wonderful things analytics can help with include creating management dashboards to view and evaluate business issues nearly instantaneously and potentially using data sets from disparate or external systems to bring information together in meaningful ways. 

Using external data such as basic weather information, or demographics can be intriguing to leverage in analytics and rightly so, but if this is going to be your first formal foray into analytics, I’m guessing there’s a whole lot that can be done with your internal data at the outset.

The possibilities of how analytics can be developed are truly endless, which can also be one of the challenges. As with anything, design is of the utmost importance. And in order to develop an effective analytic, you must first have a clear understanding of the problem, issue or question that you’re trying to answer or solve. 

Then you must determine the data you need or want to explore based on the issue and its origin. How you are using the information will depend on how reliable the data needs to be. For instance, we all know that the Internet can be a large source of information, but not all of this information is reliable. Be cautious of your data source and know how the reliability of the data will impact the result. If you’re planning to make some tough decisions based on data, then make sure your source is valid and that the data is reliable.

After obtaining the data and ensuring that it is of good quality (e.g., not missing data or containing incomplete information or is in an inappropriate format), the process of developing the analytics can begin. It may seem like a daunting task but with the right skills or training it doesn’t necessarily have to be complicated. It really depends on the question at hand.

Now comes the fun part–using analytics to understand and interpret what the data is saying.  Challenging your expectations of the data to the reality of it may sometimes surprise you. 

And finally, what you’ve been waiting for–to use the analysis as part of an informed decision-making process. How the understanding and information you were able to extract from the analytics results in a tangible benefit to you and your organization.

Now you might be saying, sure this all sounds great but it’s never this simple, which is true. There are other factors that need to be considered such as, who do you need to get on board to start this type of project? What type of investment is required in both dollars and time, and who needs to be involved in the process? Not to mention, who will develop and maintain the analytics, what skills are required and are they available in-house? And these are just samples of some the questions you may ask.

But what is the alternative? The world around us is only going to get more complex and data-driven, and burying your head in the sand will leave you making decisions based on your instincts rather than facts, using your limited resources to update spreadsheets rather than making decisions, while your competitors out-smart you in the long run (or maybe even the short-term).

And for those of you who are already using analytics and think you have this nailed, I challenge you to take a step back and ask yourself this: can I do more? Look into expanding in different ways, across silos and teams, across systems, and exploring the potential of external information or the Internet of things (which is basically electronic devices “talking” to each other) and how these sources of data can be used effectively. Explore more complex tools and techniques that are available. 

Analytics is a journey without an end–it needs to continually evolve to meet our needs and the business world in which we live in. The questions and problems that we face are ever-changing and so must our understanding of our world. Start your journey now.

[1] This Audit Data Analytics definition is taken from the AICPA White Paper “Reimagining Auditing in a Wired World 2014, reproduced as Essay 4 in the AICPA Publication Audit data analytics and Continuous Audit, Looking Toward the Future”, 2015, pages 92-93.

[2] The Analytics Maturity Model – Gartner.


Nicole Deschamps

Nicole Deschamps, Senior manager, National Services - Innovation and Analytics | Member of the CPA Canada Audit Data Analytics Committee

Nicole has been in public accounting for 15 years, serving a wide variety of clients including both public and private, in the US and in Canada with a majority of clients operating in the manufacturing industry.  Leveraging that experience, Nicole has lead the Canadian audit innovation and analytics team from the ground up; responsible for the development of innovations and analytics to be used by audit practitioners in the performance of audits.

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