Feature image for digital transformation article on the modern accountant and mindset for data analysis

The modern accountant is a hybrid technician and data analyst. For generations, the best accountants have been proficient technicians — they are competent in GAAP, tax law and compliance, audit frameworks and procedures, internal controls, or other traditional specialties of accounting. In the information age, however, accountants unwittingly have begun taking on the role of a data analyst.

This role transformation is brought on by several factors that impact accountant’s day-to-day work, such as:

  • Data Diversity: Increasingly diverse and disparate systems of record require accountants to prepare and blend data sources to derive needed information
  • Complex Problems: The scale and complexity of data needed for reporting, compliance and audits is constantly expanding with ever-changing rules and regulations, as well as the digitization and globalization of business operations.
  • Wealth of Opportunity: The burgeon of data readily available for consumption adds pressure on accountants to provide deeper insights into the operations of the businesses they serve1

Evidence of the accountant’s role transformation is easily seen by thinking broadly about the type of tasks accountants are performing, most commonly in Excel. In essence, the tasks accountants perform in Excel are often equivalent to fundamental tasks of data analysis. The below table provides examples of common data analysis tasks and Excel formulas/tools accountants use to perform them:

Data Analysis Task Excel-Based Solution
Data Merging/Blending VLOOKUP; PowerQuery; Copy & Paste
Data Filtering Excel Filters; PivotTable
Data Summarization SUM/SUMIF/SUMIFS; PivotTable
Data Parsing/Cleansing LEFT/RIGHT/MID; Remove Duplicates; Text to Columns

The Antiquated Status Quo

For years, accountants have performed well in these responsibilities by building their processes around networks of spreadsheets with increasingly sophisticated formulas, filters and PivotTables (among other things). However, the data analysis needs of most corporations and firms have now surpassed the limits of what the average accountant can keep up with.

The problem stems from the fact that only a small fraction of an accountant’s education and training is dedicated to learning to properly use Excel, let alone how to analyze data or use more advanced data analysis technologies available today. This deficiency in accountants’ education and training results in poorly designed processes, spreadsheets and workflows. The effects are seen in many of the difficulties so many corporate accounting, tax and finance teams are experiencing:

  • Manual Labor: Teams waste time with inefficient and overly mechanical data tasks, resulting in compressed “busy seasons” and difficulty meeting deadlines.
  • Human Error: Teams struggle with concerns over the accuracy and quality of data yielded from poorly designed spreadsheets.
  • Rigid Process: Teams labor needlessly on projects and changes due to lack of data agility (i.e. ability to quickly answer a new data question, change existing data processes, etc.)
  • Lost Opportunity: Teams’ inability to extract deep insights from data results in lost opportunities and competitive disadvantage

I can add from personal experience that this problem is not limited to business teams run by accountants. Nearly all departments with teams of analysts struggle with these problems. Not even IT departments are immune: I have been told more than once by IT personnel that a data-related task could not be done because they were unsure how to do it in Excel.

Technology, Process, and…

Most CFOs and other senior leadership see these problems and recognize the need for change. They generally understand that technology will permeate every step of this change and many now mandate that their teams begin using technologies better suited to their work.2

However, few senior leaders yet recognize an equally pressing need: to remain effective, their reports must develop and adopt a mindset for data analysis. Accountants must embrace and accelerate their metamorphosis into hybrid technicians and data analysts.

I don’t want financial planning people spending their time importing and exporting and manipulating data, I want them to focus on what the data telling us. — Mark Garrett, CFO of Adobe Inc. 3

What is a Mindset for Data Analysis?

Professionals with a mindset for data analysis apply their experience and mastery of the following fundamental concepts in their day-to-day work:

  • Data Structures: To be useful for reporting and analysis, data must be structured into fields and records (or columns and rows). In order to analyze multiple data structures, they must relate to one-another (generally known as keying or mapping). Accountants who understand principles of data structures design efficient workflows, eliminate unnecessary intermediate steps in data processes, build more effective Excel workbooks.
  • Data Integrity: The phrase “garbage in, garbage out” is an old one, but is just as true today as it has ever been. When accountants understand this principle and why it matters, they contribute to designing better upstream processes to avoid the generation of bad data and are mindful in how they maintain and generate the data they have stewardship over.
  • Data Querying: We derive meaning from our data by querying it. Without queries, data is lifeless and has no value. When accountants understand how data is queried and what is needed to do so, they design better processes to get the information they need the first time. They intuitively know what answers and insights can be derived from any data-set they come across. They get to the answers they need more efficiently.
  • Data Types: In the world of data analysis, data types are important. Data types impact what you can do with that data, how much space it takes up on a hard drive of database, and how fast you can process it. When accountants understand data type principles, they’re essentially becoming conversant in a universal machine language that will aid in their understanding of how countless information systems (including Excel) function and integrate with each other.

Professionals with a mindset for data analysis are guided by the above principles in the work they do everyday. It empowers them to produce better work product and to do it faster, making them more effective problem solvers. They add substantial value with insights they are uniquely able to provide because they are both technically competent in their field of accounting and in data analysis techniques. Crucially, they are also far better prepared to learn and adopt new technologies as their companies or firms implement them.

Data Analysis Mindset: Getting Started

Accountants wanting to start this mindset change can educate themselves on data management concepts and analysis techniques and then find simple ways to alter the way they do their work using new methods and tools.

For accountants who have been using Excel almost exclusively, they could begin with Microsoft Access. Microsoft Access can help introduce accountants to data structures and relations, queries, and data types. Because Access is usually included in the same Microsoft Office packages as Excel and there are lots resources available online getting started with it, it’s a great place to start.

From there, accountants can get into complex data analysis and automation with tools like Alteryx, explore data visualization with software like Microsoft Power BI (you can install Power BI Desktop for free and get started visualizing data today), or branch out into more traditional SQL-based technologies. No matter which direction an accountant chooses to go, opportunity abounds.

The Time is Now

Whatever path accountants choose for their data analyst education, they should start now and keep going. The job market for accountants is already shifting towards placing greater emphasis on these skills and will increasingly do so.4 Accountants should find small and simple ways to innovate and improve their processes with their newly acquired data analysis skills and learn from these experiences (successes or failures) to maintain their relevance in the job market.

Senior leaders who desire the benefits of digital transformation for their organizations should encourage their teams to adopt a data-centric culture. They should back this up by investing in the education initiatives of their personnel and provide them with licensing for technology that will make data analysis more accessible to them. Doing so will go a long way to ensure the long-term effectiveness of the organization as the world moves deeper into the information age.

  

Notes

  1. A compelling example of the staggering growth of available data is the “Internet of Things Effect” — in 2012, the world placed 4 billion data-generating sensor devices into service. In 2016 (yes, just four years later), the world placed more than 30 billion data-generating sensor devices into service. See Achieving Business Impact with Data by Niko Mohr and Holger Hürtgen, McKinsey & Company, Report (April 2018).
  2. See Stop Using Excel, Finance Chiefs Tell Staffs, by Tatyana Shumsky, The Wall Street Journal (Nov. 29, 2017).
  3. See Stop Using Excel, Finance Chiefs Tell Staffs, by Tatyana Shumsky, The Wall Street Journal (Nov. 29, 2017).
  4. For example, PwC now requires all incoming accountants to complete training on using Alteryx for data analysis and regularly encourages their personnel to find ways to use Alteryx to automate the work they currently do in Excel.

Blake is a CPA and a law school graduate specializing in taxology, tax and finance process automation and optimization, and cloud accounting systems.

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