Achieving Clean Data: The Tax Data Analytics Challenge

by FEI Daily Staff

The big data phenomena is no longer isolated to a few select industries. This movement has far reaching implications, with significant impacts on all operational fronts within every single industry.


For tax teams that have to deal with both internal and external driven data, changing or new tax law makes it even more challenging.  Beyond the sheer volume of data that adds to tax data analytics complexity, several market drivers have come to fruition forcing tax data analytics projects to the top of the priority list.  In light of changing tax laws, budget cut-backs, and restructuring challenges, this article explores how companies can minimize the risk of tax data errors – even with existing spreadsheets – and move beyond tax compliance alone to strategic tax planning and compliance.

Market Drivers

Taxing jurisdictions are now taking advantage of the latest technology to not only manage the collection of tax revenue and analyze tax returns at the transactional level, but to also share data across taxing jurisdictions.  In fact, the IRS uses the same enterprise software implemented by top organizations to help them identify potential corporations for audit. States are looking to do the same. This fundamental shift in which taxing jurisdictions are transforming from technology laggards into technology adopters is one of the key drivers catapulting tax planning and, therefore, tax data analytics into critical must haves.

Another major driver is the effective tax rate (ETR), which is the average rate a corporation’s pre-tax profits are taxed.  With US businesses paying among the highest corporate taxes in the world, currently at 41.1 percent (with a combined federal and assumed state tax rate of 7.1 percent), potential deductions and planning opportunities to preserve tax cash has caught the attention and focus of the C-suite.

The Modern Day Tax Data Challenge It is estimated that tax professionals spend 80 percent of their time entering and re-entering data into spreadsheets for tax compliance, provision, audits, or planning.  With so much focus on data, very little time is left for actual analysis in support of the company’s overall goals, such as reducing the ETR or protecting and enhancing cash flow.

Gathering data from multiple data sources, such as ERP systems and spreadsheets has become the accepted norm.  The result?  Even the most basic data analytics task becomes a confusing and complex journey.  Changing tax laws, budget cut-backs, restructuring challenges have compounded the issue even more, resulting in data chaos and an inordinate amount of time managing data. Simply put, the volume of tax data processed in today’s modern day company is too much to handle via manual processes.  For tax teams grappling with this issue, the question at hand becomes how can companies ensure that their data is up to par, while minimizing the resources required to achieve clean data? The answer lies in a mixture of advanced technologies and best practices.

Dealing with Multiple Data Sources

Before jumping into recommended “how-tos,” it is important to first define the goal at hand and the current tax data environment.  Data analytics is the process of cleaning, transforming, and modeling data with the goal of discovering useful information that supports decision-making. In other words, a successful data analytics project will produce quality and accurate financial data that will be the underpinning of solid tax-related business decisions.  The process of data analytics would be fairly straightforward if there was one static source for financial data, however, for the majority of corporations this is not the case.  In these corporations, data comes from many sources and in various formats – from multiple ERP systems, accounting software, best-of-breed software solutions, and manual spreadsheets.

One of the most challenging aspects of tax data analytics is the use of spreadsheets. Even just a decade ago, advanced tax planning and compliance solutions were few and far between.  As a result, tax teams turned to spreadsheets.  On average, tax professionals spend 80 percent of their time entering and re-entering data into spreadsheets.  However, unlike a trusted software application, spreadsheets are devoid of checks and balances to ensure their accuracy.  They do not undergo the rigor of a formal development cycle.  They lack regression testing and updated documentation.  The result is a fragile, interlinked system of spreadsheets that can break at any given moment in time.

But what do you do if you are one of the 80 percent of companies with spreadsheet reliance?  The ultimate goal, of course, is to move all data out of spreadsheets and into similar or the same third-party applications that taxing authorities themselves are using to analyze corporate data.  But, keep in mind that it has taken most companies years to build the hundreds or even thousands of spreadsheets they rely on, so creating an interim plan and setting realistic goals are a must.  A good initial step is to move at least 50 percent of the current spreadsheets into third-party systems, with a secondary goal of having a singular data source for data analysis. Setting incremental and attainable milestones enables companies to deal with their current spreadsheet reality, while keeping an eye on the larger goal.  For the spreadsheet portion that remains, applying best practices such as those listed here, can reduce redundant data captures, increase the accuracy and efficiency of data reconciliation, store and recycle captured data to prevent duplicate work, and improve reporting capabilities.

For data stored in ERP systems, accounting software, and other best-of-breed tax solutions, extracting and cleaning data is a much easier task, but it still requires work.  In these cases, data extraction tools (either direct from the company or off-the-shelf, such as Tableau as previously mentioned) are readily available.  These tools can process large data sets and create high-level visuals to pinpoint anomalies quickly.  As an example, an ERP data analytics tool can identify:

  • Data entry errors for items such as tax codes and GL code usage
  • Transactions where critical data, such as product codes or asset numbers are not provided
  • Instances in which new entities or accounts are added to the system
  • Potential duplicate errors
In addition, other advanced tools that build bridges between tax systems, or offer Excel add-in tools enable companies to continue to use their favorite software applications. These allow them to seamlessly move data between ERP systems, tax and accounting software and, of course, planning and analysis software.

Rising to the Tax Data Analytics Challenge

Increasingly, tech savvy taxing jurisdictions coupled with C-suite mandates are having a trickle-down effect on tax teams.  Tax executives are now chartered with not only achieving tax compliance, but also providing sophisticated tax planning capabilities that will help meet their company’s overall financial goals, and of course, avoid and/or prepare for a potential audit.  As a result, tax data analytics, which is the foundational underpinning of successful tax planning, is now a top priority for many leading companies.  However, without a feasible plan for moving tax data into vetted, third-party systems that are designed from the ground-up to support strategic tax planning, tax teams will continue to be stuck in the minutia of data entry.  Those companies that are implementing best practices today and exploring new processes and automation techniques for achieving the “holy grail” of clean data, will be in the best position to deliver on tax planning requirements now and in the future.

Diane Tinney is Senior Product Manager, Software at Bloomberg BNA