Data Analytics for Tax: A Primer

print this article

Big data is one of the most important advances the business and consumer sectors have seen since the dawn of the internet. But what does big data mean for tax? What doors can it open?

Although some skepticism is healthy every time marketers and media outlets trumpet a new, seemingly mystical fix for real business challenges—and “big data” is a notoriously overused term—tax professionals should realize the potential. If we consider how strong the ties between technology and commerce have become, big data can do for the tax industry what it has done for the consumer sector.

First, a definition: data analytics, or “big data,” broadly describes the growing size and complexity of the modern data landscape. It is also often used to describe the emerging technologies that can efficiently connect this data and drive new capabilities in data analytics.

The corporate tax environment is a perfect place to leverage the potential of data analytics, because it is a massive consumer of a company’s data, right down to the transactional level. No matter how the data is displayed, all of it must tell one consistent story.

Data analytics allows the tax professional to transition from performing time-consuming, manual processes to simply having access to useful information and conclusions on demand.

Saving Money, Time

Broadly speaking, this approach saves time and money. It also delivers strategic value: data analytics opens doors for tax departments to simplify complex information, use that information in different ways at different times, and be better prepared for strategic planning—all facets of high-performing tax departments.

One legitimate consideration is the potential downside of this approach.

Businesses today create more data than ever, and they have the technical capability to store, access, and process that data into conclusions that are more dynamic, specific, and meaningful than ever. Tax authorities, in turn, are hungry for the data that businesses collect.

At this point, data analytics can become a sensitive issue for many corporations.

Companies are, with good reason, generally reluctant to turn over to regulators data stripped of its underlying context. Shareholders and other crucial stakeholders may view the act of relinquishing company-specific data as risky and shortsighted. Once tax authorities have data, they may choose to make it public, or their security measures may be compromised.

What’s more, being able to obtain such data may whet tax authorities’ appetite for the conclusions borne from that data.

Regulatory Uncertainty

There is, in other words, regulatory uncertainty regarding data analytics in the tax environment. Whereas tax authorities are methodical about communicating the tax rules that corporations must follow, data analytics is comparatively new, and many of the regulatory details have not yet been determined or tested in the real world.

Nobody knows what regulators will do over the long term to leverage the habitual data collection and analysis going on at the companies they regulate.
Regardless of how the regulatory environment for data analytics plays out, this is the “why.” Now here’s the “how.”

Corporate data analytics programs aim mostly to connect both structured and unstructured data that originates from different functions, teams, geographies, and companies so as to provide a more efficient mechanism to access and process data and much-improved or, in many cases, new insights.

To do this right, tax must be in the mix. The right platform allows tax departments to tap into data mashups not previously available.

For the most part, the requisite data already exists—somewhere. Since tax is integral to most areas of business, tax departments are uniquely positioned to leverage structured and unstructured intracompany datasets. For example, data that exists in enterprise resource planning (ERP) systems and other relationship management tools is structured, whereas data that exists in most spreadsheets, work papers, documents, and productivity applications like email and calendars is generally unstructured.

Combining Structured and Unstructured Data

One challenge tax departments may therefore face is the combination of structured and unstructured data. Previously available tax technology generally could not bring the two datasets together without massive manual effort—and, as any tax professional will attest, manual efforts materially increase risk.

Having a single tax platform in place is the first step to overcoming these challenges.

The single-platform mandate comes with its own challenges: determining where relevant information resides and, once it is found, how to analyze and translate it for others across the organization to draw out its true value. Once a single platform is in use, however, it is easier to locate, analyze, and draw conclusions from the data using automation.

Here the potential gets really interesting.

The Holy Grail for tax teams is to shift focus from managing data, which they already do, to analyzing data for trends and making timely, high-impact decisions that can influence the company’s bottom line.

For instance, analyzing trends in revenue, expense, and currency fluctuations in the context of different business strategies that the company is considering could yield interesting trends in effective tax rates and cash taxes—insight any senior leader would value. This data could also be used in effective tax planning strategies, helping tax play a more strategic role in the business.

From what we can see, tax professionals are signaling that they are up for these challenges. They should examine existing systems and think carefully about making the business case for placing a data analytics strategy inside the tax environment.

Irish McIntyre is vice president of product management, corporate segment, tax and accounting business of Thomson Reuters.

Leave a Reply

Your email address will not be published. Required fields are marked *

XHTML: You can use these tags <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>