Data Transformation - Where to Begin? What once took tax professionals months now takes moments - but challenges remain.

AuthorOssooli, Maryam

With the advent of digitization tools comes the potential to transform and index massive data sources without the current dependency on IT and coding. We are approaching as significant a cultural shift in the tax profession as Facebook and the iPhone were in our day-to-day lives. What comes is a generation that will grow accustomed to the possibility that anything can be automated by anyone. Tasks that once took a tax professional months to perform can now take moments. The potential for material error can be better measured and controlled. This is the beginning of a significant evolution in how tax planning, provisioning, and preparation are done--an evolution accompanied by questions of how this change will be controlled, defined, and audited. With technology, tools, and app solutions developing as quickly as Amazon Web Services can power them, the speed of change can seem overwhelming.

Of course, change also comes with skepticism and resistance. How can deep institutional knowledge be fully valued for the decades of experience it took to be developed? How do we balance experience with the speed of the novel and ever-changing landscape that comes with it? But in the same way that the advent of Microsoft Excel empowered us all with spreadsheet functionality, so we collectively find ourselves on the verge of having to evolve quickly. In a tax practice with a uniquely conservative culture, a need for accurate reporting, and deep institutional knowledge that fills gaps otherwise left exposed, chief tax officers must confront a great dilemma: where to begin?

In this article we'll explore the grassroots elements of an organizational shift to a DevOps culture, which foregrounds the combination of software development and information technology to further business objectives. We will explore how to cultivate substantial advancements using focused cross-functional teams, as well as how to balance institutional knowledge with the unique contributions of millennial and gig economy skill sets. All this should begin with the recognition that the job functions in demand today didn't exist yesterday, the jobs of tomorrow are still being created, and the reality is that we all must continually learn.

Step 1: Tools to Create the Grassroots Movement

This article is born of the personal experience of this evolution within ADP's tax credits practice. One of our most complex offerings is what, on its face, seems very simple--tax credits based on year-over-year growth. It is surprising how much data and analysis and documentation go into proving to an auditor's satisfaction that growth requirements are satisfied. An auditor has myriad ways to attempt to haircut a credit calculation to the point of ineligibility. Over the years, the complexity developed to address these issues has resulted in calculations that can take months to complete, along with the added time and diligence to gather various data sources to document the facts and circumstances of growth activity.

Growth tax credits involve year-over-year comparisons and, to be done well, data sources must be consistent so a true analysis of growth patterns can be demonstrated. As with most compliance practices, in a given year hundreds of calculations may be required for tax credits compliance, and we wait in a relative state of suspension until the tax year closes. Then crunch time begins, starting with gathering data. Addressing the data gathering process itself would be an article in and of itself. But once data for the year is received, then comes the merging of data sources to document the year's full activity, performing a layered analysis from clean-source data extractions all the way through to a final calculation summary, with audit trail, that is then incorporated in the deliverables. This process requires documenting, from inception to result, all testing, calculations, and adjustments along the way.

After the tax credit calculation, there are multiple levels of review. This review often involves another person to test and evaluate the analysis, normally in a spreadsheet, with the final calculations and supporting documentation. Often the review process takes longer than producing the calculation. Inevitably, somewhere...

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