Enhance your data. Overcome indirect tax challenges

Two ways technology can help you find opportunities

Indirect tax requirements can shift frequently, rapidly, and on a large scale. The sheer volume of data can challenge the most experienced tax organizations to avoid overpayment and underpayment. However, automating elements of the indirect tax process can help companies find opportunities to save money, time, and effort.

Where to start?

When it comes to indirect taxes, avoiding overpayment and underpayment have been persistent challenges. For example, companies can overlook taxes on inventory for sale, or retailers may pay taxes on items they plan to give away as part of a promotion.

Because tax functions may too often be focused on processing returns, organizations may frequently miss chances to identify refund opportunities and quantify and address potential overpayments. But tools powered by artificial intelligence (AI) and other leading technologies may help. Investing now in indirect tax technology to automate indirect domestic tax requirements may save your organization headaches and costs in both the short and long term.

What is your organization facing?

Today, four trends are making indirect tax efficiency a complex and urgent challenge.

All of these factors are leading to an increased need for indirect tax technology that can help organizations efficiently and effectively manage indirect tax obligations. Two areas are of particular importance—ETL tools and cognitive learning solutions.

All in the data

Indirect tax management depends in large part on getting the data right. To keep pace with this demand for data accuracy, organizations may focus on the following three elements:

Data gathering

Multiple Enterprise Resource Planning (ERP) and inconsistent data sources—from spreadsheets and tax software files to e-mails and PDFs—create challenges discerning the quality of information. Additionally, different segments of an organization’s tax team have different requirements that are constantly changing with new tax laws. As a result, organizations should inventory their data sources and close any gaps between what those sources provide and what is essential for compliance, potential audits, and further processing. Organizations often benefit from a common data management template.

Data management

A single source of data is infinitely important in meeting challenging tax obligations. A strong “data lineage”—including multiple sources, such as ERP or tax software—is key to creating a “single source of truth.” It is critical to have all the information an organization needs to tie back to a tax return. That data may be adjusted—and tracked—so that four or five years later, an audit team can understand its content and source. Efficiency in this process requires not only quality checks and validation, but data completeness.

Data governance

Often decentralized, data governance can be challenging because different stakeholders own the data. This process raises additional challenges as the different stakeholders may not understand procedures or may regularly require more data. Leading practices are most efficient when driven by identifying the requirements of tax-related data up front, providing an organization’s tax team with a seat at the table during the initial process. This can help avoid any manual forensics such as retrieving a four-year-old purchase order to glean its tax information.



For all of these stakeholders, an organization should have clean and organized data, and be able to mine that data for insights—quickly. Let’s explore how to make it happen.

Step 1. ETL tools

Data wrangling tools can help automate the data extraction process. This involves a system comprising three critical steps: extracting, transforming, and loading (ETL), which is key to setting up a data pool. ETL software helps establish a secure agent that can connect directly to the underlying ERP tables. The tools comprising an ETL solution can help organizations pull and consolidate data, perform reconciliations, and execute quality checks using an automated, repeatable, and well-documented approach that leaves a clear audit trail.

Having an ETL process provides flexibility in connecting to various source systems, including ERP, e-commerce and order management, tax software, and flat files, among others. This flexibility enables users to pull in data regardless of format with agility to meet future requirements.

Adopting an ETL system substantially reduces the need to build reports and manually manipulate data. Indirect taxes in particular require massive volumes of data—in many cases millions of records per month. ETL systems can help process those large data sets in minutes, even seconds. ETL tools enable data consolidation and verification from multiple sources to streamline the reconciliation process. This time-saving feature can help save resources and costs and builds greater efficiencies.

ETL tools also allow for enhanced documentation of processes. Organizations can clearly see what is actually being done with the data, enabling a clear link and audit trail from the data back to the ERP and other reporting systems. Seeing how that data was used may be a particularly valuable asset during audits three to four years after the transaction occurred.

A key part of aligning technology to indirect tax data is using analytics to gain insights and see important elements in real time. Analytics allows organizations to flag issues as anomalies immediately rather than in the future when they likely will be compounded. Additionally, analytics enables organizations to look at data more granularly to better analyze its validity.

ETL and its benefits

Step 2. Cognitive learning solutions

Once ETL solutions are in place to help gather and manage an organization’s indirect tax data, cognitive learning tools can turn that clean and formatted data into insights for an organization’s stakeholders that can lead to savings.

For example, cognitive learning solutions can apply indirect tax decisions related to a small population of data to a broader population, allowing tax teams to determine quickly whether vendors correctly charged sales taxes or whether organizations appropriately accrued use taxes. This process enables the tax team to import raw transaction-level detail that may date back years and consolidate and map those details with other supporting information, such as contracts and purchase orders.

This process helps select representative transactions by business unit, jurisdiction, or other differentiator allowing teams to review a small group of transactions from a tax technical perspective. Cognitive learning then applies what the team learned from those transactions to the broader population, providing iterative and predictive insights. In other words, cognitive learning enables the tax team to classify all transactions via machine learning rapidly with targeted validation from specialists. These analytics help team members understand the organization’s tax profile and address common issues. These benefits can be extremely valuable from a stakeholder, reporting, and management perspective.

Gaining buy-in

To make the tax function more visible as a business partner, it is important to build clear communication among stakeholders. A strong communication strategy runs across three types of stakeholders:

Making the business case

Indirect tax is more complex than ever. Structural and policy changes are moving at a rapid pace. These swift legislative maneuvers vary by region and jurisdiction, making it more challenging for tax professionals to navigate.

Overcoming the data and stakeholder challenges tax teams face requires technology that can not only keep up with the evolving regulations and business needs, but can free up human resources for strategic, value-add work.

The business case for implementing the technology and cultural changes to address the challenges is clear. Articulating the business case may be somewhat daunting, but successfully doing so can help put an organization on a path to stay ahead of the curve.

Get in touch


Kirsten Gulotta
Deloittte Tax LLP

Holly Hamby
Deloittte Tax LLP

Brian Little
Deloitte Tax LLP

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