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How to improve your tax data value chain

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Harnessing Artificial Intelligence can help organizations refine data management for value creation.

In brief

  • Tax and finance leaders can unlock value through improved data orchestration, utilizing AI and refining data management for efficient processes.
  •  Human validation is necessary to manage risk while capitalizing on AI-driven insights, transforming the tax function from compliance to value creation.
  •  Future success hinges on prioritizing valuable data, enhancing accessibility, and embracing advanced governance and fabric technologies.

In the tax domain, data is not just an asset but an instrument that unlocks value when correctly orchestrated. However, the value may be unrealized without effective data governance and accurate analysis. Tax leaders should harness the right tools, approaches and standards to create business value from data. Businesses should also be aware of the disruption AI will potentially bring.

Tax functions strive for improved processes and technology enhancements that facilitate robust data collection, analysis and conversions into actionable insights. However, they often encounter data-related challenges. What are these challenges, and how can leaders create sustainable solutions?

The tax function should use data to be a value creator for the organization, "reinforcing the concept of being a value driver rather than just a compliance function," says Charles Brayne, EY Global Tax Platform Product Management Leader.

Embracing AI is the key to future innovation

The influence of AI on the contemporary business environment is profound and will be transformative. Those who are able to harness its potential are poised to achieve remarkable results. AI lays the groundwork for extraordinary innovation and expansion when integrated across various business areas, such as tax. As Lyn Bird, VP of Transformation at Microsoft Corp., states, "Companies with a strong foundation for AI innovation will excel, and those that don't will fail."

Finance and tax leaders should strategically align their value creation and technical feasibility approach to leverage AI. Understanding the key drivers of value, and aligning AI initiatives with overarching strategic goals, can guide decision-making processes and facilitate improvedresource utilization. "Insights emerge when value-driven evaluation and technical viability intersect to deliver on specific use cases," Bird says.

Data is the backbone of AI, playing an instrumental role in unveiling its true capabilities. However, the data's quality, availability and management significantly influence the speed and effectiveness of deriving value from AI initiatives.

Understanding the data engineering required to manage, govern and use the data effectively is essential. Despite AI's massive potential, the human element remains pivotal. As Brayne explains, "The value delivered by tax professionals will only increase if they learn how to harness the power of AI to drive different types of insight and value creation.” For many, this will require the development of new skills, including the ability to manage complex data sets with confidence, as well as the ability to detect situations where the risk of bias in AI-generated insight necessitates human validation and review.

The challenges of data management

The role of structured and unstructured data in AI cannot be overstated. According to Richard Clough, EY Global Tax Chief Data Officer, "A tax function is probably 80% a data consumer, and so therefore, to do tax well, you must have access to accurate and trusted data."

The importance of data quality, availability and management on the value derived from AI initiatives should not be underestimated. The tax sector provides a unique arena where AI can amalgamate structured and unstructured data from multiple sources to extract deeper insights to bolster decision-making processes. Leveraging AI, tax professionals need to rely on more than just data scientists or delve into the complexities of algorithms.

However, capitalizing on data goes beyond merely analyzing it; it requires a use-case-oriented approach to data management and governance. It's not about "fixing" data haphazardly but identifying specific use cases and managing data based on its value and technical feasibility. Bird advocates for a targeted, value-centric approach to data management. 

"Focusing solely on the destination without considering the journey can often lead us on a longer path,” Bird says. “Traditionally, the world has been cleaning data by any means necessary, not necessarily focusing on the route. What generative AI (GenAI) now offers, and what the world needs, is a GPS-like system that can provide the shortest path to the destination, effectively prioritizing the route."

Balancing risk and value creation

Embracing AI initiatives necessitates a delicate equilibrium. Leaders must balance the promise of value creation against potential risks, including trust, compliance, regulatory obligations and brand reputation.

"Generative AI and even more advanced technologies bring the need for trust in your data governance into greater focus. Technologies like generative AI will interpret patterns they see in the data. If that data is not trusted or well governed, that could be quite dangerous." Clough says.

Data readiness for AI, therefore, becomes an important evaluation and ongoing data governance need. Thinking about enhancing data management to treat data as a product becomes a critical focus with associated levels of data quality, data curation, and robust governance between both producers and consumers of data.

Looking towards the future, the evolution of data layers and metadata management, coupled with technological advancements, promises to enhance AI implementation's efficiency and effectiveness.

"The future revolves around how metadata is applied to manage the data assets, models, algorithms and its relation to specific data assets" Bird says. Automated data extraction is rapidly gaining popularity, offering numerous benefits such as preserving data attributes, enhancing data trust and improving data governance.

As AI and advanced technologies evolve, they will disrupt traditional tax activities. This disruption poses potential risks to professionals, mainly in data processing or content creation tasks. To truly capitalize on AI-driven transformations, financial and tax leaders must efficiently orchestrate and govern data. By prioritizing value, improving data accessibility and extraction, embracing advanced data governance and fabric technologies, and driving unique insights and value creation, these leaders can secure a place at the forefront of innovation. "Understanding data territoriality and data sovereignty is crucial for managing risks in data value chains, especially when dealing with cross-border data restrictions and regulations," says Brayne. 

To enhance efficiency, a strategic approach to data management is critical. This not only entails cleaning some data simultaneously but prioritizing data based on specific value use cases. This ensures the focus is on the most valuable data assets, thereby improving value extraction.

Data consistency and harmonization

Businesses must break away from functionally siloed operations and democratize data access, enhancing their agility and efficiency. This means moving beyond a model where data is dispensed only upon request to one where it is made readily available across the organization. Efficiency also lies in tracking data at different levels focusing on granularity. It is necessary to maintain data on a legal entity basis due to the the growing significance of tax regulatory and legislative developments resulting from the OECD’s base erosion and profit shifting (BEPS) project and operational transfer pricing.


Responsibility for this data-centric transformation, especially from a financial perspective, should ideally rest with the CFO. It extends beyond tax filings, permeating all aspects of business operations. This level of ownership would enable a holistic oversight and coordination of the enterprise's data strategy. A common challenge many companies face in their data management is the need for granular detail due to the multiplicity of enterprise resource planning (ERP) systems. Often, acquisitions lead to an enterprise-wide patchwork of information, creating complications for consolidated accounting. To counter this, businesses must aim for data consistency and harmonization across their various systems.


Indeed, this principle applies broadly to tax matters. For instance, data utilized for BEPS compliance should be consistent with that used for other tax obligations. This reduces potential reconciliation issues and aligns with the overarching need for businesses to streamline their data sources, paving the way for more efficient operations and risk management.


"Businesses need to embrace an environment where data is broadly accessible across various aspects of the company," Jill Schwieterman, EY Americas Global Compliance and Reporting Leader, notes. "This approach marks a radical departure from the traditional 'on request' data provision and signifies a vital step towards building a robust data culture within the organization."


Tax and finance leaders can harness the value of data through improved orchestration, utilization of AI, and refined data management. They need to strike a balance between AI and human validation to improve the use of data while managing risks. Businesses' future success depends on their ability to prioritize valuable data, enhance its accessibility, and incorporate advanced data governance and fabric technologies. Understanding data territoriality and sovereignty is also crucial.


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