Data Governance for Tax Administrations: A Practical Guide

1.SETTING THE LANDSCAPE

Information and knowledge are keys for organizations to fulfill their objectives.

The DAMA association¹ emphasizes that organizations with reliable, high-quality data about their users, products, services, and operations can make better decisions than those without. The absence of these properties will result in a waste of opportunities and deficient performance (DAMA-DMBoK2, 2017). This assertion is valid with greater emphasis for tax administrations, where data and its products are fundamental to accomplishing its mission.

1.1.Data, Information, Knowledge

A still current and passionate discussion in information sciences and knowledge management is the differentiation among data, information, knowledge, and (sometimes) wisdom.

Models available often present these concepts as a hierarchy, in which mastery of the lower level provides the opportunity to scale to the next level. This structured ascension is not a point of agreement among scholars, but it can be a starting point to understanding the concepts and establishing more precise communication among different users.

A theoretical model helps in understanding the transformations and relationships among these concepts.

1.2.The DIKW Model

Among the available models, one of the most visible, but not without controversies, is the so-called DIKW (Data, Information, Knowledge, Wisdom), presented in the form of a pyramid (Figure 1-1). One of the high points of the controversies is the inclusion and definition of the last attribute, wisdom².

Figure 1-1 The DIKW model.

Source: Prepared by the authors

The implicit assumption of this model is that tax administrations can use data to create information; information can be used to develop knowledge, and knowledge can be used to create wisdom.

The following definitions and associations to different types of information systems can be performed on this model:

Table 1-1 DIKW Model - elements definitions and information systems associations.

Source: Prepared by the authors

1.3.The Growing Importance of Data Governance in Tax Administrations

Tax administrations are related to the automated processing of data from the beginning. After all, they were (along with the census bureau) the first users of the so-called data processing machines in government.

Tax returns and the provision of ancillary information in digital format by taxpayers and auxiliary institutions (especially financial institutions) have been part of the life of tax administrations and taxpayers in the recent past.

In those times, the data was structured with a minimal data management schema, consisting fundamentally of a data dictionary³. IT⁴ personnel had control of the processes of extracting, transforming, and loading the data. The data needed to be cleaned⁵, mostly manually.

Data management was the responsibility of the IT area, with occasional advice from the business areas. Thus, organizations merged data management with IT management.

Nowadays, data availability has increased dramatically in quantity and formats, as well as the dependence of tax administrations on its treatment. As established in (Collosa, 2021), this is mainly due to:

The significant expansion of computer processing and storage capacity associated with the reducing their costs.

The increasing availability of communications networks and broadband Internet.

The development of effective models to capture, store and process massive data and advanced cognitive algorithms.

The emergence of new data sources and formats e.g., sensors, GPS⁶, OCR⁷ cameras for truck plates, RFID⁸ chips and antennas, social networks, etc. (Arias & Zambrano, 2020) , including electronic invoices (Barreix & Zambrano, 2018) and tax information exchange between countries.

A few years ago, the importance of using data in the work of organizations was mentioned with a quote from the famous total quality guru W. E. Deming without data, you’re just another person with an opinion (ETF-Europa, 2018). Currently, KPMG analysts have rephrased this quote: without trust in your data, you’re just another person that consumes data (KPMG, 2021).

Tax administrations are strongly linked to this reality.

Over the past several years, tax administrations worldwide have started to undergo digital transformation, collecting data from non-traditional sources and formats, and accumulating them in their databases. Tax administrations can rely heavily on data and algorithms for their internal processes and provide more and better services to the taxpayers and other stakeholders, so tax administrations can count on data accuracy, completeness, and availability.

The following numbers illustrate these aspects as presented by the OECD

From 2014 to 2019, average e-filing rates have increased significantly between 13 and 18%.

Over 80% of payments (by value and numbers) are made electronically.

Close to 50% of tax administrations pre-fill PIT (Personal Income Tax) returns with specific deductibles expenses.

New data sources allow pre-filling to move to VAT (Value-Added Tax) and CIT (Corporate Income Tax) returns.

A growing number of tax administrations use virtual assistants to respond to taxpayers enquires and support self-service.

Use artificial intelligence in services supporting taxpayers and tax officials.

Percentage of tax administrations that allow taxpayers to register online up from 70% (2015) to 97% (2019).

With the increasing availability of data, compliance work focus can change to prevention.

At the same time, society demands more responsibility from the entities that obtain and consume data from citizens and companies, establishing a series of data protection laws and regulations.

In this context, a modern data governance landscape must be set up to ensure data confidentiality, availability, quality, and integrity and reinforce the legal protection instruments (as data protection regulations) and compliance rules.

In other words, data governance must ensure that data are consistent and trustworthy and don’t get misused, so as in the transactional operations up to enable the effective use of data analytics helping to optimize operations and drive business decision-making.

This data governance landscape includes all hierarchical levels of a tax administration, intending to define policies, standards, processes, and participating in data governance committees.

1.4.Data Management vs. Data Governance

Data is an essential asset within tax administrations. Data can give tax administrations different benefits through its use and exploitation, as well as through its correct administration.

To generate value, tax administrations require data. It needs to be managed consciously; for this, the organization must put a set of fundamental practices in place to allow it to manage data like any other business asset.

1.4.1.Data Management

According to DAMA (DAMA-DMBoK2, 2017), Data Management is defined as the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.

Figure 1-2 The DAMA-DMBoK2 Data Management Framework (The DAMA Wheel).

Source: (DAMA-DMBoK2, 2017)

Organizations develop data management practices through different disciplines that cover all activities around the data lifecycles, e.g., Data Governance, Data Architecture, Data Quality, Business Intelligence, etc.

DAMA-DMBoK2 defines 11 disciplines for data management, with data governance at the center, as shown in Figure 1-2.

1.4.2.Data Governance in Data Management

As tax administrations face different challenges of information systems implementations, be it to support analytical capabilities, transactional, or business processes, it is recognized that data assets deserve to be managed correctly.

Traditionally, IT departments in organizations have been responsible for promoting data projects. Now, IT departments cannot operationalize these projects in isolation or without the commitment of the whole institution.

To manage data correctly, it is essential to have roles and responsibilities that allow accountability for the problems that data usually present and their inherent definitions. Here is where data governance intervenes as a framework that allows organizations to establish a system of rights and obligations for decision-making throughout the entire data lifecycle.

Data management requires a structure that controls and guarantees the correct administration of data, and that is why the implementation of data governance programs is gaining greater importance.

DAMA-DMBoK2 defines data governance as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets (DAMA-DMBoK2, 2017). On the other hand, Ladley (Ladley, 2020) mentions that the purpose of data governance is to ensure that the data is managed properly, according to policies and best practices.

As we can see from DAMA-DMBoK2 Management Framework (Figure 1-2), data governance is at the center of all DAMA-DMBoK2 disciplines because it is crucial to control all kinds of data projects through centered guidance.

Data governance provides the best tools to manage data correctly, e.g., principles, policies, functions, processes, procedures, etc.

1.4.3.What is data governance all about?

Data governance is a key component of data management. Tableau (Tableau Software, 2020) proposes that data governance helps answer questions like:

Who has ownership of the data?

Who can access what data?

What are security measures are in place to protect data and privacy?

How much of our data is compliant with new regulations?

Which data sources are approved to use?

Governance models and practices won’t be the same across every organization, even among tax administrations, but these models are crucial pieces of the process. As also mentioned in the paper referenced above, the following stand out:

Data quality is a pillar of data management. It doesn’t matter how robust your governance program is if you don’t have quality data. Having data that is accurate, complete, and reliable is a cornerstone of any data-driven organization.

Data security and compliance is defining and labeling data by their levels of risk and then creating secure access points, keeping a balance between user interaction and safety, considering access levels that can go at the functional, object, or even field level (Martins, Nieto, Seco, & Zambrano, 2020).

Data stewardship helps monitor how teams use data, and stewards lead by example to ensure data access, security, and quality, defining clear interactions and responsibilities of different data stakeholders.

Data transparency matters because every piece of the process and the procedures you put in place should work within a model of clarity.

Analysts and business users should quickly find out where their data comes from and know if there are any special considerations.

1.4.4.Data Lifecycle

The data lifecycle is the sequence of stages a particular data unit goes through, from its initial generation or capture to its eventual archival or deletion at the end of its useful life (Wigmore, 2017).

Figure 1-3 The data lifecycle key activities.

Source: Prepared by the authors based on (DAMA-DMBoK2, 2017)

The data governance practices must cover all data lifecycle, as it is shown in Figure 1-3.

1.5.Data Attributes

Attributes are specification or characteristic that helps define a data entity. In data management, some attributes refer to the processing characteristics of the data and its lifecycle, use and structure, security requirements, quality parameters, and compliance needs.

The following topics present summaries of several essential data attributes for their management.

Specific chapters of this document will take up these attributes.

1.5.1.Common Business Vocabulary

A typical business vocabulary is a set of commonly defined data names and definitions documented in a business glossary, for example, within a data catalog or independently.

Its