In Brief

With the popularity of and increasing reliance on data analytics to drive strategic business initiatives and achieve organizational objectives, the accounting profession’s interest in safeguarding data and ensuring its reliability is well grounded. But organizations often struggle with how to value and manage data. The authors argue for treating data like any other asset, describing the change in thinking necessary and providing a roadmap for centralized data management projects.


The operating environment of today’s organizations demands timely, efficient and effective management information to facilitate the decision-making process.

—Gregory F. Pashke, “Evaluative Criteria for Management Information Needs”

CPA Journal, August 1978

Often in the discussion of data and data analytics, the focus is on what organizations can potentially do. Unfortunately, risks associated with the use, analysis, and storage of data and the results produced may not be adequately considered or managed to appropriate risk appetite levels. The need to balance rapid analysis and data availability with safeguarding and to ensure the reliability of data continues to challenge accountants and risk management professionals. Many accountants and their stakeholders believe that it is the profession’s responsibility to balance these benefits and threats by managing data in the same way as they would manage an asset. Organizations are increasingly adopting methodologies that enable accountants to more effectively and efficiently manage, use, and profit from their organization’s data assets.

Information has become one of the most valuable assets of modern businesses, representing a new way that organizations create value for their customers and stakeholders, especially when compared to the past. The information age requires the development of information solutions, whether developing new applications to process data or to consistently manage and monitor data throughout the enterprise effectively and efficiently.

Most organizations have become aware of the data revolution that is occurring and the continual pressure to become more data driven. Few, however, have learned how to manage their data as an asset so as to derive the most value from it. Organizations recognize they need to leverage data’s value to maintain their competitive position. Agile principles provide a value-driven solution that enables organizations to provide desired value and align the use of data with organizational strategies.

This article provides accountants with a practical approach for establishing and maintaining a data asset management program. Utilizing core risk management practices used on other assets, the article explains how treating data as an asset can enhance its value, facilitate organizational activities, and achieve strategic goals.

The Profession’s View of Data

CPAs have always appreciated and respected the strategic use of data. Since the profession’s beginnings, accountants have used data to support their work and enhance its usefulness and reliability. Although it continues to be challenging to value intangible assets such as data for financial reporting purposes, most recognize the importance of data itself. Many, if not most, accountants continue to appreciate the fact that data reflects the characteristics of a financially reportable asset because it has a probable future economic benefit.

Others disagree. For some, data is something that is either loaned temporarily to accountants so that they may use it to create something of value for its owner, like a liability. Still others believe that the accountant’s role as it relates to data is a custodial one; the owner trusts the accountant with information, and the accountant implements appropriate due care controls that ensure the data’s protection. Examples include a client sharing personal data with an accountant or a controller responsible for the data under a particular division’s custody.

With the consumer, media, and regulatory concerns over the use and protection of nonpublic personal information, as well as corporate governance concerns over potential liabilities for privacy breaches, the current focus on the custodial aspects of data is well justified. Increasing attention to the effective use of data to achieve organizational objectives, however, demonstrates the need to leverage and manage data as an asset more effectively.

Data Versus Information

Despite many using the terms data and information synonymously, accountants have distinguished between the two since system technology first appeared. In reflecting on a career wherein information economics and management accounting intersected, Gerald Feltham discussed the impact that the evolving field of information economics in the late 1950s and ’60s was having on managerial accounting (“Information Economics and Management Accounting: A Brief Personal Perspective,” Journal of Management Accounting Research, December 2005, Research pioneers, including George Stigler, Jacob Marschak, and Roy Radner, had already identified how both fields intersected and provided early theories on valuing the use of information.

From an accountant’s perspective, in 1981, Stephen F. Piron, then the editor of Management Accounting, identified the need to differentiate between the two and forecast the challenges and implications of treating both the same, specifically higher expenses and missed opportunities to enhance profit. He stressed that, as the availability of data and reliance on technology increases, the ability to obtain and store data becomes more accepted:

When computers were first introduced into the business world, they were used to process data. They were workhorses … Then it became apparent that these workhorses could report summarized data to management to inform management of what was going on. Computer reports began servicing the users’ operational and information needs … Too often, they ask, what data do you want as opposed to what information do you need? (“Management Information Systems: Data vs. Information: The Difference,” Management Accounting, March 1981)

Data must be processed and converted into something other than what it is to achieve its ultimate value, like a manufacturer refining raw material and making a product.

CPAs today face a similar challenge. Organizations gather data without thinking through the implications of doing so. Actual storage costs continue to decrease, encouraging organizations to hoard data to the greatest extent possible because they believe that it has some future economic benefit, even though the accounting profession continues to struggle with assigning a financial value to an item with intangible properties.

It is the conversion of data into information—data converted to a format that facilitates a decision maker’s ability to make a more effective decision—that drives data’s ultimate asset value and facilitates its ability to generate new business opportunities or reduce fraud, waste, and abuse.

What Type of Asset Is Data?

Accepting that data is an asset and should be managed as such, what type of asset is it? A glance at a balance sheet would probably place data within inventory, especially when it is kept for eventual sale. Data sources can include information within an organization or purchased from an outside party, just as a manufacturer obtains raw materials. Until the data is used, it is stored in the same manner as raw materials, which incurs storage and other administrative expenses, including insurance. For data, this also includes the obligation and associated cost to protect the data and avoid cybersecurity breaches.

Data must be processed and converted into something other than what it is to achieve its ultimate value, like a manufacturer refining raw material and making a product. This involves subjecting the data to a process comprising analytics, machine language, and professional perspective, yielding the improved asset: information. This resultant stage of data development can be a product or it can be subjected to further refinement, enabling even more sophisticated analysis and use.

Conversion cycle–related risks will need to be identified and managed as well. These include ensuring that an inventory of all data is maintained, the conversion programs used produce reliable results, access is limited, and designated personnel responsibly dispose of data.

Data becomes an asset when, under the right circumstances, it is transformed into information that contains economic characteristics and facilitates actionable insights. Some of these characteristics relate to the reusability of data, its ability to replicate or combine with other data to create new data, and its transferability to others. To transform data, it must be fit for use and free of defects. Data must be timely, relevant, and formatted correctly to ultimately produce information that gives the organization insights about actions to be taken to meet its goals. Turning data into information requires being able to find patterns within the data that can produce insights and create value.

Data is an economic asset that can help organizations improve operations, increase revenue, solidify relationships with stakeholders, produce new revenue streams, improve the quality of current products, establish competitive differentiation, allow innovation, and reduce risks. Information can also be monetized to produce new revenue streams, such as exchanging information for goods and services or turning data assets into cash by selling them to other users.

How to Protect Data

Protecting data as if it were an asset goes beyond considering its security. Access is only one consideration; if data is used for revenue-generating activities or to help achieve organizational goals, then its reliability is also critical. Statement of Financial Accounting Concepts (SFAC) 2 defines reliability as a measure that “rests on the faithfulness with which it represents what it purports to represent, coupled with an assurance for the user that it has that representational quality.” To ensure that data was processed accurately and reliably for financial accountability or regulatory requirements, a combination of application and general controls has traditionally been considered.

Application controls include access to data through a system that enforces rules relating to authorization, segregation of duties, and business rules imposed through edit and validity checks. General controls refer to the environment that processes the application, which can include network and operating system security, physical security over related technology assets, change control, and resiliency. In addition, the process used to develop the application, typically through a system development life cycle (SDLC), is critical to ensuring the quality, and therefore reliability, of what the application processes. Adherence to SDLC processes requires a long time commitment that might delay the introduction of new systems, including those leveraging new technology solutions such as artificial intelligence and machine learning. These controls remain critical, however, in helping to ensure the quality and reliability of data.

Data becomes an asset when, under the right circumstances, it is transformed into information that contains economic characteristics and facilitates actionable insights.

Realizing Data Rewards Through Enterprise Governance

To maximize the return on its data assets, all the parts of an organization need to contribute to the asset and its conversion into useful information. The coordination of roles, responsibilities, and obligations will be less challenging for a smaller organization, whether for protecting data or ensuring its consistent use. Small organizations may use a more simplified policy that communicates executive-level expectations to a smaller data management group. For example, a smaller organization will have fewer systems, and those that it has will tend to be more integrated, thereby reducing the challenges associated with defining and ensuring the consistency of data elements. Another advantage for smaller enterprises using integrated systems is that many vendors provide built-in analytic and data management capabilities.

Medium-sized organizations require more significant structure and oversight because data is distributed throughout their systems, divisions, and departments. Typically, a data management policy and program are developed to guide divisions within the organization as to enterprise-wide objectives and expectations. These can provide provisions for data classification (e.g., from a risk perspective) or strategic data classification (e.g., to generate business development ideas). These organizations may also identify data users/stakeholders who are considered the owners of the data and are responsible for establishing rules for data use, which are then implemented by data custodians (typically IT department personnel). The roles and responsibilities of the various parties are also specified.

Large organizations may oversee data through a dedicated governance committee, comprising both user and custodian representatives from throughout the enterprise. This committee establishes roles and responsibilities for ownership, quality, consistency of use, remediation activities, program establishment and monitoring, custodial functions, risk management, and internal audit responsibilities. The committee may also define and monitor performance benchmarks.

Valuing Data as an Asset

Organizations can take steps to measure data’s value and develop a strategy for doing so. Most organizations maintain an asset register for physical assets that includes their location and other information necessary to manage them. Why would a modern organization not have the same inventory listing for data assets? Measuring or valuing data should start with preparing an inventory register of all the organization’s data, including but not limited to the location, and prioritizing the data by how it aligns with the goals of the organization. Not all data stored by the organization is needed, and it should consider discarding data that is not so aligned.

Managing data as an asset starts the process of valuing it as one. Best practices require that the asset comply with global regulatory requirements; achieve the organization’s goal; and meet reliability, accuracy, completeness, validity, and timeliness attributes. Data users in the organization will not use data if its quality does not meet their requirements and jeopardizes their ability to make decisions.

Poor data management practices include an individual department making decisions in its own best interest rather than understanding the impact of the data strategy on business processes and other enterprise-wide activities. Maintaining a “silo” style of data management results in ineffectiveness, including multiple sources of data, tolerance for poor data quality, no trust in data as a source for decision making, no collaboration across departments on data quality and usage, unclear roles and accountabilities over management of data, and overall poor data governance.

A project management methodology is needed to manage an organization’s data assets and begin transforming data into information that the organization can use to increase the data asset’s’ value. Many enterprises do not have adequate data management practices. Agile, a popular project management methodology (Exhibit 1), can be an efficient tool to jumpstart data management initiatives. Agile aligns well with the rapidly changing data environment, which demands quick analysis and adjustments to service delivery processes. Many use this project management tool as an alternative to the more traditional “waterfall” approach, which is a less flexible process for achieving data management goals.

Exhibit 1

Principles and Benefits of Agile

Agile Methodology Principles Work is performed in iterations Each iteration should increase value as learning occurs Focus on collaboration includes business process owners and IT Freedom to fail is considered learning what to do or what not to do Feedback loop from customers increases the opportunity to succeed in the next iteration The focus is on creating immediate value The initiative should align with overall goals of the business The backlog of future development initiatives is prioritized by their importance to those goals How can Agile help create a data-driven organization? Value is created from each initiative Learning occurs rapidly Ability to respond to change quickly Builds momentum for data-driven organization

Using Agile Principles

Usually, a data champion identifies the need for the organization to be more data driven, leading and promoting data asset management initiatives. Goals typically include obtaining support from executive management and the board of directors to realize the potential value of managed data to the company’s mission. Agile principles help convince these parties of the benefits of receiving their support and ownership of the initiatives by expediting impacts to the end user. The Agile approach also helps to reduce costs, maximizes opportunities to succeed, and enables an organization to react quickly to change by ensuring the right work is done at the right time by the right people in the right way.

Data champions can begin using Agile’s principles to gain momentum in the organization and increase the probability of the project’s success. Exhibit 2 outlines the steps in the Agile process. The first step in using Agile’s principles should be creating the data asset register mentioned above, which serves as a listing of all the data used and stored by the business. This register, even if only partially completed, creates immediate value to the organization. The register can now be used to identify the creators and users of each source of data identified, the next step in the process. Any other required tasks noted by the data champion in preparing the register can be placed in a backlog of future developments and prioritized by their importance to the organization.

Exhibit 2

Steps in the Agile Project Management Process

Step; Description; Completion Define core roles; Capabilities matched to role.; Name individuals to roles of champion, team, and stakeholders. Backlog creation; List of what should be developed by team for data-driven initiative.; Features from stakeholders that should be developed and are prioritized by importance to the organization. Iteration planning; Duration of iteration and what features will be developed in each iteration established.; Length of iteration defined, features to be developed established, and backlog updated for features not developed in iteration. Development of iteration; Features from iteration planning step developed; daily face-to-face meetings held by team to discuss barriers to development. Duration of iteration adhered to.; Working features developed that add immediate value to organization. Testing and feature review; Development team tests features developed, noting lessons learned.; Feature review with stakeholders for features developed and feedback loop established. Retrospective and next iteration planning; Lessons learned by team during development and feedback from feature review incorporated into backlog and next iteration planning.; Backlog updated for feedback and prioritized again. Planning for next iteration begins.

One of the benefits of using Agile is working in smaller development times known as iterations, which help to determine what work should be done next. At the end of each iteration, the data champion should share with management, data users, and data creators the value and features that have been created, looking for feedback to learn what work should be performed next and building momentum for the data-driven initiative.

With the prioritized data identified, the data champion can now start developing teams of cross-functional members, based on business processes and including data users, data creators, and IT specialists. This next iteration of the process will establish a baseline data error rate; a sample of data will be taken, and the data users and creators who are familiar with the business process and business objectives of the data will identify errors in the data, specifying error conditions and expectations. The output of this iteration is a baseline measure of a data error rate that can now be used to measure how future iterations affect the error rate. Only a few iterations into the project, the data champion has created value and has also been able to get the business working collaboratively via cross-functional teams.

The next step is to determine how the data can help in meeting organizational objectives based on the organization’s current strategy. Data users should be interviewed to determine how they currently use the data and to solicit suggestions on information they would like to have in future iterations. An understanding should also be obtained of how data users clean up the data once it has been received from data creators; these cleanup issues should be communicated to the data creators to determine whether they can be incorporated into current processes so that data can be used and stored on a centralized basis. Data quality will improve rapidly when data creators and data users communicate and collaborate. The teams will continue to take samples of the data and measure the error rate, comparing it to the benchmark already established to determine the effect iterations are having on quality.

The next iteration will come from the backlog, prioritized by how much it can help the business to achieve its objectives. The backlog is prioritized by importance to the business’s strategy and feedback from the data champion, data users, and data creators from feature reviews at the end of each iteration. As each iteration progresses, the cross-functional teams will be more likely to embrace the data-driven effort now that they are part of it and their feedback is valued. The effort should continue with a mindset of continuous improvement in managing data as an asset, in conformity with the Agile approach and methodology.

The Time for Change Is Now

Most businesses have processes in place to manage physical assets or any other assets that appear on their current financial statements. Because data is an intangible asset that is not recognized as an asset by modern accounting standards, it is often not managed as an asset. There is no good reason not to measure and manage data as the asset it is. Turning data into information is probably going to become the most critical operation of every business in the near future, if it is not already. Organizations must begin managing data and the information derived from it as real assets.

Virginia Collins, CPA/CITP, CFE is a consultant for small businesses in data initiatives and a former quality reviewer for mid-size firms in New York, N.Y.
Joel Lanz, CPA/CGMA/CITP/CFF, CISA, CISM, CISSP, CFE is the founder and principal of Joel Lanz, CPA, P.C., Jericho, N.Y. He is a member of The CPA Journal Editorial Advisory Board.