Affiliated with:

Data – An Indispensable 21st Century Employee

Data - An Indispensable 21st Century Employee

Every successful organization views data as an indispensable component of operations and decisions.  Data can be seen as an essential employee – more than as an asset

Imagine a company hiring for a new role and providing that employee with both a spacious office and robust work-from-anywhere capabilities. Call him Brent. Brent is involved in every major decision in the company and informs all business conversations and decisions. He is omnipresent, able to be in multiple places at once and understand multiple domains within the organization. Brent can bring those domains together for collaboration and can create something bigger than the sum of their parts. He is already stretched thin, but Brent’s utilization and value within the company increases every day. That is a good thing, though, because whenever Brent goes unused, he winds up costing the company revenue or lost opportunities. Surprisingly, he can be used simultaneously in multiple internal departments and shared with external vendors at the same time. Brent’s colleagues are amazed at his capabilities, which continue to expand as he is used.

Of course, Brent is entirely fictitious. Everything described above is beyond the capacity of any human employee. But these characteristics do belong to an asset every company already has: its data. Data as an asset is not an entirely new concept, and data management is not a new function. In 1999, before the advent of Big Data, Moody & Walsh[i] published seven laws of information as an asset. Fast-forward to 2022 and forward-thinking companies such as DataDiligence[ii] are showing companies that their data is just as valuable as any other item on the balance sheet. Gone are the days when IT was merely a cost center and data was a byproduct of business processes.

But what does data as an asset actually mean? Rather than making abstract definitions, let us compare data with a tangible thing. Think of it in terms of Brent and replace the word asset with employee. If data were personified and made it an employee, it would look a lot like Brent.

Data as an Asset: Four Principles

First, data is infinitely shareable without loss of value. A dataset can be replicated and shared many times over without any degradation in its quality, especially if its metadata is managed appropriately. Data can be made available on-demand across the organization instantaneously. It can be everywhere at once. A human being, however, cannot. 

Second, data is both domain-specific and universal. It is domain-specific because it comes from the specialized knowledge of various subject matter experts (SMEs) in the company and their respective departments, divisions, or domains. It is universal because it can be shared, annotated, augmented, translated, combined, and/or augmented to serve the needs of other domains within the company.

Third, data value increases with use. The massive amounts of data generated every day by any given company would break the bank if it were in physical form and housed on-premises. Why store something so expensive and never use it? Data can go from an asset to a money pit almost instantaneously if it is not used properly. Similarly, why would a company hire someone, train them, provide a major benefits package, and never utilize them? Data requires neither a massive physical storage silo nor a generous benefits package, but it must be used (following good data governance practices) across the organization. It gets stronger with more use and welcomes the opportunity for quality improvement. Human employees might get bored with continuous professional development, but data thrives on quality improvement.

Fourth—and speaking of use—data is not depletable and will wind up costing a company more if not used or managed properly. Brent, for all his effort, is one finite human being. He needs rest, time off, meals, and sleep. He can be overworked and burned out. Data, on the other hand, never sleeps. It needs no time off. It can be used, crunched, extracted, transformed, loaded, queried, traced, reported on, and it is still there, waiting to be used again. Following best practices only makes data more effective.

By thinking of data in the same way as a tangible asset, its value can be understood in easily-grasped terms. This is necessary when appealing to non-technical stakeholders and working towards a cohesive data strategy. Why is this necessary? Look at the four points above. In each case, the data is being used and valued. Doesn’t an employee expect the same thing in a company they are hired into?

For the same reasons companies create a welcoming culture around human resources, a welcoming culture must be ready for data. Brent would not be going above and beyond as described in the first paragraph if his efforts were not appreciated. Similarly, data will not work for the company if the company does not support and value it. The most common mistake is to get lost in the noise of the big data sphere. That noise makes it easy to snap up a sleek new tool and expect it to work from day one. However, that tool is only as good as the data going into it, much like other tools are only as good as the employees operating them. Focusing on tools, and not on best practices, is the quickest way to tell someone (or something) they are not valued.

So how might the relationship be balanced? Companies must start with a reflection of their own data culture. Not just a monolithic “this organization is…” but a deep dive into each department/team within the company and its strengths, weaknesses, priorities, challenges, and perceived analytics maturity. Begin with the question: If data were a new employee in this company, how well would they function in each team? One way to find out is to perform an assessment of the organization’s data culture, asking a cross-section of stakeholders to answer some questions about how the company manages and uses data.  The results can offer the information necessary to make the most of the data assets. Consider the assessment the way to develop a roadmap for appropriately valuing data assets and supporting the improvement of the company’s data culture.


An asset is defined as a valuable thing, person, or quality. Companies can no longer treat data as a byproduct of business processes; rather, they must see data as an asset on par with human resources. Doing so can enable and sustain the right data culture.

[i] D. L. Moody and P. Walsh, “Measuring the Value of Information – An Asset Valuation Approach,” ECIS (1999): 496-512.



Jonathan Fowler

Jonathan Fowler, D.C.Sc. is a practitioner and scholar in big data analytics, data culture, and data strategy. He is specifically interested in the relationship of data subculture archetypes (Collaborate/Compete/Create/Control) to analytics maturity in organizations. Jonathan is the creator of the LDIS+™ framework, which examines this phenomenon and gives companies the roadmap to harnessing it for business success.

As a consultant and instructor, Dr. Fowler has a demonstrated history of working in a variety of environments. His competencies include Big Data, statistical methods, machine learning, database design, data warehousing, ETL, master data management, and data integration.  Jonathan earned bachelor’s and master’s degrees from Clemson University and received his Doctor of Computer Science (D.C.Sc.) from Colorado Technical University.

© Since 1997 to the present – Enterprise Warehousing Solutions, Inc. (EWSolutions). All Rights Reserved

Subscribe To DMU

Be the first to hear about articles, tips, and opportunities for improving your data management career.