If you ask a data management professional: “Is data an asset of a company?”  They will undoubtedly confirm it. The next question: “How do you realize it in your daily operations?” may confuse them. Even the DAMA-DMBOK2, the prominent data management guide by DAMA International, says: “Data is widely recognized as an enterprise asset, though understanding of what it means to manage data as an asset is still evolving.”

In this article, we will:

  • Analyze the definition and core activities of asset management
  • Translate these activities into data management concepts

Data as an asset

To understand why data is an asset, let us find out the definition of an asset.

According to the Investopedia: “An asset is a resource with economic value that an individual, corporation, or countryowns or controls with the expectation that it will provide a future benefit.” (Source) The Business dictionary has another opinion. An asset is “Something valuable that an entity owns, benefits from, or has use of, in generating income.” (Source) I have marked the most important words in bold.

From these two definitions, I have derived the following definition: Data as a company asset is a resource with economic value that should deliver benefits.

So, the conclusion is obvious. If a company recognizes data as an asset, the data should deliver value and benefits. We will discuss how to get value from data in the second article of this series. Now it is time to familiarize ourselves with the core activities of asset management.

The core activities of asset management

I have done some research on the Internet and prepared a summary of the core activities of asset management, as shown in Figure 1:

The core activities of asset management.

Figure 1: The core activities of asset management.

  • Inventory

The inventory of assets should answer the following questions:

  • What are the assets?
  • Where are they?
  • What conditions are they in?
  • How much are they worth, and how much do they cost?
  • Funding

The key question is: “Does a company have enough funding to acquire and maintain assets?”

  • Lifecycle

The core concern is the ability of a company to maintain its assets.

  • The level of criticality

The level of criticality identifies the importance for a company that an asset keeps functioning.

  • The level of service

A company should identify the level of service it wants to provide for its stakeholders and customers.

This list of core activities is limited to demonstration purposes within the scope of an article. In real practice, this list can include many more positions. Now, we will translate these activities into data management concepts.

Asset management in the context of data management

In this part of the article, I will demonstrate the links between the core activities of asset management and data management concepts. First, we need to define data and data management as the subject of management.

The definitions of data and data management

First, I want to specify the terms “data” and “information.” By doing that, I want to distinguish data from data repositories like databases, ETL tools, etc.

Data is the physical or electronic representation of signals “in a manner suitable for communication, interpretation, or processing by human beings or by automatic means” (Source).

Information is data in a context that permits the explanation of its meaning and specification of relational connections.

I will use one of the key diagrams of the “Orange” model to illustrate the definition of “data management” using Figure 2.

The role of data management in the business value delivery process.

Figure 2: The role of data management in the business value delivery process.

To survive in a long-term period, a company needs to develop a business value for its customers. The company does it by designing and implementing business value chains. Data management supports the business value chain. The value proposition of data management is information for decision-making. Corresponding raw data should be acquired and processed to deliver the required information. For that, data management designs and explores data and information value chains. Data management gives answers to the following:

  1. What information/data do stakeholders need and why?

Each group of the company’s stakeholders and customers will have different information needs.

  1. Who does what?

The management of data requires the efforts of different stakeholders. Effective management requires the clear distribution of accountabilities.

  1. What does the information and data mean?

The same term can have different meanings across a company. Finance and sales departments could quite differently identify a “customer.”

  1. Where is data located, and which transformations does data undergo?

The answer to this question demonstrates the essence of data management.

  1. What is the quality of your data?

Data quality requirements and data quality checks that are built into the data chain ensure the delivery of information of the required quality.

Now, we can look at how to perform the inventory of data and information.


To answer five questions about data inventory, data management needs several capabilities and tools, as demonstrated in Figure 3:

Tools required for data inventory.

Tools required for data inventory.

  1. What information /data do stakeholders need and why?

Data modeling capability uses “data/information” requirements templates to identify data and information needs.

  1. Who does what?

Data governance capability coordinates data-related activities by identifying data management processes and roles.

  1. What does information and data mean?

Data modeling capability designs business glossaries and data dictionaries.

  1. Where is data located, and which transformations does data undergo?

Data architecture capability models data catalogs and data flows. Data modeling and IT capabilities maintain metadata and business rules repositories. They also document data lineage at the physical level.

  1. What is the quality of your data?

Data quality capability performs tasks related to gathering data quality requirements, data profiling, and documenting business rules. IT capabilities implement data quality checks.


The next core activity of asset management is funding. Data management has the same challenge. Data management is one of the core business functions. Therefore, it requires investments and permanent funding.


Data lifecycle is one of the core concepts of data management. There is a clear link between three different data management concepts, as demonstrated in Figure 4.

The link between three core data management concepts.

Figure 4: The link between three core data management concepts.

The data lifecycle is the set of processes that move and transform data from the moment of its creation to the moment of its archiving and/or destruction. Each company can describe its data lifecycle differently. The decision depends on the business model.

The data (and information value) chain is the physical realization of the data lifecycle. A company has different data chains. Different business value chains will require different data chains.

Data lineage is a model that describes a data chain at different levels of abstraction.

So, as discussed in the previous paragraph, different data management capabilities enable and maintain the data lifecycle. These are data modeling, data architecture, and IT capabilities are examples.


Data management applies the concept of critical data to scope various data management initiatives. Critical data is critical for managing business risks, making business decisions, and successfully operating a business. I elaborated on this concept in my article “Scope your data initiatives by using Critical Data Elements.”

The Service Level

Data management regulates relationships between different stakeholders along data chains using service-level agreements. Such an agreement focuses on the specification of data requirements and data delivery.

We have finalized our brief overview of similarities between asset and data management and can now make some conclusions.


  • The core tasks that asset and data management perform are similar.
  • These similarities confirm the fact that a company can treat and manage data as an asset.

So, if data is an asset, a company should get value from it. The topic of next article will address how companies can generate value from data.

For more insights, visit the Data Crossroads Academy site: //academy.datacrossroads.nl.