This is the first of a series of posts on the topic of improving the Data Management practices of an enterprise so that its data can become an asset.
Before we explain what we mean by the data being an asset for the enterprise, we should back up and define what we mean by ‘data’ in this context, and talk about why any company would want to collect and govern it in the first place. The first question anyone should ask is: Data for what? Not just the how, but the why of improving an enterprise’s data management practices.
Any 21st century organization of sufficient size is creating mountains of data – even though most of it is probably noise – on an hourly basis; even moment-to-moment if they are engaged in high volumes of web-based commerce or communications. Given those vast quantities, the data management question becomes one of determining which pockets of data the organization prioritizes for attention. If the data is used to generate reports for compliance-related or financial needs, those rise to the top. Organizations where customer deliverables are data-dependent will place a close second; after all, these are your raison d’être —the reason your customers come to you in the first place. Finally (for now) there is the analytical data that enable your organization to understand how practical information flows more accurately through the enterprise, how your suppliers and customers make use of it, and where potential bottlenecks occur and where they might be addressed.
One of the spirited discussions among data management professionals is around this very idea that data is an asset. For example, in what way can we think of data as an asset, comparable to the cars that Tesla builds? Companies have traditionally thought of assets as physical objects, either used in producing something, or the products themselves. Yet executives at many organizations today would describe their data as being their most valuable asset.
Organizations of all kinds claim to be “data-driven.” What the Oakland Athletics baseball team pioneered by using sabermetrics, enabling it to build a competitive team within tight constraints on money for player contracts, has become a case study in how data itself can make an organization competitive when it might not otherwise be. Data, when used properly, becomes a valuable stockpile, analogous to the cogs, gears, nuts, and bolts a manufacturer uses to build objects.
One hundred or more years ago, would anyone have ever asserted that data is an asset? Data, after all, is not new. Accountants have long kept financial records for enterprises (at least as far back as double-entry booking, invented in Renaissance Florence). Customer names and addresses have always been needed. What is different about today that thought leaders can regard data as an asset?
Data is both like and unlike other assets
According to the second edition of the DAMA International’s “Data Management Body of Knowledge” (DMBOK2), an asset is “an economic resource, that can be owned or controlled, and that holds or produces value. (DMBOK2, page 20).
In what ways is data like other assets?
To call data an “asset” is to use an analogy. Like physical assets, data is a resource, which may exist in potential (raw data) or in a transformed form (processed data). Organizations collect and transform data, just as they do with raw materials.
There is even the notion, commonly used in data science circles, of “cleaning data.” Some data scientists claim indeed that most of the time they spend working with data is to process it for further use (data “munging”). Data is thereby managed in ways that are meant to improve its viability as an asset, comparable to when the maker of a ring polishes gems and precious metals.
Like other assets, data can withstand the passage of time —assuming that the media on which it is stored endures intact. You can move data, just as you can drive a forklift with containers from one section of a warehouse to another. You can classify data (taxonomies and ontologies), as if you were sorting the components of an Ikea table before assembly.
Like other assets, data can be catalogued, inventoried, and given an owner. You might attach a virtual warning label to a data set, to prevent it from being misused, just as you warn the owner of a ladder not to stand on the top step.
All these examples are meant to help us understand that, while not physical, in these ways data can be considered as if it were physical.
In what ways is data unlike other assets?
Thomas C. Redman asserts, in “Data Driven: Profiting from Your Most Important Business Asset” (Dimensions, 2008), that data is the only asset that is unique to a company, while competitors can copy or “borrow” other assets.
But— you cannot physically touch, move, or put a chunk of data on a shelf in a warehouse. Despite not being tangible, data has traits that make it, in some ways, superior to objects. Data itself does not wear out. Data is easy to transmit from place to place and to copy (no forklifts needed!). More than one person can use the same data simultaneously, even for different purposes.
So, what does it mean to say that data is an asset? In this era of so-called big data, organizations of all sizes recognize that they collect, cultivate, and disseminate data in many ways. But is data really something that can be directly exchanged for money? We know that companies like Meta, Google, and others sell data that they gather. For such companies, data is obviously valuable, but would even these organizations treat their data as an economic resource?
A compromise view
One viewpoint is that data cannot be counted as an asset if revenue cannot be tied directly to it. This view makes sense to me, and I think does not prohibit Twitter from calling its data an asset, but only when that data can be directly tied to revenue. Indeed, it might be reasonable to claim that only data that has been used in a way that brings value to the business is an actual asset. All other data is merely potentially an asset. It is raw material but has not been refined or used yet to create value.
I think that DMBOK2 agrees with this idea. It highlights the notion of a company’s “goodwill” as analogous to treating data as an asset. “…the ‘value of goodwill’ commonly shows up as an item on the Profit and Loss Statement (P&L). Similarly, while not universally adopted, monetization of data is becoming increasingly common. It will not be too long before we see this as a feature of P&Ls.”
What does this mean for how we handle data?
This is where the discipline of data management comes in. To derive the most value from an organization’s data, it must effectively manage the data. Such management includes not only cleaning, transforming, and presenting data in a way that helps the enterprise derive value. It includes also mitigating and managing the risk that may come from improper handling of data.
In the next post of this series, we will discuss the concept of data governance and its importance in furthering the aim of transforming data into an organizational asset.