Originally published September 11, 2007
First, let me apologize for not posting over the past couple of months. A long-postponed and well-deserved vacation and the associated catch up with projects, clients, and other work have not allowed much time for strategic thinking. It is good to be back, and this month I am thinking about data, especially the idea of data assets.
What are data assets? Entering “data assets” into Google yields over 70 million entries that, with a cursory (and very limited) scan, identify a central theme of securing, integrating, or accessing existing data stores. This makes sense in that data stores are the physical objects that house data, but identifying a data store as a data asset completely misses the point. It isn’t the data store that’s the asset, it’s the data content.
Consider the physical assets of a business and how they are managed. Buildings and the physical objects they contain are assets. Consider the tag on your desk, chair, computer, printer, and pieces of company equipment you use. This tag means that each specific item is known, identified, and tracked. Physical assets are managed through many process steps: identification, classification, valuation, depreciation, maintenance, and replacement or destruction. Using the process in which physical assets are managed by a business as a means to evaluate treatment of data assets is a useful exercise, if only to show how we really don’t manage our data assets at all.
The first step in any asset management program is to identify and inventory all assets. This is the process by which the asset is cataloged and an identification tag is established and placed on the item. This process means that every physical asset can be listed and, through an audit, accounted for.
If only data were treated in the same way. What company has inventoried all its data elements? An inventory of data stores is like an inventory of buildings without any idea of the assets inside them. A data store is not an asset because, unlike a building, it has no intrinsic value of its own other than the disk space it uses. While a buyer might buy an empty building, no one will pay for an empty data store.
An organization that treats data as an asset inventories the data elements that exist in the business.
The process of classification allows the assets to be grouped in meaningful ways. Depreciation rules for furniture, electronic equipment and buildings are different, and are but one way in which physical assets are classified. Company divisions, office locations, and projects are other ways in which physical assets can be classified.
Data also needs several classifications: its domain (customer, product, employee, organization, and so forth); its use (orders, billing, research); its location (data store, data center, computer, etc.); where and to whom it is delivered (report, query, end user); and many others. As with physical assets, classification gives us many ways to assess groupings and individual data assets and their use.
An organization that treats data as an asset knows and uses data classifications of data elements important to the business.
The determination of the value of a physical asset is important for managing risk and reliability for the business. Value helps you decide the level of insurance and security the asset requires, as well as the investment in maintenance that should be made.
Other than disaster recovery assessments, I have never seen a business value its data. Value of data may not necessarily be in dollars, but can be tied to the importance of business processes. What would be the impact of losing customer names and addresses or billing data? The value of data is most easily applied to data classification and is used for managing the risk and reliability of data for the business.
An organization that treats data as an asset identifies the value of its data by the importance of its classification.
In general, physical assets do not last forever; and depreciation, an expense item on an income statement, recognizes that the value of a physical asset can be affected by time. Essentially, depreciation is an accounting activity that apportions asset value over time to reflect its need to be replaced at the end of its useful life.
Similarly, the value of data is affected by time. How long a period must pass since a last contact with a customer before the accuracy of the customer data is uncertain? Or time passed since the last order for a part from a supplier for confidence in its price? This illustrates a loss of value over time and is, in fact, a depreciation of data assets.
An organization that treats data as an asset depreciates each data asset by reflecting its current value as a percentage of known accuracy where 100% means absolute certainty that the data element reflects what is known to be true in the real world.
Physical assets require maintenance over their useful life. This means their current condition needs to be evaluated, risks of failure assessed, and maintenance decisions made. Maintenance is the set of activities that ensure or extend the useful life of a physical asset.
Data asset maintenance consists of data audits for accuracy, consistency, overall correctness and an ongoing data quality program that extends to all data in the business. It is a common practice to apply data quality practices to data warehouses and master data. However, it is important to extend data quality practices to other data stores and application data as well. This makes data consistent and correct wherever it is stored and is a counterpart to standardizing on a computer platform or modular office furniture for the business.
An organization that treats data as an asset maintains all its data to a high level of consistency and correctness across the business.
Replacement or Destruction
Ultimately, a physical asset comes to the end of its useful life. At this point a decision must be made to replace or destroy it, depending upon whether that particular physical asset is still needed for the business.
Business data is rarely destroyed, but I am seeing more data replacement today than ever before. As a way of keeping data from depreciating in value, many organizations, especially in financial services and retail, are replacing their data with more up-to-date information from outside firms. For data assets, the important thing to do is to evaluate the opportunities that will arise from ensuring that data assets are up to date.
This does not mean that data assets are never destroyed. When products die or are no longer supported, business units are sold off, or other business activities make data elements worthless, it is time to destroy data. This comes down to managing data assets closely to maximize their value to the business. Unlike physical assets, where these decisions are made near or at the end of each asset’s physical life, data assets are more dynamic and these decisions should be evaluated regularly.
An organization that treats data as an asset manages its data carefully to maximize its value to the business.
How does your organization treat its data assets? If it doesn’t treat data like it treats a physical asset, it is not maximizing its potential. After all, assets are to be used for the benefit of the business.
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