The previous article on information maturity (DMReview.com August 2002) reviewed several views of the subject. All of these views have a basic philosophical treatment - information must be managed. Organizations that see the need to move along a maturity spectrum usually create an information management function, that is, a formal process for treating information as an asset.
Information as an asset is a simple metaphor. Treating information like an asset is not.
By definition, treatment as an asset requires formal processes and measurement.
One example of how companies are getting closer to formal asset management is the data quality (DQ) area. Due to CRM projects and privacy legislation such as HIPAA and Gramm-Leach-Bliley, information quality is becoming not only nice, but a legal necessity.
Figure 1: Formal Accounting for Information
To further understand the implications of inventory asset management we should "track" a "piece" of information inventory. We can contrast this with a piece of hard inventory, such as an aluminum billet. (I choose a billet because many years ago, aluminum billets created great career angst with me.)
When billet is received, it is tagged and entered into inventory via a receipt transaction. When new data enters an organization (new customer or product), there is also a point of creation. The data would also be "tagged." If the aluminum billet is of lower quality alloy, perhaps it is qualified. If the data is from a source that is less than reputable, perhaps it also is tagged as to its level of reliability. If the billet can be heated and improved, then the data can also be enhanced or changed to improve its usefulness.
Any use of the billet is of course, recorded. It is eventually melted down and made into appliances, doorframes or baseball bats. The data is also used. It is read, updated and enriched. The one and really irrelevant difference is the data isn't used up. Some experts will declare that this aspect of data means that information cannot be tracked along a value chain like other consumerable items. However, this difference is insignificant. The billet is turned into valuable goods. The data is used to create value (business decisions, documents, reports, etc.) as well. The key to asset management of the non- fungible item (i.e., data) is measuring its usage vs. value. Other than that, both accrue value to organization through usage and consumption. I can look at the billet and see the potential value in its usage. The same goes for information and data.
Whew - enough academia there. What immediate value does this have to an IT shop and, more importantly, what steps can an information manager take to start to push towards information asset management.
- Determine what information needs to be tracked - you cannot track it all, no more than you can create a data model that defines every single term used by your business. Examining key data elements that appear in core indicators and metrics does this.
- Initiate a data quality (DQ) assessment to garner immediate recognition and support. Develop some hard dollar examples of where data quality (i.e., data mismanagement) hurts. For example - a data warehouse is in production, but after three years in place, it has only five active users. If the expansion of usage is hamstrung by data issues (a common reason), point out to someone (official) that the five users divided into the support costs of the data warehouse represent a pretty high investment per user. Assessing the key elements identified in step 1 is a great start. Or combine the DQ assessment with your DW reengineering efforts via CRM or other information-intensive project.
- Implement appropriate DQ programs under the information asset umbrella. These can be as simple as beefed up edits in operational application or back- end data enrichment programs.
- Leverage the success from the DQ efforts into a charter - a formal set of principles and enterprise governance that reflect the spirit of information asset management. The key is governance. This step is where it gets sticky. Governance is a polite word for "enforcement of standards." I hope that this point has established enough credibility so governance will fall automatically. That is why I recommend waiting until half way through this process before laying standards and principles out.
- Cross-reference the key element model with business processes. Find the information value chain, or where the key, crucial data is touched and where value accrues to the business through usage of information. (Remember the common theme in this series or articles: if it isn't used in a specific business action; it isn't worth doing.)
- Examine for potential improvement in processes to ensure information tracking. Again, governance enters into play as business process will be altered to accommodate the new spirit of information management.
- Coordinate with applications and project planning to build information tracking and usage tracking into applications over time. This is the second major hurdle after governance. In fact, experience in implementing information management programs has borne out an uncomfortable finding. Application areas in IT are the biggest foe of these programs. Reasons are varied but mostly boil down to fear of being slowed down by abstract procedures.
- Formally measure data quality going into the production of key metrics. Actually, do a DQ scorecard for your balanced scorecard. This is where you need to implement the formal, automated tracking of data.
- Add enterprise context: business processes, and unstructured information to be tracked. This steps folds in the truly rich content that will be required. This step is meta data enrichment.
- Identify information managers in the business ranks. Build information accountability into MBOs.
- Study how to recognize information assets on balance sheets. It will happen soon.
A business user will not take action with information in question. They will create their own. This is information mismanagement. However, society and pervasive technology are forcing organizations into taking some formal stance on their information assets. Therefore, begin to incorporate data quality and formal information management into all projects that create or rely on data to enable business actions and decisions.
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