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Information Asset Management 19

Table 2-1 Capability-based Information Management Maturity Model.

IMM Stage Description
Initial The organization is entrepreneurial; individuals have authority over data, so information
maturity is chaotic and idiosyncratic. Business rules or criteria for behavior are
nonexistent. Data quality is far from integrated, and data handling is costly.
Repeatable Departmental data becomes the norm. Any sophistication in usagedsuch as
analysisdis departmental, specialized, and costly.
Defined The organization starts to consider an enterprise view, and looks for some sort of
integration across applications and silos. A desire for data accountability evolves.
Strategic alignment to the business becomes an activity in IT. Standards are developed,
and data quality becomes formal and may centralize. Data usage becomes more
common, and efficiency of data management improves.
Managed Data and content assets are tracked, lineage of all content is understood and
documented. Analytical results are used to close process loops. Emails, documents,
and web content are also managed, and can be called up alongside “rows and
columns.” Data quality is built into processes instead of being corrected post facto.
Optimized There is no need to determine if information assets are managed effectivelydthey are
woven into the fabric of the organization. There are effective measures in place to allow
information management to support business innovation. The organization can place
a value statement on its content, if not the balance sheet.

Things Will Change

The reason you are reading this book is that something is amiss with your data. By definition, if
something is wrong, it needs to be fixed. Fixing anything means making a change to ensure that the fix
is never needed again. The bottom line is that data governance is not done with an expectation of
“business as usual” across your business and technology functions. There will be changes. Some of
them will not be well received. Part of deploying DG means managing changes.

INFORMATION ASSET MANAGEMENT

The last concept has been deliberately positioned at the end of this chapter. So far, we have mentioned
that data governance is a key element of managing data assets. We have contrasted DG with infor-
mation management and reviewed the specific solutions that may trigger IM and DG. Now we need to
talk about the asset aspect and frame our concepts within information asset management (IAM). We
have found it better to save the whole “information as an asset” discussion until after the relationship of
data governance to data management is established.
IAM describes a business-based approach to ensure that data, information, and content are all
treated as assets in the true business and accounting sensedavoiding increased risk and cost due to
data and content misuse, poor handling, or exposure to regulatory scrutiny. Please go back and review
that sentence. Applying data governance means treating data as an asset, but not in a metaphorical
sense. We truly mean as a real business asset. You may not see your “information value” on a balance