If you mention the term capability maturity model (CMM) to anyone connected with IT, chances are they will understand, or at least be aware, that the CMM represents a relative scale of capability for development and management of software. The entire concept of the CMM was originated at Carnegie Mellon University in the 1980s under the auspices of a U.S Air Force funded contract. Naturally, with the maturing of the data warehouse and the institutionalizing of Corporate Information Factories, there is now discussion related to a maturity spectrum for information management and business intelligence – an information maturity model (IMM). In fact, the CMM model does address this, but only from the standpoint of supporting development and code management, i.e., traditional repository-based management of information.

The key aspect of any discussion around maturity is that IT organizations, at a grass roots level, are beginning to see that there is a predictable curve to information production and usage. What to actually do with this understanding is still elusive, but there is guidance for that issue.

The maturity discussion for business intelligence (BI) and information management (IM) is not related to traditional information engineering (IE) or repository-based functions. A survey of the current thinking reveals it to be more focused on increasing levels of sophistication of information usage.

Perspectives on Maturity

Several vendors within the information management industry have developed messages and practices around a maturity theme. A quick overview of their perspectives sets the foundation for understanding the nature of IMM. All material below is used with the permission of the vendors mentioned.


Teradata, a division of NCR, has used a standard presentation based on maturity for several years. Their message is primarily technical and strongly centered on decreasing latency. However, it offers profound insight into how an enterprise can consider and manage information over time.

Figure 1: Teradata’s Information Evolution in a Data Warehousing Environment

The Teradata perspective represents more sophisticated uses of information driving the latency of the environment. Batch-based reports tend to be historical, while the event-based data warehouse is proactive and possibly mission critical. Teradata offers a set of services and products that are aligned with and support information use at each of these levels.


SAS Institute has also developed a perspective on maturity. Called the IEM, or information evolution model, it takes a holistic organizational view of maturity that extends beyond the technology in place. Each level is characterized in terms of four dimensions: culture, infrastructure, process and people. The core aspect of this model is that an organization performs at a level dictated by the maturity of its dimensions, regardless of the underlying information technology.

Figure 2: Information Evolution Model

For example, a company that is attempting to integrate data can be stymied by a culture that promotes “operational behavior” like individual agendas. In order to progress, a company must mature all four dimensions in synchronization.

Figure 3: The Five Levels of Evolution


Skills combined with charisma win
Power of information mavericks
Information processes and tools are individual and informal
Individual agendas drive competitiveness


Team work within functional units
Streamlined and measured processes
Functional agendas
Multiple versions of the truth
Departments implement applications


Workers think enterprise-wide
Workers understand their impact
Enterprise agenda
Information management formalized
Collaboration among peer group


Constant market alignment
Incremental improvement
Capture tacit knowledge
Focus on edge cases
Infrastructure provides context


Innovative mentality
Diversity of experience
New business models
Risk management
Change is expected

SAS has a set of criteria to assess organizations and assist them in evaluating their current investment in IT for their current maturity level. Note that the emphasis is not necessarily to force an evolution, rather understand where you are and maximize that level of understanding before moving forward.

Hybrid Example

The last example is the one used by my firm. It represents a usage-based view, consistent with the theme of this column. The fundamental precept of this model is “one person’s information is another persons data.” The usage of particular content in a particular context is key. Data and information are both used to create actions and make decisions. Much of the content is structured (rows and columns) and much of it is unstructured (documents, policies, free-form data). A usage-based view emphasizes taking existing and planned information “factories” and ensuring success through an understanding of newly enable business processes.

Figure 4: Hybrid Example

By emphasizing actionable information in this model, a user can determine what must be in place and what needs to happen to evolve their organization to new processes that exploit data and information.

So What?

The relevance of understanding these perspectives comes from applying them to current and planned projects. All of these models imply resolution of basic issues that will be confronted by any organization endeavoring to install any business intelligence system.

Should an organization understand the mix of ad hoc to batch to analytical efforts? Is it relevant to maturity? Is there a reason for an IT organization to push their infrastructure along this evolution? Should all enterprises strive to move “to the right?” Is the concept of maturity relevant to long-term management of information assets?

The key to answering these questions is to go beyond managing information via business intelligence “stuff.” Data and information are assets. This is indisputable. Treating data and information as an asset is rarely, if ever, accomplished. Therefore, a serious treatment if information as an asset must address the current and desired levels of maturity within an organization. After the current and desired states of maturity are known, an enterprise must then develop principles, guidelines and governance that affect all aspects of information technology.


If an organization desires to improve, or create a better information management function, two tactics best serve it. First, use the data warehouse or other BI related initiative as a springboard to point out the need for better management of information. Any Corporate Information Factory, if reasonably successful, has revealed a host of information sins during development. Second, before any modeling, repository selection or data administrator training takes place, carefully examine where your enterprise wants to be in its market, where your IT department needs to be to support that direction and then what level of maturity is required to support that business direction.

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access