Big Sky Thinking

Those reading this blog have noticed a couple of areas of emphasis: decision analysis and identity management. At first blush, these seem quite unrelated. . . . what does an organization's ability to make decisions at all levels have to do with how it manages identities of people and assets?

Just about everything.

Organizational decision making, regardless of scope, includes a few essential ingredients:

1) A decision making process,
2) Data and tools required to make the decision, and
3) Decision makers.

The Burton Group coined a definition of Identity Management that is fairly widely used and includes three pieces:

1) Business processes,
2) Supporting infrastructure, and
3) digital identities.

Let's line these up and see what it looks like:

1) Process. The decision process is one of many processes influenced by effective identity management, and it is probably the most important one.
2) Identity-Based Decision Architecture. An infrastructure and architecture purpose built for effective decision making must include a coherent identity management component.
3) Decision Maker Identities. The identities of decision makers will drive which decisions they are permitted to make, how they make them, and what information to which they must have access.

Organizations may certainly approach identity management and decision analysis separately, but we think that the real power of each of these two important disciplines lies in their confluence.

Identity Management is predicated on the availability of accurate data to determine who a person is, define their relationship to the organization, and what systems, applications, and assets they are entitled to have. Without accurate user information, realizing the benefits of Identity Management is impossible. Big Sky suggests that all identity data should be evaluated and rated against the following 5 criteria:

Element	Question to answer
Accuracy	Are the data correct?
Completeness	Are all the required identity data stored for each user?
Timeliness (i.e. latency)	Are data updates occurring promptly?
Relevance	Have we gathered all the data that is relevant to what we are trying to accomplish? (e.g. roles-based access control)
Availability	Are the needed data being collected as part of our processes?

Looking at your data across these 5 elements provides a straightforward check to ensure that any inconsistencies or omissions are identified. Process inconsistencies/ deficiencies are most often the root cause of data quality issues, which are generally owned by your local HR department. Here's where you need to start thinking about governance for your Identity Management program to get visibility into your organization's HR processes and a handle on data collection.

For more info on Data Quality and IdM check out: DataFlux, EIMBlog, and Sun’s Ten Best Practices for Identity Management Implementation (Data Quality is #9 on their list)

David Maister recently wrote a great post on "How we really make decisions." It centers on Lovaglia's Law: "The more important the outcome of a decision, the more people will resist using evidence to make it."

In reading blogs on decision analytics and decision sciences posts over the last few months, we have noticed that there is a strong emphasis on tactical decisions over strategic decisions. There are far more sites that provide excellent coverage of large-scale decision automation, use of metrics and analytical tools than there are sites that address fundamental, business-model changing decisions. By business-model changing decisions we mean questions like:

-- Which businesses to enter and exit
-- What large-scale strategic investments to make
-- Which customer segments to focus on

The emphasis on decision automation and tactical, volume-based decisions is not a bad thing; these efforts will dramatically improve market efficiencies. However, they don't help much with decisions that David is addressing in his piece: the big decisions.

David's piece gets to why this is the case: there is far less at stake politically and perhaps emotionally in large volume decisions that don't have widespread repercussions. David states in his post, "It sometimes seems as if, for all of us, nearly all the time, rhetoric triumphs over reason, personality over substance, politics over merits, neuroses over facts."

This is certainly the case with many of the clients we have seen when faced with the big choices: of the many programs to fund, which ones should we fund? Which customer segments do we target? On these business model-defining questions, the decisions often are made from the gut. Sometimes this works well (think "Blink"), but it often doesn't. In the government especially, those decisions are easily picked apart by bureaucratic processes or submissions to Congress.

In our view executives must exert discipline through structured decision processes and evidence for strategic decisionmaking. The only way to do this is set the rules of the decision up front, define the data required to make those decisions, and then execute the process according to plan. Furthermore, executives should hold themselves accountable for using evidence in their decisions. In many cases, their gut instinct will be validated. However, in some cases a key mistake could be avoided. In the case of business-model changing decisions, we think it's worth the upfront effort.