The new science of decision engineering

The global telecommunications network is arguably the largest, most complex and most reliable technology artifact ever created.  The reason for our success: we excel at coordinating the effort of large numbers of experts to solve difficult, complicated problems. 

Over the past two decades, operators' strategic challenges have shifted from the network to the back office.  Just as the network benefited from significant innovations in network engineering, new concepts in software engineering have been applied to streamline OSS and BSS.

In most telcos today the source of strategic risk and, therefore, opportunity, has again shifted.  It now resides in the ability to make effective decisions quickly, as telcos navigate a turbulent, rapidly evolving competitive environment.  Although network engineering and software engineering for OSS/BSS are still critical, telcos have become like a ship that can move quickly but lacks a navigational system.  Those at the helm are now those most in need of innovation. Steering very high stakes at high speed through enormous complexity has exceeded the limit of what is manageable by today's ad hoc approaches. Consequently, an opportunity exists for the industry to extend traditional engineering principles into the realm of decision making. 

By following decision engineering principles, operators can leverage previously underutilized data sources during decision making, combine the best of both worlds of data and expert judgment, and can achieve better alignment during decision implementation throughout the organization.   
These decisions may be at many levels, from the strategic choice for a new geographic market, down to a tactical decision regarding how much to charge for a new product.

Lost in complexity

As with network and software engineering, the need for a decision engineering discipline arises from complexities that cannot be effectively managed using informal approaches. Complexity may arise from many sources.  First, the sheer number of factors that must be considered in making the decision, and their inter-relationships, can overwhelm the capabilities of a decision maker who relies on informal methods.  Consider setting the parameters for a typical product - a process that must take into account features, pricing, QoS, marketing, competitors, etc.  Not only are there many such factors, but they also affect one another.  Changing pricing, for example, changes the demographics that are attracted to the product, the level of QoS that is possible, and the response of competitors.  These relationships are highly non-linear, rendering simple reasoning about them dangerously ineffective.

Time adds additional complexity.  Factors affecting a decision rarely remain static.  In addition, the criteria used to make the decision, the constraints it must satisfy, and even the outputs of the decision itself will all typically evolve.  So a change control process that can handle both the rate and extent of change, and be able to quickly assess its impact, is essential.

Another source of complexity arises from the data that populates the quantitative elements in the decision model.  Data may be obtained from sources such as an organization's operations, an external vendor or from a study commissioned to guide a decision.  Each data source adds complexity.  The first, in particular, is often problematic for at least two reasons.  Operations data repositories are often both very large and extremely hard to access without significant IT expenditure.  External and empirical data present their own challenges, principally ensuring that the data provided aligns with that needed to inform the decision.

Finally, there is the matter of uncertainty.  Data is often not available, is vague or imprecise, or is of questionable accuracy or reliability.  Clearly understanding the effect of unreliable data as it propagates through the decision process adds yet further complexity.