Decision Support Enterprise Architecture

Print Friendly

 Enterprise Architecture is a framework of decision making through its models/view which are the integral part of EA discipline. The EA models/views should be relevant to the management deicions making needs and thus they should be designed based on the suitable metamodels. These metamodels, in turn need to be properly and continuously maintained. While there exits several methods for the metamodel development and matinetanance, these typically focus on internal metamodel qualities and model engineering processes, rather than on the actual decision making needs and their impact on the metamodels used.

These metamodels, in turn need to be properly and continuously maintained. While there exists several methods for the metamodel development and matinetanance, these typically focus on internal metamodel qualities and model engineering processes, rather than on the actual decision making needs and their impact on the metamodels used.

From previous many years, Enterprise Architecture (EA) has grown into an established approach for management of information systems in enterprises. EA is a model-based in the sense that diagrammatic descriptions of the systems and their environment, constitute the core of the approach. The purpose of EA models/views is to increase the general understanding of an organization’s business along with its information system landscape, the objective is to make corporate decision making easy and responsive.

The backbone of any EA model is its metamodel, the design and structure of this metamodel greatly affects the way organization prepare its decision reports. The metamodel has to be interoperable and traceable, to get the optimum value out of it. There are few popular frameworks which propose a metamodel of their own e.g Defence Framework(DoDAF) and MODAF, while some have focus on development and maintenance processes which can be applicable to many metamodels such as FEA and TOGAF. In any case, regardless of implicit or explicit frameworks, metamodels play an important role in overall EA efforts.

For any EA model to gain success, the metamodel for it must support decision making for the business and IT of the organization. An overly done metamodel will complicate its maintenance and very simplistic metamodel will kill the purpose of EA. A middle approach is required when designing, so that it fits the purpose for the organization. To keep a metamodel align with business needs, the information prescribed by the metamodel must be relevant to management decision. Business expects a tangible value from the EA effort and  business , to some extent is interested in how there organization is operating holistically and in integrated form but what they are mostly interested in is, what decisions they should make to enhance the productivity of the operating structure. Some of the crucial areas where business has their eyes on when decision making is done is as follow:

  • Overall cost reduction of their operation
  • Mitigating future risks both internally and externally to the organization
  • Elimination in complexity of its processes and functions
  • Maximizing  reusability of its assets throughout the organization
  • Reducing overall cost of ownership
  • Smoothly implementation of change management activities
  • Enabling governance over its operations
  • Timely valid decision making

A metamodel to be designed must factor in above business elements and accordingly should be designed to have a good enough level of details which should facilitate decision making. The principals of utilizing captured metamodel data should be based on some decision making theories. There are many theories available which serves for different set of decision making. Depending on the needs of the organization, EA should utilize such theories to ensure it delivers its anticipated value proposition. Some decision theories which are quite useful are as follow:

  1. Evidential Decision theory: is a school of thought with in decision theory, which proposes the best action is what gives you the best outcomes. This applies to strong evidential information on hand based on which the rationality of selecting decision gives the best expected results.
  2. Choice modeling: attempts to model the decision process of an individual or segment in a particular context. The information used in this model is a variations of data which could generate an estimated values for decision making. Usually it’s used when decision to making changes in multiple dimension of an organization.
  3. Operations research: is a discipline that deals with the application of advanced analytical methods to help make better decisions. The results through operations research do give optimal or near optimal solutions for complex decision making problems. It heavily relies on human and technology interactions and the data produced as a result of such interaction.

Using one or combination of above theories and implementing them in EA as a part of its metamodel design, will put EA efforts at the driving seat of organization’s decision making process.

Metamodel Attributes:

Normally organizations when embark EA program, there metamodels stays more closer to the structure of the organization in terms of its people, process, information and technology. The EA views which can be  produced from such structure highlights only how the organization is operating, what links to what and where ultimately the business leads to deliver its services. However to enable EA as decision making support, we can use the same four dimensions as mentioned above and further add attributes which will build the EA capability to offer decisions facilitation.

One thing is required to be understood that chances of getting valid decisions is much higher when the data on hand is captured for both AS-IS and TO-BE architecture. The decision theories earlier mentioned utilize both states to list decisions which are most viable for business. Some of the attributes which would be handy to implement decision making EA is described in the following table:

People

Process

  • Skill set
  • People Capacity
  • People Cost
  • Succession Plans
  • Business Critical Roles
  • Power Delegations
  • Critical People Activities
  • Process classifications
  • Process Integrations
  • Activity Base Costing
  • Cost of Process Execution
  • Process Time
  • Process handoffs
  • Process overlaps

Information

Technology

  • Cost for information maintenance
  • Information storage cost
  • Information accessibility
  • Information Dissemination time
  • Risks related information management
  • Criticality of information usage

 

  • Infrastructure costs
  • Business Applications License costs
  • Network utilization cost
  • IT assets depreciation cost
  • Business Critical Applications Classification
  • Risks Impacts

 

The above attributes are the consolidated view of areas to be considered under each dimension. It necessarily doesn’t mean that all are to be used but the combination of them and based on the requirements of the business, EA should design the metamodel with the necessary enough attributes.

Where are we going from here?

As this article presents a concept of metamodel design for implementing a decision support EA, it also should, make one think about, how such information will be prescribed into the metamodel and how such information will be utilized. I believe that we are getting into a concept of building another layer of analytics which should be the core EA capability to consume such large data and develop intelligent views of the organization which would lead to number of decision choices for the organization to select. Enterprise Architecture fundamentally stands for the principal of decision making framework. If it deviates from this principal then EA is just a structure representing organization in a graphical manner and a showcase for the organization to exhibit, and nothing more.

Author: Ahsan Rauf

Strategic and solutions-focused Enterprise Architect with a strong history of successfully aligning technology strategy with business needs to support organizational growth and improve business agility. Have hands on experience with business transformation programs in Telecom, Internet Services, Supply chain and logistics, public warehousing, core and retail banking and government services. Skilled at balancing resources in complex technology environments and maintaining cross-disciplinary relationships; diligent and resourceful in uncovering solutions that create immediate impact and sustainable improvements. Effectively communicates technically complex ideas to non-technical audiences. Hands-on leadership experience complemented by strong academic background including Master`s Degree in Management Consultancy

Share This Post On

Submit a Comment