Sunday, February 26, 2017

The Enterprise Technology Architecture

Post No.1- Setting up an enterprise technology architecture
According to Gartner: “The enterprise technology architecture (ETA) viewpoint defines reusable standards, guidelines, individual parts and configurations that are technology-related (technical domains). ETA defines how these should be reused to provide infrastructure services via technical domains.”

While this might seem as pure technical choices, it is critical to note out it is just one layer of a stack, therefore, any design option should be supported and justified by requirements driven from the other layers: business, data, applications and security.
The primary objectives of developing an enterprise technology architecture are:
  •       To  effectively  remove  hardware  obsolescence  or  vendor  dependency  as  a  requirement
  •       To  periodically  review  the  systems  in  support  of  the  organization’ needs
  •       To  ensure  that  the  rapidly  changing  external  and  environmental  trends  are  enforced  to  significantly change the business and technical environments within organizations

Developing the architectural products of an Enterprise’s ETA is not an easy task. It is a process that usually starts by finding out where the business is headed and how that will affect your technical architecture. Business  strategy  is  the  primary  driver  for  the  development  and  implementation  of  the  technical  architecture. The structure of the organization, the business model and the geographical locations of the offices are some of the very basic inputs that dictate the model of your ETA.

The extracted requirements are used as an input to  develop  the  architectural  products,  which are a  set  of  models  and  views  of  the  organization.  Requirements such as business scalability, Latest technology and environmental trends and competitive advantage are all considered and developed into an architectural context.

Once the architecture is clear, Technical architecture requirements are then derived from the architectural requirements. These technical requirements will guide the engineering   of   the   organization’s   information   systems   and   technology   infrastructure.  Technical domains are then to be defined into Domain-level technical architectures along with the domain-level principles that govern the selection and use of the technologies in each domain including product and configuration standards.

The ETA can be part of a complete EA initiative or not. In both cases its implementation involves a gap analysis exercise, based on which a migration plan is developed and implementation projects are defined.

The process described in this post is taken from “A FRAMEWORK FOR DEVELOPING AND IMPLEMENTING THE ENTERPRISE TECHNICAL ARCHITECTURE” a paper by Tiko Iyamu, Tshwane University of Technology


Post No.2- Measuring the value of the ETA
Once you have an ETA in place, it is wise to define/develop a set of metrics to measure the value of enterprise technical architecture. This will help the ETA program to gain the trust of the executive team and secure the future investments. In addition to this, it will act as a source of input in the technology planning process.

To satisfy Business stakeholders, IT leaders should define Enterprise Technical Architecture Metrics that is mapped to Business Metrics, an example would how a new technology has increased the productivity of the customer services department. This metrics relates well the business people and allow them to see the technology as a business enabler function.

Some of the metrics should be pertaining to technology standards, and how much it was followed in different projects. These metrics will help us measure the compliance in order to ensure that we reap the benefits of lower total costs of ownership and costs to change. Examples of these standards are: The number of designs that are 100% compliant, the number of projects actually checked for compliance (vs. all projects), Time taken to design and the Year-over-year improvement across many projects.

Other metrics can measure the degree and value of reuse. The number of technical patterns reused, the number of components reused and the number of technical serviced reused, are all examples of metrics that shed the light on the value of the ETA.

In general, metrics that demonstrate costs savings should be tracked and reflected to the business. Without such metrics, business will continue to see technology as a cost center and fails to see beyond this.

For more in this topic please read the Gartner article “Develop Enterprise Technical Architecture Metrics to Demonstrate Enterprise Architecture Value” at https://www.gartner.com/document/1330249


Post No.3- Auditing the ETA
ETA is often perceived as a mature field, yet many organizations fail to commit to many aspects of it, in particularly the documentation.

Last semester, I was asked by a colleague to review the ETA of his organization. I listed down some questions that resembles my understanding of what to expect in an ETA.
First and Foremost, I found out that the mission, vision, goals and objectives of the enterprise are not part of the architectural sets. Although some of the architectural decisions were backed with business requirements, not all of the technical requirements could be traced back to business drivers in the documents. I had to ask them for it to get the answer, verbally.

In other areas, they had great documentation. Conceptual, logical and physical models were there. Inter-operability requirements were well demonstrated along with functional and non-functional requirements. In addition to these, various products standards and configuration standards were stated in the documents.

They however lacked in to major areas:
  •        They did not have metrics of any sort
  •       No clear governance on how this ETA is maintained. They should have considered defining how a new technology is introduced and the decision process behind it

Overall, I felt their ETA was so complex and not well acquainted to the business and its future plans.


Sunday, February 12, 2017

Posts in Data Architecture

Post No.1- Major Components of Data Architectures

Now more than ever, organization has started to see data as a strategic asset that can be sold and exchanged. In the near future, the digital revolution with concepts such as the Internet of Things (IoT) will push companies to collect, and analyze vast volumes of data and therefore, architect in a way that adds to its business value.

The enterprise data architecture should be defined and modeled at the three levels of abstractions:
  •      Conceptual level to describe the high level business perspective of the data entities, how they deliver value and how they will be used. At this level data can be seen as Business information conceptual entities whether it is structured data, enterprise contents or taxonomies.
  •      A logical level that describes in much detailed modeling how data is represented and interrelated. At this level Schemas are defined and data are mapped to applications or taxonomies.
  •        A Physical or implementation level modeling that reflects the builder perspective such as Physical data stores and repositories for both structured data and enterprise wide information contents.

Without a proper data architecture, organizations won’t be able to drive strategic change. Therefore, it is very important not to see data architecting as a technical job. Old data models are no longer sufficient to fulfill business demands. Experts suggest the following eight components to go into the building of a modern data architecture:
  •         Engage business users in identifying the most valuable types of data
  •         Make data governance a first priority
  •          Ensure your data architecture is not developed around a specific technology.
  •          Develop a real-time foundation to support analysis and movement
  •          Build security within the foundation
  •          Develop a master data management strategy
  •          Position data as a service to enable information to be pulled from multiple sources.
  •          Offer self-service environments to allow business users to build their own queries.

The link below contains useful information on the topic:

Post No.2- Data Architecture Governance

Data architectures does not involve only designing data models but it includes the rules, policies, and standards that govern how data is used, stored, managed and integrated within an organization.

Gartner defines data governance as “the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archiving and deletion of information. It includes the processes, roles, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals. “

Data governance addresses many pain areas that face organizations today. It can improve the organization ability to deploy data for external use. It can also improve Data error remediation process and thus enhance data quality by addressing root causes of data issues.

A data governance document in an organization typically includes the following sections: Roles & Organizations, Data Strategy, Policies & Standards, Compliance, Issue Management, Projects & Services, Data Asset Valuation and Communication.

The emerging digital business that deal with vast amount of data requires data governance to cover areas such as: Big Data, Mobile Data Platforms, Social media, Data Demand Management and Regulatory Coordination.

Typical roles in Data governance are: Steering Committee, Data Governance Sponsor, Data Governance Head, Data owners and Data Stewards.

Data architects may recommend enabling technologies to support data governance and stewardship workflows such as Data profiling, Data discovery, and Business glossary and data lineage.

For more on this interesting topic, please pay a visit to this link:

Post No.3- Data Architecture and Informational Architecture

I have recently read a blog whose author is among a team that work to clean up the Wikipedia pages dealing with Enterprise Architecture. The author was discussing how his team found out two separate pages in Wikipedia one dedicated for Information architecture and the other for Data Architecture. The author made a simple survey to determine whether they should keep Data Architecture and Informational Architecture as two distinct terms in Wikipedia or as a single term, and in this case, which one should they keep Data Architecture or Informational Architecture.

The results of the 55 respondents was split even. Most of those who claimed that it is one field, favored Information Architecture over Data Architecture.

I went through different articles and blogs over the internet that have different interesting viewpoint on the topic. Some would argue that Information is data within context, therefore data architecture is a subset of Information architecture. Others see Data architecture as the bigger picture whereas how information is modeled as being part of it.

My personal viewpoint is that it depends on the context on which the term is used. In different levels of abstraction for instance the terms can be Data or Information. To Business Intelligence experts working to extract data from its sources it is no doubt data, whereas it is information for those working on Enterprise Content management system for instance. In general, Data architecture represent the technical view and Information architecture represent the business view.

I certainly go with the term data when I am setting architectural principles, stewardship requirements and other governance requirements. However, regardless of any debates on the topic, the most important thing is that Data and Information architects should know how to build their models, methods, standards and governance in support of the business and its drivers.

Here is the link of the blog and the survey results:


See you the in the next blog!