In 1987, John Zachman published an article in the IBM Systems Journal called A Framework for Information Systems Architecturewhich laid the formalized foundation of Enterprise Architecture. In the 1990s, John Zachman further develop the idea to classify, organize and understand an organization by creating The Zachman Framework™. The Zachman Framework™ talks about understanding an organization in terms of:
Data
Function
Network
People
Time
Motivation
Today, the field of Enterprise Architecture (EA) also draws from the fields of Engineering, Computer Science, Business Administration, Operations Research, Psychology, Sociology, Political Science, Public Administration, and Management. Due to the advancements and inclusion of various fields, the definition of what EA is continues to evolve depending upon if you are a practitioner, academic, vendor or government but the basic premise of Enterprise Architecture is to holistically understand the entire organization to make management decisions.
In addition to The Zachman Framework™, there are many other EA frameworks that have emerged over the years to help an organization understanding where they are (current state or as-is), where they want to be (future state or to-be) and what steps (transitions) they should take to get to the future. Some of these EA frameworks include:
The Open Group Architecture Framework (TOGAF)
Federal Enterprise Architecture Framework (FEAF)
Department of Defense Enterprise Architecture Framework (DoDAF)
To be clear, EA is not only about frameworks but its also about the EA methodology, tools, artifacts, and best practices. As you develop EA within your organization, you will realize that not all frameworks and tools would fit perfectly but it is a continuous improvement over time. Regardless of the size of the organization, EA can help create a holistic thinking mentality, optimize business processes and improve decision-making.
By now you might be thinking that of course, EA is the answer to your woes. But hold on! Before you jump into EA, it is critical to know: 1) The term EA and its jargon can confuse people, 2) EA is about the entire enterprise (aka organization) and not about just certain functions of the organization, 3) People working under the EA function should have a complete grasp of Business operations and IT capabilities, 4) EA is not an IT activity and 5) EA’s purpose is to communicate what is happening and what could happen.
For organizations, EA is like an overarching umbrella which when used effectively can have a profound impact but if used incorrectly can turn into a burden to carry. Keeping these things in mind, let’s ask the following questions:
Today
Tomorrow
Who is demanding the need for EA and who is creating it?
Who should be demanding a need for EA and who should be creating it?
What if EA fails?
What should happen when EA fails?
Where EA is helping in decision-making?
Where EA should help in decision-making?
When EA artifacts are being collected?
When should EA artifacts be collected?
Why EA is being used?
Why EA should be used?
As we can see, whoever sees a need for EA matters, EA champions within various organizational functions matters, EA execution matters, EA measurement matters and EA best practices for organizational-wide improvement matters. It should be noted that all organizations do EA in some way (unformalized, semi-formalized or fully formalized).
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I swear to fulfill, to the best of my ability and judgment, this pledge:
I will respect but also appropriately challenge the hard-won technological gains of those technology professionals in whose steps I walk, and gladly share such knowledge as is mine with that diversity of people who are to follow.
I will apply, for the benefit of organizations and society, all measures which are required, avoiding those twin traps of technology-first and technology-last.
I will remember that there is art in technological implementations in addition to business and technology frameworks, and that warmth, empathy, ethics, and understanding should outweigh the manager’s bias, the analyst’s conclusion, and the system’s algorithms.
I will not be ashamed to say “I know not”, nor will I fail to call in my colleagues when the skills of another one are needed for technological improvements.
I will respect the privacy of my users, for their information is not disclosed to me that the world may know. Most especially I must tread with care in matters of using data to positively or negatively affect decisions. If it is given to me to have a positive impact, all thanks. But it may also be within my power to have a negative impact; this must never happen as I should make every effort to be aware of my own biases while being aware of the big picture. Above all, I must realize although important, technology shouldn’t be the de facto solution to everything.
I will remember that I do not just address a support ticket, a software bug, a cybersecurity threat, a network issue, a system feature, but my actions can directly and indirectly adversely affect people in organizations and societies. My responsibility includes taking into account these interconnected issues and solutions if I am to improve anything.
I will prevent misuse of technology whenever I can, for addressing issues immediately is preferable to delaying it.
I will remember that I remain a member of society, with special obligations to all my fellow human beings, those who understand the technology and those who do not.
If I do not violate this oath, may I enjoy life and art, respected while I live and remembered with inspiration thereafter? May I always act so as to preserve the finest traditions of my calling and may I long experience the joy of helping those who seek my technological expertise.
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In 1956, John McCarthy, the father of Artificial Intelligence (AI), brought together expert thinkers from multiple disciplines to explore how machines could “mimic” certain human traits. These expert thinkers came from the fields of Computer Science, Engineering, Logic, Mathematics, and Psychology and wanted to find out how machines could:
Use language
Form abstractions and concepts
Improve problems reserved for humans
Improve themselves
Today, the field of AI also draws from the fields of Linguistics, Philosophy, Statistics, Economics, and others. Due to the advancements and inclusion of various fields, the definition of what AI is has also evolved. What was once considered AI, is now considered just one of many things a computer system does. In my view, AI is a capability and thus a computer system that can independently solve routine and non-routine problems through self-learning has AI capabilities. These capabilities of a computer system can range from Object Character Recognition (OCR), Natural Language Processing (NLP), Computer Vision, Motion Manipulation (in Robotics) and others.
Under the hood, AI-capable computer systems are a combination of algorithms, data, hardware, and software. When writing algorithms and eventually code for AI, software developers cannot really take into account all the various scenarios a computer system might encounter and what to do in those scenarios. Thus, AI-capable computer systems are coded in a way where they can learn from experience through training by using baseline datasets and then extrapolating them to other scenarios.
However, the problem with creating AI-capable computer systems is that these systems are still highly dependent on the quality of the underlying algorithms and the datasets, both of which can be created/provided by humans. As humans, we are prone to biases in not only creating algorithms but also incomplete data that can create AI-capable computer systems that are biased and would be making incorrect decisions.
For organizations that are looking to improve themselves, AI-capable computer systems can be used to help enhance customer experiences, improve operations and provide insights for making decisions. On the flip side, AI-capable computer systems that have weak algorithms and/or bad data can result in horrible decision-making. Now that we understand what is AI and how it can potentially be used, let’s ask the following questions:
Today
Tomorrow
Who is creating the underlying algorithms and cleaning the data?
Who should be creating the underlying algorithms and cleaning the data?
What happens when AI-capable computer systems make bad decisions?
What should happen when AI-capable computer systems make bad decisions?
Where AI-capable computer systems are relevant for decision-making?
Where should AI-capable computer systems be relevant for decision-making?
When is data being acquired?
When should data be acquired?
Why AI-capable computer systems are being used?
Why AI-capable computer systems should be used?
As we can see, the human factor in AI-capable computer systems is a real threat/opportunity. And while we are far away from creating sentient beings that are capable of general intelligence, right now we do have AI-capable computer systems that can perform narrower tasks better than humans. What this means is that today and in the near future, specific tasks would be given to these AI-capable computer systems rather than humans. Keeping this in mind, organizations and governments are trying to figure out how to address this AI wave and put programs in place when certain jobs would go extinct.
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