I have come to realize that much of what fascinates me about my craft is largely due to my grandparents. I have had a lifelong interest in English language grammar, semantics, and other linguistic elements that was ignited by my grandmother. Grandma was an ex-school teacher who had loved words and their meanings. She taught me invaluable language lessons that I had no idea would serve me so well my entire life, especially in my chosen profession.
I have also had a lifelong interest in logic and logic games that was instilled in me by my grandfather. My grandpa loved puzzles and logic games. When our parents were gone to town or otherwise busy, my grandparents often occupied us by playing cards, puzzle games, and word games. Because I share many of their same interests, I sometimes wonder if the DNA passed down to me by my grandparents is the reason I am interested in complex concepts and feel the need to constantly learn something new.
A person extremely important to my career, as well as a dear friend, is the late Cissy Jordan. She was the woman who started me on my journey in metadata management and modeling. I have never met anyone who was more gifted than Cissy at recognizing raw talent. For me her talents paid off handsomely because based on a gut feeling she had about me combined with her knowledge of how I function personally, she offered me my first job in Data Administration (now called Metadata Management, Data Governance, Master Data Management, Data Quality, Semantic and Logical Data Modeling, etc). She took a chance on me, and I will always be grateful to her for doing so. But most importantly, she was the one who ignited the passion I have for my craft.
One of my all-time favorite people in this industry is John Zachman. Excuse me for name dropping – that is not my point here. Even though I doubt he knows it, John Zachman has been one of my unofficial mentors since I first learned about the Zachman Framework in the late 1980’s. I feel very strongly that the framework holds up as well today as it did when John Zachman first published it. It has helped me understand how all the pieces fit together, and it guides my basic approach for architecting information.
There are many other people who have been instrumental in my career. They are responsible for a lot of the knowledge I have gained about this field and for my continued interest in my craft. However, if I start acknowledging all of them, it would be enough material on its own to start another website.
The computer industry had been in existence for quite some time when I started my career in IT, but Information Management and Information Modeling were in their infancies. When I first started modeling, working with metadata repositories, and of course learning about naming conventions and definition standards, there were basically zero computerized modeling tools, only one or two computerized “data dictionary” tools, and very few standards.
The few so-called modeling tools existing at the time only depicted functions broken down into their components ending up in individual steps or operations. In fact, they were not really modeling tools nor did they produce actual diagrams. They were simply sentences or word phrases structured and reported in a type of hierarchy similar to the structure of an organization chart. The one or two data dictionaries that existed were very limited in their content and capabilities. As for the few standards I could find back then? At best, they were informal. And, few organizations recognized them as being industry standards.
In the very beginning of my career, modeling was not done on a computer - it was done by hand. Data models and flowcharts (workflow models) were created using paper, pencils with very big erasers, and special plastic templates consisting of a handful of shapes relative to the type of model being drawn. We modelers thought our lives would be so much easier when we would have access to computerized modeling tools.
When the first computerized modeling tools came out, the models were very primative and difficult to update. The models had to printed on computer paper and it took hours to tape all the pieces together. Those taped together models also took up large amounts of real estate on the walls where they were hung. This was primarily because the entire model had to be printed each time there was an update to it, modelers did not have a choice as to the placement of any of the objects, the algorithms for placing the objects on the model were primative. As a result, models were still created and managed by hand most the time and the computerized models were primarily updated for show and tell with management.
Because the early data models were primarily physical in nature, data modelers worked very closely with DBAs (Database Administrators or Database Analysts). Most data models were created for flat files, but some depicted the structure of early databases, which were typically referred to as Data Banks. At that time in history, databases were hierarchical, primarily IMS databases. Relational databases were new concepts, not well accepted in the computer industry, and few companies employed them. Relational database management systems didn’t become considered more than just an intellectual exercise until a few years into my career.
Computer organizations' offices were very different early in my career compared to what they are like today. In the office where I worked for my second IT job, the very large computer monitors were located on a couple of long tables in the middle of the floor. Each computer station had a light bulb hanging over it. When one finished using the computer monitor they reached up and turned on the light so others would know the monitor was available for use. A few months later, the IT department moved to a new building. In the new, modern office our desks were organized such that two employees shared one monitor and they took turns accessing the few available computer systems or tools (No, computer systems were not referred to as applications in the 1980s.)
The actual computers the systems ran on were contained in other rooms, often taking up entire floors of the computer building. Some programmers and DBAs worked in "clean rooms", which required them to wear special clothing, hair nets, and walk through air blowers on their way in and out of the doorway.
In order to access the computer systems or tools via the very large monitors, we all had to learn TSO/SPF. Additionally, many of us learned how to write JES3 JCL in order to manage the data dictionary, databases, or modeling tools as well as to produce the limited number of available reports. Customized reports from data dictionaries and modeling tools were basically non-existent at that time. And, any report changes deemed necessary were very expensive because they had to be made by the vendors.
IF we were lucky, once we ordered our reports it only took 2 days for the printouts to arrive in the mail room. Then the printouts had to be collated by one of us in our department, and delivered to whomever ordered the reports. If I remember correctly, the printouts were called green bar printouts. The name came from the fact they were printed on continuous sheets of green lined paper. (Google says green bar printouts are still in use today, but I haven’t seen them in use for decades.)
As I said previously, we had an automated system used to store metadata called a data dictionary and/or data directory (an early version of what we now call an information repository or a metadata repository). Once we switched to a data dictionary that allowed us to have customized reports, we had to write our own programs to get the information back out. So, at that time, I took some in-house classes to learn how to write JES3 JCL, COBOL, and a little bit of Assembler. (I later took college courses in COBOL so I could be an official programmer if I later decided that's what I wanted to do. However, that was a career choice I decided not to pursue.)
In addition to the typical data dictionary reports, I wrote JES3 JCL and Cobol to produce a glossary and an entity affinity analysis report. These types of analysis and reports were very new to our company at the time, as well as to the IT industry, so they were not available for purchase.
In the 1980s most people outside the IT industry as well as many people working in the computer industry believed that computers had limited use in the average business organization. This was especially true of "those small home computers", as I often heard co-workers call them. I remember many people with whom I worked stating that “those IBM and Macintosh home computers or PCs” (now referred to as desktop PCs) were only a passing fad. Most importantly, they said, those “small” computers will never, ever be used by any "real" computer organization, let alone any “real” programmer.
Computers for personal use or in homes (PCs) in the 1980s were quite rare and very expensive. If one did own their own computer at home, all of the associated equipment occupied about half of a home office. In fact, the average person in the 1980s didn’t really know about computers, and even fewer understood their value. People often asked why anyone besides a math professor, a space scientist, or a department of defense or NASA employee would want or need to use a computer. This was because many people thought computers were primarily used to calculate complex numbers and perform accounting related functions, in other words, glorified calculators. The few people at that time outside the computer industry believed that computers would never be used by typical business organizations.
Laptops had not yet been invented when I first started working. At least they were not yet known by most of us let alone being manufactured for use on a mass scale. When laptops did arrive on the market, they were not what I would consider a "lap" top. They were huge, heavy, and very cumbersome to use. Early laptops did not have batteries and they ran off from floppy drives that were literally "floppy" since they did not have hard drives. I found it sort of amusing that the term "floppy drive" stuck even after the drives stopped being floppy.
About 5 years into my IT career the Data Management team at the company where I was employed received a brand-new Lisa by Macintosh. For those who don’t know, that was one of the first Macintosh desktop computers. Almost exclusively, we used the Lisa to create and print presentation foils for use on overhead projectors. (Google it if you don't know what that is.)
The internet in the beginning of my career? It didn’t even exist. Even if it existed in the minds of its creators at that time and in some limited capacity was being used by a few universities, the military, and defense contractors, it was not really the internet - at least not as we know it now. Most certainly, most of the world was not even remotely aware of the internet at that time. I think it was the very late 1980s, or maybe even the early 1990s when I first started using the internet on a regular basis at work. When I first started using the it, and even during the early years after becoming the world wide web and available to the general public, it was unreliable (meaning it was down a LOT), it took quite a bit of time to learn the technical lines of code required to access it, and it was EXTREMELY slow – especially compared to today.
Finally, there was no such thing as a "Google Search" back then. In fact, I’m not sure the man responsible for Google was even born yet. Not only was searching the internet a bit difficult in the beginning, but it was hard to find much useful information. Not only was there little information to find, but it was often out of date and it was rarely trustworthy. Most people considered the vast majority of the information found on the early internet to be inaccurate. At work we were often asked to verify the information obtained from the internet using other research methods, such as going to the library, reading books handed to us, or using other research methods. I am quite well aware the internet still contains quite a bit of outdated and inaccurate information, but at least it also contains a lot of current and accurate information.
Data modeling, functional modeling, metadata management, data administration, and data repositories were extremely new concepts for businesses when I started working, and they were relatively new to the computer industry. I was in my early 20's when I started my computer career, and in those days few people had even heard of modelers or data administrators. I received puzzled looks from most people, including other computer industry associates, when I told them I was a modeler or that I was modeling for work. (Those looks were followed by a variety of comments - most of them not worth repeating. PC, meaning politically correct, was not a concept back then, so most male chauvinists didn't even try to hide the fact that they were one.) Those of us creating model related deliverables were not yet referred to as architects, and our products were not referred to as architectures. (The difference between a modeler and an architect will be discussed at a later time, but basically it's the same as the difference between a drafter and an architect of buildings or houses.)
Because of all the new concepts and technology we were using, every new project as well as most meetings started the same way - regardless of whether they were with end users or with other computer industry associates. Those of us on the Data Administration team had to spend several hours educating the others about metadata, data dictionaries, and modeling. We had to explain the value of doing "all that extra paperwork” and why anyone should “take all that time” to define fields in a database or on a report when programmers and users "already know what they mean".
Time after time we answered questions (which were really statements) managers and others asked about all money being spent on all those "extra" activities when the programmers building the systems "already know what that data is". When we started to explain the value being able to reduce data storage (which was very expensive back then) by using a common database, that also required a common understanding of the data being stored, we frequently heard "Who cares if the same data is stored in multiple computer systems? That’s the way it has always been done."
Usually, the next step in the process was to repeat all those same conversations with each and every one of the meeting attendees’ managers – often all the way up to the General Manager or CEO. Then, and only then, were we able to start the actual work of Data Administration and Modeling. (Hmmm …. now that I think about it, in some respects not all that much has changed in some organizations since I first started in this line of work. Those conversations I just described are pretty darn close to some conversations I have had the past few years!)
Data Modeling that incorporates Business Rules along with some Process Modeling, Workflow Modeling, Metadata Management and Information or metadata repositories have been core to just about everything I have been involved over the course of my career. As I said earlier, I feel the concepts of the Zachman Framework are just as pertinent to my craft in today's world as they were when I first learned about them. I realize those of us in this line of work didn’t always consciously know all of those concepts and skills were involved in our work and the products we produced. Yet, in one form or another, and often informally, they have always been a part of effectively managing information as a resource.
Luckily architecture and metadata repository technology, formalisms, tools, and techniques have advanced in the past few years. Life cycle methodologies have also advanced, but I am not so sure the advancements are really all that advanced or necessary. In my opinion, one of the biggest problems that still remains today is the seamless and effective integration of system life cycle methodologies with appropriate modeling tools and metadata repositories.
Recently I was so grateful to be trained in and able to practice, for a short period of time, the science of ontology modeling using a Semantic Technology formalism called OWL2. Yes, we in this industry have created semantic models, and even ontologies, for many years now, but creating them using Semantic Technology is a whole different animal. Although it’s been around for a while, Semantic Technology is relatively new to the Information and Knowledge Management industries.
In my opinion, creating and managing ontologies using Semantic Technology or ontology modeling formalisms and languages, such as OWL (The Web Ontology Language) and other RDF (Resource Description Framework) systems, are the future of data modeling combined with business rule modeling. Semantic Technology will most certainly impact the future of metadata management tools. I believe that Semantic Technology or ontology modeling formalisms and languages, are the most important advances that have come about in the computer and information/knowledge management industries since modeling was first invented.
I also feel that Artificial Intelligence will be an important part of many, if not all, future IT and Information Management systems. Artificial Intelligence has been of great interest to me for many years now. Yet, I have barely scratched the surface of how my craft supports and works with that technology. I am sure that Semantic Technology and/or its predecessor, the art and science of modeling and metadata management, and long existing English Language grammar and linguistic rules are all important components in the future of Information and Knowledge management as well as the computer industry.
When I first started in this line of work, I had no idea what I was getting into nor how much of my life would revolve around my work and my craft. I started out being intrigued in all the topics associated with my craft, and quickly became fascinated. It has also caused me many sleepless nights, and at times, the desire to pull my hair out (not literally). If you had told me the first 5 years of my career I'd be having the exact same conversations over 30 years later, I would probably had said you were nuts. I assumed my line of work would become routine in all IT shops by the mid-1990s. (Yes, I know .... but I was quite young when I began learning my craft.) But regardless of the mental anguish often involved in my line of work, I am very grateful for the opportunities and experiences I have had. And my craft has become one of my lifelong passions, especially because it is so intertwined with the way we as humans both think and communicate.
Some of the concepts and principles I have learned I believe will stay relevant for a long time to come because they are independent of tools and techniques. Applying basic logic and English Language (or other language) semantic, grammar, and other linguistic rules to models, especially data models, is high on this list. In fact, my craft is primarily independent of any physical implementation technology. Over the years I have often told the students in my modeling classes that modeling, especially semantic and logical data modeling, is not just a way of preparing information to be automated, it's an analysis technique. Most importantly, it's a way of thinking.
The experiences I have had learning about Artificial Intelligence, learning about and practicing Semantic Technology, and my recent venture into designing and managing small websites, have only cemented my beliefs. I firmly believe in the importance of Information or Knowledge Architectures, especially at the enterprise level. Semantic Technology, as I said before, is in my opinion one of the most important developments in the world of modeling. It's a major step in taking us where we need to be for the next few decades. I also believe that that combining Semantic Technology with bonafide model driven application development tools allows the full realization of all the benefits of modeling we architects have promised all these years.
There are relatively few times I wish I had been born later in time. But, I especially wish I were going to be around and viable on this earth a few more decades when I think of the future of Semantic Technology and Artificial Intelligence combined with Robotics. I truly wish I could be around to see how all of these technologies take us into the future, especially how they take us into space and to other worlds - literally and figuratively.
While I am grateful for the gift of knowledge and the opportunities I have had up to this point in my life, I look forward to the future of my craft and my career. In addition to continuing my work in metadata management and information architectures, I plan to continue my education and experience in Semantic Technology and Artificial Intelligence. Right now I am now using my existing knowledge and skills, as well as any newly acquired skills, to design and manage websites for small companies or organizations.
As far as this website is concerned, I started with a few simple goals: to share my knowledge and associated standards, to continue practicing my craft, to practice my craft in a new medium, and most importantly, to keep learning.
As I was writing this page for the website and looked back on what I had written, I realized I am starting to sound quite a bit like my grandparents when they talked about the homesteading and the depression. My grandparents used to amuse all of us grandchildren with stories of what it was like when they were young.
When we complained about the long bus rides to and from school, they reminded us that as children they had to take a horse and buggy to school along with enough hay to feed the horse for the day. In fact, they reminded us, most travel during that time was done by horse and buggy since only the very rich owned motorized vehicles. When we complained about having to spend the summers on tractors farming and riding motorcycles to go "change pipe" (for irrigation), they recounted how rough it was to homestead in Idaho, how in the early days most farm work was done by a man behind a horse and plow (followed by tractors without cabs), and how grandpa and other men spent many years hand digging irrigation ditches.
Grandma loved technology and was often in awe of how far it had advanced the world since she was born. She experienced the birth of cell phones and their early evolution. Almost from the start cell pones were used by many farmers where I am from in southeastern Idaho, but in the beginning my grandma didn't think they would be used for anything other than work or urgent situations as they were so expensive. TVs were not invented until shortly before my parents were born, but my grandmother never understood why anyone would use them to occupy their children, or as babysitters. In fact, she often told us, when my parents were young the concept of babysitters didn’t even exist - kids just went everywhere their parents went.
I’m sure my grandparents would never have guessed a few decades after homesteading and digging ditches by hand there would be huge automatic sprinklers watering the crops that would send text messages to my Dad’s mobile phone when they ran off line or had other problems. Or that eventually those cordless cell phones would become smartphones with color monitors more advanced than any TV my parents had when I was a kid, with more computing power than any of the computers I first worked on in this industry, or small enough to fit in a person’s pocket or on their wrist - just like the wrist phones worn by Maxwell Smart and Agent 99 on Get Smart. I also can’t help but chuckle when I think of what my grandparents would have said if they had known just a few decades after homesteading there would be multi-billions of dollars spent every year not just for childcare and babysitting, but also for pet sitting.
I now understand that everything my grandparents said was very true, but as kids we thought they were exaggerating. As I grew older and became more knowledgeable and experienced, I realized they probably only told us the highlights of how tough it was back then, especially on a farm. I can't imagine traveling by horse and buggy all the time, and essentially being alone during long, tough winters. In fact, I’m not sure I could have survived homesteading back then. And, I know I would have done anything to avoid digging ditches by hand.
Yet, there are many parallels between my grandparents experiences and mine when it comes to our work and the equipment used for that work. When I look back on the beginning of my career, I feel like modeling and managing metadata in the 1980s for IT professionals is similar to what homesteading and ditch digging was like in the early 1900s for farmers and ranchers. Yes, I know, in some respects they are not comparable because we early modelers and data managers did not put our lives and well-being at risk like farmers and ranchers did when they moved out west to homestead. But, the advancement of computer and information management technology and equipment from the time I started working in IT until today is very much comparable to the advancement of farming technology and equipment from the time my grandparents homesteaded until they retired (which for a farmer/rancher is very late in life - if ever).
But, I digress …