#️⃣Software Engineering: Mea Culpa (Part IX: A Look Inward)

Software Engineering Series

Paraphrasing a saying attributed to Lao Tzu, "a journey starts with a look inward". Of course, there are also people walking just for the sake of walking, without a purpose or afterthought, though usually when we follow a path we ask ourselves: where we are? what's the destination? how long is the road? how much it takes to reach the intermediary and final goals? what do I need for the road? what can I find on the road? what else do I need to know? who can help me? with whom or what can I travel? does the journey make sense? am I missing something? am I fit for the road and obstacles ahead? what challenges I might meet?

The traveler's metaphor can be easily applied to the professional life as well, especially when one reached a junction or a point on which a decision must be made about the road ahead. Usually, the first step is being aware where one is, and this applies from all points of view: geographical, physical, mental, spiritual, social and even ideological or religious. Of course, not everything is equally important, and it depends on each individual's values, state of mind, situation in life, etc. The more dimensions one considers into the equations of life, the more challenging it is to find a solution, even if not optimal. Even if some things can be ignored, the overall texture can become quite complex, more complex than our linear way of thinking allows us to cope with!

It would be great if one could always choose what's the destination! Unfortunately, the destination is available based on a serios of variables we are constrained to: our capabilities and knowledge, our experience, the work ahead, on what others want to achieve, and this can change from day to day according to the priorities set. Ideally, we should be aware of all the strings attached to us, respectively which strings are pulled and who's pulling the strings, however life is usually at least one step ahead of us! The lower we find ourselves in the texture that decides upon our life, the more challenging it becomes. Ideally, we should have no attachment to things, situations, people or other material things. However, we aren't hermits living in a cave!

Once we set a destination, is important to consider what one needs for the road, respectively for reaching the intermediary steps. That's probably one of the most challenging aspects of all experience. How can I prepare one for the road not taken? The past is probably the only one that can offer a sense of the future, though what if the future doesn't resemble the past in many essential points? One can always make jumps of faith, though faith alone is not enough! Blind faith without knowledge or wisdom can one bring in the opposite or other wrong directions. One can but try to map the territory and establish a plan, however the contact with reality can be harsher than one can imagine. One can but try to adapt, though one must be built for this! Some fundament must be already there!

A good recommendation for the road ahead is to expect the unexpected. Planning is good for preparing the mind, though what one finds on the way might be surprising as well challenging to overcome. Harsh long-distance journeys are not for everybody! Starting small and training the qualities needed for such journeys might be the best advice one can get. Though, the beginning of wisdom takes more steps than this, no matter the road, no matter the experience or one’s aims!

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#️⃣Software Engineering: Mea Culpa (Part VIII: A Look Beyond)

Software Engineering Series

With AI on the verge, blogging and bloggers can easily become obsolete. Why bother navigating through the many blogs to get a broader perspective when the same can be obtained with AI? Just type in a prompt of the type "write a blogpost of 600 words on the importance of AI in society" and Copilot or any other similar AI agent will provide you an answer that may look much better than the first draft of most of the bloggers out there! It doesn't matter whether the text follows a well-articulated idea, a personal perspective or something creative! One gets an acceptable answer with a minimum of effort and that's what matters for many.

The results tend to increase in complexity the more models are assembled together, respectively the more uncontrolled are the experiments. Moreover, solutions that tend to work aren't necessarily optimal. Machines can't offer instant enlightenment or anything close to it. Though they have an incomparable processing power of retrieval, association, aggregation, segregation and/or iteration, which coupled with the vast amount of data, information and knowledge can generate anything in just a matter of seconds. Probably, the only area in which humans can compete with machines is creativity and wisdom, though how many will be able to leverage these at scale? Probably, machines have some characteristics that can be associated with these intrinsic human characteristics, though usually more likely the brute computational power will prevail.

At Microsoft Build, Satya Nadella mentioned that foundry encompasses already more than 1900 supported models. In theory, one can still evaluate and test such models adequately. What will happen when the scale increases with a few orders of magnitude? What will happen when for each person there are one or more personalized AI models? AI can help in many areas by generating and evaluating rapidly many plausible alternatives, though as soon the models deal with some kind of processing randomization, the chances for errors increase exponentially (at least in theory).

It's enough for one or more hallucinations or other unexpected behavior to lead to more unexpected behavior. No matter how well a model was tested, as long as there's no stable predictable mathematical model behind it, the chances for something to go wrong increase with the number of inputs, parameters, uses, or changes of context the model deals with. Unfortunately, all these aspects are seldom documented. It's not like using a formula and you know that given a set of inputs and operations, the result is the same. The evolving nature of such models makes them unpredictable in the long term. Therefore, there must always be a way to observe the changes occurring in models.

One of the important questions is how many errors can we afford in such models? How long it takes until errors impact each other to create effects comparable with a tornado. And what if the tornado increases in magnitude to the degree that it wrecks everything that crosses its path? What if multiple tornadoes join forces? How many tornadoes can destroy a field, a country or a continent? How many or big must be the tornadoes to trigger a warning?

Science-Fiction authors love to create apocalyptic scenarios, and all happens in just a few steps, respectively chapters. In nature, usually it takes many orders of magnitude to generate unpredictable behavior. But, as nature often reveals, unpredictable behavior does happen, probably more often than we expect and wish for. The more we are poking the bear, the higher the chances for something unexpected to happen! Do we really want this? What will be the price we must pay for progress?

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#️⃣Software Engineering: Mea Culpa (Part VII: A Look Forward)

Software Engineering Series

I worked for more than 20 years in various areas related to ERP systems - Data Migrations, Business Intelligence/Analytics, Data Warehousing, Data Management, Project Management, (data) integrations, Quality Assurance, and much more, having experience with IFS IV, Oracle e-Business Suite, MS Dynamics AX 2009 and during the past 3-7 years also with MS Dynamics 365 Finance, SCM & HR (in that order). Much earlier, I started to work with SQL Server (2000-2019), Oracle, and more recently with Azure Synapse and MS Fabric, writing over time more than 800 ad-hoc queries and reports for the various stakeholders, covering all the important areas, respectively many more queries for monitoring the various environments. 

In the areas where I couldn’t acquire experience on the job, I tried to address this by learning in my free time. I did it because I take seriously my profession, and I want to know how (some) things work. I put thus a lot of time into trying to keep actual with what’s happening in the MS Fabric world, from Power BI to KQL, Python, dataflows, SQL databases and much more. These technologies are Microsoft’s bet, though at least from German’s market perspective, all bets are off! Probably, many companies are circumspect or need more time to react to the political and economic impulses, or probably some companies are already in bad shape. 

Unfortunately, the political context has a broad impact on the economy, on what’s happening in the job market right now! However, the two aspects are not the only problem. Between candidates and jobs, the distance seems to grow, a dense wall of opinion being built, multiple layers based on presumptions filtering out voices that (still) matter! Does my experience matter or does it become obsolete like the technologies I used to work with? But I continued to learn, to keep actual… Or do I need to delete everything that reminds the old?

To succeed or at least be hired today one must fit a pattern that frankly doesn’t make sense! Yes, soft skills are important though not all of them are capable of compensating for the lack of technical skills! There seems to be a tendency to exaggerate some of the qualities associated with skills, or better said, of hiding behind big words. Sometimes it feels like a Shakespearian inaccurate adaptation of the stage on which we are merely players.

More likely, this lack of pragmatism will lead to suboptimal constructions that will tend to succumb under their own structure. All the inefficiencies need to be corrected, or somebody (or something) must be able to bear their weight. I saw this too often happening in ERP implementations! Big words don’t compensate for the lack of pragmatism, skills, knowledge, effort or management! For many organizations the answer to nowadays problems is more management, which occasionally might be the right approach, though this is not a universal solution for everything that crosses our path(s).

One of society’s answers to nowadays’ problem seems to be the refuge in AI. So, I wonder – where I’m going now? Jobless, without an acceptable perspective, with AI penetrating the markets and making probably many jobs obsolete. One must adapt, but adapt to what? AI is brainless even if it can mimic intelligence! Probably, it can do more in time to the degree that many more jobs will become obsolete (and I’m wondering what will happen to all those people). 

Conversely, to some trends there will be probably other trends against them, however it’s challenging to depict in clear terms the future yet in making. Society seems to be at a crossroad, more important than mine.

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#️⃣Software Engineering: Mea Culpa (Part VI: A Look Back)

Software Engineering Series

Looking back at my university years, I'd say that there are three teachers, respectively courses that made a considerable impact on students' lives. In the second year I learned Category Algebra, which despite the fact that it reflected past knowledge and the topics were too complex for most of us, it provided us with a unprecedented layer of abstraction that showed us that Mathematics is not what we thought it to be!

The second course was related to the Complex plane theory, a course in which, the decan of the university at those times, challenged our way of thinking about relatively basic concepts. It was a big gap between what we thought about Mathematics, and what the subject proved to be. The course was thought in a post-university year together with a course on Relativity Theory, in which even we haven't understood much about the concepts and theories, it was the first time (except the Graph theory), we saw applied Mathematics to a broader context. Please don't misunderstand me! There were many other valuable teachers and courses, though these were the three courses that made the most important impact for me!

During those times, we attended also courses on Fortran, Pascal, C++, HTML and even dBase, and, even if each programming language brought something new in the landscape, I can't say they changed how we thought about the world (some of us had similar courses during the lyceum years) and problem solving. That's what for example SQL or more generally a database-related course brought, even if I had to wait for the first MooC courses to appear. Equally important was also Scott E Page's course on Model Theory, which introduced the model-thinking approach, a structured way of thinking about models, with applicability to the theoretical and practical aspects of life.

These are the courses that anybody interested in programming and/or IT should attend! Of course, there are also courses on algorithms, optimization, linear and non-linear programming, and they bring an arsenal of concepts and techniques to think about, though, even if they might have a wide impact, I can't compare them with the courses mentioned above. A course should (ideally) change the way we think about the world to make a sensible difference! Same goes for programming and theoretical concepts too!...

Long after I graduated, I found many books and authors that I wished I had met earlier! Quotable Math reflects some of the writings I found useful, though now it seems already too late for those books to make a considerable impact! Conversely, it's never too late to find new ways to look at life, and this is what some books achieve! This is also a way of evaluating critically what we want to read or what is worth reading!

Of course, there are many courses, books or ideas out there, though if they haven't changed the way you think about life, directly or indirectly, are they worth attending, respectively reading? Conversely, if one hasn't found a new perspective brought by a topic, probably one barely scratched the surface of the subject, independently if we talk here about students or teachers. For some topics, it's probably too much to ask, though pragmatically talking, that's the intrinsic value of what we learn! 

That's a way to think about life and select the books worth reading! I know, many love reading for the sake of reading, though the value of a book, theory, story or other similar artifacts should be judged especially by the impact they have on our way of thinking, respectively on our lives. Just a few ideas that's maybe worth reflective upon... 

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#️⃣Software Engineering: Mea Culpa (Part V: All-Knowing Developers are Back in Demand?)

Software Engineering Series

I’ve been reading many job descriptions lately related to my experience and curiously or not I observed that many organizations look for developers with Microsoft Dynamics experience in the CRM, respectively Finance and Operations (F&O) and Business Central (BC) areas. It’s a good sign that the adoption of Microsoft solutions for CRM and ERP increases, especially when one considers the progress made in the BI and AI areas with the introduction of Microsoft Fabric, which gives Microsoft a considerable boost. Conversely, it seems that the "developers are good for everything" syntagma is back, at least from what one reads in job descriptions. 

Of course, it’s useful to have an inhouse developer who can address all the aspects of an implementation, though that’s a lot to ask considering the different non-programming areas that need to be addressed. It’s true that a developer with experience can handle Requirements, Data and Process Management, respectively Data Migrations and Business Intelligence topics, though if one considers that each of the topics can easily become a full-time job before, during and post-project implementations. I’ve been there and I (hopefully) know that the jobs imply. Even if an experienced programmer can easily handle the different aspects, there will be also times when all the topics combined will be too much for a person!

It's not a novelty that job descriptions are treated like Christmas lists, but it’s difficult to differentiate between essential and nonessential skillset. I read many jobs descriptions lately in which among a huge list of demands, one of the requirements is to program in the F&O framework, sign that D365 programmers are in high demand. I worked for many years as programmer and Software Engineer, respectively in the BI area, where SQL and non-SQL code is needed. Even if I can understand the code in F&O, does it make sense to learn now to program in X++ and the whole framework? 

It's never too late to learn new tricks, respectively another programming language and/or framework. It even helps to provide better solutions in usual areas, though frankly I would invest my time in other areas, and AI-related topics like AI prompting or Data Science seem to be more interesting on the long run, especially when they are already in demand!

There seems to be a tendency for Data Science professionals to do everything, building their own solutions, ignoring the experience accumulated respectively the data models built in BI and Data Analytics areas, as if the topics and data models are unrelated! It’s also true that AI-modeling comes with its own requirements in what concerns data modeling (e.g. translating non-numeric to numeric values), though I believe that common ground can be found!

Similarly, the notebook-based programming seems to replicate logic in each solution, which occasionally makes sense, though personally I wouldn’t recommend it as practice! The other day, I was looking at code developed in Python to mimic the joining of tables, when a view with the same could be easier (re)used, maintained, read and probably more efficient, even if different engines will be used. It will be interesting to see how the mix of spaghetti solutions will evolve over time. There are developers already complaining of the number of objects used in the process by building logic for each layer from the medallion architecture! Even if it makes sense from architectural considerations, it will become a nightmare in time.

One can wonder also about nomenclature used – Data Engineer or Prompt Engineering for the simple manipulation of data between structures in data transformations, respectively for structuring the prompts for AI. I believe that engineering involves more than this, no matter the context! 

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🧭Business Intelligence: A Software Engineer's Perspective (Part VII: Think for Yourself!)

Business Intelligence Series

After almost a quarter-century of professional experience the best advice I could give to younger professionals is to "gather information and think for themselves", and with this the reader can close the page and move forward! Anyway, everybody seems to be looking for sudden enlightenment with minimal effort, as if the effort has no meaning in the process!

In whatever endeavor you are caught, it makes sense to do upfront a bit of thinking for yourself - what's the task, or more general the problem, which are the main aspects and interpretations, which are the goals, respectively the objectives, how a solution might look like, respectively how can it be solved, how long it could take, etc. This exercise is important for familiarizing yourself with the problem and creating a skeleton on which you can build further. It can be just vague ideas or something more complex, though no matter the overall depth is important to do some thinking for yourself!

Then, you should do some research to identify how others approached and maybe solved the problem, what were the justifications, assumptions, heuristics, strategies, and other tools used in sense-making and problem solving. When doing research, one should not stop with the first answer and go with it. It makes sense to allocate a fair amount of time for information gathering, structuring the findings in a reusable way (e.g. tables, mind maps or other tools used for knowledge mapping), and looking at the problem from the multiple perspectives derived from them. It's important to gather several perspectives, otherwise the decisions have a high chance of being biased. Just because others preferred a certain approach, it doesn't mean one should follow it, at least not blindly!

The purpose of research is multifold. First, one should try not to reinvent the wheel. I know, it can be fun, and a lot can be learned in the process, though when time is an important commodity, it's important to be pragmatic! Secondly, new information can provide new perspectives - one can learn a lot from other people’s thinking. The pragmatism of problem solvers should be combined, when possible, with the idealism of theories. Thus, one can make connections between ideas that aren't connected at first sight.

Once a good share of facts was gathered, you can review the new information in respect to the previous ones and devise from there several approaches worthy of attack. Once the facts are reviewed, there are probably strong arguments made by others to follow one approach over the others. However, one can show that has reached a maturity when is able to evaluate the information and take a decision based on the respective information, even if the decision is not by far perfect.

One should try to develop a feeling for decision making, even if this seems to be more of a gut-feeling and stressful at times. When possible, one should attempt to collect and/or use data, though collecting data is often a luxury that tends to postpone the decision making, respectively be misused by people just to confirm their biases. Conversely, if there's any important benefit associated with it, one can collect data to validate in time one's decision, though that's a more of a scientist’s approach.

I know that's easier to go with the general opinion and do what others advise, especially when some ideas are popular and/or come from experts, though then would mean to also follow others' mistakes and biases. Occasionally, that can be acceptable, especially when the impact is neglectable, however each decision we are confronted with is an opportunity to learn something, to make a difference! 

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🔖Book Review: Zhamak Dehghani's Data Mesh: Delivering Data-Driven Value at Scale (2021)

	Zhamak Dehghani's "Data Mesh: Delivering Data-Driven Value at Scale" (2021) is a must read book for the data professional. So, here I am, finally managing to read it and give it some thought, even if it will probably take more time and a few more reads for the ideas to grow. Working in the fields of Business Intelligence and Software Engineering for almost a quarter-century, I think I can understand the historical background and the direction of the ideas presented in the book. There are many good ideas but also formulations that make me circumspect about the applicability of some assumptions and requirements considered.  So, after data marts, warehouses, lakes and lakehouses, the data mesh paradigm seems to be the new shiny thing that will bring organizations beyond the inflection point with tipping potential from where organization's growth will have an exponential effect. At least this seems to be the first impression when reading the first chapters.
The book follows to some degree the advocative tone of promoting that "our shiny thing is much better than previous thing", or "how bad the previous architectures or paradigms were and how good the new ones are" (see [2]). Architectures and paradigms evolve with the available technologies and our perception of what is important for businesses. Old and new have their place in the order of things, and the old will continue to exist, at least until the new proves its feasibility.   The definition of the data mash as "a sociotechnical approach to share, access and manage analytical data in complex and large-scale environments - within or across organizations" [1] is too abstract even if it reflects at high level what the concept is about. Compared to other material I read on the topic, the book succeeds in explaining the related concepts as well the goals (called definitions) and benefits (called motivations) associated with the principles behind the data mesh, making the book approachable also by non-professionals.  Built around four principles "data as a product", "domain-oriented ownership", "self-serve data platform" and "federated governance", the data mesh is the paradigm on which data as products are developed; where the products are "the smallest unit of architecture that can be independently deployed and managed", providing by design the information necessary to be discovered, understood, debugged, and audited. It's possible to create Lego-like data products, data contracts and/or manifests that address product's usability characteristics, though unless the latter are generated automatically, put in the context of ERP and other complex systems, everything becomes quite an endeavor that requires time and adequate testing, increasing the overall timeframe until a data product becomes available.  The data mesh describes data products in terms of microservices that structure architectures in terms of a collection of services that are independently deployable and loosely coupled. Asking from data products to behave in this way is probably too hard a constraint, given the complexity and interdependency of the data models behind business processes and their needs. Does all the effort make sense? Is this the "agility" the data mesh solutions are looking for? Many pioneering organizations are still fighting with the concept of data mesh as it proves to be challenging to implement. At a high level everything makes sense, but the way data products are expected to function makes the concept challenging to implement to the full extent. Moreover, as occasionally implied, the data mesh is about scaling data analytics solutions with the size and complexity of organizations. The effort makes sense when the organizations have a certain size and the departments have a certain autonomy, therefore, it might not apply to small to medium businesses. Previous Post <<||>>  Next Post References: [1] Zhamak Dehghani (2021) "Data Mesh: Delivering Data-Driven Value at Scale" (link) [2] SQL-troubles (2024) Zhamak Dehghani's Data Mesh - Monolithic Warehouses and Lakes (link)

🧭Business Intelligence: A Software Engineer's Perspective (Part VI: The Data Citizen)

Business Intelligence Series

More than a century ago, Jerbert G Wells wrote on mathematical literacy: "[...] the time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex world-wide States that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write" [1]. The quote is occasionally misquoted as referring to Statistics, though frankly the boundaries of mathematical, statistical, numerical and data literacy tend to melt into each other, existing multiple dependencies between them.

In the age of big data, data citizens, business people able to use data, data processing and visualization tools for building solutions that enable their job, become steadily a necessity for businesses in their quest of making data-driven decisions, gaining insight and whatever valuable use data might have for the organizations. The need is not new,  Microsoft Access and Excel were used for similar purposes already in the 90s, becoming a maintenance nightmare for IT, data islands without proper backup or documentation existing through the organizations, diverse numbers being reported and contradicting each other. 

Then IT took over, trying to find alternatives for the data islands, implementing concepts like single source(s) of truth, quality gates and supporting processes, designing data models and infrastructures for self-service, allowing users to tap into the data for data exploration, discovery, reporting, etc. Getting all this right required to redesign existing infrastructures, making one step forward and a few steps back, in the end everything is a learning process. Such an effort can easily consume an organization's resources. 

Microsoft and other vendors for data-driven solutions keep insisting on how much potential exist in their tools for the data citizen, how the citizens can bring competitive advantage for organizations, automating business and supporting processes. The potential is not to neglect, though it requires a considerable investment from organizations in training and mentoring data citizens, in building data warehouses or data meshes that focus on end-user self-service needs. The data citizen needs time to learn, to play with the data, build solutions, test their usefulness in the daily tasks, respectively incorporate and disseminate the knowledge gained within the organization. 

There are many scenarios in which results can be obtained with a minimum of effort, however there are also hard limits. Besides the learning effort and the time available, there are cognitive, knowledge and ability limits that vary from person to person. Understanding what good architecture, design and techniques means is unfortunately not for everybody, and here's where the concept of citizen data analyst or citizen scientist breaks, and this independently of the tools used. There are also IT people who have similar challenges. 

It must be also recognized that the solutions built in the early stages by data citizens are primarily personal solutions that need to be reviewed and brought to the standards adopted by the organization. In time, it's expected to reduce considerably such effort by evolving data citizen's knowledge and skillset. Without this further work, the solutions built will tend to display some of the shortcomings of the solutions built on MS Access or Excel. 

The concept of data citizen can work as long the various assumptions and needs are adequately addressed, however progress will not happen overnight. The effort needs to become part of organization's long-term strategy, and the effort can be considerable for many organizations. Mentorship in terms of technical and non-technical support is needed. It's advisable to proceed in small iterative steps and integrate gradually the lessons learned.

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Resources:

[1] "Mankind in the Making", by Herbert G Wells, 1903 [Source]

🧭Business Intelligence: A Software Engineer's Perspective (Part V: From Process Management to Mental Models in Knowledge Gaps)

Business Intelligence Series 

An organization's business processes are probably one of its most important assets because they reflect the business model, philosophy and culture, respectively link the material, financial, decisional, informational and communicational flows across the whole organization with implication in efficiency, productivity, consistency, quality, adaptability, agility, control or governance. A common practice in organizations is to document the business-critical processes and manage them accordingly over their lifetime, making sure that the employees understand and respect them, respectively improve them continuously. 

In what concerns the creation of data artifacts, data without the processual context are often meaningless, no matter how much a data professional knows about data structures/models. Processes allow to delimit the flow and boundaries of data, respectively delimit the essential from non-essential. Moreover, it's the knowledge of processes that allows to reengineer the logic behind systems especially when no proper documentation about the logic is available. 

Therefore, the existence of documented processes allows to bridge the knowledge gaps existing on the factual side, and occasionally also on the technical side. In theory, the processes should provide a complete overview of the procedures, rules, policies and responsibilities existing in the organization, respectively how the business operates. However, even if people tend to understand how the world works locally, when broken down into parts, their understanding is systemically flawed, missing the implications of causal relationships that span time with delays, feedback, variable confusion, chaotic behavior, and/or other characteristics borrowed from the vocabulary of complex systems.  

Jay W Forrester [3], Peter M Senge [1], John D Sterman [2] and several other systems-thinking theoreticians stressed the importance of mental models in making-sense about the world especially in setups that reflect the characteristics of complex systems. Mental models frame our experience about the world in congruent mental constructs that are further used to think, understand and navigate the world. They are however tacit, fuzzy, incomplete, imprecisely stated, inaccurate, evolving simplifications with dual character, enabling on one side, while impeding on the other side cognitive processes like sense-making, learning, thinking or decision-making, limiting the range of action to what is familiar and comfortable. 

On one side one of the primary goals of Data Analytics is to provide new insights, while on the other side the new insights fail to be recognized and put into practice because they conflict with existing mental models, limiting employees to familiar ways of thinking and acting. 

Externalizing and sharing mental models allow besides making assumptions explicit and creating a world view also to strategize, make tests and simulations, respectively make sure that the barriers and further constraints don't impact the decisional process. Sange goes further and advances that mental models, especially at management level, offer a competitive advantage, allowing to maintain coherence and direction, people becoming more perceptive and responsive about environmental or circumstance changes.

The whole process isn't about creating a unique congruent mental model, even if several mental models may converge toward one or more holistic models, but of providing different diverse perspectives and enabling people to make leaps in abstraction (by moving from direct observations to generalizations) while blending advocacy and inquiry to promote collaborative learning. Gradually, people and organizations should recognize a shift from mental models dominated by events to mental models that recognize longer-tern patterns of change and the underlying structures producing those patterns [1].

Probably, for many the concept of mental models seems to be still too abstract, respectively that the effort associated with it is unnecessary, or at least questionable on whether it can make a difference. Conversely, being aware of the positive and negative implications the mental models hold, can makes us explore, even if ad-hoc, the roads they open.

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Resources:
[1] Peter M Senge (1990) The Fifth Discipline: The Art & Practice of The Learning Organization
[2] John D Sterman (2000) "Business Dynamics: Systems thinking and modeling for a complex world"
[3] Jay W Forrester (1971) "Counterintuitive Behaviour of Social Systems", Technology Review

🧭Business Intelligence: A Software Engineer's Perspective (Part IV: The Loom of Interactions)

Business Intelligence Series 

The process of developing or creating a report is quite simple - there's a demand for data, usually a business problem, the user (aka requestor) defines a set of requirements, the data professional writes one or more queries to address the requirements, which are then used to build one or more reports. The report(s) is/are reviewed by the requestor and with this the process should be over in most of the cases. However, this is rather the exception - a long series of changes over multiple iterations are usually necessary, the queries and the reports get modified and even rewritten until they reach the final form, lot of effort being wasted in the process on both sides.

Common practices for improving the process behind resume to assuring that the requirements are complete and understood upfront, that best practices are followed, that the user gets an early review of the work and that there's a continuous communication, that process' performance is monitored, that controls are in place, etc. Standardizing the process helps to reduce the number of iterations, but only by a factor. Unfortunately, the bigger issue - the knowledge gap - is often ignored.

There's lot of literature on problem solving, on what steps to follow, on how to define the problem, what aspects should be considered, etc. Recipes are good when one knows how to follow them, respectively how to cook, and that can be a tedious process. It is said that framing the right problem is half the way to its solving, and that's so true. Part of the bigger issue is that users need data to better understand the problem, however the drives can be different - sometimes is problem's complexity, while other times the need is apparent, only with the first set of data the users start thinking seriously about the problem. 

So, the first major gap is between the problem and user's knowledge about the problem. Experience and theory can help reduce the gap, however the most important progress comes when the user understands the data behind the various processes that overlap with the problem. Sometimes, it's enough to explore the data visually, while other times deeper explorations are needed. Data literacy is important, though more important are the exposure to the data and problems of different variety and complexity, respectively having the time for this. 

The second gap concerns the data professional - building the data model and the logic for the report requires domain knowledge. The level of knowledge depends from case to case, and typically what one doesn't know has the biggest impact. A data professional can help to the degree of the information, respectively knowledge he has about the business. The expectation to provide a report based on a set of fields might be valid for simple requirements, though the more complex a problem, the more domain knowledge is needed. Moreover, the data professional might need to reengineer the logic from the source system, which can prove challenging only by looking at the data.

Ideally, the two parties should work together starting with problem's framing and build common ground while covering the knowledge gaps on both sides. Of course, the user doesn't need to dive into the technical knowledge unless the organization leverages this interaction further by adopting the data citizen mindset. Such interactions can help to build trust, respectively a basis for further collaboration. Conversely, the more isolated the two parties, the higher the chances for more iterations to occur. 

Covering the knowledge gaps might look like a redistribution of the effort, though by keeping the status quo there is little chance for growth!

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🧭Business Intelligence: A Software Engineer's Perspective (Part III: More of a One-Man Show)

Business Intelligence Series 

Probably, in some organizations there are still recounted stories about a hero who knew so much about the business and was technically proficient that he/she was able to provide data-driven answers to most business questions. Unfortunately, the times of solo representations are for long gone - the world moves too fast, there are too many questions looking for an answer, many of them requiring a solution before the problem was actually defined, a whole infrastructure is needed to be able to harness the potential of  technologies and data, the volume of knowledge required grows exponentially, etc. 

One of the approaches of handling the knowledge gap between the initial and required knowledge in solving problems based on data is to build all the required knowledge in one person, either on the business or the technical side. More common is to hire a data analyst and build the knowledge in the respective resource, and the approach has great chances to work until the volume of work exceeds a person's limits. The data analyst is forced to request to have the workload prioritized, which might work in certain occasions, while in others one needs to compromise on quality and/or do overtime, and all the issues deriving from this. 

There are also situations in which the complexity of the problem exceeds a person's ability to handle it, and that's not necessarily a matter of intelligence but of knowhow. Some organizations respond with complexity to complexity, while others are more creative and break the complexity in manageable pieces. In both cases, more resources are needed to cover the knowledge and resource gap. Hiring more data analysts can get the work done though it's not a recipe for success. The more diverse the team, the higher the chances to succeed, though again it's a matter of creativity and of covering the knowledge gaps. Sometimes, it's more productive to use the resources already available in organization, though this can involve other challenges. 

Even if much of the knowledge gets documented, as soon the data analyst leaves the organization a void is created until a similar resource is able to fill it. Organizations can better cope with these challenges if they disseminate the knowledge between data professionals respectively within the business. The more resources are involved the higher the level of retention and higher the chances of reusing the knowledge. However, the more people are involved, the higher the costs, especially the one associated with the waste of effort. 

Organizations can compromise by choosing 1-2 resources from each department to be involved in knowledge dissemination, ideally people with data and technology affinity. They shall become data citizens, people who use  data, data processing and visualization for building solutions that enable their job. Data citizens are expected to act as showmen in their knowledge domain and do their magic whenever such requirements arise.

Having a whole team of data citizens opens new opportunities for organizations, though such resources will need beside domain knowledge and data literacy also technical knowledge. Unfortunately, many people will reach their limitations in this area. Besides the learning effort, understanding what good architecture, design and techniques means is unfortunately not for everybody, and here's where the concept of citizen data analyst or citizen scientist breaks, and this independently of the tools used.

A data citizen's effort works best in data discovery, exploration and visualization scenarios where the rapid creation of prototypes reduces the time from idea to solution. However, the results are personal solutions that need to be validated by a technical person, pieces of the solutions maybe redesigned and moved until enterprise solutions result.

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🧭Business Intelligence: A Software Engineer's Perspective (Part II: Major Knowledge Gaps)

Business Intelligence Series

Solving a problem requires a certain degree of knowledge in the areas affected by the problem, degree that varies exponentially with problem's complexity. This requirement applies to scientific fields with low allowance for errors, as well as to business scenarios where the allowance for errors is in theory more relaxed. Building a report or any other data artifact is closely connected with problem solving as the data artifacts are supposed to model the whole or parts of what is needed for solving the problem(s) in scope.

In general, creating data artifacts requires: (1) domain knowledge - knowledge of the concepts, processes, systems, data, data structures and data flows as available in the organization; (2) technical knowledge - knowledge about the tools, techniques, processes and methodologies used to produce the artifacts; (3) data literacy - critical thinking, the ability to understand and explore the implications of data, respectively communicating data in context; (4) activity management - managing the activities involved. 

At minimum, creating a report may require only narrower subsets from the areas mentioned above, depending on the complexity of the problem and the tasks involved. Ideally, a single person should be knowledgeable enough to handle all this alone, though that's seldom the case. Commonly, two or more parties are involved, though let's consider the two-parties scenario: on one side is the customer who has (in theory) a deep understanding of the domain, respectively on the other side is the data professional who has (in theory) a deep understanding of the technical aspects. Ideally, both parties should be data literates and have some basic knowledge of the other party's domain. 

To attack a business problem that requires one or more data artifacts both parties need to have a common understanding of the problem to be solved, of the requirements, constraints, assumptions, expectations, risks, and other important aspects associated with it. It's critical for the data professional to acquire the domain knowledge required by the problem, otherwise the solution has high chances to deviate from the expectations. The general issue is that there are multiple interactions that are iterative. Firstly, the interactions for building the needed common ground. Secondly, the interaction between the problem and reality. Thirdly, the interaction between the problem and parties’ mental models und understanding about the problem. 

The outcome of these interactions is that the problem and its requirements go through several iterations in which knowledge from the previous iterations are incorporated successively. With each important piece of knowledge gained, it's important to revise and refine the question(s), respectively the problem. If in each iteration there are also programming and further technical activities involved, the effort and costs resulted in the process can explode, while the timeline expands accordingly. 

There are several heuristics that could be devised to address these challenges: (1) build all the required knowledge in one person, either on the business or the technical side; (2) make sure that the parties have the required knowledge for approaching the problems in scope; (3) make sure that the gaps between reality and parties' mental models is minimal; (4) make sure that the requirements are complete and understood before starting the development; (5) adhere to methodologies that accommodate the necessary iterations and endeavor's particularities; (6) make sure that there's a halt condition for regularly reviewing the progress, respectively halting the work; (7) build an organizational culture to support all this. 

The list is open, and the heuristics aren't exclusive, so in theory any combination of them can be considered. Ideally, an organization should reflect all these heuristics in one form or another. The higher the coverage, the more mature the organization is. The question is how organizations with a suboptimal setup can change the status quo?

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🧭Business Intelligence: A Software Engineer's Perspective I (Houston, we have a Problem!)

Business Intelligence Series

One of the critics addressed to the BI/Data Analytics, Data Engineering and even Data Science fields is their resistance to applying Software Engineering (SE) methods in practice. SE can be regarded as the application of sound methods, methodologies, techniques, principles, and practices to obtain high quality economic software in a reproducible manner. At minimum, should be applied SE techniques and practices proven to work, for example the use of best practices, reference technologies, standardized processes for requirements gathering and management, etc. This doesn't mean that one should apply the full extent of SE but consider a minimum that makes sense to adopt.

Unfortunately, the creation of data artifacts (queries, reports, data models, data pipelines, data visualizations, etc.) as process seem to be done after the principle of least action, though least action means here the minimum interaction to push pieces on a board rather than getting the things done. At high level, the process is as follows: get the requirements, build something, present results, get more requirements, do changes, present the results, and the process is repeated ad infinitum.

Given that data artifact's creation finds itself at the intersection of two or more knowledge areas in which knowledge is exchanged in several iterations between the parties involved until a common ground is achieved, this process is totally inefficient from multiple perspectives. First of all, it takes considerably more time than planned to reach a solution, resources being wasted in the process, multiple forms of waste being involved. Secondly, the exchange and retention of knowledge resulting from the process is minimal, mainly on a need by basis. This might look as an efficient approach on the short term, but is inefficient overall.

BI reflects the general issues from SE - most of the issues can be traced back to requirements - if the requirements are incorrect and there's no magic involved in between, then one can't expect for the solution to be correct. The bigger the difference between the initial and final requirements elicited in the process, the more resources are wasted. The more time passes between the start of the development phase and the time a solution is presented to the customer, the longer it takes to build the final solution. Same impact have the time it takes to establish a common ground and other critical factors for success involved in the process.

One can address these issues through better requirements elicitation, rapid prototyping, the use of agile methodologies and similar approaches, though the general feeling is that even if they bring improvements, they don't address the root causes - lack of data literacy skills, lack of knowledge about the business, lack of maturity in planning and executing tasks, the inexistence of well-designed processes and procedures, respectively the lack of an engineering mindset.

These inefficiencies have low impact when building a report occasionally, though they accumulate and tend to create systemic issues in what concerns the overall BI effort. They are addressed locally by experts and in general through a strategic approach like the elaboration of a BI strategy, though organizations seldom pay attention to them. Some organizations consider that they are automatically addressed as part of the data culture, though data culture focuses in general on data literacy and not on the whole set of assumptions mentioned above.

An experienced data professional sees more likely the inefficiencies, tries to address them locally in his interactions with the various stakeholders, he/she can build a business case for addressing them, though it depends on organizations to recognize that they have a problem, respective address the inefficiencies in a strategic and systemic manner!

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🔖Book Review: Laurent Bossavit's The Leprechauns of Software Engineering (2015)

	Software Engineering should be the "establishment and use of sound engineering principles to obtain economically software that is reliable and works on real machines efficiently" [2]. Working for more than 20 years in the field I feel sometimes that its foundation is a strange mix of sound and questionable ideas that take the form of methodologies, principles, standards, myths, folklore, statistics and other similar concepts that form its backbone. I tend to look with critical eyes at the important numbers advanced in research and pseudo-scientific papers especially when they’re related to my job, this because I know that statistics are seldom what they appear to be - there are accidental and sometimes even intended errors made to support the facts. Unfortunately, the missing row data and often the information about the methodologies used in collecting and processing the respective data make numbers and/or graphics' understanding more challenging, not to mention the considerable amount of effort and time spent to uncover the evidence trail.
Fortunately, there are other professionals who went further down the path of bibliographical references and shared their findings in blogs, papers, books and other media content. It’s also the case of Laurent Bossavit, who in his book, "The Leprechauns of Software Engineering" (2015), looks behind some of the numbers that over time become part of the leprechaunish folklore of IT professionals, puts them into the historical context and provides in appendix the evidence trails for the reader to validate his findings. Over several chapters the author focuses mainly on the cost of defects, Boehm’s cone of uncertainty, the differences in productivity amount individual programmers (aka 10x claim), respectively the relation between poor requirements and defects. His most important finding is that the references used in most of the researched sources advancing the above numbers were secondary, while the actual sources provide no direct information of empirical data or the methodology for its collection. The way the numbers are advanced and used makes one question the validity of the measurements performed, respectively the character of the mistakes the authors made. Many of the cited papers hardly match the academic requirements of other scientific fields, being a mix of false claims, improperly conducted research and citations. Secondly, he argues that the small sample sizes used as basis for the experiments, the small population formed usually of students, respectively the way numbers were mixed without any reliable scientific character makes him (and the reader as well) question even more how the experiments were performed in the respective papers. With this, it is more likely that a bigger number of research based on these sources should raise further concerns. The reader can thus ask himself/herself how deep the domino effect goes inside of the Software Engineering field. In author’s opinion Software Engineering as social process "needs to be studied with tools that borrow as much from the social and cognitive sciences as they do from the mathematical theories of computation". How much is possible to extend the theories and models of the respective fields is an open topic. The bottom line, the field of Software Engineering needs better and scientific empirical experiments that are based on commonly agreed definitions, data collection and processing techniques, respectively higher standards for research publications. Without this, we’ll continue to compare apples with peaches and mix them in calculations so we can get some stories that support our leprechaunish theories. Overall, the book is a good read for software engineers as well as for other IT professionals. Even if it barely scratched the surface of software myths and folklore, there’s enough material for the readers who want to dive deeper. Previous Post  <<||>>  Next Post References: [1] Laurent Bossavit (2015) "The Leprechauns of Software Engineering" [2] Friedrich Bauer (1972) "Software Engineering", Information Processing

💼Project Management: Methodologies (Part I: Agile Manifesto Reloaded I - An Introduction)

 

There are so many books written on agile methodologies, each attempting to depict the realities of software development projects. There are many truths considered in them, though they seem to blend in a complex texture in which the writer takes usually the position of a preacher in which the sins of the traditional technologies are contrasted with the agile principles. In extremis everything done in the past seems to be wrong, while the agile methods seem to be a panacea, which is seldom the case.

There are already 20 years since the agile manifesto was published and the methodologies adhering to the respective principles don’t seem to provide the expected success, suffering from the same chronical symptoms of their predecessors - they are poorly understood and implemented, tend to function after hammer’s principle, respectively the software development projects still deliver poor results. Moreover, there are more and more professionals who raise their voice against agile practices.

Frankly, the principles behind the agile manifesto make sense. A project should by definition satisfy stakeholders’ requirements, ideally through regular deliveries that incorporate the needed functionality while gradually seeking to get early feedback from customers, respectively involve the customer through all project’s duration, working together to deliver a feasible product. Moreover, self-organizing teams, face-to-face meetings, constant pace, technical excellence should allow minimizing the waste, respectively maximizing the efficiency in the project. Further aspects like simplicity, good design and architecture should establish a basis for success.

Re-reading the agile manifesto, even if each read pulls from experience more and more pro and cons, the manifesto continues to look like a Christmas wish-list. Even if the represented ideas make sense and satisfy a specific need, they are difficult to achieve in a project’s context and setup. Each wish introduces a constraint that brings with it its own limitations. Unfortunately, each policy introduced by a methodology follows the same pattern, no matter of the methodology considered. Moreover, the wishes cover only a small subset from a project’s texture, are general and let lot of space for interpretation and implementation, though the same can be said about any principles that don’t provide a coherent worldview or a conceptual model.

The software development industry needs a coherent worldview that reflects its assumptions, models, characteristics, laws and challenges. Software Engineering (SE) attempts providing such a worldview though unfortunately is too complex for many and there seem to be a big divide when considered in respect to the worldviews introduced by the various Project Management (PM) methodologies. Studying one or two PM methodologies, learning a few programming languages and even the hand on experience on a few projects won’t fill the gaps in knowledge associated with the SE worldview.

Organizations don’t seem to see the need for professionals of having a formal education in SE. On the other side is expected from employees to have by default some of the skillset required, which is not the case. Besides understanding and implementing a technology there are a set of knowledge areas in which the IT professional must have at least a high-level knowledge if it’s expected from him/her to think critically about the respective areas. Unfortunately, the lack of such knowledge leads sometimes to situations which can impact negatively projects.

Almost each important word from the agile manifesto pulls with it a set of concepts from a SE’ worldview – customer satisfaction, software delivery, working software, requirements management, change management, cooperation, teamwork, trust, motivation, communication, metrics, stakeholders’ management, good design, good architecture, lessons learned, performance management, etc. The manifesto needs to be regarded from a SE’s eyeglasses if one expects value from it.

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𖣯Strategic Management: Strategy Design (Part I: Simple, but not that Simple)

Strategic Management Series

Simplicity of design has been for centuries the wholly grail of architects, while software designers seem to situate themselves in opposition with the trend, as they aim using a mix of technologies that usually increase architecture’s complexity (sometimes the many, the newer and fancier, the better). Unfortunately, despite the implied but not necessarily reachable potential, each component added to an information system or infrastructure has the potential of increasing the overall complexity by a factor proportional to the degree of interactions it creates, respectively by the number of issues it creates or allows to propagate through these interactions.

Conversely, one talks about simplicity in IT without stating what is intended by it, and it can mean many things. Quite often the aim is packed within the ‘keep it simple stupid’ (aka KISS) mantra, a modern and pejorative alternative of Occam’s razor. KISS became a principle in software architecture design, and it can mean that a simple solution works better than a complex one, or that pursuing something in the simplest manner possible is usually better. The nuances are wide enough to cover a wide spectrum of solutions, arriving at statements that the simplest choice to make is the most appropriate one to make, thing that’s not necessarily true in IT, where complexity finds itself home.

Starting with the important number of technologies coexisting in integrations and ending with the exceptions existing in processes or the quality of data, things are almost never as simple as one may wish. An IT infrastructure’s complexity is dependent on the number of existing components, on whether they come from different generations or come from different vendor, on whether are deployed on different operating systems or are supported by different service providers, on the number of customizations made, on the degree of overlapping of the data and integrations needed to keep the data in synch, respectively of the differences existing in data models, quality and use. In general, the more variance, randomness, and challenges one has, the higher the overall complexity.

Paraphrasing Saint Exupéry, in IT simplicity is reached when there is no longer anything to add or anything to take away, or in Hans Hofmann’s words, simplicity is reflected in ‘the ability to simplify means to eliminate the unnecessary so that the necessary may speak’. This refers to the features, what a piece of software can do, respectively the functionality, how a certain outcome is reached, which arrive to be packed in various logical aggregations (function point, functional requirement, story, epic, model, product, etc.) or physical aggregations (classes, components, packages, services, models, etc.). These are the levels at which one needs to address simplicity adequately.

To make something simple one must be able either to design a solution up to the detail that there’s nothing to add or remove, or to start with something and remove or things to reach simplicity. Both approaches involve a considerable effort, time, and multiple iterations, however the first approach can easily become utopian as some architectures are so complex that sooner or later the second approach comes into play. Therefore, one needs in general to focus on what seems an optimal solution and optimize it continuously in further iterations. Aiming for perfection from the beginning or also later in the improvement process is a foolhardy wish.

Even if simplicity is hard to achieve, one can still talk about the elegance of a solution, scenarios in which the various components fit together like the pieces of a puzzle, or about robustness, reliability, correctness, maintainability, (re)usability, or learnability. These latter characteristics are known in Software Engineering as (software) quality attributes.

#️⃣☯Software Engineering: Concept Documents (The Good, the Bad and the Ugly)

A concept document (simply a concept) is a document that describes at high level the set of necessary steps and their implications in order to achieve a desired result, typically making the object of a project. In other words, it describes how something can be done or achieved, respectively how a problem can be solved.

The Good: The main aim of the document is to give all the important aspects and to assure that the idea is worthy of consideration, that the steps considered provide a good basis for further work, respectively to provide a good understanding for the various parties involved, Therefore, concepts are used as a basis for the sign-off, respectively for the implementation of software and hardware solutions.

 A concept provides information about the context, design, architecture, security, usage, purpose and/or objectives of the future solution together with the set of assumptions, constraints and implications. A concept is not necessarily a recipe because it attempts providing a solution for a given problem or situation that needs a solution. Even if it bears many similarities in content and structure a concept it also not a strategy, because the strategy offers an interpretation of the problem, and also not a business case, because the later focuses mainly on the financial aspects.

A concept proves thus to be a good basis for implementing the described solution, being often an important enabler. On the other side, a written concept is not always necessary, even if conceptualization must exist in implementers’ head.

The Bad: From these considerations projects often consider the elaboration of a concept before further work can be attempted. To write such a document is needed to understand the problem/situation and be capable of sketching a solution in which the various steps or components fit together as the pieces of a puzzle. The problem is that the more complex the problem to be solved, the fuzzier the view and understanding of the various pieces becomes, respectively, the more challenging it becomes to fit the pieces together. In certain situations, it becomes almost impossible for a single person to understand and handle all the pieces. Solving the puzzle becomes a collective approach where the complexity is broken in manageable parts in the detriment of other aspects.

Writing a concept is a time-consuming task. The more accuracy and details are needed, the longer it takes to write and review the document, time that’s usually stolen from other project phases, especially when the phases are considered as sequential. It takes about 20% from the total effort needed to write a ‘perfect’ concept for writing a concept that covers only 80% of the facts, while 80% from the effort to consider the remaining 20% of the facts as the later involve multiple iterations. In extremis, aiming for perfection will make one start the implementation late or not start at all. It’s a not understandable pedantry with an important impact on projects'
 timeline and quality in the hope of a quality increase, which is sometimes even illusory.

The Ugly: The concept-based approach is brought to extreme in ERP implementations where for each process or business area is needed to write a concept, which often carries fancy names – solution design document, technical design document, business process document, etc. Independently how it is called, the purpose is to describe how the solution is implemented. The problem is that the conceptualization phase tends to take much longer than planned given the dependencies between the various business area in terms of functionality and activities. The complexity can become overwhelming, with an important impact on project’s budget, time and quality.

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#️⃣Software Engineering: Mea Culpa (Part II: The Beginnings)

Software Engineering Series

I started programming at 14-15 years old with logical schemas made on paper, based mainly on simple mathematical algorithms like solving equations of second degree, finding prime or special numbers, and other simple tricks from the mathematical world available for a student at that age. It was challenging to learn programming based only on schemas, though, looking back, I think it was the best learning basis a programmer could have, because it allowed me thinking logically and it was also a good exercise, as one was forced to validate mentally or on paper the outputs.

Then I moved to learning Basic and later Pascal on old generation Spectrum computers, mainly having a keyboard with 64K memory and an improvised monitor. It felt almost like a holiday when one had the chance to work 45 minutes or so on an IBM computer with just 640K memory. It was also a motivation to stay long after hours to write a few more lines of code. Even if it made no big difference in what concerns the speed, the simple idea of using a more advanced computer was a big deal.

The jump from logical schemas to actual programming was huge, as we moved from static formulas to exploratory methods like the ones of finding the roots of equations of upper degrees by using approximation methods, working with permutations and a few other combinatoric tools, interpolation methods, and so on. Once I got my own 64K Spectrum keyboard, a new world opened, having more time to play with 2- and 3-dimensional figures, location problems and so on. It was probably the time I got most interesting exposure to things not found in the curricula.  

Further on, during the university years I moved to Fortran, back to Pascal and dBASE, and later to C and C++, the focus being further on mathematical and sorting algorithms, working with matrices, and so on. I have to admit that it was a big difference between the students who came from 2-3 hours of Informatics per week (like I did) and the ones coming from lyceums specialized on Informatics, this especially during years in which learning materials were almost inexistent. In the end all went well.

The jumping through so many programming languages, some quite old for the respective times, even if allowed acquiring different perspectives, it felt sometimes like  a waste of time, especially when one was limited to using the campus computers, and that only during lab hours. That was the reality of those times. Fortunately, the university years went faster than they came. Almost one year after graduation, with a little help, some effort and benevolence, I managed to land a job as web developer, jumping from an interlude with Java to ASP, JavaScript, HTML, ColdFusion, ActionScript, SQL, XML and a few other programming languages ‘en vogue’ during the 2000.

Somewhere between graduation and my first job, my life changed when I was able to buy my own PC (a Pentium). It was the best investment I could make, mainly because it allowed me to be independent of what I was doing at work. It allowed me learning the basics of OOP programming based on Visual Basic and occasionally on Visual C++ and C#. Most of the meaningful learning happened after work, from the few books available, full of mistakes and other challenges.

That was my beginning. It is not my intent to brag about how much or how many programming languages I learned - knowledge is anyway relative - but to differentiate between the realities of then and today, as a bridge over time.

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