#️⃣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 other 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 in the long term, 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: Mea Culpa (Part IV: Generalist or Specialist in an AI Era?)

Business Intelligence Series

Except the early professional years when I did mainly programming for web or desktop applications in the context of n-tier architectures, over the past 20 years my professional life was a mix between BI, Data Analytics, Data Warehousing, Data Migrations and other topics (ERP implementations and support, Project Management, IT Service Management, IT, Data and Applications Management), though the BI topics covered probably on average at least 60% of my time, either as internal or external consultant. 

I can consider myself thus a generalist who had the chance to cover most of the important aspects of a business from an IT perspective, and it was thus a great experience, at least until now! It’s a great opportunity to have the chance to look at problems, solutions, processes and the various challenges and opportunities from different perspectives. Technical people should have this opportunity directly in their jobs through the communication occurring in projects or IT services, though that’s more of a wish! Unfortunately, the dialogue between IT and business occurs almost only over the tickets and documents, which might be transparent but isn’t necessarily effective or efficient! 

Does working only part time in an area make one person less experienced or knowledgeable than other people? In theory, a full-time employee should get more exposure in depth and/or breadth, but that’s relative! It depends on the challenges one faces, the variation of the tasks, the implemented solutions, their depth and other technical and nontechnical factors like training, one’s experience in working with the various tools, the variety of the tasks and problem faced, professionalism, etc. A richer exposure can but not necessarily involve more technical and nontechnical knowledge, and this shouldn’t be taken as given! There’s no right or wrong answer even if people tend to take sides and argue over details.

Independently of job's effective time, one is forced to use his/her time to keep current with technologies or extend one’s horizon. In IT, a professional seldom can rely on what is learned on the job. Fortunately, nowadays one has more and more ways of learning, while the challenge shifts toward what to ignore, respectively better management of one’s time while learning. The topics increase in complexity and with this blogging becomes even more difficult, especially when one competes with AI content!

Talking about IT, it will be interesting to see how much AI can help or replace some of the professions or professionals. Anyway, some jobs will become obsolete or shift the focus to prompt engineering and technical reviews. AI still needs explicit descriptions of how to address tasks, at least until it learns to create and use better recipes for problem definition and solving. The bottom line, AI and its use can’t be ignored, and it can and should be used also in learning new things. It’s amazing what one can do nowadays with prompt engineering! 

Another aspect on which AI can help is to tailor the content to one’s needs. A high percentage in the learning process is spent on fishing in a sea of information for content that is worth knowing, respectively for a solution to one’s needs. AI must be able to address also some of the context without prompters being forced to give information explicitly!

AI opens many doors but can close many others. How much of one’s experience will remain relevant over the next years? Will AI have more success in addressing some of the challenges existing in people’s understanding or people will just trust AI blindly? Anyway, somebody must be smarter than AI, and here people’s collective intelligence probably can prove to be a real match. 

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🗄️Data Management: Data Governance (Part II: Heroes Die Young)

Data Management Series

In the call for action there are tendencies in some organizations to idealize and overcharge main actors' purpose and image when talking about data governance by calling them heroes. Heroes are those people who fight for a goal they believe in with all their being and occasionally they pay the supreme tribute. Of course, the image of heroes is idealized and many other aspects are ignored, though such images sell ideas and ideals. Organizations might need heroes and heroic deeds to change the status quo, but the heroism doesn't necessarily payoff for the "heroes"! 

Sometimes, organizations need a considerable effort to change the status quo. It can be people's resistance to new, to the demands, to the ideas propagated, especially when they are not clearly explained and executed. It can be the incommensurable distance between the "AS IS" and the "TO BE" perspectives, especially when clear paths aren't in sight. It can be the lack of resources (e.g., time, money, people, tools), knowledge, understanding or skillset that makes the effort difficult. 

Unfortunately, such initiatives favor action over adequate strategies, planning and understanding of the overall context. The call do to something creates waves of actions and reactions which in the organizational context can lead to storms and even extreme behavior that ranges from resistance to the new to heroic deeds. Finding a few messages that support the call for action can help, though they can't replace the various critical for success factors.

Leading organizations on a new path requires a well-defined realistic strategy, respectively adequate tactical and operational planning that reflects organizations' specific needs, knowledge and capabilities. Just demanding from people to do their best is not enough, and heroism has chances to appear especially in this context. Unfortunately, the whole weight falls on the shoulders of the people chosen as actors in the fight. Ideally, it should be possible to spread the whole weight on a broader basis which should be considered the foundation for the new. 

The "heroes" metaphor is idealized and the negative outcome probably exaggerated, though extreme situations do occur in organizations when decisions, planning, execution and expectations are far from ideal. Ideal situations are met only in books and less in practice!

The management demands and the people execute, much like in the army, though by contrast people need to understand the reasoning behind what they are doing. Proper execution requires skillset, understanding, training, support, tools and the right resources for the right job. Just relying on people's professionalism and effort is not enough and is suboptimal, but this is what many organizations seem to do!

Organizations tend to respond to the various barriers or challenges with more resources or pressure instead of analyzing and depicting the situation adequately, and eventually change the strategy, tactics or operations accordingly. It's also difficult to do this as long an organization doesn't have the capabilities and practices of self-check, self-introspection, self-reflection, etc. Even if it sounds a bit exaggerated, an organization must know itself to overcome the various challenges. Regular meetings, KPIs and other metrics give the illusion of control when self-control is needed. 

Things don't have to be that complex even if managing data governance is a complex endeavor. Small or midsized organizations are in theory more capable to handle complexity because they can be more agile, have a robust structure and the flow of information and knowledge has less barriers, respectively a shorter distance to overcome, at least in theory. One can probably appeal to the laws and characteristics of networks to understand more about the deeper implications, of how solutions can be implemented in more complex setups.

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🗄️Data Management: Data Culture (Part V: Quid nunc? [What now?])

Data Management Series

Despite the detailed planning, the concentrated and well-directed effort with which the various aspects of data culture are addressed, things don't necessarily turn into what we want them to be. There's seldom only one cause but a mix of various factors that create a network of cause and effect relationships that tend to diminish or increase the effect of certain events or decisions, and it can be just a butterfly's flutter that stirs a set of chained reactions. The butterfly effect is usually an exaggeration until the proper conditions for the chaotic behavior appear!

The butterfly effect is made possible by the exponential divergence of two paths. Conversely, success needs probably multiple trajectories to converge toward a final point or intermediary points or areas from which things move on the "right" path. Success doesn't necessarily mean reaching a point but reaching a favorable zone for future behavior to follow a positive trend. For example, a sink or a cone-like structure allow water to accumulate and flow toward an area. A similar structure is needed for success to converge, and the structure results from what is built in the process. 

Data culture needs a similar structure for the various points of interest to converge. Things don't happen by themselves unless the force of the overall structure is so strong that allows things to move toward the intended path(s). Even then the paths can be far from optimal, but they can be favorable. Probably, that's what the general effort must do - bring the various aspects in the zone for allowing things to unfold. It might still be a long road, though the basis is there!

A consequence of this metaphor is that one must identify the important aspects, respectively factors that influence an organization's culture and drive them in the right direction(s) – the paths that converge toward the defined goal(s). (Depending on the area of focus one can consider that there are successions of more refined goals.)

The structure that allows things to converge is based on the alignment of the various paths and implicitly forces. Misalignment can make a force move in other direction with all the consequences deriving from this behavior. If its force is weak, probably will not have an impact over the overall structure, though that's relative and can change in time. 

One may ask for what's needed all this construct, even if it doesn’t reflect the reality. Sometimes, even a not entirely correct model can allow us to navigate the unknown. Model's intent is to depict what's needed for a initiative to be successful. Moreover, success doesn’t mean to shoot bulls eye but to be first in the zone until one's skillset enables performance.

Conversely, it's important to understand that things don't happen by themselves. At least this seems to be the feeling some initiatives let. One needs to build and pull the whole structure in the right direction and the alignment of the various forces can reduce the overall effort and increase the chances for success. Attempting to build something just because it’s written in documentation without understanding the whole picture (or something close to it) can easily lead to failure.

This doesn’t mean that all attempts that don’t follow a set of patterns are doomed to failure, but that the road will be more challenging and will probably take longer. Conversely, maybe these deviations from the optimal paths are what an organization needs to grow, to solidify the foundation on which something else can be built. The whole path is an exploration that doesn’t necessarily match what is written in books, respectively the expectations!

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🗄️Data Management: Data Culture (Part IV: Quo vadis? [Where are you going?])

Data Management Series

The people working for many years in the fields of BI/Data Analytics, Data and Process Management probably met many reactions that at the first sight seem funny, though they reflect bigger issues existing in organizations: people don’t always understand the data they work with, how data are brought together as part of the processes they support, respectively how data can be used to manage and optimize the respective processes. Moreover, occasionally people torture the data until it confesses something that doesn’t necessarily reflect the reality. It’s even more deplorable when the conclusions are used for decision-making, managing or optimizing the process. In extremis, the result is an iterative process that creates more and bigger issues than whose it was supposed to solve!

Behind each blunder there are probably bigger understanding issues that need to be addressed. Many of the issues revolve around understanding how data are created, how are brought together, how the processes work and what data they need, use and generate. Moreover, few business and IT people look at the full lifecycle of data and try to optimize it, or they optimize it in the wrong direction. Data Management is supposed to help, and it does this occasionally, though a methodology, its processes and practices are as good as people’s understanding about data and its use! No matter how good a data methodology is, it’s as weak as the weakest link in its use, and typically the issues revolving around data and data understanding are the weakest link. 

Besides technical people, few businesspeople understand the full extent of managing data and its lifecycle. Unfortunately, even if some of the topics are treated in the books, they are too dry, need hands on experience and some thought in corroborating practices with theories. Without this, people will do things mechanically, processes being as good as the people using them, their value becoming suboptimal and hinder the business. That’s why training on Data Management is not enough without some hands-on experience!

The most important impact is however in BI/Data Analytics areas - how the various artifacts are created and used as support in decision-making, process optimization and other activities rooted in data. Ideally, some KPIs and other metrics should be enough for managing and directing a business, however just basing the decisions on a set of KPIs without understanding the bigger picture, without having a feeling of the data and their quality, the whole architecture, no matter how splendid, can breakdown as sandcastle on a shore meeting the first powerful wave!

Sometimes it feels like organizations do things from inertia, driven by the forces of the moment, initiatives and business issues for which temporary and later permanent solutions are needed. The best chance for solving many of the issues would have been a long time ago, when the issues were still small to create any powerful waves within the organizations. Therefore, a lot of effort is sometimes spent in solving the consequences of decisions not made at the right time, and that can be painful and costly!

For building a good business one needs also a solid foundation. In the past it was enough to have a good set of products that are profitable. However, during the past decade(s) the rules of the game changed driven by the acerb competition across geographies, inefficiencies, especially in the data and process areas, costing organizations on the short and long term. Data Management in general and Data Quality in particular, even if they’re challenging to quantify, have the power to address by design many of the issues existing in organizations, if given the right chance!

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🗄️Data Management: Data Culture (Part III: A Tale of Two Cities)

One of the curious things is that as part of their change of culture organizations try to adopt a new language, to give new names to things, try to make distinction between the "AS IS" and "TO BE" states, insisting how the new image will replace the previous one. Occasionally, they even stress how bad things were in the past and how great will be in the future, trying to depict the future in vivid images. 

Even if this might work occasionally, it tends to confuse people and this not necessarily because of the language and the metaphors used, or the fact that same people were in the same positions, but the lack of belief or conviction, respectively half-hearted enthusiasm personified by the parties. To "convert" people to new philosophies one needs to believe in them or mimic that in similar terms. The lack of conviction can easily have a false effect that spreads within the organization. 

Dissociation from the past, from what an organization was, tends to increase the resistance against the new because two different images are involved. On one side there’s the attachment to the past, and even if there were mistakes made, or things didn’t go optimally, the experiences and decisions made are part of the organization, of the people who made them. People as individuals and as an organization should embrace their mistakes and good deeds altogether, learn from them, improve what is to improve and move forward. Conversely, there’s the resistance to the new, to the change, words they don’t believe in yet, the bigger picture is still fuzzy in their minds, and there can be many other reasons that don’t agree with one’s understanding. 

There are images, memories, views, decisions, objectives of the past and people need to recognize the road from what it was to what should be. One can hypothesize that embracing one’s mistake and understanding, the chain of reasoning from then and from now will help an organization transition towards the new. Awareness of one’s situation most probably will help in the transition process. Unfortunately, leaders and technology gurus tend to depict the past as negative, creating thus more negative emotions, respectively reactions in the process. The past is still part of the people, of the organization and will continue to be.

Conversely, the disassociation from the past can create more resistance to the new, and probably more unnecessary barriers. Probably, it’s easier for the gurus to build the new if the past weren’t there! Forgetting the past would be an error because there are many lessons that can be still useful. All the experience needs to be redirected in new directions. It’s more important to help people see the vision of the future, understand their missions, the paths to be followed and the challenges ahead, . 

It sounds more of a rambling from a psychology course, though organizations do have an image they want to change, to bring forth to cope with the various challenges, an image they want to reflect when needed. There are also organizations that want to change but keep their image intact, which leads to deeper conflicts. Unfortunately, changes of image involve conflicts that can become complex from what they bring forth.  

A data culture should increase people’s awareness of the present, respectively of the future, of what it takes to bridge the gap, the challenges ahead, how to embrace change, how to keep a realistic perspective, how to do a reality check, etc. Methodologies can increase people’s awareness and provide the theoretical basis, though walking the path will be a different story for everyone. 

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🗄️Data Management: Data Governance (Part I: No Guild of Heroes)

Data Management Series

Data governance appeared around 1980s as topic though it gained popularity in early 2000s [1]. Twenty years later, organizations still miss the mark, respectively fail to understand and implement it in a consistent manner. As usual, the reasons for failure are multiple and they vary from misunderstanding what governance is all about to poor implementation of methodologies and inadequate management or leadership. 

Moreover, methodologies tend to idealize the various aspects and is not what organizations need, but pragmatism. For example, data governance is not about heroes and heroism [2], which can give the impression that heroic actions are involved and is not the case! Actions for the sake of action don’t necessarily lead to change by themselves. Organizations are in general good at creating meaningless action without results, especially when people preoccupy themselves, miss or ignore the mark. Big organizations are very good at generating actions without effects. 

People do talk to each other, though they try to solve their own problems and optimize their own areas without necessarily thinking about the bigger picture. The problem is not necessarily communication or the lack of depth into business issues, people do communicate, know the issues without a business impact assessment. The challenge is usually in convincing the upper management that the effort needs to be consolidated, supported, respectively the needed resources made available. 

Probably, one of the issues with data governance is the attempt of creating another structure in the organization focused on quality, which has the chances to fail, and unfortunately does fail. Many issues appear when the structure gains weight and it becomes a separate entity instead of being the backbone of organizations. 

As soon organizations separate the data governance from the key users, management and the other important decisional people in the organization, it takes a life of its own that has the chances to diverge from the initial construct. Then, organizations need "alignment" and probably other big words to coordinate the effort. Also such constructs can work but they are suboptimal because the forces will always pull in different directions.

Making each manager and the upper management responsible for governance is probably the way to go, though they’ll need the time for it. In theory, this can be achieved when many of the issues are solved at the lower level, when automation and further aspects allow them to supervise things, rather than hiding behind every issue. 

When too much mircomanagement is involved, people tend to busy themselves with topics rather than solve the issues they are confronted with. The actual actors need to be empowered to take decisions and optimize their work when needed. Kaizen, the philosophy of continuous improvement, proved itself that it works when applied correctly. They’ll need the knowledge, skills, time and support to do it though. One of the dangers is however that this becomes a full-time responsibility, which tends to create a separate entity again.

The challenge for organizations lies probably in the friction between where they are and what they must do to move forward toward the various objectives. Moving in small rapid steps is probably the way to go, though each person must be aware when something doesn’t work as expected and react. That’s probably the most important aspect. 

So, the more functions are created that diverge from the actual organization, the higher the chances for failure. Unfortunately, failure is visible in the later phases, and thus self-awareness, self-control and other similar “qualities” are needed, like small actors that keep the system in check and react whenever is needed. Ideally, the employees are the best resources to react whenever something doesn’t work as per design. 

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Resources:
[1] Wikipedia (2023) Data Management [link]
[2] Tiankai Feng (2023) How to Turn Your Data Team Into Governance Heroes [link]