🧭💹Business Intelligence: Perspectives (Part XXI: Data Visualization Revised)

Data Visualization Series

Creating data visualizations nowadays became so easy that anybody can do it with a minimum of effort and knowledge, which on one side is great for the creators but can be easily become a nightmare for the readers, respectively users. Just dumping data in visuals can be barely called data visualization, even if the result is considered as such. The problems of visualization are multiple – the lack of data culture, the lack of understanding processes, data and their characteristics, the lack of being able to define and model problems, the lack of educating the users, the lack of managing the expectations, etc.

There are many books on data visualization though they seem an expensive commodity for the ones who want rapid enlightenment, and often the illusion of knowing proves maybe to be a barrier. It's also true that many sets of data are so dull, that the lack of information and meaning is compensated by adding elements that give a kitsch look-and-feel (aka chartjunk), shifting the attention from the valuable elements to decorations. So, how do we overcome the various challenges? 

Probably, the most important step when visualizing data is to define the primary purpose of the end product. Is it to inform, to summarize or to navigate the data, to provide different perspectives at macro and micro level, to help discovery, to explore, to sharpen the questions, to make people think, respectively understand, to carry a message, to be artistic or represent truthfully the reality, or maybe is just a filler or point of attraction in a textual content?

Clarifying the initial purpose is important because it makes upfront the motives and expectations explicit, allowing to determine the further requirements, characteristics, and set maybe some limits in what concern the time spent and the qualitative and/or qualitative criteria upon which the end result should be eventually evaluated. Narrowing down such aspects helps in planning and the further steps performed. 

Many of the steps are repetitive and past experience can help reduce the overall effort. Therefore, professionals in the field, driven by intuition and experience probably don't always need to go through the full extent of the process. Conversely, what is learned and done poorly, has high chances of delivering poor quality. 

A visualization can be considered as effective when it serves the intended purpose(s), when it reveals with minimal effort the patterns, issues or facts hidden in the data, when it allows people to explore the data, ask questions and find answers altogether. One can talk also about efficiency, especially when readers can see at a glance the many aspects encoded in the visualization. However, the more the discovery process is dependent on data navigation via filters or other techniques, the more difficult it becomes to talk about efficiency.

Better criteria to judge visualizations is whether they are meaningful and useful for the readers, whether the readers understood the authors' intent, the further intrinsic implication, though multiple characteristics can be associated with these criteria: clarity, specificity, correctedness, truthfulness, appropriateness, simplicity, etc. All these are important in lower or higher degree depending on the broader context of the visualization.

All these must be weighted in the bigger picture when creating visualizations, though there are probably also exceptions, especially on the artistic side, where artists can cut corners for creating an artistic effect, though also in here the authors need to be truthful to the data and make sure that their work don't distort excessively the facts. Failing to do so might not have an important impact on the short term considerably, though in time the effects can ripple with unexpected effects.

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𖣯Strategic Management: Strategic Perspectives (Part II: The Elephant in the Room)

Strategic Management Perspectives

There’s an ancient parable about several blind people who touch a shape they had never met before, an elephant, and try to identify what it is. The elephant is big, more than each person can sense through direct experience, and people’s experiences don’t correlate to the degree that they don’t trust each other, the situation escalating upon case. The moral of the parable is that we tend to claim (absolute) truths based on limited, subjective experience [1], and this can easily happen in business scenarios in which each of us has a limited view of the challenges we are facing individually and as a collective. 

The situation from the parable can be met in business scenarios, when we try to make sense of the challenges we are faced with, and we get only a limited perspective from the whole picture. Only open dialog and working together can get us closer to the solution! Even then, the accurate depiction might not be in sight, and we need to extrapolate the unknown further.  

A third-party consultant with experience might be the right answer, at least in theory, though experience and solutions are relative. The consultant might lead us in a direction, though from this to finding the answer can be a long way that requires experimentation, a mix of tactics and strategies that change over time, more sense-making and more challenges lying ahead. 

We would like a clear answer and a set of steps that lead us to the solution, though the answer is as usual, it depends! It depends on the various forces/drivers that have the biggest impact on the organization, on the context, on the organization’s goals, on the resources available directly or indirectly, on people’s capabilities, the occurrences of external factors, etc. 

In many situations the smartest thing to do is to gather information, respectively perspectives from all the parties. Tools like brainstorming, SWOT/PESTLE analysis or scenario planning can help in sense-making to identify the overall picture and where the gravity point lies. For some organizations the solution will be probably a new ERP system, or the redesign of some processes, introduction of additional systems to track quality, flow of material, etc. 

A new ERP system will not necessarily solve all the issues (even if that’s the expectation), and some organizations just try to design the old processes into a new context. Process redesign in some areas can be upon case a better approach, at least as primary measure. Otherwise, general initiatives focused on quality, data/information management, customer/vendor management, integrations, and the list remains open, can provide the binder/vehicle an organization needs to overcome the current challenges.

Conversely, if the ERP or other strategical systems are 10-20 years old, then there’s indeed an elephant in the room! Moreover, the elephant might be bigger than we can chew, and other challenges might lurk in its shadow(s). Everything is a matter of perspective with no apparent unique answer. Thus, finding an acceptable solution might lurk in the shadow of the broader perspective, in the cumulated knowledge of the people experiencing the issues, respectively in some external guidance. Unfortunately, the guides can be as blind as we are, making limited or no important impact. 

Sometimes, all it’s needed is a leap of faith corroborated with a set of tactics or strategies kept continuously in check, redirected as they seem fit based on the knowledge accumulated and the challenges ahead. It helps to be aware of how others approached the same issues. Unfortunately, there’s no answer that works for all! In this lies the challenge, in identifying what works and makes sense for us!

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Resources:
[1] Wikipedia (2024) Blind men and an elephant [link]

🧭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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🧭Business Intelligence: Mea Culpa (Part III: Problem Solving)

Business Intelligence Series

I've been working for more than 20 years in BI and Data Analytics area, in combination with Software Engineering, ERP implementations, Project Management, IT services and several other areas, which allowed me to look at many recurring problems from different perspectives. One of the things I learnt is that problems are more complex and more dynamic than they seem, respectively that they may require tailored dynamic solutions. Unfortunately, people usually focus on one or two immediate perspectives, ignoring the dynamics and the multilayered character of the problems!

Sometimes, a quick fix and limited perspective is what we need to get started and fix the symptoms, and problem-solvers usually stop there. When left unsupervised, the problems tend to kick back, build up momentum and appear under more complex forms in various places. Moreover, the symptoms can remain hidden until is too late. To this also adds the political agendas and the further limitations existing in organizations (people, money, know-how, etc.).

It seems much easier to involve external people (individual experts, consultancy companies) to solve the problem(s), though unless they get a deep understanding of the business and the issues existing in it, the chances are high that they solve the wrong problems and/or implement the wrong solutions. Therefore, it's more advisable to have internal experts, when feasible, and that's the point where business people with technical expertise and/or IT people with business expertise can help. Ideally, one should have a good mix and the so called competency centers can do a great job in handling the challenges of organizations. 

Between business and IT people there's a gap that can be higher or lower depending on resources know-how or the effort made by organizations to reduce it. To this adds the nature of the issues existing in organizations, which can vary considerable across departments, organizations or any other form of establishment. Conversely, the specific skillset can be transmuted where needed, which might happen naturally, though upon case also considerable effort needs to be involved in the process.

Being involved in similar tasks, one may get the impression that one can do whatever the others can do. This can happen in IT as well on the business side. There can be activities that can be done by parties from the other group, though there are also many exceptions in both directions, especially when one considers that one can’t generalize the applicability and/or transmutation of skillset. 

A more concrete example is the know-how needed by a businessperson to use the BI infrastructure for answering business questions, and ideally for doing all or at least most of the activities a BI professional can do. Ideally, as part of the learning path, it would be helpful to have a pursuable path in between the two points. The mastery of tools helps in the process though there are different mindsets involved.

Unfortunately, the data-related fields are full of overconfident people who get the problem-solving process wrong. Data-based problem-solving resumes in gathering the right facts and data, building the right conceptual model, identifying the right questions to ask, collecting more data, refining methods and solutions, etc. There’s aways an easy wrong way to solve a problem!

The mastery of tools doesn’t imply the mastery of business domains! What people from the business side can bring is deeper insight in the business problems, though getting from there to implementing solutions can prove a long way, especially when problems require different approaches, different levels of approximations, etc. No tool alone can bridge such gaps yet! Frankly, this is the most difficult to learn and unfortunately many data professionals seem to get this wrong!

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🧭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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𖣯Strategic Management: The Impact of New Technologies (Part II - The Technology-oriented Patient)

Looking at the way data, information and knowledge flow through an organization, with a little imagination one can see the resemblance between an organization and the human body, in which the networks created by the respective flows spread through organization as nervous, circulatory or lymphatic braids do, each with its own role in the good functioning of the organization. Each technology adopted by an organization taps into these flows creating a structure that can be compared with the nerve plexus, as the various flows intersect in such points creating an agglomeration of nerves and braids.

The size of each plexus can be considered as proportional to the importance of the technology in respect to the overall structure. Strategic technologies like ERP, BI or planning systems, given their importance (gravity), resemble with the organs from the human body, with complex networks of braids in their vicinity. Maybe the metaphor is too far-off, though it allows stressing the importance of each technology in respect to its role and the good functioning of the organization. Moreover, each such structure functions as pressure points that can in extremis block any of the flows considered, a long-term block having important effects.

The human organism is a marvelous piece of work reflecting the grand design, however in time, especially when neglected or driven by external agents, diseases can clutch around any of the parts of the human body, with all the consequences deriving from this. On the other side, an organization is a hand-made structure found in continuous expansion as new technologies or resources are added. Even if the technologies are at peripheral side of the system, their good or bad functioning can have a ripple effect trough the various networks.

Replacing any of the above-mentioned strategic systems can be compared with the replacement of an organ in the human body, having a high degree of failure compared with other operations, being complex in nature, the organism needing long periods to recover, while in extreme situations the convalescence prolongs till the end. Fortunately, organizations seem to be more resilient to such operations, though that’s not necessarily a rule. Sometimes all it takes is just a small mistake for making the operation fail.

The general feeling is that ERP and BI implementations are taken too lightly by management, employees and implementers. During the replacement operation one must make sure not only that the organ fits and functions as expected, but also that the vital networks regained their vitality and function as expected, and the latter is a process that spans over the years to come. One needs to check the important (health) signs regularly and take the appropriate countermeasures. There must be an entity having the role of the doctor, who/which has the skills to address adequately the issues.

Moreover, when the physical structure of an organization is affected, a series of micro-operations might be needed to address the deformities. Unfortunately, these areas are seldom seen in time, and can require a sustained effort for fixing, while a total reconstruction might apply. One works also with an amorphous and ever-changing structure that require many attempts until a remedy is found, if a remedy is possible after all.

Even if such operations are pretty well documented, often what organizations lack are the skilled resources needed during and post-implementation, resources that must know as well the patient, and ideally its historical and further health preconditions. Each patient is different and quite often needs its own treatment/medication. With such changes, the organization lands itself on a discovery journey in which the appropriate path can easily deviate from the well-trodden paths.

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🧭Business Intelligence: New Technologies, Old Challenges (Part I: An Introduction)

Each important technology has the potential of creating divides between the specialists from a given field. This aspect is more suggestive in the data-driven fields like BI/Analytics or Data Warehousing. The data professionals (engineers, scientists, analysts, developers) skilled only in the new wave of technologies tend to disregard the role played by the former technologies and their role in the data landscape. The argumentation for such behavior is rooted in the belief that a new technology is better and can solve any problem better than previous technologies did. It’s a kind of mirage professionals and customers can easily fall under.

Being bigger, faster, having new functionality, doesn’t make a tool the best choice by default. The choice must be rooted in the problem to be solved and the set of requirements it comes with. Just because a vibratory rammer is a new technology, is faster and has more power in applying pressure, this doesn’t mean that it will replace a hammer. Where a certain type of power is needed the vibratory rammer might be the best tool, while for situations in which a minimum of power and probably more precision is needed, like driving in a nail, then an adequately sized hammer will prove to be a better choice.

A technology is to be used in certain (business/technological) contexts, and even if contexts often overlap, the further details (aka requirements) should lead to the proper use of tools. It’s in a professional’s duties to be able to differentiate between contexts, requirements and the capabilities of the tools appropriate for each context. In this resides partially a professional’s mastery over its field of work and of providing adequate solutions for customers’ needs. Especially in IT, it’s not enough to master the new tools but also have an understanding about preceding tools, usage contexts, capabilities and challenges.

From an historical perspective each tool appeared to fill a demand, and even if maybe it didn’t manage to fill it adequately, the experience obtained can prove to be valuable in one way or another. Otherwise, one risks reinventing the wheel, or more dangerously, repeating the failures of the past. Each new technology seems to provide a deja-vu from this perspective.

Moreover, a new technology provides new opportunities and requires maybe to change our way of thinking in respect to how the technology is used and the processes or techniques associated with it. Knowledge of the past technologies help identifying such opportunities easier. How a tool is used is also a matter of skills, while its appropriate use and adoption implies an inherent learning curve. Having previous experience with similar tools tends to reduce the learning curve considerably, though hands-on learning is still necessary, and appropriate learning materials or tutoring is upon case needed for a smoother transition.

In what concerns the implementation of mature technologies, most of the challenges were seldom the technologies themselves but of non-technical nature, ranging from the poor understanding/knowledge about the tools, their role and the implications they have for an organization, to an organization’s maturity in leading projects. Even the most-advanced technology can fail in the hands of non-experts. Experience can’t be judged based only on the years spent in the field or the number of projects one worked on, but on the understanding acquired about implementation and usage’s challenges. These latter aspects seem to be widely ignored, even if it can make the difference between success and failure in a technology’s implementation.

Ultimately, each technology is appropriate in certain contexts and a new technology doesn’t necessarily make another obsolete, at least not until the old contexts become obsolete.

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💼Project Management: Project Execution (Part III: Projects' Dynamics - An Introduction)

Despite the considerable collection of books on Project Management (PM) and related methodologies, and the fact that projects are inherent endeavors in professional as well personal life (setups that would give in theory people the environment and exposure to different project types), people’s understanding on what it takes to plan and execute a project seems to be narrow and questionable sometimes. Moreover, their understanding diverges considerably from common sense. It’s also true that knowledge and common sense are relative when considering any human endeavor in which there are multiple roads to the same destination, or when learning requires time, effort, skills, and implies certain prerequisites, however the lack of such knowledge can hurt when endeavor’s success is a must and a team effort. 

Even if the lack of understanding about PM can be considered as minor when compared with other challenges/problems faced by a project, when one’s running fast to finish a race, even a small pebble in one’s running shoes can hurt a lot, especially when one doesn’t have the luxury to stop and remove the stone, as it would make sense to do.

It resides in the human nature to resist change, to seek for information that only confirm own opinions, to follow the same approach in handling challenges, even if the attempts are far from optimal, even if people who walked the same path tell you that there’s a better way and even sketch the path and provide information about what it takes to reach there. As it seems, there’s the predisposition to learn on the hard way, if there’s significant learning involved at all. Unfortunately, such situations occur in projects and the solutions often overrun the boundaries of PM, where social and communication skills must be brought into play. 

On the other side, there’s still hope that change can be managed optimally once the facts are explained to a certain level that facilitates understanding. However, such an attempt can prove to be quite a challenge, given the various setups in which PM takes place. The intersection between technologies and organizational setups lead to complex scenarios which make such work more difficult, even if projects’ challenges are of organizational rather than technological nature. 

When the knowledge we have about the world doesn’t fit our expectation, a simple heuristic is to return to the basics. A solid edifice can be built only on a solid foundation and the best foundation in coping with reality is to establish common ground with other people. One can achieve this by identifying their suppositions and expectations, by closing the gap in perception and understanding, by establishing a basis for communication, in which feedback is a must if one wants to make significant progress.

Despite of being explorative and time-consuming, establishing common ground can be challenging when addressing to an imaginary audience, which is quite often the situation. The practice shows however that progress can be made by starting with a set of well-formulated definitions, simple models, principles, and heuristics that have the potential of helping in sense-making.

The goal is thus to identify first the definitions that reflect the basic concepts that need to be considered. Once the concepts defined, they can be related to each other with the help of a few models. Even if fictitious, as simplifications of the reality, the models should allow playing with the concepts, facilitating concepts’ understanding. Principles (set of rules for reasoning) can be used together with heuristics (rules of thumb methods or techniques) for explaining the ‘known’ and approaching the ‘unknown’. Even maybe not perfect, these tools can help building theories or explanatory constructs.

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📦Data Migrations (DM): Conceptualization (Part VII: Data Import Layer)

Data Migrations Series

The data requirements for the Data Migration (DM) and Data Quality (DQ) are driven by the processes implemented in the target system(s). Therefore, a good knowledge of these requirements can decrease the effort needed for these two subprojects considerably. The needed knowledge basis starts with the entities and their attributes, the dependencies existing between them and the various rules that apply, and ends with the parametrization requirements, respectively the architecture(s) that can be used to import the data.

The DM process starts with defining the entities in scope and their attributes, respectively identifying the corresponding entities and attributes from the legacy systems. The attributes not having a correspondent in the legacy system need to be provided by the business and integrated in the DM logic. In addition, it’s needed to consider also the attributes needed by the business and not available in the target system, some of them more likely available in the legacy systems. For such attributes is needed either to misuse an attribute from the target or to extend the target system.

For each entity is created a data mapping that basically documents the data transformations needed for migrating the data. In the process is needed to consider also attributes’ data types, the (standard) formatting, their domain of definition, as well the various rules that apply. Their implementation belongs into the DM layer from which the data are exported in a standard format as needed by the target system.

Exporting the data from the DM layer directly into the target system’s tables has in theory the lowest overhead even if the rejected records are difficult to track, the rejections resulting only from records’ ‘validation against database’s schema. For this approach to work, one must have a good knowledge of the database schema and of the business rules implemented into the target system.

To solve the issue with errors’ logging, systems have a further layer on top of the database model, which also allow running data validation against target system’s business rules. Modern import frameworks allow loading the data via a set of standard files with a predefined structure. The data can be thus imported manually or via load jobs into the system a log with the issues being generated in the process. Some frameworks allow even the manual editing of failed records, respectively to import the data. Unfortunately, calling the layer from the DM layer is not possible from a database, though this would bring seldom a benefit. Some third-party tools attempt to improve the import functionality by calling the target system’s import layer.

The import files must be generated from the DM layer in the required structure with the appropriate formatting. The challenge however resides in identifying all the attributes that should make scope of the load. It’s an iterative process which sometimes is backed by try-and-error heuristics. Unless target system’s validation rules are known beforehand, the rules need to be discovered in this process, which can prove time-consuming. The discoveries need to be integrated also in the DM and from here results the big number of changes that need to be performed.

Given the dependencies existing between entities the files need to be generated and loaded in a predefined order. These dependencies are reflected also in the data processing and the validation rules considered in the DM layer.

A quality checkpoint can be implemented between the export from the DM layer and import to enforce the four-eyes principle. It’s normally the last opportunity for trapping the eventual issues. A further quality check is performed after import by validating on whether the data were imported as expected.

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🧮ERP: Panning (Part I: It’s all about Planning - An Introduction)

Ideally the total volume of work can be uniformly distributed for all project’s duration though in praxis the curve representing the effort has the form of a wave or aggregation of waves that tend to reach the peak shortly before or during the Go-Live(s). More important, higher fluctuations occur in the various areas of the project on whole project’s duration, as there are dependencies between the various functional areas, as one needs to wait for decisions to be made, people are not available, etc. Typically, the time wasted on waiting, researching or other non-value-added activities have the potential of leading to such peaks. Therefore, the knowledge must be available, and decisions must be taken when required, which can be challenging but not impossible to achieve. 

To bridge the time wasted on waiting, the team members need to work on other topics. If on customer’s side the resources can handle maybe other activities, on vendor’s side the costs can be high and proportional with the volume of waiting. Therefore, vendor’s resources must be involved at least in two projects or do work in other areas in advance, which is not always possible. However, when vendor’s resources are involved in two or more projects, unless the planning is perfect or each resource can handle the work as it comes, there are further waiting times added. The customer is then forced either to book the resources exclusively, or to wait and carry the costs associated with it. 

On the other side ERP Implementations tend to become exploration projects, especially when the team has only partial knowledge about the system, or the requirements have a certain degree of specialization that deviates from the standard processes. The more unknowns an ERP implementation has, the more difficult is to plan. To be able to plan one must know the activities ahead, how long they take, and of course, one must adhere to the delivery dates, because each delay can have a cascading effect that can impact project’s schedule considerably. 

Probably the best approach to planning is to group the activities into packages and plan the packages, being in each subteam’s duty to handle the planning for each package, following to manage at upper level only the open issues, risks or opportunities. This shifts the burden from Project Manager’s shoulders within the project. Moreover, even if in theory the plan can consider each activity, it will become obsolete as soon it’s updated given the considerable volume of work requested to maintain it. Periodically, one can still revise the whole plan to identify opportunities and risks. What the team can do is to plan for a certain time interval (e.g. 4-6 weeks) and build from there. This allows focusing on the most important activities. 

To further shift the burden, activities like Data Migration, Data Cleaning or the integrations with third party systems should be treated when possible as subprojects. Despite having interdependencies with the main project (e.g. parameters, master data, decisions) and share same resources, they have their own schedule whose deadlines need to be aligned with main project’s milestones. 

Unless the team and business put all effort to respect the plan and, as long the plan is realistic, the initial plan can seldom be respected – it’s anyway just a sketch of the road ahead that can change as the project progresses – and this aspect needs to be understood by the business otherwise will lead to false expectations. On the other side, the team must try respecting the deadlines and communicate in time inability to do so. It’s an interplay in which communication is more important than ever.

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🧮ERP: Planning (Part I: It’s all about Partnership - An Introduction)

ERP Implementations Series

Unless the organization (customer) implementing an ERP system has a strong IT team and the knowledge required for the implementation is available already in-house, the resources need to be acquired from the market, and probably the right thing to do is to identify a certified implementer (partner) which can fill the knowledge and skillset gaps, respectively which can help splitting the risks associated with such an implementation.

In theory, the customer provides knowledge about its processes, while the partner comes with expertise about the system to be implemented and further technologies, industry best practices, project methodologies, etc. Further on, the mix is leveraged to harness the knowledge and reach project’s objectives. 

In praxis however finding an implementer which can act as partner might be more challenging than expected. This because the implementer needs to understand customer’s business and where it’s heading, bridge the gap between functional requirements and system’s functionality, advise on areas of improvement, prepare the customer for the project and lead the customer through the changes, respectively establish a basis for the future. Some of the implications are seldom made explicit even if they are implied by what is needed by the project. 

Technology is seldom the issue in an ERP implementation, the challenges residing in handing the change and the logistics required. There are so many aspects to be considered and handled, and this can be challenging for any implementer no matter how long has been on the market or how experienced the resources are. Somebody needs to lead the change and the customer seldom has the knowledge to handle the change. In some cases, the implementer must make the customer aware of the implications, while in others needs to take the initiative and lead the change, though the customer needs to play along, which can be challenging also. 

Many aspects need to be handled at management level from a strategical point of view on customer’s side. It starts with assuring that the most important aspects of the business where considered, that the goals and objectives are clear, that the proper environment is created, and ends with the timely decision-making, with assuring that the resources are available when needed, that the needed organization structures and roles are in place, that the required knowledge is available before, during and after implementation, that the potential brought by the ERP system is harnessed for the years to come. 

A partnership allows in theory splitting the implementation risks as ERP implementations have a high rate of failure. Quite often the outcomes of such projects don’t meet the expectations, the systems being in extremis unusable or a bottleneck for the organization. Ideally one should work with the partner(s) and attempt solving the issues, split eventually the incurred cost overruns, find a middle way. Most of the times it’s recommended to find a solution together rather than coming to a litigation. 

Given the complex dependencies existing between the various parts of the project, the causes that lead to poor implementations are difficult to prove, as there are almost always grey areas. Moreover, the litigations can require a considerable time and resources to settle. These can be just extreme situations, and as long one has a good partner, there’s no need to think that far. On the other side, even if undesirable, one must be prepared also for such outcomes, even if the countermeasures may involve an additional effort. Therefore, one must address such issues in contracts by establishing the areas of accountability/responsibilities for each party, document adequately the requirements and further (important) communication, make sure that the deliverables have the expected quality, etc.

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🧭Business Intelligence: Perspectives (Part VI: Data Soup - Reports vs. Data Visualizations)

Business Intelligence Series

Considering visualizations, John Tukey remarked that ‘the greatest value of a picture is when it forces us to notice what we never expected to see’, which is not always the case for many of the graphics and visualizations available in organizations, typically in the form of simple charts and dashboards, quite often with no esthetics or meaning behind.

In general, reports are needed as source for operational activities, in which the details in form of raw or aggregate data are important. As one moves further to the tactical or strategic aspects of a business, visualizations gain in importance especially when they allow encoding data and information, respectively variations, trends or relations in smaller places with minimal loss of information.

There are also different aspects of visualizations that need to be considered. Modern tools allow rapid visualization and interactive navigation of data across different variables which is great as long one knows what is searching for, which is not always the case.

There are junk charts in which the data drowns in graphical elements that bring no value to the reader, in extremis even distorting the message/meaning.

There are graphics/visualizations that attempt bringing together and encoding multiple variables in respect to a theme, and for which a ‘project’ is typically needed as data is not ad-hoc available, don’t have the desired quality or need further transformations to be ready for consumption. Good quality graphics/visualizations require time and a good understanding of the business, which are not necessarily available into the BI/Analytics teams, and unfortunately few organizations do something in that direction, ignoring typically such needs. In this type of environments is stressed the rapid availability of data for decision-making or action-relevant insight, which depends typically on the consumer.

The story-telling capabilities of graphics/visualizations are often exaggerated. Yes, they can tell a story though stories need to be framed into a context/problem, some background and further references need to be provided, while without detailed data the graphics/visualizations are just nice representations in which each consumer understands what he can.

In an ideal world the consumer and the ‘designer’ would work together to identify the important data for the theme considered, to find the appropriate level of detail, respectively the best form of encoding. Such attempts can stop at table-based representations (aka reports), respectively basic or richer forms of graphical representations. One can consider reports as an early stage of the visualization process, with the potential to derive move value when the data allow meaningful graphical representations. Unfortunately, the time, data and knowledge available seldom make this achievable.

In addition, a well-designed report can be used as basis for multiple purposes, while a graphic/visualization can enforce more limitations. Ideal would be when multiple forms of representation (including reports) are combined to harness the value of data. Navigations from visualizations to detailed data can be useful to understand what happens; learning and understanding the various aspects being an iterative process.

It’s also difficult to demonstrate the value of insight derived from visualizations, especially when graphical literacy goes behind the numeracy and statistical literacy - many consumers lacking the skills needed to evaluate numbers and statistics adequately. If for a good artistic movie you need an assistance to enjoy the show and understand the message(s) behind it, the same can be said also about good graphics/visualizations. Moreover, this requires creativity, abstraction-based thinking, and other capabilities to harness the value of representations.

Given the considerable volume of requirements related to the need of basis data, reports will continue to be on high demand in organizations. In exchange visualizations can complement them by providing insights otherwise not available.

Initially published on Medium as answer to a post on Reporting and Visualizations. 

Data Science: Data Strategy (Part II: Generalists vs Specialists in the Field)

Division of labor favorizes the tasks done repeatedly, where knowledge of the broader processes is not needed, where aspects as creativity are needed only at a small scale. Division invaded the IT domains as tools, methodologies and demands increased in complexity, and therefore Data Science and BI/Analytics make no exception from this.

The scale of this development gains sometimes humorous expectations or misbelieves when one hears headhunters asking potential candidates whether they are upfront or backend experts when a good understanding of both aspects is needed for providing adequate results. The development gains tragicomical implications when one is limited in action only to a given area despite the extended expertise, or when a generalist seems to step on the feet of specialists, sometimes from the right entitled reasons. 

Headhunters’ behavior is rooted maybe in the poor understanding of the domain of expertise and implications of the job descriptions. It’s hard to understand how people sustain of having knowledge about a domain just because they heard the words flying around and got some glimpse of the connotations associated with the words. Unfortunately, this is extended to management and further in the business environment, with all the implications deriving from it. 

As Data Science finds itself at the intersection between Artificial Intelligence, Data Mining, Machine Learning, Neurocomputing, Pattern Recognition, Statistics and Data Processing, the center of gravity is hard to determine. One way of dealing with the unknown is requiring candidates to have a few years of trackable experience in the respective fields or in the use of a few tools considered as important in the respective domains. Of course, the usage of tools and techniques is important, though it’s a big difference between using a tool and understanding the how, when, why, where, in which ways and by what means a tool can be used effectively to create value. This can be gained only when one’s exposed to different business scenarios across industries and is a tough thing to demand from a profession found in its baby steps. 

Moreover, being a good data scientist involves having a deep insight into the businesses, being able to understand data and the demands associated with data – the various qualitative and quantitative aspects. Seeing the big picture is important in defining, approaching and solving problems. The more one is exposed to different techniques and business scenarios, with right understanding and some problem-solving skillset one can transpose and solve problems across domains. However, the generalist will find his limitations as soon a certain depth is reached, and the collaboration with a specialist is then required. A good collaboration between generalists and specialists is important in complex projects which overreach the boundaries of one person’s knowledge and skillset. 

Complexity is addressed when one can focus on the important characteristic of the problem, respectively when the models built can reflect the demands. The most important skillset besides the use of technical tools is the ability to model problems and root the respective problems into data, to elaborate theories and check them against reality. 

Complex problems can require specialization in certain fields, though seldom one problem is dependent only on one aspect of the business, as problems occur in overreaching contexts that span sometimes the borders of an organization. In addition, the ability to solve problems seem to be impacted by the diversity of the people involved into the task, sometimes even with backgrounds not directly related to organization’s activity. As in evolution, a team’s diversity is an important factor in achievement and learning, most gain being obtained when knowledge gets shared and harnessed beyond the borders of teams.

Note:
Written as answer to a Medium post on Data Science generalists vs specialists.

𖣯🧮Strategic Management: Simplicity VI (ERP Implementations' Story II)

Besides the witty sayings and theories advanced in defining what simplicity is about, life shows that there’s a considerable gap between theory and praxis. In the attempt at a definition, one is forced to pull more concepts like harmony, robustness, variety, balance, economy, or proportion, which can be grouped under organic unity or similar concepts. However, intuitionally one can advance the idea that from a cybernetic perspective simplicity is achieved when the information flows are not disrupted and don’t meet unnecessary resistance. By information here are considered the various data aggregations – data, information, knowledge, and eventually wisdom (aka DIKW pyramid) – though it can be extended to encompass materials, cash and vital energy.

One can go further and say that an organization is healthy when the various flows mentioned above run smoothly through the organization nourishing it. The comparison with the human body can go further and say that a blockage in the flow can cause minor headaches or states that can take a period of convalescence to recover from them. Moreover, the sustained effort applied by an organization can result in fatigue or more complex ailments or even diseases if the state is prolonged. 

For example, big projects like ERP implementations tend to suck the vital energy of an organization to the degree that it will take months to recover from the effort, while the changes in the other types of flow can lead to disruptions, especially when the change is not properly managed. Even if ERP implementations provide standard solutions for the value-added processes, they represent vendors’ perspective into the respective processes, which don’t necessarily fit an organization’s needs. One is forced then to make compromises either by keeping close to the standard or by expanding the standard processes to close the gap. Either way processual changes are implied, which affect the information flow, especially for the steps where further coordination is needed, respectively the data flow in respect to implementation or integration with the further systems. A new integration as well as a missing integration have the potential of disrupting the data and information flows.

The processual changes can imply changes in the material flow as the handling of the materials can change, however the most important impact is caused maybe by the processual bottlenecks, which can cause serious disruptions (e.g. late deliveries, production is stopped), and upon case also in the cash-flow (e.g. penalties for late deliveries, higher inventory costs). The two flows can be impacted by the data and information flows independently of the processual changes (e.g. when they have poor quality, when not available, respectively when don’t reach the consumer in timely manner). 

With a new ERP solution, the organization needs to integrate the new data sources into the existing BI infrastructure, or when not possible, to design and implement a new one by taking advantage of the technological advancements. Failing to exploit this potential will impact the other flows, however the major disruptions appear when the needed knowledge about business processes is not available in-house, in explicit and/or implicit form, before, during and after the implementation. 

Independently on how they are organized – in center of excellence or ad-hoc form – is needed a group of people who can manage the various flows and ideally, they should have the appropriate level of empowerment. Typically, the responsibility resides with key users, IT and one or two people from the management. Without a form of ‘organization’ to manage the flows, the organization will reside only on individual effort, which seldom helps reaching the potential. Independently of the number of resources involved, simplicity is achieved when the activities flow naturally. 

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Written: Sep-2020, Last Reviewed: Mar-2024

𖣯Strategic Management: Strategy Design (Part II: A System's View)

Each time one discusses in IT about software and hardware components interacting with each other, one talks about a composite referred to as a system. Even if the term Information System (IS) is related to it, a system is defined as a set of interrelated and interconnected components that can be considered together for specific purposes or simple convenience.

A component can be a piece of software or hardware, as well persons or groups if we extend the definition. The consideration of people becomes relevant especially in the context of ecologies, in which systems are placed in a broader context that considers people’s interaction with them, as this raises to important behavior that impacts system’s functioning.

Within a system each part has a role or function determined in respect to the whole as well as to the other parts. The role or function of the component is typically fixed, predefined, though there are also exceptions especially when the scope of a component is enlarged, respectively reduced to the degree that the component can be removed or ignored. What one considers or not considers as part of system defines a system’s boundaries; it’s what distinguishes it from other systems within the environment(s) considered.

The interaction between the components resumes in the exchange, transmission and processing of data found in different aggregations ranging from signals to complex data structures. If in non-IT-based systems the changes are determined by inflow, respectively outflow of energy, in IT the flow is considered in terms of data in its various aggregations (information, knowledge).  The data flow (also information flow) represents the ‘fluid’ that nourishes a system’s ‘organism’.

One can grasp the complexity in the moment one attempts to describe a system in terms of components, respectively the dependencies existing between them in term of data and processes. If in nature the processes are extrapolated, in IT they are predefined (even if the knowledge about them is not available). In addition, the less knowledge one has about the infrastructure, the higher the apparent complexity. Even if the system is not necessarily complex, the lack of knowledge and certainty about it makes it complex. The more one needs to dig for information and knowledge to get an acceptable level of knowledge and logical depth, the more time is needed for designing a solution.

Saint Exupéry’s definition of simplicity applies from a system’s functional point of view, though it doesn’t address the relative knowledge about the system, which often is implicit (in people’s heads). People have only fragmented knowledge about the system which makes it difficult to create the whole picture. It’s typically the role of system or process operational manuals, respectively of data descriptions, to make that knowledge explicit, also establishing a fundament for common knowledge and further communication and understanding.

Between the apparent (perceived) and real complexity of a system there’s an important gap that needs to be addressed if one wants to manage the systems adequately, respectively to simplify the systems. Often simplification happens when components or whole systems are replaced, consolidated, or migrated, a mix between these approaches existing as well. Simplifications at data level (aka data harmonization) or process level (aka process optimization and redesign) can have an important impact, being inherent to the good (optimal) functioning of systems.

Whether these changes occur in big-bang or gradual iterations it’s a question of available resources, organizational capabilities, including the ability to handle such projects, respectively the impact, opportunities and risks associated with such endeavors. Beyond this, it’s important to regard the problems from a systemic and systematic point of view, in which ecology’s role is important.

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Written: Jun-2020, Last Reviewed: Mar-2024

💻IT: Crowdsourcing (Definitions)

"Obtaining information by tapping the collective knowledge of many people." (W Roy Schulte & K Chandy, "Event Processing: Designing IT Systems for Agile Companies", 2009)

"A model of problem solving and idea generation that marshals the collective talents of a large group of people." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"the act of outsourcing a task to an undefined, generally large group of people or community, typically in the form of some sort of post on the Internet." (Bill Holtsnider & Brian D Jaffe, "IT Manager's Handbook" 3rd Ed., 2012)

"Tapping into collective online knowledge by inviting large numbers of people, via the Internet, to contribute ideas on different aspects of a business’s operations. A related concept is 'crowdfunding', which involves funding a project or venture by raising capital from individual investors via the Internet." (DK, "The Business Book", 2014)

"The process by which ideas, services, or other needs are solicited from predominantly amorphous and undefined large groups of people." (Evan Stubbs, "Big Data, Big Innovation", 2014)

"A method of resource gathering where interested potential customers pledge money to innovators for a product that has not yet been created." (Rachel Heinen et al, "Tools for the Process: Technology to Support Creativity and Innovation", 2015)

"The practice of outsourcing organisational tasks by placing a call on the internet and inviting all-comers to post submissions, often with the lure of a prize or commission for the 'best entry'." (Duncan Angwin & Stephen Cummings, "The Strategy Pathfinder" 3rd Ed., 2017)

"Dividing the work of collecting a substantial amount of data into small tasks that can be undertaken by volunteers." (Open Data Handbook)

#️⃣Software Engineering: Programming (Part XII: Misconceptions about Programming - Part I)

Software Engineering Series

Besides equating the programming process with a programmer’s capabilities, minimizing the importance of programming and programmers’ skills in the whole process (see previous post), there are several other misconceptions about programming that influence process' outcomes.

Having a deep knowledge of a programming language allows programmers to easily approach other programming languages, however each language has its own learning curve ranging from a few weeks to half of year or more. The learning curve is dependent on the complexity of the languages known and the language to be learned, same applying to frameworks and architectures, the scenarios in which the languages are used, etc. One unrealistic expectation is that the programmers are capablle of learning a new programming language or framework overnight, this expectation pushing more pressure on programmers’ shoulders as they need to compensate in a short time for the knowledge gap. No, the programming languages are not the same even if there’s high resemblance between them!

There’s lot of code available online, many of the programming tasks involve writing similar code. This makes people assume that programming can resume to copy-paste activities and, in extremis, that there’s no creativity into the act of programming. Beside the fact that using others’ code comes with certain copyright limitations, copy-pasting code is in general a way of introducing bugs in software. One can learn a lot from others’ code, though programmers' challenge resides in writing better code, in reusing code while finding the right the level of abstraction.  

 

There’s the tendency on the market to build whole applications using wizard-like functionality and of generating source-code based on data or ontological models. Such approaches work in a range of (limited) scenarios, and even if the trend is to automate as much in the process, is not what programming is about. Each such tool comes with its own limitations that sooner or later will push back. Changing the code in order to build new functionality or to optimize the code is often not a feasible solution as it imposes further limitations.

Programming is not only about writing code. It involves also problem-solving abilities, having a certain understanding about the business processes, in which the conceptual creativity and ingenuity of design can prove to be a good asset. Modelling and implementing processes help programmers gain a unique perspective within a business.

For a programmer the learning process never stops. The release cycle for the known tools becomes smaller, each release bringing a new set of functionalities. Moreover, there are always new frameworks, environments, architectures and methodologies to learn. There’s a considerable amount of effort in expanding one's (necessary) knowledge, effort usually not planned in projects or outside of them. Trainings help in the process, though they hardly scratch the surface. Often the programmer is forced to fill the knowledge gap in his free time. This adds up to the volume of overtime one must do on projects. On the long run it becomes challenging to find the needed time for learning.

In resource planning there’s the tendency to add or replace resources on projects, while neglecting the influence this might have on a project and its timeline. Each new resource needs some time to accommodate himself on the role, to understand project requirements, to take over the work of another. Moreover, resources are replaced on project with a minimal or even without the knowledge transfer necessary for the job ahead. Unfortunately, same behavior occurs in consultancy as well, consultants being moved from one known functional area into another unknown area, changing the resources like the engines of different types of car, expecting that everything will work as magic.

Continued…

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