Business Intelligence: Perspectives (Part VIII: There’s More to Noise)

Business Intelligence Series

Visualizations should be built with an audience's characteristics in mind! Upon case, it might be sufficient to show only values or labels of importance (minima, maxima, inflexion points, exceptions, trends), while other times it might be needed to show all or most of the values to provide an accurate extended perspective. It even might be useful to allow users switching between the different perspectives to reduce the clutter when navigating the data or look at the patterns revealed by the clutter. 

In data-based storytelling are typically shown the points, labels and further elements that support the story, the aspects the readers should focus on, though this approach limits the navigability and users’ overall experience. The audience should be able to compare magnitudes and make inferences based on what is shown, and the accurate decoding shouldn’t be taken as given, especially when the audience can associate different meanings to what’s available and what’s missing. 

In decision-making, selecting only some well-chosen values or perspectives to show might increase the chances for a decision to be made, though is this equitable? Cherry-picking may be justified by the purpose, though is in general not a recommended practice! What is not shown can be as important as what is shown, and people should be aware of the implications!

One person’s noise can be another person’s signal. Patterns in the noise can provide more insight compared with the trends revealed in the "unnoisy" data shown! Probably such scenarios are rare, though it’s worth investigating what hides behind the noise. The choice of scale, the use of special types of visualizations or the building of models can reveal more. If it’s not possible to identify automatically such scenarios using the standard software, the users should have the possibility of changing the scale and perspective as seems fit. 

Identifying patterns in what seems random can prove to be a challenge no matter the context and the experience in the field. Occasionally, one might need to go beyond the general methods available and statistical packages can help when used intelligently. However, a presenter’s challenge is to find a plausible narrative around the findings and communicate it further adequately. Additional capabilities must be available to confirm the hypotheses framed and other aspects related to this approach.

It's ideal to build data models and a set of visualizations around them. Most probable some noise may be removed in the process, while other noise will be further investigated. However, this should be done through adjustable visual filters because what is removed can be important as well. Rare events do occur, probably more often than we are aware and they may remain hidden until we find the right perspective that takes them into consideration. 

Probably, some of the noise can be explained by special events that don’t need to be that rare. The challenge is to identify those parameters, associations, models and perspectives that reveal such insights. One’s gut feeling and experience can help in this direction, though novel scenarios can surprise us as well.

Not in every set of data one can find patterns, respectively a story trying to come out. Whether we can identify something worth revealing depends also on the data available at our disposal, respectively on whether the chosen data allow identifying significant patterns. Occasionally, the focus might be too narrow, too wide or too shallow. It’s important to look behind the obvious, to look at data from different perspectives, even if the data seems dull. It’s ideal to have the tools and knowledge needed to explore such cases and here the exposure to other real-life similar scenarios is probably critical!

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Data Science: Rare Events (Just the Quotes)

"We must rather seek for a cause, for every event whether probable or improbable must have some cause." (Polybius, "The Histories", cca. 100 BC)

"There is nothing in the nature of a miracle that should render it incredible: its credibility depends upon the nature of the evidence by which it is supported. An event of extreme probability will not necessarily command our belief unless upon a sufficiency of proof; and so an event which we may regard as highly improbable may command our belief if it is sustained by sufficient evidence. So that the credibility or incredibility of an event does not rest upon the nature of the event itself, but depends upon the nature and sufficiency of the proof which sustains it." (Charles Babbage, "Passages from the Life of a Philosopher", 1864)

"Events with a sufficiently small probability never occur, or at least we must act, in all circumstances, as if they were impossible." (Émile Borel, "Probabilities and Life", 1962)

"Most accidents in well-designed systems involve two or more events of low probability occurring in the worst possible combination." (Robert E Machol, "Principles of Operations Research", 1975)

"[…] all human beings - professional mathematicians included - are easily muddled when it comes to estimating the probabilities of rare events. Even figuring out the right question to ask can be confusing." (Steven Strogatz, "Sync: The Emerging Science of Spontaneous Order", 2003)

"Bell curves don't differ that much in their bells. They differ in their tails. The tails describe how frequently rare events occur. They describe whether rare events really are so rare. This leads to the saying that the devil is in the tails." (Bart Kosko, "Noise", 2006)

"A Black Swan is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was. […] The Black Swan idea is based on the structure of randomness in empirical reality. [...] the Black Swan is what we leave out of simplification." (Nassim N Taleb, “The Black Swan”, 2007)

"A forecaster should almost never ignore data, especially when she is studying rare events […]. Ignoring data is often a tip-off that the forecaster is overconfident, or is overfitting her model - that she is interested in showing off rather than trying to be accurate."  (Nate Silver, "The Signal and the Noise: Why So Many Predictions Fail-but Some Don't", 2012)

"[…] according to the bell-shaped curve the likelihood of a very-large-deviation event (a major outlier) located in the striped region appears to be very unlikely, essentially zero. The same event, though, is several thousand times more likely if it comes from a set of events obeying a fat-tailed distribution instead of the bell-shaped one." (John L Casti, "X-Events: The Collapse of Everything", 2012)

"[…] both rarity and impact have to go into any meaningful characterization of how black any particular [black] swan happens to be." (John L Casti, "X-Events: The Collapse of Everything", 2012)

"Black Swans (capitalized) are large-scale unpredictable and irregular events of massive consequence - unpredicted by a certain observer, and such un - predictor is generally called the 'turkey' when he is both surprised and harmed by these events. [...] Black Swans hijack our brains, making us feel we 'sort of' or 'almost' predicted them, because they are retrospectively explainable. We don’t realize the role of these Swans in life because of this illusion of predictability. […] An annoying aspect of the Black Swan problem - in fact the central, and largely missed, point - is that the odds of rare events are simply not computable." (Nassim N Taleb, "Antifragile: Things that gain from disorder", 2012)

"Behavioral finance so far makes conclusions from statics not dynamics, hence misses the picture. It applies trade-offs out of context and develops the consensus that people irrationally overestimate tail risk (hence need to be 'nudged' into taking more of these exposures). But the catastrophic event is an absorbing barrier. No risky exposure can be analyzed in isolation: risks accumulate. If we ride a motorcycle, smoke, fly our own propeller plane, and join the mafia, these risks add up to a near-certain premature death. Tail risks are not a renewable resource." (Nassim N Taleb, "Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

"But note that any heavy tailed process, even a power law, can be described in sample (that is finite number of observations necessarily discretized) by a simple Gaussian process with changing variance, a regime switching process, or a combination of Gaussian plus a series of variable jumps (though not one where jumps are of equal size […])." (Nassim N Taleb, "Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

"[…] it is not merely that events in the tails of the distributions matter, happen, play a large role, etc. The point is that these events play the major role and their probabilities are not (easily) computable, not reliable for any effective use. The implication is that Black Swans do not necessarily come from fat tails; the problem can result from an incomplete assessment of tail events." (Nassim N Taleb, "Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

"Once we know something is fat-tailed, we can use heuristics to see how an exposure there reacts to random events: how much is a given unit harmed by them. It is vastly more effective to focus on being insulated from the harm of random events than try to figure them out in the required details (as we saw the inferential errors under thick tails are huge). So it is more solid, much wiser, more ethical, and more effective to focus on detection heuristics and policies rather than fabricate statistical properties." (Nassim N Taleb, "Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

Strategic 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]

Graphical Representation: Composition (Just the Quotes)

"Nothing is so illuminating as a set of properly proportioned diagrams. [...] In addition to the significance of graphics in analytical work, it is likewise a valuable aid to the memory. A picture is manifestly more readily retained in mind than a description of the same subject, no matter how vividly it may have been expressed. A pictorial or diagrammatic illustration usually produces a firmer and more lasting impression than any composition of words or tabulation of figures, however well they may be arranged or set forth." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)

"Without adequate planning, it is seldom possible to achieve either proper emphasis of each component element within the chart or a presentation that is pleasing in its entirely. Too often charts are developed around a single detail without sufficient regard for the work as a whole. Good chart design requires consideration of these four major factors:" (1) size," (2) proportion," (3) position and margins, and" (4) composition." (Anna C Rogers, "Graphic Charts Handbook", 1961)

"As a general rule, plotted points and graph lines should be given more 'weight' than the axes. In this way the 'meat' will be easily distinguishable from the 'bones'. Furthermore, an illustration composed of lines of unequal weights is always more attractive than one in which all the lines are of uniform thickness. It may not always be possible to emphasise the data in this way however. In a scattergram, for example, the more plotted points there are, the smaller they may need to be and this will give them a lighter appearance. Similarly, the more curves there are on a graph, the thinner the lines may need to be. In both cases, the axes may look better if they are drawn with a somewhat bolder line so that they are easily distinguishable from the data." (Linda Reynolds & Doig Simmonds, "Presentation of Data in Science" 4th Ed, 1984)

"While visuals are an essential part of data storytelling, data visualizations can serve a variety of purposes from analysis to communication to even art. Most data charts are designed to disseminate information in a visual manner. Only a subset of data compositions is focused on presenting specific insights as opposed to just general information. When most data compositions combine both visualizations and text, it can be difficult to discern whether a particular scenario falls into the realm of data storytelling or not." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

"A semantic approach to visualization focuses on the interplay between charts, not just the selection of charts themselves. The approach unites the structural content of charts with the context and knowledge of those interacting with the composition. It avoids undue and excessive repetition by instead using referential devices, such as filtering or providing detail-on-demand. A cohesive analytical conversation also builds guardrails to keep users from derailing from the conversation or finding themselves lost without context. Functional aesthetics around color, sequence, style, use of space, alignment, framing, and other visual encodings can affect how users follow the script." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Aligning on data ink can be a powerful way to build relationships across charts. It can be used to obscure the lines between charts, making the composition feel more seamless. [....] Alignment paradigms can also influence the layout design needed. [...] The layout added to the alignment further supports this relationship." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Beyond basic charts, practitioners must also learn to compose visualizations together elegantly. The perceptual stage focuses on making the literal charts more precise as well as working to de-emphasize the entire piece. Design choices start to consider distractions, reducing visual clutter and centering on the message. Minimalism is espoused as a core value with an emphasis on shifting toward precision as accuracy. This is the most common next step for practitioners. Minimalism is also a key stage in maturation. It is experimentation at one extreme that helps practitioners distill down to core, shared practices." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Chart choices can also create weight within the entire composition. Presenting information as a comprehensive visualization, such as in a dashboard, requires thinking beyond individual charts. In writing, we not only craft sentences, but write the composition as an entire piece. Certain sentences may drive the writing more, but all sentences play a role in conveying the message." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

"Visualizations are abstractions, relying on primary graphicacy skills to fully understand the composition." (Vidya Setlur & Bridget Cogley, "Functional Aesthetics for data visualization", 2022)

Data Management: Data Governance (Part III: Taming the Complexity)

Data Management Series

The Chief Data Officer (CDO) or the “Head of the Data Team” is one of the most challenging jobs because is more of a "political" than a technical role. It requires the ideal candidate to be able to throw and catch curved balls almost all the time, and one must be able to play ball with all the parties having an interest in data (aka stakeholders). It’s a full-time job that requires the combination of management and technical skillsets, and both are important. The focus will change occasionally in one direction more than in the other, with considerable fluctuations. 

Moreover, even if one masters the technical and managerial aspects, the combination of the two gives birth to situations that require further expertise – applied systems thinking being probably the most important. This, also because there are so many points of failure that it's challenging to address all the important causes. Therefore, it’s critical to be a system thinker, to have an experienced team and make use adequately of its experience! 

In a complex word, in which even the smallest constraint or opportunity can have an important impact especially when it’s involved in the early stages of the processes taking place in organizations. It relies on the manager’s and team’s skillset, their inspiration, the way the business reacts to the tasks involved and probably many other aspects that make things work. It takes considerable effort until the whole mechanism works, and even more time to make things work efficiently. The best metaphor is probably the one of a small combat team in which everybody has their place and skillset in the mechanism, independently if one talks about strategy, tactics or operations. 

Unfortunately, building such teams takes time, and the more people are involved, the more complex this endeavor becomes. The manager and the team must meet somewhere in the middle in what concerns the philosophy, the execution of the various endeavors, the way of working together to achieve the same goals. There are multiple forces pulling in all directions and it takes time until one can align the goals, respectively the effort. 

The most challenging forces are the ones between the business and the data team, respectively the business and data requirements, forces that don’t necessarily converge. Working in small organizations, the two parties have in theory more challenges to overcome the challenges and a team’s experience can weight a lot in the process, though as soon the scale changes, the number of challenges to be overcome changes exponentially (there are however different exponential functions in which the basis and exponent make the growth rapid). 

In big organizations can appear other parties that have the same force to pull the weight in one direction or another. Thus, the political aspects become more complex to the degree that the technologies must follow the political decisions, with all the positive and negative implications deriving from this. As comparison, think about the challenges from moving from two to three or more moving bodies orbiting each other, resulting in a chaotic dynamical system for most initial conditions. 

Of course, a business’ context doesn’t have to create such complexity, though when things are unchecked, when delays in decision-making as well as other typical events occur, when there’s no structure, strategy, coordinated effort, or any other important components, the chances for chaotic behavior are quite high with the pass of time. This is just a model to explain real life situations that seem similar on the surface but prove to be quite complex when diving deeper. That’s probably why a CDO’s role as tamer of complexity is important and challenging!

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Bart Kosko - Collected Quotes

"A bell curve shows the 'spread' or variance in our knowledge or certainty. The wider the bell the less we know. An infinitely wide bell is a flat line. Then we know nothing. The value of the quantity, position, or speed could lie anywhere on the axis. An infinitely narrow bell is a spike that is infinitely tall. Then we have complete knowledge of the value of the quantity. The uncertainty principle says that as one bell curve gets wider the other gets thinner. As one curve peaks the other spreads. So if the position bell curve becomes a spike and we have total knowledge of position, then the speed bell curve goes flat and we have total uncertainty (infinite variance) of speed." (Bart Kosko, "Fuzzy Thinking: The new science of fuzzy logic", 1993)

"Bivalence trades accuracy for simplicity. Binary outcomes of yes and no, white and black, true and false simplify math and computer processing. You can work with strings of 0s and 1s more easily than you can work with fractions. But bivalence requires some force fitting and rounding off [...] Bivalence holds at cube corners. Multivalence holds everywhere else." (Bart Kosko, "Fuzzy Thinking: The new science of fuzzy logic", 1993)

"Fuzziness has a formal name in science: multivalence. The opposite of fuzziness is bivalence or two-valuedness, two ways to answer each question, true or false, 1 or 0. Fuzziness means multivalence. It means three or more options, perhaps an infinite spectrum of options, instead of just two extremes. It means analog instead of binary, infinite shades of gray between black and white." (Bart Kosko, "Fuzzy Thinking: The new science of fuzzy logic", 1993)

"The binary logic of modern computers often falls short when describing the vagueness of the real world. Fuzzy logic offers more graceful alternatives." (Bart Kosko & Satoru Isaka, "Fuzzy Logic,” Scientific American Vol. 269, 1993)

"A bit involves both probability and an experiment that decides a binary or yes-no question. Consider flipping a coin. One bit of in-formation is what we learn from the flip of a fair coin. With an unfair or biased coin the odds are other than even because either heads or tails is more likely to appear after the flip. We learn less from flipping the biased coin because there is less surprise in the outcome on average. Shannon's bit-based concept of entropy is just the average information of the experiment. What we gain in information from the coin flip we lose in uncertainty or entropy." (Bart Kosko, "Noise", 2006)

"A signal has a finite-length frequency spectrum only if it lasts infinitely long in time. So a finite spectrum implies infinite time and vice versa. The reverse also holds in the ideal world of mathematics: A signal is finite in time only if it has a frequency spectrum that is infinite in extent." (Bart Kosko, "Noise", 2006)

"Bell curves don't differ that much in their bells. They differ in their tails. The tails describe how frequently rare events occur. They describe whether rare events really are so rare. This leads to the saying that the devil is in the tails." (Bart Kosko, "Noise", 2006)

"Chaos can leave statistical footprints that look like noise. This can arise from simple systems that are deterministic and not random. [...] The surprising mathematical fact is that most systems are chaotic. Change the starting value ever so slightly and soon the system wanders off on a new chaotic path no matter how close the starting point of the new path was to the starting point of the old path. Mathematicians call this sensitivity to initial conditions but many scientists just call it the butterfly effect. And what holds in math seems to hold in the real world - more and more systems appear to be chaotic." (Bart Kosko, "Noise", 2006)

"'Chaos' refers to systems that are very sensitive to small changes in their inputs. A minuscule change in a chaotic communication system can flip a 0 to a 1 or vice versa. This is the so-called butterfly effect: Small changes in the input of a chaotic system can produce large changes in the output. Suppose a butterfly flaps its wings in a slightly different way. can change its flight path. The change in flight path can in time change how a swarm of butterflies migrates." (Bart Kosko, "Noise", 2006)

"I wage war on noise every day as part of my work as a scientist and engineer. We try to maximize signal-to-noise ratios. We try to filter noise out of measurements of sounds or images or anything else that conveys information from the world around us. We code the transmission of digital messages with extra 0s and 1s to defeat line noise and burst noise and any other form of interference. We design sophisticated algorithms to track noise and then cancel it in headphones or in a sonogram. Some of us even teach classes on how to defeat this nemesis of the digital age. Such action further conditions our anti-noise reflexes." (Bart Kosko, "Noise", 2006)

"Linear systems do not benefit from noise because the output of a linear system is just a simple scaled version of the input [...] Put noise in a linear system and you get out noise. Sometimes you get out a lot more noise than you put in. This can produce explosive effects in feedback systems that take their own outputs as inputs." (Bart Kosko, "Noise", 2006)

"Many scientists who work not just with noise but with probability make a common mistake: They assume that a bell curve is automatically Gauss's bell curve. Empirical tests with real data can often show that such an assumption is false. The result can be a noise model that grossly misrepresents the real noise pattern. It also favors a limited view of what counts as normal versus non-normal or abnormal behavior. This assumption is especially troubling when applied to human behavior. It can also lead one to dismiss extreme data as error when in fact the data is part of a pattern." (Bart Kosko, "Noise", 2006)

"Noise is a signal we don't like. Noise has two parts. The first has to do with the head and the second with the heart. The first part is the scientific or objective part: Noise is a signal. [...] The second part of noise is the subjective part: It deals with values. It deals with how we draw the fuzzy line between good signals and bad signals. Noise signals are the bad signals. They are the unwanted signals that mask or corrupt our preferred signals. They not only interfere but they tend to interfere at random." (Bart Kosko, "Noise", 2006)

"Noise is an unwanted signal. A signal is anything that conveys information or ultimately anything that has energy. The universe consists of a great deal of energy. Indeed a working definition of the universe is all energy anywhere ever. So the answer turns on how one defines what it means to be wanted and by whom." (Bart Kosko, "Noise", 2006)

"The central limit theorem differs from laws of large numbers because random variables vary and so they differ from constants such as population means. The central limit theorem says that certain independent random effects converge not to a constant population value such as the mean rate of unemployment but rather they converge to a random variable that has its own Gaussian bell-curve description." (Bart Kosko, "Noise", 2006)

"The flaw in the classical thinking is the assumption that variance equals dispersion. Variance tends to exaggerate outlying data because it squares the distance between the data and their mean. This mathematical artifact gives too much weight to rotten apples. It can also result in an infinite value in the face of impulsive data or noise. [...] Yet dispersion remains an elusive concept. It refers to the width of a probability bell curve in the special but important case of a bell curve. But most probability curves don't have a bell shape. And its relation to a bell curve's width is not exact in general. We know in general only that the dispersion increases as the bell gets wider. A single number controls the dispersion for stable bell curves and indeed for all stable probability curves - but not all bell curves are stable curves." (Bart Kosko, "Noise", 2006)

More quotes from Bart Kosko at QuotableMath.blogspot.com.

Business Intelligence: Perspectives (Part VII: Creating Value for Organizations)

Business Intelligence Series

How does one create value for an organization in BI area? This should be one of the questions the BI professional should ask himself and eventually his/her colleagues on a periodic basis because the mere act of providing reports and good-looking visualizations doesn’t provide value per se. Therefore, it’s important to identify the critical to success and value drivers within each area!

One can start with the data, BI or IT strategies, when organizations invest the time in their direction, respectively with the considered KPIs and/or OKRs defined, and hopefully the organizations already have something similar in place! However, these are just topics that can be used to get a bird view over the overall landscape and challenges. It’s advisable to dig deeper, especially when the strategic, tactical and operational plans aren’t in sync, and let’s be realistic, this happens probably in many organizations, more often than one wants to admit!

Ideally, the BI professional should be able to talk with the colleagues who could benefit from having a set of reports or dashboards that offer a deeper perspective into their challenges. Talking with each of them can be time consuming and not necessarily value driven. However, giving each team or department the chance to speak their mind, and brainstorm what can be done, could in theory bring more value. Even if their issues and challenges should be reflected in the strategy, there’s always an important gap between the actual business needs and those reflected in formal documents, especially when the latter are not revised periodically. Ideally, such issues should be tracked back to a business goal, though it’s questionable how much such an alignment is possible in practice. Exceptions will always exist, no matter how well structured and thought a strategy is!

Unfortunately, this approach also involves some risks. Despite their local importance, the topics raised might not be aligned with what the organization wants, and there can be a strong case against and even a set of negative aspects related to this. However, talking about the costs involved by losing an opportunity can hopefully change the balance favorably. In general, transposing the perspective of issues into the area of their associated cost for the organization has (hopefully) the power to change people’s minds.

Organizations tend to bring forward the major issues, addressing the minor ones only after that, this having the effect that occasionally some of the small issues increase in impact when not addressed. It makes sense to prioritize with the risks, costs and quick wins in mind while looking at the broader perspective! Quick wins are usually addressed at strategic level, but apparently seldom at tactical and operational level, and at these levels one can create the most important impact, paving the way for other strategic measures and activities.

The question from the title is not limited only to BI professionals - it should be in each manager and every employee’s mind. The user is the closest to the problems and opportunities, while the manager is the one who has a broader view and the authority to push the topic up the waiting list. Unfortunately, the waiting lists in some organizations are quite big, while not having a good set of requests on the list might pinpoint that issues might exist in other areas!  

BI professionals and organizations probably know the theory well but prove to have difficulties in combining it with praxis. It’s challenging to obtain the needed impact (eventually the maximum effect) with a minimum of effort while addressing the different topics. Sooner or later the complexity of the topic kicks in, messing things around!

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

Software Engineering Series

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

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

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

It's never too late to learn new tricks, respectively another programming language and/or framework. It even helps to provide better solutions in 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" images, 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]

Business Intelligence: Perspectives (Part V: From Data to Storytelling III)

Business Intelligence Series 

As children we heard or later read many stories, and even if few remained imprinted in memory, we can still recognize some of the metaphors and ideas used. Stories prepared us for life, and one can suppose that the business stories we hear nowadays have similar intent, charge and impact. However, if we dig deeper into each story and dissect it, we may be disappointed by its simplicity, the resemblance to other stories, to what we've heard over time. Moreover, stories can bring also negative connotations, that can impact any other story we hear. 

From the scores or hundreds of distinct stories that have been told, few reach a magnitude that can become more than the stories themselves, few become a catalyst for the auditorium, and even then they tend to manipulate. Conversely, well-written transformative stories can move mountains when they resonate with the auditorium. In a leader’s motivational speech such stories can become a catalyst that moves people in the intended direction.

Children stories are quite simple and apparently don’t need special constructs even if the choice of words, structure and messages is important. Moving further into organizations, storytelling becomes more complex, upon case, structures and messages need to follow certain conventions within some politically correct scripts. Facts become important to the degree they serve the story, though the purposes they serve change with time, becoming secondary to the story. Storytelling becomes thus just of way of changing the facts as seems fit to the storyteller. 

Storytelling has its role in organizations for channeling the multitude of messages across various structures. However, the more one hears the word storytelling, the more likely one is closer to fiction than to business decision-making. It's also true that the word in itself carries a power we all tasted during childhood and why not much later. The word has a magic power that appeals to our memories, to our feelings, to our expectations. However, as soon one's expectations are not met, the fight with the chimeras turns into a battle of our own. Yes, storytelling has great power when used right, when there's a story to tell, when the business narratives are worth telling. 

The problem with stories is that no matter how much they are based on real facts or happenings, they become fictitious in time, to the degree that they lose some of the most important facts they were based on. That’s valid especially when there’s no written track of the story, though even then various versions of the story can multiply outside of the standard channels and boundaries. 

Even if the author tried to keep the story as close to the facts, the way stories are understood, remembered and retold depend on too many factors - the words used, the degree to which metaphors and similar elements are understood, remembered and transmitted correctly, the language used, the mental structure existing in the auditorium, the association of words, ideas or metaphors, etc.

Unfortunately, the effect of stories can be negative too, especially when stories are designed to manipulate the auditorium beyond any ethical norms. When they don’t resonate with the crowd or are repeated unnecessary, the narratives may have adverse effects and the messages can get lost in the crowd or create resistance. Moreover, stories may have a multifold and opposite effect within different segments of the auditorium. 

Storytelling can make hearts and minds resonate with the carried messages, though misdirected, improper or poorly conceived stories have also the power to destroy all that have been built over the years. Between the two extremes there’s a small space to send the messages across!

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