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

Business Intelligence Series

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

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

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

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

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

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

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

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Business Intelligence: A Software Engineer's Perspective 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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Project Management: Projects' Dynamics I (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 III (Heuristics)

Probably one of the most difficult things to learn as a technical person is using the right technology for a given purpose, this mainly because one’s inclined using the tools one knows best. Moreover, technologies’ overlapping makes the task more and more challenging, the difference between competing technologies often residing in the details. Thus, identifying the gaps resumes in understanding the details of the problem(s) or need(s), respectively the advantages or disadvantages of a technology over the other. This is true especially about competing technologies, including the ones that replace other technologies.

There are simple heuristics, that can allow approaching such challenges. For example, heavy data processing belongs usually in databases, while import/export functionality belongs in an ETL tool.  Therefore, one can start looking at the problems from these two perspectives. Would the solution benefit from these two approaches or are there more appropriate technologies (e.g. data streaming, ELT, non-relational databases)? How much effort would involve building the solution? 

Commercial Off-The-Shelf (COTS) tools provided by third-party vendors usually offer specialized functionality in each area. Gartner and Forrester provide regular analyses of the main players in the important areas, analyses which can be used in theory as basis for further research. Even if COTS tend to be more expensive and can have some important functionality gaps, as long they are extensible, they can prove a good starting point for developing a solution. 

Sometimes it helps researching on the web what other people or organizations did, how they approached the same aspects, what technologies, techniques and best practices they used to overcome the challenges. One doesn’t need to reinvent the wheel even if it’s sometimes fun to do so. Moreover, a few hours of research can give one a basis of useful information and a better understanding over the work ahead.

On the other side sometimes it’s advisable to use the tools one knows best, however this can lead also to unusable and less performant solutions. For example, MS Excel and Access have been for years the tools of choice for building personal solutions that later grew into maintenance nightmares for the IT team. Ideally, they can still be used for data entry or data cleaning, though building solutions exclusively based on (one of) them can prove to be far than optimal. 

When one doesn’t know whether a technology or mix of technologies can be used to provide a solution, it’s recommended to start a proof-of-concept (PoC) that would allow addressing most important aspects of the needed solution. One can start small by focusing on the minimal functionality needed to check the main aspects and evolve the PoC during several iterations as needed.

For example, in the case of a Data Migration (DM) this would involve building the data extraction layer for an entity, implement several data transformations based on the defined mappings, consider building a few integrity rules for validation, respectively attempt importing the data into the target system. Once this accomplished, one can start increasing the volume of data to check how the solution behaves under stress. The volume of data can be increased incrementally or by considering all the data available. 

As soon the skeleton was built one can consider all the mappings, respectively add several entities to build the dependencies existing between them and other functionality. The prototype might not address all the requirements from the beginning, therefore consider the problems as they arise. For example, if the volume of data seems to cause problems then attempt splitting the data during processing in batches or considering specific optimization techniques like indexing or scaling techniques like increasing computing resources. 

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Project Management: The Choice of Tools in Project Management

“Beware the man of one book” (in Latin, “homo unius libri”), a warning generally attributed to Thomas Aquinas and having a twofold meaning. In its original interpretation it was referring to the people mastering a single chosen discipline, however the meaning degenerated in expressing the limitations of people who master just one book, and thus having a limited toolset of perspectives, mental models or heuristics. This later meaning is better reflected in Abraham Maslow adage: “If the only tool you have is a hammer, you tend to see every problem as a nail”, as people tend to use the tools they are used to also in situations in which other tools are more appropriate.

It’s sometimes admirable people and even organizations’ stubbornness in using the same tools in totally different scenarios, expecting though the same results, as well in similar scenarios expecting different results. It’s true, Mathematics has proven that the same techniques can be used successfully in different areas, however a mathematician’s universe and models are idealistically fractionalized to a certain degree from reality, full of simplified patterns and never-ending approximations. In contrast, the universe of Software Development and Project Management has a texture of complex patterns with multiple levels of dependencies and constraints, constraints highly sensitive to the initial conditions.

Project Management has managed to successfully derive tools like methodologies, processes, procedures, best practices and guidelines to address the realities of projects, however their use in praxis seems to be quite challenging. Probably, the challenge resides in stubbornness of not adapting the tools to the difficulties and tasks met. Even if the same phases and multiple similarities seems to exist, the process of building a house or other tangible artefact is quite different than the approaches used in development and implementation of software.

Software projects have high variability and are often explorative in nature. The end-product looks totally different than the initial scaffold. The technologies used come with opportunities and limitations that are difficult to predict in the planning phase. What on paper seems to work often doesn’t work in praxis as the devil lies typically in details. The challenges and limitations vary between industries, businesses and even projects within the same organization.

Even if for each project type there’s a methodology more suitable than another, in the end project particularities might pull the choice in one direction or another. Business Intelligence projects for example can benefit from agile approaches as they enable to better manage and deliver value by adapting the requirements to business needs as the project progresses. An agile approach works almost always better than a waterfall process. In contrast, ERP implementations seldom benefit from agile methodologies given the complexity of the project which makes from planning a real challenge, however this depends also on an organization’s dynamicity.

Especially when an organization has good experience with a methodology there’s the tendency to use the same methodology across all the projects run within the organization. This results in chopping down a project to fit an ideal form, which might be fine as long the particularities of each project are adequately addressed. Even if one methodology is not appropriate for a given scenario it doesn’t mean it can’t be used for it, however in the final equation enter also the cost, time, effort, and the quality of the end-results.

In general, one can cope with complexity by leveraging a broader set of mental models, heuristics and set of tools, and this can be done only though experimentation, through training and exposing employees to new types of experiences, through openness, through adapting the tools to the challenges ahead.

Data Science: Heuristic (Definitions)

"Problem solving or analysis by experimental and especially trial-and-error methods." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"The mode of analysis in which the next step is determined by the results of the current step. Used for decision support processing." (Margaret Y Chu, "Blissful Data ", 2004)

"A type of analysis in which the next step is determined by the results of the current step of analysis." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling 2nd Ed.", 2005)

"The mode of analysis in which the next step is determined by the results of the current step of analysis. Used for decision-support processing." (William H Inmon, "Building the Data Warehouse", 2005)

"An algorithmic technique designed to solve a problem that ignores whether the solution can be proven to be correct." (Omar F El-Gayar et al, "Current Issues and Future Trends of Clinical Decision Support Systems", 2008)

"General advice that is usually efficient but sometimes cannot be used; also it is a validate function that adds a number to the state of the problem." (Attila Benko & Cecília S Lányi, "History of Artificial Intelligence", 2009) 

"These methods, found through discovery and observation, are known to produce incorrect or inexact results at times but likely to produce correct or sufficiently exact results when applied in commonly occurring conditions." (Vineet R Khare & Frank Z Wang, "Bio-Inspired Grid Resource Management", Handbook of Research on Grid Technologies and Utility Computing, 2009)

"Refers to a search and discovery approach, in which we proceed gradually, without trying to find out immediately whether the partial result, which is only adopted on a provisional basis, is true or false. This method is founded on a gradual approach to a given question, using provisional hypotheses and successive evaluations." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence: Anticipation Tool for Managers", 2011)

"'Rules of thumb' and approximation methods for obtaining a goal, a high quality solution, or improved performance. It sacrifices completeness to increase efficiency, as some potential solutions would not be practicable or acceptable due to their 'rareness' or 'complexity'. This method may not always find the best solution, but it will find an acceptable solution within a reasonable timeframe for problems that will require almost infinite or longer than acceptable times to compute." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"An experience-based technique for solving problems that emphasizes personal knowledge and quick decision making." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

[heuristic process:] "An iterative process, where the next step of analysis depends on the results attained in the current level of analysis" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"An algorithm that gives a good solution for a problem but that doesn’t guarantee to give you the best solution possible." (Rod Stephens, "Beginning Software Engineering", 2015)

"Rules of thumb derived by experience, intuition, and simple logic." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Problem-solving technique that yields a sub-optimal solution judged to be sufficient." (Karl Beecher, "Computational Thinking - A beginner's guide to problem-solving and programming", 2017)

"An algorithm to solve a problem simply and quickly with an approximate solution, as compared to a complex algorithm that provides a precise solution, but may take a prohibitively long time." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

Software Engineering: Heuristic (Just the Quotes)

"Heuristic reasoning is reasoning not regarded as final and strict but as provisional and plausible only, whose purpose is to discover the solution of the present problem. We are often obliged to use heuristic reasoning. We shall attain complete certainty when we shall have obtained the complete solution, but before obtaining certainty we must often be satisfied with a more or less plausible guess. We may need the provisional before we attain the final. We need heuristic reasoning when we construct a strict proof as we need scaffolding when we erect a building." (George Pólya, "How to Solve It", 1945)

"The aim of heuristics is to study the methods and rules of discovery and invention. [...] Heuristic, as an adjective, means 'serving to discover'." (George Pólya, "How to Solve It", 1945)

"An algorithm gives you the instructions directly. A heuristic tells you how to discover the instructions for yourself, or at least where to look for them." (Steve McConnell, "Code Complete", 1993)

"Heuristic (it is of Greek origin) means discovery. Heuristic methods are based on experience, rational ideas, and rules of thumb. Heuristics are based more on common sense than on mathematics. Heuristics are useful, for example, when the optimal solution needs an exhaustive search that is not realistic in terms of time. In principle, a heuristic does not guarantee the best solution, but a heuristic solution can provide a tremendous shortcut in cost and time." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Heuristic methods may aim at local optimization rather than at global optimization, that is, the algorithm optimizes the solution stepwise, finding the best solution at each small step of the solution process and 'hoping' that the global solution, which comprises the local ones, would be satisfactory." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Models of bounded rationality describe how a judgement or decision is reached (that is, the heuristic processes or proximal mechanisms) rather than merely the outcome of the decision, and they describe the class of environments in which these heuristics will succeed or fail." (Gerd Gigerenzer & Reinhard Selten [Eds., "Bounded Rationality: The Adaptive Toolbox", 2001)

"A heuristic is a rule applied to an existing solution represented in a perspective that generates a new (and hopefully better) solution or a new set of possible solutions." (Scott E Page, "The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies", 2008)

"There are two parts to learning craftsmanship: knowledge and work. You must gain the knowledge of principles, patterns, practices, and heuristics that a craftsman knows, and you must also grind that knowledge into your fingers, eyes, and gut by working hard and practicing." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"A second class of metaphors - mathematical algorithms, heuristics, and models - brings us closer to the world of computer science programs, simulations, and approximations of the brain and its cognitive processes." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"[...] heuristics are simple, efficient rules - either hard-wired in our brains or learned - that kick in especially when we're facing problems with incomplete information." (David DiSalvo, "What Makes Your Brain Happy and Why You Should Do the Opposite", 2011)

"This is the essence of intuitive heuristics: when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution." (Daniel Kahneman, "Thinking, Fast and Slow", 2011)

"Heuristics are simplified rules of thumb that make things simple and easy to implement. But their main advantage is that the user knows that they are not perfect, just expedient, and is therefore less fooled by their powers. They become dangerous when we forget that." (Nassim N Taleb, "Antifragile: Things that gain from disorder", 2012)

"A good heuristic decision is made by 1) knowing what to look for, 2) knowing when enough information is enough (the 'threshold of decision' ), and 3) knowing what decision to make." (Patrick Van Horne, "Left of Bang", 2014)

"Heuristic decision making is fast and frugal and is often based on the evaluation of one or two salient bits of information." (Amitav Chakravarti, "Why People (Don’t) Buy: The Go and Stop Signals", 2015)

"A heuristic is a strategy we derive from previous experience with a similar problem." (Darius Foroux, "Think Straight", 2017)

More quotes on "Heuristic" at the-web-of-knowledge.blogspot.com.