📊Graphical Representation: Graphics We Live By II (Discount Rates in MS Excel)

Graphical Representation Series

It's difficult, if not impossible, to give general rules on how data visualizations should be built. However, the data professional can use a set of principles, which are less strict than rules, and validate one's work against them. Even then one might need to make concessions and go against the principles or common sense, though such cases should be few, at least in theory. One of such important principles is reflected in Tufte's statement that "if the statistics are boring, then you've got the wrong numbers" [1].

So, the numbers we show should not be boring, but that's the case with most of the numbers we need to show, or we consume in news and other media sources. Unfortunately, in many cases we need to go with the numbers we have and find a way to represent them, ideally by facilitating the reader to make sense of the respective data. This should be our first goal when visualizing data. Secondly, because everybody talks about insights nowadays, one should identify the opportunity for providing views into the data that go beyond the simple visualization, even if this puts more burden on data professional's shoulder. Frankly, from making sense of a set of data and facilitating an 'Aha' moment is a long leap. Thirdly, one should find or use the proper tools and channels for communicating the findings. 

A basic requirement for the data professional to be able to address these goals is to have clear definitions of the numbers, have a good understanding of how the numbers reflect the reality, respectively how the numbers can be put into the broader context. Unfortunately, all these assumptions seem to be a luxury. On the other side, the type of data we work with allows us to address at least the first goal. Upon case, our previous experience can help, though there will be also cases in which we can try to do our best. 

Let's consider a simple set of data retrieved recently from another post - Discount rates (in percentage) per State, in which the values for 5 neighboring States are considered (see the first two columns from diagram A). Without knowing the meaning of data, one could easily create a chart in Excel or any other visualization tool. Because the State has categorical values, probably some visualization tools will suggest using bar and not column charts. Either by own choice or by using the default settings, the data professional might end up with a column chart (see diagram B), which is Ok for some visualizations. 

One can start with a few related questions:

(1) Does it make sense to use a chart to represent 5 values which have small variability (the difference between the first and last value is of only 6%)? 

(2) Does it make sense to use a chart only for the sake of visualizing the data?

(3) Where is the benefit for using a chart as long there's no information conveyed? 

One can see similar examples in the media where non-aggregated values are shown in a chart just for the sake of visualizing the data. Sometimes the authors compensate for the lack of meaning with junk elements, fancy titles or other tricks. Usually, sense-making in a chart takes longer than looking at the values in a table as there are more dimensions or elements to consider. For a table there's the title, headers and the values, nothing more! For a chart one has in addition the axes and some visualization elements that can facilitate or complicate visualization's decoding. Where to add that there are also many tricks to distort the data. 

Tables tend to maximize the amount of digital ink used to represent the data, and minimize the amount used to represent everything else not important to understanding. It's what Tufte calls the data-to-ink ratio (see [1]), a second important principle. This can be translated in (a) removing the border of the chart area, (b) minimizing the number of gridlines shown, (c) minimizing the number of ticks on the axis without leading to information lost, (d) removing redundant information, (e) or information that doesn't help the reader. 

However, the more data is available in the table, the more difficult it becomes to navigate the data. But again, if the chart shows the individual data without any information gained, a table might be still more effective. One shouldn't be afraid to show a table where is the case!

(4) I have a data visualization, what's next?

Ideally, the data professional should try to obtain the maximum of effect with minimum of elements. If this principle aims for the efficiency of design, a fourth related principle aims for the efficiency of effort - one should achieve a good enough visualization with a minimum of effort. Therefore, it's enough maybe if we settle to any of the two above results. 

On the other side, maybe by investing a bit more effort certain aspects can be improved. In this area beginners start playing with the colors, formatting the different elements of the chart. Unfortunately, even if color plays a major role in the encoding and decoding of meaning, is often misused/overused. 

(5) Is there any meaning in the colors used?

In the next examples taken from the web (diagram C and D), the author changed the color of the column with the minimal value to red to contrast it with the other values. Red is usually associated with danger, error, warning, or other similar characteristics with negative impact. The chances are high that the reader will associate the value with a negative connotation, even if red is used also for conveying important information (usually in text). Moreover, the reader will try to interpret the meaning of the other colors. In practice, the color grey has a neutral tone (and calming effect on the mind). Therefore, it's safe to use grey in visualization (see diagram D in contrast with diagram C). Some even advise setting grey as default for the visualization and changing the colors as needed later. 

In these charts, the author signalized in titles that red denotes the lowest value, though it just reduces the confusion. One can meet titles in which several colors are used, reminding of a Christmas tree. Frankly, this type of encoding is not esthetically pleasing, and it can annoy the reader. 

(6) What's in a name?

The titles and, upon case, the subtitles are important elements in communicating what the data reflects. The title should be in general short and succinct in the information it conveys, having the role of introducing, respectively identifying the chart, especially when multiple charts are used. Some charts can also use a subtitle, which can be longer than the title and have more of a storytelling character by highlighting the message and/or the finding in the data. In diagrams C and D the subtitles were considered as tiles, which is not considerably wrong. 

In the media and presentations with influencing character, subtitles help the user understand the message or the main findings, though it's not appropriate for hardcoding the same in dynamic dashboards. Even if a logic was identified to handle the various scenarios, this shifts users' attention, and the chance is high that they'll stop further investigating the visualization. A data professional should present the facts with minimal interference in how the audience and/or users perceive the data. 

As a recommendation, one should aim for clear general titles and avoid transmitting own message in charts. As a principle this can be summarized as "aim for clarity and equidistance".

(7) What about meaning?

Until now we barely considered the meaning of data. Unfortunately, there's no information about what the Discount rate means. It could be "the minimum interest rate set by the US Federal Reserve (and some other national banks) for lending to other banks" or "a rate used for discounting bills of exchange", to use the definitions given by the Oxford dictionary. Searching on the web, the results lead to discount rates for royalty savings, resident tuitions, or retail for discount transactions. Most probably the Discount rates from the data set refer to the latter.

We need a definition of the Discount rate to understand what the values represent when they are ordered. For example, when Texas has a value of 25% (see B), does this value have a negative or a positive impact when compared with other values? It depends on how it's used in the associated formula. The last two charts consider that the minimum value has a negative impact, though without more information the encoding might be wrong! 

Important formulas and definitions should be considered as side information in the visualization, accompanying text or documentation! If further resources are required for understanding the data, then links to the required resources should be provided as well. At least this assures that the reader can acquire the right information without major overhead. 

(8) What do readers look for? 

Frankly, this should have been the first question! Readers have different expectations from data visualizations. First of all, it's the curiosity - how the data look in row and/or aggregated form, or in more advanced form how are they shaped (e.g. statistical characteristics like dispersion, variance, outliers). Secondly, readers look in the first phase to understand mainly whether the "results" are good or bad, even if there are many shades of grey in between. Further on, there must be made distinction between readers who want to learn more about the data, models, and processes behind, respectively readers who just want a confirmation of their expectations, opinions and beliefs (aka bias). And, in the end, there are also people who are not interested in the data and what it tells, where the title and/or subtitle provide enough information. 

Besides this there are further categories of readers segmented by their role in the decision making, the planning and execution of operational, tactical, or strategic activities. Each of these categories has different needs. However, this exceeds the scope of our analysis. 

Returning to our example, one can expect that the average reader will try to identify the smallest and highest Discount rates from the data set, respectively try to compare the values between the different States. Sorting the data and having the values close to each other facilitates the comparison and ranking, otherwise the reader needing to do this by himself/herself. This latter aspect and the fact that bar charts better handle the display of categorical data such as length and number, make from bar charts the tool of choice (see diagram E). So, whenever you see categorical data, consider using a bar chart!

Despite sorting the data, the reader might still need to subtract the various values to identify and compare the differences. The higher the differences between the values, the more complex these operations become. Diagram F is supposed to help in this area, the comparison to the minimal value being shown in orange. Unfortunately, small variances make numbers' display more challenging especially when the visualization tools don't offer display alternatives.

For showing the data from Diagram F were added in the table the third and fourth columns (see diagram A). There's a fifth column which designates the percentage from a percentage (what's the increase in percentages between the current and minimal value). Even if that's mathematically possible, the gain from using such data is neglectable and can create confusion. This opens the door for another principle that applies in other areas as well: "just because you can, it doesn't mean you should!". One should weigh design decisions against common sense or one's intuition on how something can be (mis)used and/or (mis)understood!

The downside of Diagram F is that the comparisons are made only in relation to the minimum value. The variations are small and allow further comparisons. The higher the differences, the more challenging it becomes to make further comparisons. A matrix display (see diagram G) which compares any two values will help if the number of points is manageable. The upper side of the numbers situated on and above the main diagonal were grayed (and can be removed) because they are either nonmeaningful, or the negatives of the numbers found below the diagram. Such diagrams are seldom used, though upon case they prove to be useful.

Choropleth maps (diagram H) are met almost everywhere data have a geographical dimension. Like all the other visuals they have their own advantages (e.g. relative location on the map) and disadvantages (e.g. encoding or displaying data). The diagram shows only the regions with data (remember the data-to-ink ratio principle).

(9) How about the shape of data?

When dealing with numerical data series, it's useful to show aggregated summaries like the average, quartiles, or standard deviation to understand how the data are shaped. Such summaries don't really make sense for our data set given the nature of the numbers (five values with small variance). One can still calculate them and show them in a box plot, though the benefit is neglectable. 

(10) Which chart should be used?

As mentioned above, each chart has advantages and disadvantages. Given the simplicity and the number of data points, any of the above diagrams will do. A table is simple enough despite not using any visualization effects. Also, the bar charts are simple enough to use, with a plus maybe for diagram F which shows a further dimension of the data. The choropleth map adds the geographical dimension, which could be important for some readers. The matrix table is more appropriate for technical readers and involves more effort to understand, at least at first sight, though the learning curve is small. The column charts were considered only for exemplification purposes, though they might work as well. 

In the end one should go with own experience and consider the audience and the communication channels used. One can also choose 2 different diagrams, especially when they are complementary and offer an additional dimension (e.g. diagrams F and H), though the context may dictate whether their use is appropriate or not. The diagrams should be simple to read and understand, but this doesn't mean that one should stick to the standard visuals. The data professional should explore other means of representing the data, a fresh view having the opportunity of catching the reader's attention.

As a closing remark, nowadays data visualization tools allow building such diagrams without much effort. Conversely, it takes more effort to go beyond the basic functionality and provide more value for thyself and the readers. One should be able to evaluate upfront how much time it makes sense to invest. Hopefully, the few methods, principles and recommendations presented here will help further!

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Resources:
[1] Edward R Tufte (1983) "The Visual Display of Quantitative Information"

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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🧊☯Data Warehousing: Data Vault 2.0 (The Good, the Bad and the Ugly)

Data Warehousing Series

One of the interesting concepts that seems to gain adepts in Data Warehousing is the Data Vault – a methodology, architecture and implementation for Data Warehouses (DWH) developed by Dan Linstedt between 1990 and 2000, and evolved into an open standard with the 2.0 version.

According to its creator, the Data Vault is a detail-oriented, historical tracking and uniquely linked set of normalized tables that support one or more business functional areas [2]. To hold data at the lowest grain of detail from the source system(s) and track the changes occurred in the data, it splits the fact and dimension tables into hubs (business keys), links (the relationships between business keys), satellites (descriptions of the business keys), and reference (dropdown values) tables [3], while adopting a hybrid approach between 3rd normal form and star schemas. In addition, it provides a two- or three-layered data integration architecture, a series of standards, methods and best practices supposed to facilitate its use.

It integrates several other methodologies that allow bridging the gap between the technical, logistic and execution parts of the DWH life-cycle – the PMI methodology is used for the various levels of planning and execution, while the Scrum methodology is used for coordinating the day-to-day project tasks. Six Sigma is used together with Total Quality Management for the design and continuous improvement of DWH and data-related processes. In addition, it follows the CMMI maturity model for providing a clear baseline for benchmarking an organization’s DWH capabilities in development, acquisition and service areas.

The Good: The decomposition of the source data models into hub, link and satellite tables provides traceability and auditability at raw data level, allowing thus to address the compliance requirements of Sarabanes-Oxley, HIPPA and Basel II by design.

The considered standards, methods, principles and best practices are leveraged from Software Engineering [1], establishing common ground and a standardized approach to DWH design, implementation and testing. It also narrows down the learning and implementation paths, while allowing an incremental approach to the various phases.

Data Vault 2.0 offers support for real-time, near-real-time and unstructured data, while new technologies like MapReduce, NoSQL can be integrated within its architecture, though the same can be said about other approaches as long there’s compatibility between the considered technologies. In fact, except business entities’ decomposition, many of the notions used are common to DWH design.

The Bad: Further decomposing the fact and dimension tables can impact the performance of the queries run against the tables as more joins are required to gather the data from the various tables. The further denormalization of tables can lead to higher data storage needs, though this can be neglectable compared with the volume of additional objects that need to be created in DWH. For an ERP system with a few hundred of meaningful tables the complexity can become overwhelming.

Unless one uses a COTS tool which automates some part of the design and creation process, building everything from scratch can be time-consuming, increasing thus the time-to-market for solutions. However, the COTS tools can introduce restrictions of their own, which can negatively impact the overall experience with the methodology.

The incorporation of non-technical methodologies can have positive impact, though unless one has experience with the respective methodologies, the disadvantages can easily overshadow the (theoretical) advantages.

The Ugly: The dangers of using Data Vault can be corroborated as usual with the poor understanding of the methodology, poor level of skillset or the attempt of implementing the methodology without allowing some flexibility when required. Unless one knows what he is doing, bringing more complexity in a field which is already complex, can easily impact negatively projects’ outcomes.

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References:
[1] Dan Linstedt & Michael Olschimke (2015) Building a Scalable Data Warehouse with Data Vault 2.0
[2] Dan Linstedt (?) Data Vault Basics [source]
[3] Dan Linstedt (2018) Data Vault: Data Modeling Specification v 2.0.2 [source]

𖣯Strategic Management: Strategy Design (Part IV: Designing for Simplicity)

More than two centuries ago, in his course on the importance of Style in Literature, George Lewes wisely remarked that 'the first obligation of Simplicity is that of using the simplest means to secure the fullest effect' [1]. This is probably the most important aspect the adopters of the KISS mantra seem to ignore – solutions need to be simple while covering all or most important aspects to assure the maximum benefit. The challenge for many resides in defining what the maximum benefit is about. This state of art is typically poorly understood, especially when people don’t understand what’s possible, respectively of what’s necessary to make things work smoothly. 

To make the simplicity principle work, one must envision the desired state of a product or solution and trace back what’s needed to achieve that vision. One can aim for the maximum or for the minimum possible, respectively for anything in between. That’s at least true in theory, in praxis there are constraints that limit the range of achievement, constraints ranging from the availability of resources, their maturity or the available time, respectively to the limits for growth - the learning capacity of individuals and organization as a whole. 

On the other side following the 80/20 principle, one could achieve in theory 80% of a working solution with 20% of the effort needed in achieving the full 100%. This principle comes with a trick too because one needs to focus on the important components or aspects of the solution for this to work. Otherwise, one is forced to do exploratory work in which the learning is gradually assimilated into the solution. This implies continuous feedback, respectively changing the targets as one progresses in multiple iterations. The approach is typically common to ERP implementations, BI and Data Management initiatives, or similar transformative projects which attempt changing an organization’s data, information, or knowledge flows - the backbones organizations are built upon.     

These two principles can be used together to shape an organization. While simplicity sets a target or compass for quality, the 80/20 principle provides the means of splitting the roadmap and effort into manageable targets while allowing to identify and prioritize the critical components, and they seldom resume only to technology. While technologies provide a potential for transformation, in the end is an organization’s setup that has the transformative role. 

For transformational synergies to happen, each person involved in the process must have a minimum of necessary skillset, knowledge and awareness of what’s required and how a solution can be harnessed. This minimum can be initially addressed through training and self-learning, however without certain mechanisms in place, the magic will not happen by itself. Change needs to be managed from within as part of an organization’s culture, by the people close to the flow, and when necessary, also from the outside, by the ones who can provide guiding direction. Ideally, a strategic approach is needed the vision, mission, goals, objectives, and roadmap are sketched, where intermediary targets are adequately mapped and pursued, and the progress is adequately tracked.

Thus, besides the technological components is needed to consider the required organizational components to support and manage change. These components form a structure which needs to adhere by design to the same principle of simplicity. According to Lewes, the 'simplicity of structure means organic unity' [1], which can imply harmony, robustness, variety, balance, economy or proportion. Without these qualities the structure of the resulting edifice can break under its own weight. Moreover, paraphrasing Eric Hoffer, simplicity marks the end of a continuous process of designing, building, and refining, while complexity marks a primitive stage.

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

References:
[1] George H Lewes (1865) "The Principles of Success in Literature"

Considered quotes:
"Simplicity of structure means organic unity, whether the organism be simple or complex; and hence in all times the emphasis which critics have laid upon Simplicity, though they have not unfrequently confounded it with narrowness of range." (George H Lewes, "The Principles of Success in Literature", 1865)
"The first obligation of Simplicity is that of using the simplest means to secure the fullest effect. But although the mind instinctively rejects all needless complexity, we shall greatly err if we fail to recognise the fact, that what the mind recoils from is not the complexity, but the needlessness." (George H Lewes, "The Principles of Success in Literature", 1865)
"In products of the human mind, simplicity marks the end of a process of refining, while complexity marks a primitive stage." (Eric Hoffer, 1954)

💼Project Management: Methodologies (Part III: Agility under Eyeglasses I)

There are more and more posts in the cyberspace voicing against the agile practices, the way they are understood and implemented by organizations. Some try to be hilarious [5]; others try to keep the scholastic seriousness [1] [2] [3] [4], and all of them make some valid points. In each remark there’re some seeds of truth, even if context-dependent.

Personally, I embrace an agile approach when possible, however I find it difficult to choose between the agile methodologies available on the market because each of them introduces some concepts that contradict what it means to be agile – to respond promptly to business needs. It doesn’t mean that one must consider each requirement, but that’s appropriate to consider those which have business justification. Moreover, organizations need to adapt the methodologies to their needs, and seldom vice-versa.

Considering the Agile Manifesto, it’s difficult to take as serious statements that lack precision, formulations like “we value something over something else” are more of a wish than principles. When people don’t understand what the agile “principles” mean, one occasionally hears statements like “we need no documentation”, “we need no project plan”, “the project plan is not important”, “Change Management doesn’t apply to agile projects” or “we need only high-level requirements because we’ll figure out where we’re going on the way”. Because of the lack of precision, a mocker can variate the lesser concept to null and keep the validity of the agile “principles”.

The agile approaches seem to lack control. If you’re letting the users in charge of the scope then you risk having a product that offers a lot though misses the essential, and thus unusable or usable to a lower degree. Agile works good for prototyping something to show to the users or when the products are small enough to easily fit within an iteration, or when the vendor wants to gain a customer’s trust. Therefore, agile works good with BI projects that combine in general all three aspects.

An abomination is the work in fix sprints or iterations of one or a few weeks, and then chopping the functionality to fit the respective time intervals. If you have the luck of having sign-offs and other activities that steal your time, then the productive time reduces up to 50% (the smaller the iterations the higher the percentage). What’s even unconceivable is that people ignore the time spent with bureaucracy. If this way of working repeats in each iteration then the project duration multiplies by a factor between 2 or 4, the time spent on Project Management increasing by the same factor. What’s not understandable is that despite bureaucracy the adherence to delivery dates, budget and quality is still required.

Sometimes one has the feeling that people think that software development and other IT projects work like building a house or like the manufacturing of a mug. You choose the colors, the materials, the dimensions and voila the product is ready. IT projects involve lot of unforeseen and one must react agilely to it. Here resides one of the most important challenges.   

Communication is one important challenge in a project especially when multiple interests are involved. Face-to-face conversation is one of the nice-to-have items on the wish list however in praxis isn’t always possible. One can’t expect that all the resources are available to meet and decide. In addition, one needs to document everything from meeting minutes, to Business Cases and requirements. A certain flexibility in changing the requirements is needed though one can’t change them arbitrarily, there must be a concept behind otherwise the volume of overwork can easily make the budget for a project explode exponentially.

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Resources:
[1] Harvard Business Review (2018) Why Agile Goes Awry - and How to Fix It, by Lindsay McGregor & Neel Doshi (Online) Available from: https://hbr.org/2018/10/why-agile-goes-awry-and-how-to-fix-it
[2] Forbes (2012) The Case Against Agile: Ten Perennial Management Objections, by Steve Denning  (Online) Available from:
https://www.forbes.com/sites/stevedenning/2012/04/17/the-case-against-agile-ten-perennial-management-objections/#6df0e6ea3a95 
[3] Springer (2018) Do Agile Methods Work for Large Software Projects?, by Magne Jørgensen  (Online) Available from:
https://link.springer.com/chapter/10.1007/978-3-319-91602-6_12
[4] Michael O Church (2015) Why “Agile” and especially Scrum are terrible  (Online) Available from:
https://michaelochurch.wordpress.com/2015/06/06/why-agile-and-especially-scrum-are-terrible/
[5] Dev.to (2019) Mockery of agile, by Artur Martsinkovskyi (Online) Available from: https://dev.to/arturmartsinkovskyi/mockery-of-agile-5bdf

𖣯Strategic Management: Strategy Definition (Part II: Defining the Strategy)

In a previous post an organization’s strategy was defined as a set of coordinated and sustainable actions following a set of well-defined goals, actions devised into a plan and designed to create value and overcome an organization’s challenges. In what follows are described succinctly the components of the strategy.

A strategy’s definition should start with the identification of organization’s vision, where the organization wants to be in the future, its mission statement, a precise description of what an organization does in turning the vision from concept to reality, its values - traits and qualities that are considered as representative, and its principles – the guiding laws and truths for action. All these components have the purpose at defining at high-level the where (the vision), the why (the mission), the what (the core values) and by which means (the principles) of the strategy.

One of the next steps that can be followed in parallel is to take inventory of the available infrastructure: systems, processes, procedures, practices, policies, documentation, resources, roles and their responsibilities, KPIs and other metrics, ongoing projects and initiatives. Another step resumes in identifying the problems (challenges), risks and opportunities existing in the organization as part of a SWOT analysis adjusted to organization’s internal needs. One can extend the analysis to the market and geopolitical conditions and trends to identify further opportunities and risks. Within another step but not necessarily disconnected from the previous steps is devised where the organization could be once the problems, risks, threats and opportunities were addressed.

Then the gathered facts are divided into two perspectives – the “IS” perspective encompasses the problems together with the opportunities and threats existing in organization that define the status quo, while the “TO BE” perspective encompasses the wished state. A capability maturity model can be used to benchmark an organization’s current maturity in respect to industry practices, and, based on the wished capabilities, to identify organization’s future maturity.

Based on these the organization can start formulating its strategic goals – a set of long-range aims for a specific time-frame, from which are derived a (hierarchical) set of objectives, measurable steps an organization takes in order to achieve the goals. Each objective carries with it a rational, why the objective exists, an impact, how will the objective change the organization once achieved, and a target, how much of the objective needs to be achieved. In addition, one can link the objectives to form a set of hypothesis - predictive statements of cause and effect that involve approaches of dealing with the uncertainty. In order to pursue each objective are devised methods and means – the tactics (lines of action) that will be used to approach the various themes. It’s important to prioritize the tactics and differentiate between quick winners and long-term tactics, as well to define alternative lines of actions.

Then the tactics are augmented in a strategy plan (roadmap) that typically covers a minimum of 3 to 5 years with intermediate milestones. Following the financial cycles the strategy is split in yearly units for each objective being assigned intermediate targets. Linked to the plan are estimated the costs, effort and resources needed. Last but not the least are defined the roles, management and competency structures, with their responsibilities, competencies and proper level of authority, needed to support strategy’s implementation. Based on the set objectives are devised the KPIs used to measure the progress (success) and stir the strategy over its lifecycle.

By addressing all these aspects is created thus a first draft of the strategy that will need several iterations to mature, further changes deriving from the contact with the reality.

𖣯Strategic Management: Strategy Definition (Part I: The Reason behind a Strategy)

Many of the efforts that go on in organizations are just castles built into the thin air, and even if some of the architectures are wonderful, without a foundation they tend to crash under their own weight. For example, the investment in a modern BI solution, in an ERP or CRM system, seldom meets an organization’s expectations, and what’s even more unfortunate is that the potential introduced by the investments is only to a small degree harnessed, while the same old problems continue to exist, typically in new contexts.

An architect more likely would ask himself: What would be that foundation needed to support a castle or the whole settlement the castle belongs to? From what needs to be made? How should it be structured? How often needs to be reconsolidated and when? Who will participate in its building and its maintenance? What it still needed to make the infrastructure self-reliant? What other architects do? What’s best practice in the field? Many questions for which the architect needs to find optimal answers.

The strength of an edifice lies in its foundation. Its main purpose is to provide a solid, durable, self-reliant and maintainable structure on which the edifice can be anchored, that can support the current and future load of the edifice, and that keeps the edifice standing in face of calamities. It must therefore address the core challenges faced by the edifice during its lifetime. When one has a group of edifices holding together as a settlement, there’s needed a foundation to support the whole settlement and not only one edifice. Moreover, the foundation needs to be customized to address environment’s characteristics and owners’ plans for further development.

The foundation on which modern organizations build their edifice is a strategy rooted in organizations’ reason of existence (the mission), wishes of becoming (the vision), beliefs (the core values) and fundamental truths (the principles). A strategy, a term borrowed from military, is a set of coordinated and sustainable actions following a set of well-defined goals, actions devised into a plan and designed to create value and overcome an organization’s challenges. Through its character a strategy is the perfect tool for addressing holistically the problems, opportunities, strengths and weaknesses existing in an organization, of aligning the objectives toward the same goals, of providing transparency and common understanding into the status quo and the road ahead.

Having defined a strategy will not make things happen by themselves, one needs also the capabilities of executing the strategy as a whole, one needs clear roles with responsibilities and proper authority. In addition, the strategy needs to be adapted in time to serve its purpose. This might mean changing the level of detail, changing the strategy when opportunities or threats were identified, when goals become obsolete. To make this possible is needed to define several processes to support the strategy through its whole lifecycle and a set of metrics to make the progress visible.

There are organizations that make it without having a written strategy, some go with the inertia provided by the adoption of tools, with the experience of individual workers that through their cooperation provide the improvement needed. In a higher or lower degree there’s a strategy fragmented in each individual or group, however the strategies don’t necessarily converge. The problem with such approaches is that the results are often suboptimal, especially because they are fragmented efforts, more likely with different contradictory goals.

As any other tool a strategy has a potential power that when adequately harnessed can help organizations achieve their (strategic) goals, though it depends on each organization to harness that potential.

#️⃣Software Engineering: Programming (Part VIII: Pair Programming)

Software Engineering Series

“Two heads are better than one” – a proverb whose wisdom is embraced today in the various forms of harnessing the collective intelligence. The use of groups in problem solving is based on principles like “the collective is more than the sum of its individuals” or that “the crowds are better on average at estimations than the experts”. All well and good, based on the rationality of the same proverb has been advanced the idea of having two developers working together on the same piece of code – one doing the programming while the other looks over the shoulder as a observer or navigator (whatever that means), reviewing each line of code as it is written, strategizing or simply being there.

This approach is known as pair programming and considered as an agile software development technique, adhering thus to the agile principles (see the agile manifesto). Beyond some intangible benefits, its intent is to reduce the volume of defects in software and thus ensure an acceptable quality of the deliverables. It’s also an extreme approach of the pear review concept.

Without considering whether pair programming adheres to the agile principles, the concept has several big loopholes. The first time I read about pair programming it took me some time to digest the idea – I was asking myself what programmer will do that on a daily basis, watching as other programmers code or being watched while coding, each line of code being followed by questions, affirmative or negative nodding… Beyond their statute of being lone wolves, programmers can cooperate when the tasks ahead requires it, however to ask a programmer watch actively as others program it won’t work on the long run!

Talking from my own experience as programmer and of a professional working together with other programmers, I know that a programmer sees each task as a challenge, a way of learning, of reaching beyond his own condition. Programming is a way of living, with its pluses and minuses.

Moreover, the complexity of the tasks doesn’t resume at handling the programming language but of resolving the right problem. Solving the right problem is not something that can one overcome with brute force but with intelligence. If using the programming language is the challenge then the problem lies somewhere else and other countermeasures must be taken!

Some studies have identified that the use of pair programming led to a reduction of defects in software, however the numbers are misleading as long they compare apples with pears. To statistically conclude that one method is better than the other means doing the same experiment with the different methods using a representative population. Unless one addressees the requirements of statistics the numbers advanced are just fiction!

Just think again about the main premise! One doubles the expenditure for a theoretical reduction of the defects?! Actually, it's more than double considering that different types of communication takes place. Without a proven basis the effort can be somewhere between 2.2 and 2.5 and for an average project this can be a lot! The costs might be bearable in situations in which the labor is cheap, however programmers’ cooperation is a must.

The whole concept of pair programming seems like a bogus idea, just like two drivers driving the same car! This approach might work when the difference in experience and skills between developers is considerable, that being met in universities or apprenticeship environments, in which the accent is put on learning and forming. It might work on handling complex tasks as some adepts declare, however even then is less likely that the average programmer will willingly do it!

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🤝Governance: Accountability (Just the Quotes)

"To hold a group or individual accountable for activities of any kind without assigning to him or them the necessary authority to discharge that responsibility is manifestly both unsatisfactory and inequitable. It is of great Importance to smooth working that at all levels authority and responsibility should be coterminous and coequal." (Lyndall Urwick, "Dynamic Administration", 1942)

"Complete accountability is established and enforced throughout; and if there there is any error committed, it will be discovered on a comparison with the books and can be traced to its source." (Alfred D Chandler Jr, "The Visible Hand", 1977)

"If responsibility - and particularly accountability - is most obviously upwards, moral responsibility also reaches downwards. The commander has a responsibility to those whom he commands. To forget this is to vitiate personal integrity and the ethical validity of the system." (Roger L Shinn, "Military Ethics", 1987)

"Perhaps nothing in our society is more needed for those in positions of authority than accountability." (Larry Burkett, "Business By The Book: Complete Guide of Biblical Principles for the Workplace", 1990)

"Corporate governance is concerned with holding the balance between economic and social goals and between individual and communal goals. The governance framework is there to encourage the efficient use of resources and equally to require accountability for the stewardship of those resources. The aim is to align as nearly as possible the interests of individuals, corporations and society." (Dominic Cadbury, "UK, Commission Report: Corporate Governance", 1992)

"Accountability is essential to personal growth, as well as team growth. How can you improve if you're never wrong? If you don't admit a mistake and take responsibility for it, you're bound to make the same one again." (Pat Summitt, "Reach for the Summit", 1999)

"Responsibility equals accountability equals ownership. And a sense of ownership is the most powerful weapon a team or organization can have." (Pat Summitt, "Reach for the Summit", 1999)

"There's not a chance we'll reach our full potential until we stop blaming each other and start practicing personal accountability." (John G Miller, "QBQ!: The Question Behind the Question", 2001)

"Democracy is not about trust; it is about distrust. It is about accountability, exposure, open debate, critical challenge, and popular input and feedback from the citizenry." (Michael Parenti, "Superpatriotism", 2004)

"No individual can achieve worthy goals without accepting accountability for his or her own actions." (Dan Miller, "No More Dreaded Mondays", 2008)

"In putting together your standards, remember that it is essential to involve your entire team. Standards are not rules issued by the boss; they are a collective identity. Remember, standards are the things that you do all the time and the things for which you hold one another accountable." (Mike Krzyzewski, "The Gold Standard: Building a World-Class Team", 2009)

"Nobody can do everything well, so learn how to delegate responsibility to other winners and then hold them accountable for their decisions." (George Foreman, "Knockout Entrepreneur: My Ten-Count Strategy for Winning at Business", 2010)

"Failing to hold someone accountable is ultimately an act of selfishness." (Patrick Lencioni, "The Advantage, Enhanced Edition: Why Organizational Health Trumps Everything Else In Business", 2012)

"We cannot have a just society that applies the principle of accountability to the powerless and the principle of forgiveness to the powerful. This is the America in which we currently reside." (Chris Hayes, "Twilight of the Elites: America After Meritocracy", 2012)

"Artificial intelligence is a concept that obscures accountability. Our problem is not machines acting like humans - it's humans acting like machines." (John Twelve Hawks, "Spark", 2014)

"In order to cultivate a culture of accountability, first it is essential to assign it clearly. People ought to clearly know what they are accountable for before they can be held to it. This goes beyond assigning key responsibility areas (KRAs). To be accountable for an outcome, we need authority for making decisions, not just responsibility for execution. It is tempting to refrain from the tricky exercise of explicitly assigning accountability. Executives often hope that their reports will figure it out. Unfortunately, this is easier said than done." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Some hierarchy is essential for the effective functioning of an organization. Eliminating hierarchy has the frequent side effect of slowing down decision making and diffusing accountability." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Accountability makes no sense when it undermines the larger goals of education." (Diane Ravitch, "The Death and Life of the Great American School System", 2016)

"[...] high-accountability teams are characterized by having members that are willing and able to resolve issues within the team. They take responsibility for their own actions and hold each other accountable. They take ownership of resolving disputes and feel empowered to do so without intervention from others. They learn quickly by identifying issues and solutions together, adopting better patterns over time. They are able to work without delay because they don’t need anyone else to resolve problems. Their managers are able to work more strategically without being bogged down by day-to-day conflict resolution." (Morgan Evans, "Engineering Manager's Handbook", 2023)

"In a workplace setting, accountability is the willingness to take responsibility for one’s actions and their outcomes. Accountable team members take ownership of their work, admit their mistakes, and are willing to hold each other accountable as peers." (Morgan Evans, "Engineering Manager's Handbook", 2023)

"Low-accountability teams can be recognized based on their tendency to shift blame, avoid addressing issues within the team, and escalate most problems to their manager. In low-accountability teams, it is difficult to determine the root of problems, failures are met with apathy, and managers have to spend much of their time settling disputes and addressing performance. Members of low-accountability teams believe it is not their role to resolve disputes and instead shift that responsibility up to the manager, waiting for further direction. These teams fall into conflict and avoidance deadlocks, unable to move quickly because they cannot resolve issues within the team."

🔭Data Science: Phenomena (Just the Quotes)

"The word ‘chance’ then expresses only our ignorance of the causes of the phenomena that we observe to occur and to succeed one another in no apparent order. Probability is relative in part to this ignorance, and in part to our knowledge.” (Pierre-Simon Laplace, "Mémoire sur les Approximations des Formules qui sont Fonctions de Très Grands Nombres", 1783)

"The aim of every science is foresight. For the laws of established observation of phenomena are generally employed to foresee their succession. All men, however little advanced make true predictions, which are always based on the same principle, the knowledge of the future from the past." (Auguste Compte, "Plan des travaux scientifiques nécessaires pour réorganiser la société", 1822)

"The insights gained and garnered by the mind in its wanderings among basic concepts are benefits that theory can provide. Theory cannot equip the mind with formulas for solving problems, nor can it mark the narrow path on which the sole solution is supposed to lie by planting a hedge of principles on either side. But it can give the mind insight into the great mass of phenomena and of their relationships, then leave it free to rise into the higher realms of action." (Carl von Clausewitz, "On War", 1832)

"Theories usually result from the precipitate reasoning of an impatient mind which would like to be rid of phenomena and replace them with images, concepts, indeed often with mere words." (Johann Wolfgang von Goethe, "Maxims and Reflections", 1833)

"[…] in order to observe, our mind has need of some theory or other. If in contemplating phenomena we did not immediately connect them with principles, not only would it be impossible for us to combine these isolated observations, and therefore to derive profit from them, but we should even be entirely incapable of remembering facts, which would for the most remain unnoted by us." (Auguste Comte, "Cours de Philosophie Positive", 1830-1842)

"The dimmed outlines of phenomenal things all merge into one another unless we put on the focusing-glass of theory, and screw it up sometimes to one pitch of definition and sometimes to another, so as to see down into different depths through the great millstone of the world." (James C Maxwell, "Are There Real Analogies in Nature?", 1856) 

"Isolated facts and experiments have in themselves no value, however great their number may be. They only become valuable in a theoretical or practical point of view when they make us acquainted with the law of a series of uniformly recurring phenomena, or, it may be, only give a negative result showing an incompleteness in our knowledge of such a law, till then held to be perfect." (Hermann von Helmholtz, "The Aim and Progress of Physical Science", 1869)

"If statistical graphics, although born just yesterday, extends its reach every day, it is because it replaces long tables of numbers and it allows one not only to embrace at glance the series of phenomena, but also to signal the correspondences or anomalies, to find the causes, to identify the laws." (Émile Cheysson, cca. 1877)

"There is no doubt that graphical expression will soon replace all others whenever one has at hand a movement or change of state - in a word, any phenomenon. Born before science, language is often inappropriate to express exact measures or definite relations." (Étienne-Jules Marey, "La méthode graphique dans les sciences expérimentales et principalement en physiologie et en médecine", 1878)

"Most surprising and far-reaching analogies revealed themselves between apparently quite disparate natural processes. It seemed that nature had built the most various things on exactly the same pattern; or, in the dry words of the analyst, the same differential equations hold for the most various phenomena." (Ludwig Boltzmann, "On the methods of theoretical physics", 1892)

"Some of the common ways of producing a false statistical argument are to quote figures without their context, omitting the cautions as to their incompleteness, or to apply them to a group of phenomena quite different to that to which they in reality relate; to take these estimates referring to only part of a group as complete; to enumerate the events favorable to an argument, omitting the other side; and to argue hastily from effect to cause, this last error being the one most often fathered on to statistics. For all these elementary mistakes in logic, statistics is held responsible." (Sir Arthur L Bowley, "Elements of Statistics", 1901)

"A model, like a novel, may resonate with nature, but it is not a ‘real’ thing. Like a novel, a model may be convincing - it may ‘ring true’ if it is consistent with our experience of the natural world. But just as we may wonder how much the characters in a novel are drawn from real life and how much is artifice, we might ask the same of a model: How much is based on observation and measurement of accessible phenomena, how much is convenience? Fundamentally, the reason for modeling is a lack of full access, either in time or space, to the phenomena of interest." (Kenneth Belitz, Science, Vol. 263, 1944)

"The principle of complementarity states that no single model is possible which could provide a precise and rational analysis of the connections between these phenomena [before and after measurement]. In such a case, we are not supposed, for example, to attempt to describe in detail how future phenomena arise out of past phenomena. Instead, we should simply accept without further analysis the fact that future phenomena do in fact somehow manage to be produced, in a way that is, however, necessarily beyond the possibility of a detailed description. The only aim of a mathematical theory is then to predict the statistical relations, if any, connecting the phenomena." (David Bohm, "A Suggested Interpretation of the Quantum Theory in Terms of ‘Hidden’ Variables", 1952)

"The sciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena. The justification of such a mathematical construct is solely and precisely that it is expected to work" (John Von Neumann, "Method in the Physical Sciences", 1955)

"As shorthand, when the phenomena are suitably simple, words such as equilibrium and stability are of great value and convenience. Nevertheless, it should be always borne in mind that they are mere shorthand, and that the phenomena will not always have the simplicity that these words presuppose." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"Can there be laws of chance? The answer, it would seem should be negative, since chance is in fact defined as the characteristic of the phenomena which follow no law, phenomena whose causes are too complex to permit prediction." (Félix E Borel, "Probabilities and Life", 1962)

"Theories are usually introduced when previous study of a class of phenomena has revealed a system of uniformities. […] Theories then seek to explain those regularities and, generally, to afford a deeper and more accurate understanding of the phenomena in question. To this end, a theory construes those phenomena as manifestations of entities and processes that lie behind or beneath them, as it were." (Carl G Hempel, "Philosophy of Natural Science", 1966)

"The less we understand a phenomenon, the more variables we require to explain it." (Russell L Ackoff, "Management Science", 1967)

 "As soon as we inquire into the reasons for the phenomena, we enter the domain of theory, which connects the observed phenomena and traces them back to a single ‘pure’ phenomena, thus bringing about a logical arrangement of an enormous amount of observational material." (Georg Joos, "Theoretical Physics", 1968)

"A model is an abstract description of the real world. It is a simple representation of more complex forms, processes and functions of physical phenomena and ideas." (Moshe F Rubinstein & Iris R Firstenberg, "Patterns of Problem Solving", 1975)

"A real change of theory is not a change of equations - it is a change of mathematical structure, and only fragments of competing theories, often not very important ones conceptually, admit comparison with each other within a limited range of phenomena." (Yuri I Manin, "Mathematics and Physics", 1981)

"In all scientific fields, theory is frequently more important than experimental data. Scientists are generally reluctant to accept the existence of a phenomenon when they do not know how to explain it. On the other hand, they will often accept a theory that is especially plausible before there exists any data to support it." (Richard Morris, 1983)

"Nature is disordered, powerful and chaotic, and through fear of the chaos we impose system on it. We abhor complexity, and seek to simplify things whenever we can by whatever means we have at hand. We need to have an overall explanation of what the universe is and how it functions. In order to achieve this overall view we develop explanatory theories which will give structure to natural phenomena: we classify nature into a coherent system which appears to do what we say it does." (James Burke, "The Day the Universe Changed", 1985) 

"The science of statistics may be described as exploring, analyzing and summarizing data; designing or choosing appropriate ways of collecting data and extracting information from them; and communicating that information. Statistics also involves constructing and testing models for describing chance phenomena. These models can be used as a basis for making inferences and drawing conclusions and, finally, perhaps for making decisions." (Fergus Daly et al, "Elements of Statistics", 1995)

"[…] the simplest hypothesis proposed as an explanation of phenomena is more likely to be the true one than is any other available hypothesis, that its predictions are more likely to be true than those of any other available hypothesis, and that it is an ultimate a priori epistemic principle that simplicity is evidence for truth." (Richard Swinburne, "Simplicity as Evidence for Truth", 1997)

"The point is that scientific descriptions of phenomena in all of these cases do not fully capture reality they are models. This is not a shortcoming but a strength of science much of the scientist's art lies in figuring out what to include and what to exclude in a model, and this ability allows science to make useful predictions without getting bogged down by intractable details." (Philip Ball," The Self-Made Tapestry: Pattern Formation in Nature", 1998)

"A scientific theory is a concise and coherent set of concepts, claims, and laws (frequently expressed mathematically) that can be used to precisely and accurately explain and predict natural phenomena." (Mordechai Ben-Ari, "Just a Theory: Exploring the Nature of Science", 2005)

"Complexity arises when emergent system-level phenomena are characterized by patterns in time or a given state space that have neither too much nor too little form. Neither in stasis nor changing randomly, these emergent phenomena are interesting, due to the coupling of individual and global behaviours as well as the difficulties they pose for prediction. Broad patterns of system behaviour may be predictable, but the system's specific path through a space of possible states is not." (Steve Maguire et al, "Complexity Science and Organization Studies", 2006)

"Humans have difficulty perceiving variables accurately […]. However, in general, they tend to have inaccurate perceptions of system states, including past, current, and future states. This is due, in part, to limited ‘mental models’ of the phenomena of interest in terms of both how things work and how to influence things. Consequently, people have difficulty determining the full implications of what is known, as well as considering future contingencies for potential systems states and the long-term value of addressing these contingencies. " (William B. Rouse, "People and Organizations: Explorations of Human-Centered Design", 2007)

"A theory is a speculative explanation of a particular phenomenon which derives it legitimacy from conforming to the primary assumptions of the worldview of the culture in which it appears. There can be more than one theory for a particular phenomenon that conforms to a given worldview. […]  A new theory may seem to trigger a change in worldview, as in this case, but logically a change in worldview must precede a change in theory, otherwise the theory will not be viable. A change in worldview will necessitate a change in all theories in all branches of study." (M G Jackson, "Transformative Learning for a New Worldview: Learning to Think Differently", 2008)

"[...] construction of a data model is precisely the selective relevant depiction of the phenomena by the user of the theory required for the possibility of representation of the phenomenon."  (Bas C van Fraassen, "Scientific Representation: Paradoxes of Perspective", 2008)

"Put simply, statistics is a range of procedures for gathering, organizing, analyzing and presenting quantitative data. […] Essentially […], statistics is a scientific approach to analyzing numerical data in order to enable us to maximize our interpretation, understanding and use. This means that statistics helps us turn data into information; that is, data that have been interpreted, understood and are useful to the recipient. Put formally, for your project, statistics is the systematic collection and analysis of numerical data, in order to investigate or discover relationships among phenomena so as to explain, predict and control their occurrence." (Reva B Brown & Mark Saunders, "Dealing with Statistics: What You Need to Know", 2008)

"A theory is a set of deductively closed propositions that explain and predict empirical phenomena, and a model is a theory that is idealized." (Jay Odenbaugh, "True Lies: Realism, Robustness, and Models", Philosophy of Science, Vol. 78, No. 5, 2011)

"Mathematical modeling is the modern version of both applied mathematics and theoretical physics. In earlier times, one proposed not a model but a theory. By talking today of a model rather than a theory, one acknowledges that the way one studies the phenomenon is not unique; it could also be studied other ways. One's model need not claim to be unique or final. It merits consideration if it provides an insight that isn't better provided by some other model." (Reuben Hersh, ”Mathematics as an Empirical Phenomenon, Subject to Modeling”, 2017)

"Repeated observations of the same phenomenon do not always produce the same results, due to random noise or error. Sampling errors result when our observations capture unrepresentative circumstances, like measuring rush hour traffic on weekends as well as during the work week. Measurement errors reflect the limits of precision inherent in any sensing device. The notion of signal to noise ratio captures the degree to which a series of observations reflects a quantity of interest as opposed to data variance. As data scientists, we care about changes in the signal instead of the noise, and such variance often makes this problem surprisingly difficult." (Steven S Skiena, "The Data Science Design Manual", 2017)

"The first epistemic principle to embrace is that there is always a gap between our data and the real world. We fall headfirst into a pitfall when we forget that this gap exists, that our data isn't a perfect reflection of the real-world phenomena it's representing. Do people really fail to remember this? It sounds so basic. How could anyone fall into such an obvious trap?" (Ben Jones, "Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations", 2020)

"We live on islands surrounded by seas of data. Some call it 'big data'. In these seas live various species of observable phenomena. Ideas, hypotheses, explanations, and graphics also roam in the seas of data and can clarify the waters or allow unsupported species to die. These creatures thrive on visual explanation and scientific proof. Over time new varieties of graphical species arise, prompted by new problems and inner visions of the fishers in the seas of data." (Michael Friendly & Howard Wainer, "A History of Data Visualization and Graphic Communication", 2021)

"Although to penetrate into the intimate mysteries of nature and hence to learn the true causes of phenomena is not allowed to us, nevertheless it can happen that a certain fictive hypothesis may suffice for explaining many phenomena." (Leonhard Euler)

🔭Data Science: Discovery (Just the Quotes)

"A good method of discovery is to imagine certain members of a system removed and then see how what is left would behave: for example, where would we be if iron were absent from the world: this is an old example." (Georg C Lichtenberg, Notebook J, 1789-1793)

"Every science has for its basis a system of principles as fixed and unalterable as those by which the universe is regulated and governed. Man cannot make principles; he can only discover them." (Thomas Paine, "The Age of Reason", 1794)

"We learn wisdom from failure much more than from success. We often discover what will do, by finding out what will not do; and probably he who never made a mistake never made a discovery." (Samuel Smiles, "Facilities and Difficulties", 1859)

"The process of discovery is very simple. An unwearied and systematic application of known laws to nature, causes the unknown to reveal themselves. Almost any mode of observation will be successful at last, for what is most wanted is method." (Henry Thoreau, "A Week on the Concord and Merrimack Rivers", 1862)

"Every process has laws, known or unknown, according to which it must take place. A consciousness of them is so far from being necessary to the process, that we cannot discover what they are, except by analyzing the results it has left us." (Lord William T Kelvin , "An Outline of the Necessary Laws of Thought", 1866)

"Accurate and minute measurement seems to the nonscientific imagination a less lofty and dignified work than looking for something new. But nearly all the grandest discoveries of science have been but the rewards of accurate measurement and patient long contained labor in the minute sifting of numerical results." (William T Kelvin, "Report of the British Association For the Advancement of Science" Vol. 41, 1871)

"Modern discoveries have not been made by large collections of facts, with subsequent discussion, separation, and resulting deduction of a truth thus rendered perceptible. A few facts have suggested an hypothesis, which means a supposition, proper to explain them. The necessary results of this supposition are worked out, and then, and not till then, other facts are examined to see if their ulterior results are found in Nature." (Augustus de Morgan, "A Budget of Paradoxes", 1872)

"Science arises from the discovery of Identity amid Diversity." (William S Jevons, "The Principles of Science: A Treatise on Logic and Scientific Method", 1874)

"It would be an error to suppose that the great discoverer seizes at once upon the truth, or has any unerring method of divining it. In all probability the errors of the great mind exceed in number those of the less vigorous one. Fertility of imagination and abundance of guesses at truth are among the first requisites of discovery; but the erroneous guesses must be many times as numerous as those that prove well founded. The weakest analogies, the most whimsical notions, the most apparently absurd theories, may pass through the teeming brain, and no record remain of more than the hundredth part. […] The truest theories involve suppositions which are inconceivable, and no limit can really be placed to the freedom of hypotheses." (W Stanley Jevons, "The Principles of Science: A Treatise on Logic and Scientific Method", 1877)

"A discoverer is a tester of scientific ideas; he must not only be able to imagine likely hypotheses, and to select suitable ones for investigation, but, as hypotheses may be true or untrue, he must also be competent to invent appropriate experiments for testing them, and to devise the requisite apparatus and arrangements." (George Gore, "The Art of Scientific Discovery", 1878)

"All great scientists have, in a certain sense, been great artists; the man with no imagination may collect facts, but he cannot make great discoveries." (Karl Pearson, "The Grammar of Science", 1892)

"The folly of mistaking a paradox for a discovery, a metaphor for a proof, a torrent of verbiage for a spring of capital truths, and oneself for an oracle, is inborn in us." (Paul Valéry, "Introduction to the Method of Leonardo da Vinci", 1895)

"If we study the history of science we see happen two inverse phenomena […] Sometimes simplicity hides under complex appearances; sometimes it is the simplicity which is apparent, and which disguises extremely complicated realities. […] No doubt, if our means of investigation should become more and more penetrating, we should discover the simple under the complex, then the complex under the simple, then again the simple under the complex, and so on, without our being able to foresee what will be the last term. We must stop somewhere, and that science may be possible, we must stop when we have found simplicity. This is the only ground on which we can rear the edifice of our generalizations." (Henri Poincaré, "Science and Hypothesis", 1901)

"The only true voyage of discovery […] would be not to visit new landscapes, but to possess other eyes, to see the universe through the eyes of another, of a hundred others, to see the hundred universes that each of them sees." (Marcel Proust, "À la recherche du temps perdu", 1913)

"To come very near to a true theory, and to grasp its precise application, are two very different things, as the history of a science teaches us. Everything of importance has been said before by somebody who did not discover it." (Alfred N Whitehead, "The Organization of Thought", 1917)

"Great scientific discoveries have been made by men seeking to verify quite erroneous theories about the nature of things." (Aldous L Huxley, "Life and Letters and the London Mercury" Vol. 1, 1928)

"The art of discovery is confused with the logic of proof and an artificial simplification of the deeper movements of thought results. We forget that we invent by intuition though we prove by logic." (Sarvepalli Radhakrishnan, "An Idealist View of Life", 1929)

"Science is the attempt to discover, by means of observation, and reasoning based upon it, first, particular facts about the world, and then laws connecting facts with one another and (in fortunate cases) making it possible to predict future occurrences." (Bertrand Russell, "Religion and Science, Grounds of Conflict", 1935) 

"A great discovery solves a great problem but there is a grain of discovery in the solution of any problem. Your problem may be modest; but if it challenges your curiosity and brings into play your inventive faculties, and if you solve it by your own means, you may experience the tension and enjoy the triumph of discovery." (George Polya, "How to solve it", 1944)

"It is always more easy to discover and proclaim general principles than it is to apply them." (Winston Churchill, "The Second World War: The gathering storm", 1948)

"Scientific discovery consists in the interpretation for our own convenience of a system of existence which has been made with no eye to our convenience at all." (Norbert Wiener, "The Human Use of Human Beings", 1949)

"The scientist who discovers a theory is usually guided to his discovery by guesses; he cannot name a method by means of which he found the theory and can only say that it appeared plausible to him, that he had the right hunch or that he saw intuitively which assumption would fit the facts." (Hans Reichenbach, "The Rise of Scientific Philosophy", 1951)

"It is important for him who wants to discover not to confine himself to one chapter of science, but to keep in touch with various others." (Jacques S Hadamard, "An Essay on the Psychology of Invention in the Mathematical Field", 1954)

"The true aim of science is to discover a simple theory which is necessary and sufficient to cover the facts, when they have been purified of traditional prejudices." (Lancelot L Whyte, "Accent on Form", 1954)

"There comes a point where the mind takes a leap - call it intuition or what you will - and comes out upon a higher plane of knowledge, but can never prove how it got there. All great discoveries have involved such a leap." (Albert Einstein, [interview in Life, "Death of a Genius"] 1955)

"Discovery follows discovery, each both raising and answering questions, each ending a long search, and each providing the new instruments for a new search." (J Robert Oppenheimer, "Prospects in the Arts and Sciences", 1964)

"Discovery always carries an honorific connotation. It is the stamp of approval on a finding of lasting value. Many laws and theories have come and gone in the history of science, but they are not spoken of as discoveries. […] Theories are especially precarious, as this century profoundly testifies. World views can and do often change. Despite these difficulties, it is still true that to count as a discovery a finding must be of at least relatively permanent value, as shown by its inclusion in the generally accepted body of scientific knowledge." (Richard J. Blackwell, "Discovery in the Physical Sciences", 1969)

"A discovery must be, by definition, at variance with existing knowledge." (Albert Szent-Gyorgyi, "Dionysians and Apollonians", Science 176, 1972)

"It is one of our most exciting discoveries that local discovery leads to a complex of further discoveries. Corollary to this we find that we no sooner get a problem solved than we are overwhelmed with a multiplicity of additional problems in a most beautiful payoff of heretofore unknown, previously unrecognized, and as-yet unsolved problems." (Buckminster Fuller, "Synergetics: Explorations in the Geometry of Thinking", 1975)

"You cannot learn, through common sense, how things are you can only discover where they fit into the existing scheme of things." (Stuart Hall, 1977)

"Every discovery, every enlargement of the understanding, begins as an imaginative preconception of what the truth might be. The imaginative preconception - a ‘hypothesis’ - arises by a process as easy or as difficult to understand as any other creative act of mind; it is a brainwave, an inspired guess, a product of a blaze of insight. It comes anyway from within and cannot be achieved by the exercise of any known calculus of discovery. " (Sir Peter B Medawar, "Advice to a Young Scientist", 1979)

"Metaphors can have profound significance because, as images or figures, they allow the mind to grasp or discover unsuspected ideal and material relationships between objects." (Giuseppe Del Re, "Cosmic Dance", 1999)

"Alternative models are neither right nor wrong, just more or less useful in allowing us to operate in the world and discover more and better options for solving problems." (Andrew Weil," The Natural Mind: A Revolutionary Approach to the Drug Problem", 2004)

"We tackle a multifaceted universe one face at a time, tailoring our models and equations to fit the facts at hand. Whatever mechanical conception proves appropriate, that is the one to use. Discovering worlds within worlds, a practical observer will deal with each realm on its own terms. It is the only sensible approach to take." (Michael Munowitz, "Knowing: The Nature of Physical Law", 2005)

"Equations seem like treasures, spotted in the rough by some discerning individual, plucked and examined, placed in the grand storehouse of knowledge, passed on from generation to generation. This is so convenient a way to present scientific discovery, and so useful for textbooks, that it can be called the treasure-hunt picture of knowledge." (Robert P Crease, "The Great Equations", 2009)

"Models do not only describe reality, they are also instruments for exploring reality. They are not only involved in the integration of known data, but also in the discovery of new data." (Andreas Bartels, "The Standard Model of Cosmology as a Tool for Interpretation and Discovery", 2013)

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