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

Data Visualization Series

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

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

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

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

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

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

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

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

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🤝Governance: Criteria (Definitions)

"Standards by which alternatives are judged. Attributes that describe certain (information) characteristics." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"Conditions that enable a decision to be made, especially at a decision point within the areas of work related to New Product Planning and New Product Introduction." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"Standards, rules, or tests on which a judgment or decision can be based, or by which a product, service, result, or process can be evaluated." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"Standards or expectation specifying what should exist (what success looks like)." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

[definite criteria] "A special purpose framework using a definite set of criteria having substantial support that is applied to all material items appearing in financial statements, such as the price-level basis of accounting." (Tom Klammer, "Statement of Cash Flows: Preparation, Presentation, and Use", 2018)

[common criteria:] "A set of internationally accepted semantic tools and constructs for describing the security needs of customers and the security attributes of products." (NIST SP 800-32)

[common criteria:] "Governing document that provides a comprehensive, rigorous method for specifying security function and assurance requirements for products and systems." (CNSSI 4009-2015)

[evaluation criteria:] "The standards by which accomplishments of technical and operational effectiveness or suitability characteristics may be assessed. Evaluation criteria are a benchmark, standard, or factor against which conformance, performance, and suitability of a technical capability, activity, product, or plan is measured." (NIST SP 800-137A)

🔭Data Science: Criteria (Just the Quotes)

"[Precision] is the very soul of science; and its attainment afford the only criterion, or at least the best, of the truth of theories, and the correctness of experiments." (John F W Herschel, "A Preliminary Discourse on the Study of Natural Philosophy", 1830)

"A primary goal of any learning model is to predict correctly the learning curve - proportions of correct responses versus trials. Almost any sensible model with two or three free parameters, however, can closely fit the curve, and so other criteria must be invoked when one is comparing several models." (Robert R Bush & Frederick Mosteller, "A Comparison of Eight Models?", Studies in Mathematical Learning Theory, 1959)

"A satisfactory prediction of the sequential properties of learning data from a single experiment is by no means a final test of a model. Numerous other criteria - and some more demanding - can be specified. For example, a model with specific numerical parameter values should be invariant to changes in independent variables that explicitly enter in the model." (Robert R Bush & Frederick Mosteller,"A Comparison of Eight Models?", Studies in Mathematical Learning Theory, 1959)

"[...] 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 - that is, correctly to describe phenomena from a reasonably wide area. Furthermore, it must satisfy certain aesthetic criteria - that is, in relation to how much it describes, it must be rather simple.” (John von Neumann, "Method in the physical sciences", 1961)

"Adaptive system - whether on the biological, psychological, or sociocultural level - must manifest (1) some degree of 'plasticity' and 'irritability' vis-a-vis its environment such that it carries on a constant interchange with acting on and reacting to it; (2) some source or mechanism for variety, to act as a potential pool of adaptive variability to meet the problem of mapping new or more detailed variety and constraints in a changeable environment; (3) a set of selective criteria or mechanisms against which the 'variety pool' may be sifted into those variations in the organization or system that more closely map the environment and those that do not; and (4) an arrangement for preserving and/or propagating these 'successful' mappings." (Walter F Buckley," Sociology and modern systems theory", 1967)

"The advantage of this way of proceeding is evident: insights and skills obtained on the model-side can be - certain transference criteria satisfied - transferred to the original, [in this way] the model-builder obtains a new knowledge about the modeled original […]" (Herbert Stachowiak, "Allgemeine Modelltheorie", 1973)

"In most engineering problems, particularly when solving optimization problems, one must have the opportunity of comparing different variants quantitatively. It is therefore important to be able to state a clear-cut quantitative criterion." (Yakov Khurgin, "Did You Say Mathematics?", 1974)

"The principal aim of physical theories is understanding. A theory's ability to find a number is merely a useful criterion for a correct understanding." (Yuri I Manin, "Mathematics and Physics", 1981)

"It is often the scientist’s experience that he senses the nearness of truth when such connections are envisioned. A connection is a step toward simplification, unification. Simplicity is indeed often the sign of truth and a criterion of beauty." (Mahlon B Hoagland, "Toward the Habit of Truth", 1990)

"A model for simulating dynamic system behavior requires formal policy descriptions to specify how individual decisions are to be made. Flows of information are continuously converted into decisions and actions. No plea about the inadequacy of our understanding of the decision-making processes can excuse us from estimating decision-making criteria. To omit a decision point is to deny its presence - a mistake of far greater magnitude than any errors in our best estimate of the process." (Jay W Forrester, "Policies, decisions and information sources for modeling", 1994)

"The amount of understanding produced by a theory is determined by how well it meets the criteria of adequacy - testability, fruitfulness, scope, simplicity, conservatism - because these criteria indicate the extent to which a theory systematizes and unifies our knowledge." (Theodore Schick Jr., "How to Think about Weird Things: Critical Thinking for a New Age", 1995)

♜Strategic Management: Resilience (Definitions)

"The ability to recover from challenges or to overcome obstacles. In a social-ecological context this refers to the innovation capacity of the organization to successfully address societal and environmental challenges." (Rick Edgeman & Jacob Eskildsen, "Social-Ecological Innovation", 2014)

"The quality of being able to absorb systemic 'shocks' without being destroyed even if recovery produces an altered state to that of the status quo ante." (Philip Cooke, "Regional Innovation Systems in Centralised States: Challenges, Chances, and Crossovers", 2015)

"The ability of an organization to quickly adapt to disruptions while maintaining continuous business operations and safeguarding people, assets, and overall brand equity. Business resilience goes a step beyond disaster recovery, by offering post-disaster strategies to avoid costly downtime, shore up vulnerabilities, and maintain business operations in the face of additional, unexpected breaches." (William Stallings, "Effective Cybersecurity: A Guide to Using Best Practices and Standards", 2018)

"A capability to anticipate, prepare for, respond to, and recover from significant multi-hazard threats with minimum damage to social well-being, the economy, and the environment." (Carolyn N Stevenson, "Addressing the Sustainable Development Goals Through Environmental Education", 2019)

"The ability of a project to readily resume from unexpected events, threats or actions." (Phil Crosby, "Shaping Mega-Science Projects and Practical Steps for Success", 2019)

"The ability of an infrastructure to resist, respond and overcome adverse events" (Konstantinos Apostolou et al, "Business Continuity of Critical Infrastructures for Safety and Security Incidents", 2020)

"The capacity to respond to, adapt and learn from stressors and changing conditions." (Naomi Borg & Nader Naderpajouh, "Strategies for Business Sustainability in a Collaborative Economy", 2020)

"The word resilience refers to the ability to overcome critical moments and adapt after experiencing some unusual and unexpected situation. It also indicates return to normal." (José G Vargas-Hernández, "Urban Socio-Ecosystems Green Resilience", 2021)

"Operational resilience is a set of techniques that allow people, processes and informational systems to adapt to changing patterns. It is the ability to alter operations in the face of changing business conditions. Operationally resilient enterprises have the organizational competencies to ramp up or slow down operations in a way that provides a competitive edge and enables quick and local process modification." (Gartner)

[Operational resilience:] "The ability of an organization to absorb the impact of any unexpected event without failing to deliver on its brand promise." (Forrester)

[Business resilience:] "The ability to thrive in the face of unpredictable events and circumstances without deteriorating customer experience or sacrificing the long-term viability of the company." (Forrester)