🧭💹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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🧭Business Intelligence: Perspectives (Part IX: Dashboards Are Dead & Other Crap)

Business Intelligence Series

I find annoying the posts that declare that a technology is dead, as they seem to seek the sensational and, in the end, don't offer enough arguments for the positions taken; all is just surfing though a few random ideas. Almost each time I klick on such a link I find myself disappointed. Maybe it's just me - having too great expectations from ad-hoc experts who haven't understood the role of technologies and their lifecycle.

At least until now dashboards are the only visual tool that allows displaying related metrics in a consistent manner, reflecting business objectives, health, or other important perspective into an organization's performance. More recently notebooks seem to be getting closer given their capabilities of presenting data visualizations and some intermediary steps used to obtain the data, though they are still far away from offering similar capabilities. So, from where could come any justification against dashboard's utility? Even if I heard one or two expert voices saying that they don't need KPIs for managing an organization, organizations still need metrics to understand how the organization is doing as a whole and taken on parts. 

Many argue that the design of dashboards is poor, that they don't reflect data visualization best practices, or that they are too difficult to navigate. There are so many books on dashboard and/or graphic design that is almost impossible not to find such a book in any big library if one wants to learn more about design. There are many resources online as well, though it's tough to fight with a mind's stubbornness in showing no interest in what concerns the topic. Conversely, there's also lot of crap on the social networks that qualify after the mainstream as best practices. 

Frankly, design is important, though as long as the dashboards show the right data and the organization can guide itself on the respective numbers, the perfectionists can say whatever they want, even if they are right! Unfortunately, the numbers shown in dashboards raise entitled questions and the reasons are multiple. Do dashboards show the right numbers? Do they focus on the objectives or important issues? Can the number be trusted? Do they reflect reality? Can we use them in decision-making? 

There are so many things that can go wrong when building a dashboard - there are so many transformations that need to be performed, that the chances of failure are high. It's enough to have several blunders in the code or data visualizations for people to stop trusting the data shown.

Trust and quality are complex concepts and there’s no standard path to address them because they are a matter of perception, which can vary and change dynamically based on the situation. There are, however, approaches that allow to minimize this. One can start for example by providing transparency. For each dashboard provide also detailed reports that through drilldown (or also by running the reports separately if that’s not possible) allow to validate the numbers from the report. If users don’t trust the data or the report, then they should pinpoint what’s wrong. Of course, the two sources must be in synch, otherwise the validation will become more complex.

There are also issues related to the approach - the way a reporting tool was introduced, the way dashboards flooded the space, how people reacted, etc. Introducing a reporting tool for dashboards is also a matter of strategy, tactics and operations and the various aspects related to them must be addressed. Few organizations address this properly. Many organizations work after the principle "build it and they will come" even if they build the wrong thing!

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

Business Intelligence Series

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

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

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

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

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

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

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

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

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

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

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

🕋Data Warehousing: Drill Down (Definitions)

"A technique for navigating through levels of data ranging from the most summarized (up) to the most detailed (down). For example, to view the details of sales data by year, a user can drill down to display sales data by quarter, and drill down further to display data by month." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"The act of adding a row header or replacing a row header in a report to break down the rows of the answer set more finely." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)

"To delve deeper into data by going from a summary value to more detailed values." (Margaret Y Chu, "Blissful Data ", 2004)

"The process of exposing progressively more detail by making selections of items in a dynamic report or further enhancing a query." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"Method of analysis for retrieving lower levels of detailed data starting from summary data." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)

"A method of exploring detailed data that was used in creating a summary level of data. Drill down levels depend on the granularity of data within a dimension." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"To decompose data into more atomic components, that is, data at lower levels of aggregation." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"An analytical technique that lets a DSS user navigate among levels of data ranging from the most summarized (up) to the most detailed (down)." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence 3rd Ed.", 2017)

"A method of exploring detailed data that was used in creating a summary level of data. Drill Down levels depend on the granularity of the data in the data warehouse." (Intrafocus) 

🕋Data Warehousing: Roll/Drill Up (Definitions)

"The act of removing a row header or replacing a row header in a report to summarize the rows of the answer set. Sometimes called dynamic aggregation." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed , 2002)

"To present higher levels of summarization. " (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)

"Method of analysis for retrieving higher levels of summary data starting from detailed data." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)

"Data analysis performed on a data set with applied mathematical functions, associated with fewer dimensions, higher levels of hierarchy in one or more dimensions, or both." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"In SQL, an OLAP extension used with the GROUP BY clause to aggregate data by different dimensions. (Rolling up the data is the exact opposite of drilling down the data.)" (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"To collect subsets of data from multiple locations in one location." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The act of summarizing or aggregating data to a level higher than the previous level of detail." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling 2nd Ed.", 2005)