A visualisation love story

By , strategic BI manager at PBT Group.

The fellowship of humanity is founded in stories – stories that evolved from cave drawings to Shakespearian writings to the modern-day stories on the cinema canvas. I know the debate between reading versus watching movies is probably as old as the television itself and is still ongoing. However, the fact that cannot be debated is that people are infatuated with stories.

The visualisation of stories in movies has simply made stories more accessible to the portion of the population not inclined to find reading as enjoyable as others. This does not mean books are redundant, as some people will never sacrifice the joy of immersing in their own imaginations through the written word, but ultimately, visualisation of stories continues to enrich a much wider part of the population.

In a similar way, one of the latest buzzwords in business intelligence (BI) – visualisation – is causing quite a significant uproar. Let’s set the record straight from the beginning: Visualisation is not the new BI. Visualisation is just an added medium through which one can publish the intelligence in the data so a larger portion of the company can benefit from discovering the story ‘hidden’ therein.

Picture it

Way back, in the Shakespearian BI era, data and intelligence were expressed in data tables. It never ceases to amaze me when I come across BI end-users who can glance at a data table with 25 columns and 72 rows showing regional weekly sales data for the last 18 months, and – within seconds – become excited by the trends they observe in the data. Yes, such data whizzes exist, and sometimes leave me reeling, convinced that Neo stepped out of ‘The Matrix’ through my computer screen. However, I tend to fall on the side of the masses, and for me to make sense of large data sets, the golden rule applies: “A picture is worth a thousand words (or numbers, in the case of BI).”

Over the past couple of years, managers have started to realise that, instead of being solely dependent on a small team of highly skilled quantitative analysts to analyse and interpret the data for the masses in lengthy book reports, the latest technological advancement in visualisation capabilities of tools, like PowerBI, Qlikview, and Tableau, among others, may unlock the story in the data to a wider audience, much faster.

However, don’t be fooled into thinking it is as simple as putting the tools and the data in the BI users’ hands, and – ‘hey presto’ – BI value is unlocked by the masses for the masses. It is not that easy. Returning to my analogy of books versus movies, consider how many people are involved in publishing a book versus bringing a movie to the silver screen. The book involves the writer, an editing team, the back-cover writer and the publisher. Judging from the credits on a movie, it can take a team of more than 100 people to effectively tell one story on the telly.

One must understand that to unlock the story in the data through visualisation takes very careful planning and design, to ensure the right visualisation mechanism (bar graph, line graph, heat map, xy plots, etc) is used that best tells the story. I’m yet to come across a company where all BI users just intuitively know how to match the right mechanism to the data to effectively answer the business question they have.

Unlocking value

Cue the role of the data visualisation architect (Google it, such a person exists), who is almost like the scriptwriter, location manager, set designer, casting director and director of photography all rolled into one. This person effectively combines the right data (script) with the right visualisation mechanism (set designer), and the right formatting and structure (director of photography), and then publishes it on the right platform (location manager) to the selected target audience (casting director), who will then effectively utilise the insight to the value of the organisation, resulting in a conclusion of the story. The data visualisation architect will know the science behind how visualisation is perceived and physiologically processed by the viewer, and use this specialist skill to design the optimal visualisation for each and every insight story embedded in the data.

As a side note, I must caution the reader not to confuse the data visualisation architect with the other buzzword, data scientist. That would be like demoting the director to become the set designer. Granted, data scientists have a very strong understanding and a keen, almost intrinsic, ability to design visualisations that tell a very clear story. However, a data scientist is usually the person who drives and steers the whole journey of discovering a new story, scripting, designing, casting, recording, and producing the movie. Data scientists are far more valuable in the exploratory analytics space where the business question is still being formulated, the hypothesis must still be defined, the suitable data sourced and analysed, and finally, the conclusions drawn and presented to decision-makers.

Just like the data scientist discovers untold stories hidden in the data, the data visualisation architect can enable business to unlock the value intrinsic in their existing data for decisions made on a daily/weekly/monthly basis. This is done by converting existing reams of data-table book reports into well-crafted visualisation views that tell the story succinctly in an aesthetically pleasing way – empowering the other 80% who cannot intuitively see the picture in the data, like Neo.

Source : IT Web


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