Sex sells. And, although it may seem absurdly out of context, it definitely sells Business Intelligence
Though somewhat ironic, many BI purchase decisions are made on abstract (or outright flimsy), rather than tangible benefits-based grounds. – A stunning interface
, fancy shapes or colors, can push one Philly over the line in a close two-horse race.
At Yellowfin, we’re about to release the latest version of our BI solution – Yellowfin 5.2. Amongst other enhancements and additions, the update will include a number of new charting options. And whilst their aesthetic appeal is undeniable, we think it’s important to understand their purpose and place.
Confabulation in Business Intelligence and data visualization
Jim Harris, in a recent Information Management
blog post – Data Confabulation in Business Intelligence
– stated that in an environment of forever avalanching data assets, he feared a new trend. His fear was that: “data-driven decision-making may simply become intuition-driven decisions validated after the fact by selectively choosing the data that supports the decision already made. The human mind is already exceptionally good at doing this – the term for it in psychology is confabulation
He explained that: “Data confabulation in Business Intelligence occurs when intuition-driven business decisions are claimed to be data-driven and justified after the fact using the results of selective post-decision
I concur with Harris, and hold a similar fear for data visualization
– that this component of BI is also being applied improperly. My fear is two-fold:
- Amidst a growing, cluttered, swirling sea of BI products, organizations looking to select a BI solution will move through a mountainous number of POCs (Proof of Concept) and emerge utterly confused by a combination of expansive choice, and myriad of sweet-smelling vendor promises. Spoilt (and understandably perplexed) for choice – a choice that business users now have a larger say in (as they should) – many organizations’ ultimate purchase decision will be based on which vendor generates the prettiest 3D pie chart, rather than which BI solution (and visualizations) can provide the best-fit (clearest insight).
- Decision-making will suffer. Decision-makers, report users and writers alike, will be caught up in the euphoria of spiffy-looking infographics that carry meagre meaning, but project the impression of authority and decisiveness. This infatuation with the superficial will lead to the utilization of inappropriate, less incisive data visualizations, misunderstandings/misinterpretation (or at the very least, lesser understandings) of the underlying data, and poorer decisions.
A lively blog post – Business intelligence vs. infotainment
– on data visualization and infographics forum, FlowingData
, speaks of this trend. The post posits that, at its core, despite the “bright and shiny tools”, “Business Intelligence, which is really just statistics for business, is analysis. It’s not entertainment.”
Many organizations seem to confuse the two terms and their respective purposes.
These thoughts are echoed by BI expert, Stephen Few, who in his latest blog post – Teradata, David McCandless, and yet another detour for analytics
– critiques the work of infographic designer, David McCandless.
Few exhibits the same frustration regarding the fundamental purpose of data visualization, stating that: “McCandless rarely chooses forms of display that our eyes and brains can perceive with ease and precision. He selects what will appeal superficially to the viewer (lots of circles, swirls, and vibrant colors), not what will most effectively express what’s essential and meaningful.”
Few continues, saying that “too many of his [McCandless’] visualizations display information in ways that hide much that’s relevant and essential, leaving little of value for the viewer to see.”
The problem with data visualization: We’ve been here before
It seems that, despite the talk, the world of data visualization has been failing to walk-the-walk for some time, according to the 2002 publication, Information Visualization in Data Mining and Knowledge Discovery
In a forward from the series editor, Jim Gray, the research novel laments that: “Data visualization has lagged its sister disciplines of data capture, data storage, data analysis, and knowledge discovery… there is still a huge gap between our ability to extract answers and out ability to present the information in meaningful ways.” Although the book was published in 2002, with a suggestion that the problem was on the cusp of being resolved, essentially, the sentiment remains true.
The book defines the principle function of data visualization in stark, uncompromising terms:
“Visualization, well done, harnesses the perceptual capabilities of humans to provide visual insight into data… (it is) fundamentally about data reduction… Finding a view or projection of the data that reduces complexity while capturing important information.
“A successful visualization is one that emphasizes the information of interest and presents it as a resolution sufficient to perform the task.”
Did the last visual re-presentation of your corporate data match this lofty, rigid definition?
Conclusion: Don’t let data visualization take your eyes off the prize
So sex does sell BI. And that’s fine – it should be fun and engaging. Just make sure that your corporate data remains the star of the show and point of focus, rather than the data visualizations themselves.
But is it poor data visualization, or its poor application, that is hampering this much-maligned element of business analytics?
Whatever the case, just remember this: If it takes five people and an hour-long meeting to explain what that
chart means, and its implications and impact on your organization’s financial outlook, then it’s time to send it back to the nunnery. - Overtly eye-catching visualizations have their place, but they’re not always the best answer. Sometimes a humble line or bar chart conveys meaning most clearly.