September 29, 2013

Visualization or navigation?

I’ve suggested in the past, approximately, that the platform technology side of business intelligence is more significant than the user interface. That formulation, however, doesn’t exactly capture what I believe. To be more precise, let’s differentiate between a couple aspects of business intelligence UI.

It might seem that a lot of the action in business intelligence revolves around ever-better visualization. After all, Tableau is clearly identified as a visualization-centric technology; who’s hotter than Tableau? And numerous other vendors talk of “visualizations” too. But I don’t think that’s exactly right — rather, I see navigation as being a much bigger deal. And unlike most pure visualization, navigation usually depends strongly on underlying platform capabilities.

Examples of what I mean by innovative navigation — all of which have been developed or have gained prominence over the past decade or so — include:

By way of contrast, how has pure visualization improved over the same time period? Mainly, it’s been ported from fat Windows clients to browser-based technologies and then again to mobile platforms.

The comparison would be similar, I think, if we looked back 25 years instead of 10.

A natural rebuttal to this view is to note, correctly, that BI is commonly sold on the strength of glitzy demos. But if we’re just talking about static displays, those demos were just as glitzy a decade ago, and indeed pretty good as soon as PCs replaced monochromatic terminals. Even Tableau brags that its users find its product “fun”, not that they find it pretty.

It might be reasonable to say that most BI differentiation focuses on either:

But you know what? One of the most important consideration in getting the information users want is — you guessed it — navigation.

Navigation wins.

Comments

6 Responses to “Visualization or navigation?”

  1. ClearStory, Spark, and Storm | DBMS 2 : DataBase Management System Services on September 29th, 2013 7:23 pm

    [...] Has put a lot of effort into user interface, but in ways that fit my theory that UI is more about navigation than actual display. [...]

  2. Ken Chestnut on October 1st, 2013 1:11 am

    A lot of people confuse user interface (look and feel) with usability (ease of use). Just because it looks pretty does not necessarily make it easy to use.

    IMHO, the biggest issues affecting BI ease of use are data loading and data modeling (what you would presumably deem platform technologies). End user navigation is important but secondary to successfully loading and modeling the data.

    One of the things QlikView did to improve usability was to simplify data modeling through their associative architecture (limitations notwithstanding). This had a secondary benefit in terms of providing end users with navigational flexibility.

    Similarly, Tableau has done some nice things to simplify navigation and automate aggregation along typical dimensions (e.g., time) in an effort to improve ease of use (relative to traditional data loading and modeling).

    I believe these companies have been successful (partially) due to the underlying platform technologies that simplify/automate some of these mundane but esoteric and important tasks.

  3. What matters in investigative analytics? | DBMS 2 : DataBase Management System Services on October 7th, 2013 4:09 am

    [...] recently suggested that navigation is a huge part of business intelligence differentiation. That’s because good navigation pretty much equates to BI agility. With luck, a BI tool that [...]

  4. DB2 Hub | Visualization or navigation? on October 16th, 2013 9:38 am

    [...] Visualization or navigation? Tags: business, business objects, clear, data warehouse, datameer, enp, gis and geospatial, greenplum, hbase, sas instituteYou might also like ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ /* [...]

  5. Stephen McDaniel on October 17th, 2013 10:45 am

    Thanks for sharing this critical point!

    I agree. Except, I would call it “flow”. The flow of how people think versus the interface analytic applications present are often quite different. As someone who has designed and used many analytic applications, I think this area is a huge miss by many vendors. As Stephen Few, has observed, many who develop analytic/BI applications don’t use their application (at least not like customers in the real-world.)

    People have taken to data visualization (more about the appearance being just “right”) and visual analytics (more about iterative questioning) because there was at least a chart or graph that meshes with their uncertainty about the analysis they are attempting to perform, what each application is truly doing, did they set the right options, etc.? In the analytic product world of countless dialogs and decisions required, even for analyses of modest complexity, people are left wondering, “what have I really done” and “is this conclusion trustworthy?”

    In almost every analytic and BI product I have used, it is often a mystery to end-users what steps they have taken and can they trust their conclusions. So, while an application like Tableau is superior to SAS Enterprise Guide at rapidly iterating (I worked on the futures for both products), a product like Enterprise Guide gives you a better sense upon completion of all the steps that you have followed to reach a conclusion for a complex analysis.
    Flow is hard, analytic flow is not natural to developers. Understanding how non-developers think has is not a matter of intelligence, but is gained through real-world analysis experience, especially working with new users repeatedly.

    Examples

    Quick and dirty analysis with Tableau 8 (video)
    http://www.freakalytics.com/2013/07/05/quick-dirty-analysis-with-tableau/

    Business analytics and more with SAS Enterprise Guide (video)
    http://www.freakalytics.com/2013/06/24/business-analytics-more-sas-enterprise-guide/

    Best regards,
    Stephen McDaniel

  6. Curt Monash on October 17th, 2013 6:01 pm

    Stephen,

    I agree that it’s a huge problem — famously in medical research, for example, as well as in business — that people do a poor job of establishing how reliable a conclusion is.

    In business, the ideal case would be that people make good use of unreliably, quickly-obtained conclusions, and also exploit rigorous ones to the extent that they’re available. The value of non-rigorous conclusions is several-fold — they can stimulate thought and creativity in general, and they might stimulate what amount to “experimental” business initiatives in particular. Resource allocation/experimental design are much knottier issues in business than in a laboratory, and I can’t imagine coming to truly rigorous answers about them.

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