November 2008 – OpenI for Digital Health

BI 2.0, Next Generation BI, and Everythig New and Improved

Fellow blogger Bhupendra Khanal has an interesting post that mentions the challenges associated with BI 2.0/Information 2.0 (he also plugs our very own OpenI, which is much appreciated — Bhupendra, may OpenI karma come back to you thousand-fold :-)Software industry, not unlike any other, contains a lot of hype and probably sometimes even more so with all this 2.0 buzz, which probably seems cool to the industry insiders, but is definitely confusing to the market.

Take BI 2.0 (or Information 2.0) for example – what in the world does it mean? Well, turns out, at the end of the day, to most BI vendors, it means more fancy charts and graphs and dashboards, except this time they’ll have rounded corners, larger fonts with brighter colors, and maybe a fit of Flash and/or Ajax thrown in for a good measure to demonstrate live interactivity.

All this is fine and well, but all this is also pure BS if you are not helping your user make better decisions, or informing them of something new.

If BI 2.0 or Information 2.0 is to be seen as the “next generation” (it seems you cant’ escape these cliches), then it needs to go beyond charts/graphs/dashboard paradigm. BI applications and tools need to be rooted in the knowledge worker’s workflow – and should be cognizant of the types of decisions that need supporting.

BI needs to be aware of the domain context – i.e. which industry are you supporting? which area – marketing, finance, operations, research..? Because without this, the best BI can do is to provide nice visuals and hope and pray that the user knows how to translates them into intelligence and action.

But software can be better than that if is stops being lazy. And that’s my hope with our work in OpenI. We certainly started in the charts/graphs/dashboards paradigm, so we are as guilty as anybody. But as they say in any 12-step plan, “acceptance” is the first step — and now, we are moving towards a future of BI software that caters to the root need for intelligence — i.e. not only that you see your data clearly, but you also see it in your specific business context, and get immediate option to act upon it.

For e.g. a marketing analytics BI application – once it incorporates the data about customers and marketing campaigns and resulting purchases — should not wait for a user to define dashboards and reports, but rather already provide a suite of analyses that answer the most typical marketer’s questions – i.e. how effective are my campaigns , who are my best customers/prospects, and what tactics work the best for individual customer segments? And don’t stop there btw — if you have identified some new and interesting customer segments – it should integrate with an online campaign manager tool to immediately launch new campaigns; or publish the list of your most valuable customers to your e-commerce engine or call center platform which know how to treat them in a special way; etc. etc.

Similar scenarios can be applied to other industries and domains too. We as BI software developers just need to imagine differently. And that’s what 2.0, new version, or next generation is all about – next level of imagination.

Taking BI Beyond Charts and Graphs

I attended a talk at the monthly BI SIG meeting at SDForum by Christian Marcazzo from Spotfire, now a part of Tibco. I have long admired Spotfire’s innovations on data visualization front, so I was curious how they see BI from the whole Human-Computer Interaction (HCI) aspect, and couple of things stood out.

First – if we look at consumer-centric data applications (Zillow, Google Finance, etc.) and compare their interfaces to more traditional enterprise BI applications, it’s amazing to see how the latter just doesn’t even attempt to look good.

Why is that?

Because enterprise BI app developers aren’t under the same pressure to seduce their users like consumer data apps. Zillow, Google Finance, et al live and die by the community they create, so for them, user experience in paramount, and it shows. Most BI apps, on the other hand, are almost developed under the assumption that users are under a “thou shalt always use this BI software” executive order, and as such don’t have much leverage in rejecting a software based on poor or sub-optimal user experience.

So they begrudgingly use the BI software for its least interesting/effective use – churn out one report after another. The BI app basically becomes a report production factory.

That brings me to my second point – for BI to be more than charts/graphs/dashboards, it needs to be part of the user’s workflow. Now the term “workflow” means a lot of different thing to differnt people, and has recently become a popular box in BI markitechture diagrams – but to me, it basically means that BI app needs to know the various contexts under which its users are using it, and provide a way to add intelligence/insight to the process. BI app by itself should almost be invisible.

Zillow users think of themselves as a home buyers/sellers, not a real estate data analysts. Typical Google Finance users are checking out their portfolio and evaluating stocks, but don’t think of themselves as financial analysts. So, why in the world BI applications are hell bent to think of their users are data analyst first, rather than understanding the specific tasks they are trying to accomplish more intelligently?

That’s where workflow comes in. BI app needs to understand the nuances of the business domains their users are in, make intelligence available in their task workflow where it’s needed, and provide a clear way to act upon that intelligence. Too often we think of BI as a separate app where a user will do analysis, and then the users will jump to other apps where they can take actions — that’s now how users see the world. And without understanding the users, BI apps can’t really provide intelligence.

It’s time to turn this model around. BI apps should think more like mashups –pull data from any “public” repository with REST like API’s, make anlayses available to share and tweak, and make the resulting insights be integrateable to other apps. The more lines get blurred between BI apps and the rest, more successful its adoption is going to be.