Ask anyone who is involved in an open source project, getting recognized is one of the greatest kicks you get out of the whole deal.

So, when my Google alert picked up this news on OpenI listed in the Top 10 Free BI Apps list, it absolutely made my day. Of course, a lot of the credit goes to all the folks who have contributed to this project, and the open source community that has supported us all this time. And thanks to Tamina Vahidy for recognizing the project.

When we started OpenI back in July 2005, we just wanted to subsidize our R&D. We needed a BI platform to deploy our analytical models, and instead of opting for commercial BI platforms which would have never fit into our cost model, we decided to develop a BI platform using available open source components, and also as an open source project of its own. We figured if we get even a couple of people outside of our company to pitch — whether it was design help, or just thinking through requirements, use cases we hadn’t encountered — that alone would pay for the efforts to make it open source.

Well, not only we got design help and advice from a great deal of smart folks in the space, we even have people contributing code. I remember someone (maybe Steve Weber) making a point about open source development model — not all the smart people in the world work for you, so the only way to get them involved in your projects is via open source (ok, you may argue crowdsourcing ideas such as Netflix’s contest, but I don’t have a $ 1 million to give away in prize money 🙂

So — here we are — working on version 1.3 of the product. We are using it internally as the web front-end of our commercial product. Of course, it has ways to go — but as contribution and recognition keep coming in, it just seems like a much more rewarding way to develop software.

I discovered this on TED talks, and I completely agree that this is a true display of “passion for information visualization”.

The software used for the presentation is at http://www.gapminder.org and is also a google tool (http://tools.google.com/gapminder) It appears to be “bundled” with the global economic data, not sure if there is an open decoupled version that one can point to their data and play around. Although it seems flash-based and pulling data from static data sources (didn’t seem like rdbms, but I could be wrong) — but this would be a great way visualize OLAP data.

What’s interesting is the concept of a “play” button for the time dimension, which makes a great use of animation to see how different quantities (measures) change over time. It also manages the screen real-estate well to put different dimensions on X or Y-axis. But most of all, this truly exemplifies what data visualization is all about — it goes beyond the realm of charts and graphs that take a while to decipher, and rather tells a very clear, compelling, and visual story. Very impressive!

Recently I had a potential partnership discussion to evaluate whether our predictive analytics technology could provide key insights from this potential partner’s (I’ll call them company XYZ) marketing database. Here’s how my conversation went with Mr. X, an exec at company XYZ, (which is a marketing technology company):

Me: “So, what are they key business pain points for your clients that we can analyze?”
Mr. X: “Well, you know, the usual stuff — marketing ROI, cut costs, increase sales, etc.”
Me: “ah.. yes, but can we delve a bit deeper? Where exactly clients’ marketing programs need help?”
Mr. X: “what do you mean?”
Me: “Well, are they more worried about increasing acquisition volume, or is it more about predicting high-LTV customers, or is it more about retention? I’m trying to get a sense of what is their #1 issue?”
Mr. X: “they don’t know.. it’s probably all of that stuff”
Me: “It’s important that we get a sense of priority, because otherwise we are talking about applying analytics without really knowing what we are trying to optimize”
Mr. X: “well, you are the analytics expert — you need to tell them what to analyze. They don’t think like you, worrying about success metrics, etc. They ask us to run marketing programs, and now we’d like to sell them some analytics. I can tell you what data we have on their marketing programs, now you tell me what kind of analytics you can provide me that I can sell.”
Me: “But we do need to understand their business objectives before determining what analytics is relevant enough so that they’ll pay for it”
Mr. X: “What I need from you is some screenshots — some charts and graphs that show what kind of analytics you can do — I’ll be more than happy to review that and tell you if we can work together”

Needless to say, I didn’t sense a true spirit of partnership here, but I did sense an attitude that I find more often than I’d like that analytics is all about producing charts and graphs that the user will somehow find useful.

Which, IMHO, is total BS!

But I can’t blame Mr. X too much because this is a pretty common perception of analytics in the marketplace. Recently I talked to a marketing exec who said –“everytime I meet with the analytics guys from our agency, they basically have this big ream of a powerpoint deck filled with one chart after another — and I don’t want to see all that stuff; all I want them to do is to tell me what relevant insight(s) did they find, and what course of marketing action would they recommend, and it’s like pulling teeth to get them to move beyond charts and graphs and talk about action.”

Look at the websites of any business intelligence software provider, or analytical software provider — and I will guarantee you that you will see a bunch of fancy charts and graphs, and dashboards with enough dials and speedometers to make you dizzy. Somewhere along the line, maybe we have forgotten that the purpose of analytics is to equip us with insights that enable better decision making.

So, first off, the type of analysis being done has to be aware of what type of decisions we are exactly expecting to improve; and second, the result of the analysis needs to be presented in a fashion that is “integrated” into the decision making. Maybe you list your recommended actions next to your charts and graphs, or maybe you somehow highlight the figures and trends that demand attention. My point — don’t leave it upto your user figure out the action based on the fancy charts and graphs, find out what decisions users are trying to make, and provide information that fills in that gap between analytics and actionable insight.

Yesterday during a call with a potential client, this topic came up. This is a well-known desktop software company, and they have a unique challenge: Their primary measure of customer loyalty is the “Reichheld Score” aka the Net Promoter Score, which is based on customer responses to a single question — “Would you recommend us to a friend?”. Now, the interesting thing is that while this company is doing rather well in the market, their overall net promoter score is not that great. In fact, their score is lagging behind other comparable software manufacturers.

So, the obvious question is — why doesn’t their net promoter score correlate with company growth? Which metric should they rather measure as a driver of company growth?
As we talked, I also found out that this company hasn’t done much in terms of evaluating the behavioral data on their customers — you know, stuff like actual purchase transactions, new purchases versus repeat purchases, customer complaints, returns, etc. And I couldn’t help but think that perhaps this is where they should start look first. A great deal of research work has shown us that past behavior is the best predicator of future behavior, and this is true when it comes to measuring customer loyalty as well. A 2002 HBR article “mismanagement of customer loyalty” describes this as:

Simply put: Not all loyal customers are profitable, and not all profitable customers are loyal. Traditional tools for segmenting customers do a poor job of identifying that latter group, causing companies to chase expensively after initially profitable customers who hold little promise of future profits. The authors suggest an alternative approach, based on well-established “event-history modeling” techniques, that more accurately predicts future buying probabilities. Armed with such a tool, marketers can correctly identify which customers belong in which category and market accordingly.

So why isn’t this company looking at behavioral data on its coustomers? I didn’t get a clear answer, but could it be that it is easier to conduct surveys rather than dig deep into data, specially when the data volumes are huge and the data is scattered around different corporate silos? Could this company be viewing the analysis of behavioral data as a long, complex exercise that involves getting down and dirty with data warehouses and analytical modeles, when all they needed was a nice simple metric that would measure customer loyalty and nicely correlate with company growth?

Yes, I am being a bit facetious — but while I don’t dispute the value of qualitative research, I think in this case, they are more applicable AFTER an initial study of behavioral data. This company needs to understand the “WHAT” first (i.e. what is going on with my customers? which customer attributes/behavior are best indicators of company growth?), and then they can apply qualitative reserach to understnd the “WHY”, i.e. why things are happening the way they are.