You Can Take a Horse to Water

I mostly wear a heart rate monitor when I work out.  I do it so I don't sandbag myself.  It is a way old Garmin unit, and once it is full, I download it into my Garmin desktop program (pre-app days) and I look it and then forget it.

I work with a guy who has one of those Under Armour bands.  I see them on other folks as well.  HBR has a quick blog post about this trend.

We're creating our own little Big Data networks on our heart rates, sleeping patterns, blood chemistry, and all sorts of great stuff, but will the data from these 485,000,000 auto-analytics devices by 2018 (in the HBR article) actually be used?

Like all BI projects, your reports need to support a decision.  For me, it is stop lolly-gagging, but that is a moment in time.  I don't use the data again.  I don't have a good BI application to compare and contrast my workouts and to make that useable.  Do they exist?  I'm sure Olympic and professional athletes use them - but for this middle-aged, balding desk jockey?  And even if I paid for the best BI auto-analytics tool and I can increase my cycling efficiency by 5% - so what?

Just because we can collect data and create reports - it doesn't mean we should.  Make sure to align reports with a true decision making need or you'll run the risk of wasting money on something no one will use.

Now, if you will excuse me, I need to update my food diary app...

NSA Big Data is Really a BI Problem

Currently, we are hearing a multitude of thoughts regarding the recently exposed NSA Big Data program.  The current issue of BusinessWeek has a rational essay on it.

From a BI point of view, the essay doesn't say much that we already don't know - the struggle is in collecting data, organizing it, and relating it to other "indexes."  These are basic Big Data/data warehouse concepts.

What I found most thought provoking is this sentence - that the NSA, "if it needs to, it can actively cross the between your statistical self and your real, physical self."  In its purest sense, that's the point of BI - creating a meaningful, actionable, reliable, data-driven proxy that crosses to reality and can be used to influence decision making.

The NSA BI program is built for just a handful of uses (thank goodness) at the beginning of the decision making journey and our BI projects are built for regular, decision-making.  One measure of BI success is decisions per quantity of data collected.  On this basis, our projects have a higher success rate, but let's check this out:

My most popular BI environment has under 200 global users of 24 dashboards - and the data size is about 50 MB.  No matter how you do the success rate ratio it'll be much better than the 18,000 NSA annual uses (what I remember reading someplace) of what has to be a gazillion gigabytes.  Sounds like a negative BI ROI - obviously the NSA would disagree, but then again, their success (no terrorist attack) completely changes the equation.

But it does lead to a BI question for you - are your BI projects getting used enough?  How do you monitor it?  When would you decide to stop a program because it is not being used?

Transparency

I enjoy reading Kevin Coupe's Morning News Beat - he provides insights into the retail industry, particularly grocery. He also has a column in Forbes discussing transparency in the food industry in relations to food labeling GMO products.

From a food point of view, I agree with him, but he makes me think about transparency in BI.  Too many report users have no idea where the reports come from or how definitions are made.  Questions exist about application of the metrics and incorporating report insights in decision making.

The standard answer is documentation - here's a data dictionary, here's the technical requirements, here's a huge pdf that answers all your questions (if you can find them).  Honestly, the true standard answer is nothing other than, "Oh, that's Fred's report - go ask him."

For my projects, I have a governance committee - a group of end-users who help figure out what's next for the reports to keep them actionable, relevant, and trustworthy.  But that transparency only goes so far.  Some of my clients even have user group meetings, which is fantastic - if people attend.

Kevin says:

"Don’t do it, and I have to ask why...  But keep fighting the calls for labeling, and you create uncertainty and mistrust."

He has a great point.  In today's world, in which content is ubiquitous, the absence of BI transparency in a single report is curious.  In previous work, Kevin has mentioned that food labels exist with QR codes in which you can scan it and see videos and explanation of the farms where the food came from.

Why can't we do the same thing for a report?  How can we do it with our existing tools? Why does it have to be so hard?  Is BI fighting the calls for report transparency?