Bryant Duhon, our indefatigable community manager, has been doing a “Member of the Week” feature on this site for the last while. I thought it would be fun to turn the tables and feature Bryant. Laurence Hart (AIIM CIO, blogger extraordinaire, and Twitteratti) helped convince Bryant to take part.

In order to really switch things up, Bryant will answer questions posed by the community. Anyone that has questions for Bryant can DM me on Twitter at chris_p_walker or email me at walkerchrisp@gmail.com. I’ll compile the questions, have Bryant answer them, and post the results to this site.

I think 10-15 questions is about right. So, as soon as I’ve collected the questions we’ll get this thing started.

By the way, I will identify submitters of questions. No anonymity allowed.

Big data is too big for most organizations. It’s not because there is too much raw material to deal with, nor is it because of a lack of applicable tools. It doesn’t even have anything to do with the inclusion of unstructured data (which doesn’t really exist but that’s a topic for blog posts that I’ve already written). No, big data’s too big because of what’s possible.

The possibilities to slice & dice are virtually endless. Numerical data is bombarding organisations from within and without. Text based data (I’m calling it data because until it’s put in context it’s not really information) is being generated almost at the speed of thought, in quantities heretofore unimagined. Every transaction, every search request, every Tweet, and every Like generates an entry in some repository that organizations may or may not be aware of, have control of, or have access to.

That all sorta sucks, but ...

But what really sucks is that organizations jump onto the Big Data bandwagon with not an iota of a clue as to what they want to do with it. Boys and girls, Uncle Chris is gonna try and wise you up.

Like any other project that involves expending time and money, you need to know what you want to achieve when you’re done. E.g.: Many years ago I worked on a service management reporting project for a big organization providing managed network services to an even bigger organization. It came down to this ... approximately 17,000,000 rows/day of raw data were collected, $pooploads/mo of revenue depended on meeting SLA targets, unmet SLA resulted in pooploads-alot being lost. The specific metrics and their data sources were identified prior to spending a dime on tools. My point is, you need to figure out what the business requirements are. That was true in 2002 and it is true today.

Much has stayed the same, and some has changed over the last 10+ years. We’ve even got some new stuff we can play around with thanks to social media, text analytics, sentiment analysis, etc. But knowing at least a few of the questions we’re trying to answer, before actually doing something, is still a valid and necessary first step.

It’s really cool that we can now ask “How does [demographic of choice] feel about our support organization?” in addition to asking about how many units of blue-widget-A we sold last quarter in the mountain time zone north of the 49^th parallel. But before we ask the question we need to know to ask it and we need to know what we’re gonna do if we don’t like the answer (we should also have a social media strategy in place). We also need to know niggling little details like where the data is, whether or not we can access the data, and whether or not the data is reliable (whatever that means). Oh, we should also have some sort of governance in place to deal with all that personal and payment data we’re collecting, storing, massaging, analyzing, and interpreting to generate more profits than ever before.

I’d like to end today’s sermon with another little story ...

Back in 2004 I was a project manager at a municipality. One of my periodic tasks was to compile the results detailing uptake of certain web-enabled municipal services related to planning and development. Each month I would get the results from the various sub departments, enter them into my fancy-schmancy reporting tool, compare the numbers against the projections, and then present them at a monthly meeting. We used a standard red-black-green thingy and it was all so easy. Easy until the dude in charge asked me if they were supposed to do anything about the red (bad) numbers. My question, and the take-away from this anecdote, to him was “If you’re not going to address the issues highlighted, why are you spending time and money on this?”

Big data is full of big possibilities. However, before you jump in make sure you have a plan. Understand what it is you’re trying to achieve. Have a plan for how you’re going to react to negative results as well as positive. Know that you won’t figure it all out on your first attempt, but that’s okay because a cool thing about analytics is that the more you play, the more you learn and then you discover more possibilities.

Many, many years ago I lived not far from the hardware store that used to be here. (My favourite barber was in the same complex. They let you smoke, served espresso, and had a pile of out-of-date Penthouse.) I really liked that hardware store, but abandoned it when one of those big-ass, everything-under-one-roof, meg-gigantic (by Canadian standards) box stores opened up. I remember the excitement and anticipation leading up to the grand opening (actually, I don’t, but this story wouldn’t be nearly as compelling if I didn’t embellish just a wee bit). It mattered not that the monogigantilithic store was a further 8 minutes away. It was worth it! We were going to have every conceivable hardware, lawn care, renovation, major appliance, and home improvement item known to human-kind under one roof. At prices that can’t be beat. Well, bugger that!

It seems that this retailer forgot to include a couple of key items: product knowledge and customer service. Oh sure, you could ask a staff member (in 1 of Canada’s official languages) where an item was and they’d send you off in the right general direction. You could even ask them product oriented questions and if they’d read the correct documentation you’d likely get a reasonable answer. But what you couldn’t get (as a standard practice) was that feeling that you were valued as a customer and, more importantly, as a person. How could this be fixed? you may well ask. Easy ...

I returned to the more expensive, closer to my house hardware store. You see, when I went in wondering about paint choices and application techniques they talked to me. Hell, they even listened to me. When my wife and I bickered about paint or whatever, the staff provided the voice of reason and helped us make the right choice for our situation. When I returned they knew me enough to ask about how my project, whatever it happened to be, turned out. They offered crappy coffee and a human touch. I was even able to chat with other customers.

What does all this have to do with customer experience today, you may ask? Everything. We’re still people who like to connect with other people. There’s a certain humanness and sociability that we look for in our lives. Certainly we sometimes like the convenience of online shopping in our (or our partner’s) underwear from the comforts of our homes, but I like to believe that we still want and need that contact with other people.

I’m not for a second saying that we need to replicate that intimate experience online (except for certain sites of an adult nature); it’s impossible. What we need to do is redefine how excellent customer experience is delivered online. We need to understand that true customer experience involves multiple channels, without the customer having to repeat themselves again and again and again.

I’m going to close with a couple of examples from a big name retailer. They’re two stories about two very different experiences, make of them what you will...

Back in December my wife and I went Christmas shopping for our daughter. Our daughter is a voracious reader, who turned 12 years old earlier this month. We wanted to get her some books that were a bit different from her usual fare. Kerry (the staff member whose name I remember because she was really cute) spent several minutes with us asking about our daughter’s preferences and our thoughts on what we felt were appropriate topics for her to read. Kerry made some recommendations that proved to be spot on. Well done, Kerry!

Same retailer, different channel ... To date I’ve purchased about $300 of ebooks from this retailer. One of the things I like is that they make recommendations about what I may like to try next. What I dislike, vehemently, is that they keep recommending titles that I have previously purchased. Repeatedly.

Do you see my point? Do you feel my pain (uhm, irritation is probably more accurate)?