It may not be recognized by most but today (May 25th) is Towel Day. Fans of Douglas Adams carry a towel (as referred to in Adams' The Hitchhiker's Guide to the Galaxy) with them to demonstrate their appreciation for the books and the author,

Don’t Panic -- no matter what the day throws at you, draw comfort from the knowledge that you’re armed with your trusty towel.

Tried an experiment this week that may be of interest to some -- although I am not the first I'm sure. When I go to a conference, I usually try and take decent notes about what I heard and more importantly what it made me think.

This week I was at the MIT CIO symposium (#MITCIO) and the MIT Center for Digital Business Research (#MITCDB) research report out. Rather than taking note on paper, I just wrote down all my thoughts on my Twitter account (@CEBESS). Not all that novel, but an active change in behavior for me. Hopefully, my few followers were not too annoyed.

The side effect of this change is I could see the comments by others who used the same hash tag in a chronological context. With only 140 characters, it is easy to lose context about what the post was actually about when you are typing on a phone furiously. But with all the other posts being done by others it’s more useful.

I used my Samsung S2 SkyRocket phone for all the posts at the #MITCIO session and my Slate7 for the #MITCDB event. Both worked well, but on the Slate7 I was able to use TweetChat, so I didn’t ever forget that hashtag, which is all too easy to do when you’re in a hurry.

I was a bit shocked by the number of people I ran into at the #MITCIO event that I already knew. @kimStephenson, @Thedodgeretort, @mkrigsman, @M3Wilkinson and a number of others were there that I have interacted with before.

Running a bit behind on getting blog posts out this week, but I thought I’d try to get one out early about the MIT CIO symposium I attended yesterday.

Jeff Cutler did a great job of summarizing what went on at the Big Data session and wrote it up in a blog post, with pictures and everything.

A couple of the key elements discussed were:

• Correlation is NOT causality.
• When dealing with Big Data, you need to Measure, Experiment, Analyze and Replicate. Having expectations is important.

I did ask the panel a question about their view on:

When you expand your knowledge about the organizational behavior and management process using big data techniques, management may be one of the most well understood and best targets for automation so what are the implications for business and business schools. I don’t think they really understood the question, since their answer was targeted at a whole other set of issues related to how management uses the output of Big Data efforts.

I did talk to Erik (the moderator) after the session and he agreed that this is an area where organizations have significant opportunity in the future. When you think about business processes and the data available, there is structured and yet-to-be-structured data as well as well understood, yet-to-be-understood processes and chaotic behavior (I almost said chaotic process, but if it is chaotic it can’t be a process). Most of that management work is ripe for automation, freeing up people to work on other creative (leadership) tasks.

IEEE had an article D-Wave's Quantum Computing Claim Gets Boost in Testing that looked into D-Wave’s claims of having a quantum computer that companies can buy. Organizations like Google are buying and NASA has committed to some testing on the Google system, so it is clear there is some momentum behind what they sell. The tests are showing that a different kind of computing is involved that is useful for certain kinds of optimization and security related problems.

Although it is unlikely that quantum computers will be hitting the mainstream business computing market in the foreseeable future, there are some industries (like logistics and energy management) where they could prove useful sooner than others. This technology is something organizations should be aware of, even if it will not be useful for years in their area, since the approach is so radically different.

Lately, I’ve been a number of conversations with people about the strategic use of technologies. I mentioned the criteria I use to evaluate trends and technologies. We then typically get into a discussion about the difference in impact between some of the technologies that are much discussed today and how the tactical use differs from the strategic use.

For example:

• Analytics – Although you may need to gather more data and keep it longer, there is not enough attention space to sustain the effort unless you simplify, automate and focus attention only on what needs human involvement. Time to action/decisions has to be the measure of impact.
• Cloud – Although it may reduce costs in certain circumstances, the strategic impact of cloud techniques (whether it is infrastructure, processes or people) is to increase flexibility. If through the use of cloud techniques you end up increasing the flexibility, it cannot be sustained.
• Mobility – The mobility strategy for a business has to focus on improving the access to corporate information and reducing the latency in the decision-making process. If the focus remains on the devices, it will also fail.

These current technology directions (and others) have a strategic side and a tactical manifestation – make sure you know what is important to your business over the long haul when creating your plan of attack. If you want to reach the top you still go up one step at a time, but it is easy to lose sight of the goal along the way. Identify the metrics to measure progress and then measure the impact along the way and make adjustments.

When I was writing this post I felt it was a bit risky, since these technologies are viewed as so important today. The real point of the post is to view them strategically and not just a buzzword or fad. This tactical approach may be the reason that for some organizations, innovation is not working out.