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.

Yesterday, the New York Times had an article titled: Pills Tracked From Doctor to Patient to Aid Drug Marketing. The articles discussed how the new analytics capabilities are allowing drug marketers to locate influential doctors by their social behavior as well as patient behavior. This article was a good example of where additional insight can be used to define action.

I normally view much of the “Big Data” trend to be focused too much on insight and not enough on action, but this article did talk through some of the interesting issues at least as it relates to the healthcare provider market. 

After I got my Slate7 last week (which I have been very happy with by the way), I now see a whole new set of tablet-based platforms being discussed in the press. The Split x2 (for Windows 8) and the SlateBook x2 a serious tablet/laptop for Android.

It is clear there is a great deal of innovation and anticipation taking place in this space. When I think about how you use a tablet (e.g., less than an arm’s length away but a relatively fixed distance) it seems to by crying out for glasses free 3D – if you could only spare the power.