I had an interesting day today following the live-tweets from the SAS Inside Intelligence Analyst Event. There were some very interesting tweets that came along, like this one from Ray Wang (Enterprise Analyst with the Altimeter Group, and the most prolific tweeter for the event with around 15% of the total tweets):
Finally, I thought, organizations are starting to understand the value of data and that we can begin to use it for strategic needs. Then Dan Vesset (IDC Analyst and author of a terrific paper entitled Decision Management: A Strategy for Organizationwide Decision Support and Automation – must be an IDC customer or pay to read it) tweeted what I consider best news from the Event:
Now I started to get excited — we are finally getting to the point where systems can make decisions, look at the data and make sense of it and not only recommend or report on the data, but actually make the decision and maybe, just maybe even act on it. Ah, the possibilities — all those years of Star Trek and Star Wars finally coming to fruition!
As a big proponent of automation for organizations to truly leverage technology and data management, my head was spinning — could it be possible? Are we really that close to making something like this happen?
Later in the day, I caught a tweet from Venessa Miemis (Futurist, Student, Amazing Brain, and the writer behind the very famous and well read emergent by design blog) that talked to a different (yet similar) reality:
A different opinion indeed.
This got me thinking: do we need Sensemakers, people who can make sense of the data — or can we trust the systems to make sense and make the decisions for us?
I had a conversation via Twitter with Venessa about this, but there is only so much you can do with just 140 characters at the time. I told her I would write this post to explain my positions further.
Here we go.
I fully believe that there are three factors standing in the way of Sensemakers as Venessa tweeted:
- Scalability – There are around 6.5 Billion of us in this planet, and we are growing towards 9 Billion in the next ten years. Too much information that needs to be processed to those many people. Sure, the counter argument would suggest, with those many more people you can have more Sensemakers — thus you can feed the needs of more and more people. That would be true if Sensemakers were easy to find, train, and deploy. As it was pointed out to me in discussions in my previous post, we still don’t know very well the type of people we need to analyze the information — how can we expect of have more of them? To me the model is not scalable and thus would not fit the purpose. To be fair, Venessa feels that this big-box thinking is what got us in trouble before — so why try again? Well, for starters…
- Globalization – We are no longer limited to the information in our near-and-dear communities. The local, small-town mentality that most of us had (yes, even in corporations) has recently been replaced by a global perspective. This is a big world (before you say Duh!, please read on) and to feed the knowledge needs of a global world you need a global mentality. Human beings are nurtured in local groups and communities; we are not global in actions or thoughts. The ability to think global is not innate, and it is not easy to do for one person. Finding, training, and deploying that person – in addition to being a Sensemaker – becomes an almost impossible task. Now multiply that by 6.5 Billion people or so. Computer systems can handle the magnitude of this need, human beings can only say “Huh?”. Further, globalization has also brought the issue of…
- Complexity and Volume of Information – Raise your hand if you don’t feel overwhelmed by knowledge and information coming at you (OK, the funny person who raised their hand can now put it down). The sheer magnitude of data, knowledge and information is mind-blowing. Add to that the complexity level of the information we receive and you get an idea of why you feel so overwhelmed. Now, you have to find the potential Sensemakers to take that complex information, make sense and connect the dots and then communicate it and explain it to the people who need it. Wanna apply for that job? Me neither.
What I do want is to use computers and sytems designed to handle very complex, very large data sets and put them to work the right way. We saw in the last few months the launch of machines so fast and powerful that my old Ti-99/4A seems like a — well even my phone is 100s times faster and more powerful than my old computer. Why not leverage those systems for what they are supposed to do? Take large to gigantic data sets, organize them, make sense of them, and then act on it.
To me, this is the way we are moving in the next five to ten years. This is the reality I want to build towards, what I see as our future.
Wanna join me? Why not? What do you think is a better way to handle these demands and needs? let me know your thoughts, would love to know what you are thinking…