Recently, I’ve been working with our executive briefing folks and a number of others on megatrends – the industry independent trends that will shape our lives in the future and their effect on business decision making. These will naturally shape how technology is consumed as well.
In the process, a number of meta-drivers fell out that may shape the megatrends. Yes, this is turning into a convoluted network of interactions and that is why some models to assess these interactions are so important. These categories for these meta-drivers seemed to be:
- Engagement – this is what drives social, concepts like flow and maybe even the Internet of Things
- Simplicity – addressing the limitations of our ability to consume
- Efficiency – this embraces the concept of abundance and scarcity
- Flexibility – the need to adjust quickly (probably the sustained driving factor for cloud techniques)
- Security – we all know about this, if you don’t feel safe almost nothing else matters
- Visibility – the need for contextual understanding in order to act (one of the reasons for the current focus on Big Data)
Are these too simple? What have I left out?? It surprised me how old some of the links I identified were to link to this post.
It seems like many of our decisions could use an indicator showing how they increase or decrease these categories. We could use this as part of defining our expectations.
How many times do we make decisions that increase security but radically decrease flexibility or visibility, for example? You hear that discussion about our personal as well as our business lives today.
This week I had the opportunity to attend one of Leon Kappelman’s classes at the University of North Texas to participate in interactions with students about their senior project/presentation. The teams of students were covering a number of topics like BYOD, Cloud adoption, Biometric based security… All topics where I felt fairly comfortable.
One presentation was focused on Data Management in the Age of Big Data and they had one concept well understood that many analysts miss.
The opportunity for better decision making.
The team focused on 5 key issues. The lack of:
- Data Governance
- Data Quality standards and management
- Data Architecture and Security
- Operations support
- Business buy-in
We had quite a discussion about the business buy-in issue, since we needed them to explain why it would get this far without buy-in but they explained that the issue orbited around business culture and the implications advanced analytic techniques would have on the culture.
I was happy to see these students internalized these concepts, and hope the organizations they move into after graduation are ready for their perspective.
I wrote a post about what a technologists can do to be relevant a while back and at the time I thought that a list like this would be relatively transient. It turns out that unlike buzzwords, the underlying technologies are usually here for the long haul -- just ask a COBOL programmer. The half-life of the experience is likely much longer than I thought.
I was in a discussion today where we talked about a list of experiences a technologist needs to have in order to talk with some degree of authority about the next big thing in an enterprise context. Naturally, a person can’t know everything to the same level of depth, but there is a basic, useful level for every strategic technologist to have.
Some of the obvious ones I’ve mentioned before were:
- Install a public cloud-based virtual machine and use it for something
- Write an application for a mobile device and get the app listed in the app store
- Take an on-line class (or maybe a couple every year) through a tool like coursera
A couple of those items would have been as applicable 2-3 years ago as they are today. Some have changed quite radically in their capability in that timeframe. I’ve done each of them at least twice for one reason or another and each time I learned something new.
If I were to add a new one that I haven’t touched in a very long time, it would likely be something to do analytics. There is a bit of a problem with this one though, since having enough data to do something useful and interesting may be tough.
I mentioned I was going to experiment with 3D printing. I now need to find something in the Internet-of-Things space as well.
I’ve probably looked at all these things enough to understand what their good for, but actually tackling a project brings that perspective to a whole other level. The hands-on experience doesn’t need to be production ready quality, since the goal is as much generating the exposure to the issues and ideas as it is solving a particular problem.
What other areas should a technologist tackle? And how? I haven’t even mentioned anything in the networking space. Anyone who has looked under the covers of Software Defined Networks probably knows the depth of impact changes in this space will have for the future.
The book Outliers talked about spending 10,000 hours on an area to become great. I wonder how tackling 400 technology domain experiences allows you to be successful - that’s 10 a year for 40 years.
I was looking at a NYT article titled Big Data is Opening Doors, but Maybe Too Many. The article discusses some of the unintended uses that the metadata and derived context information and privacy. In particular it talks about its use by the insurance industry…
While reading it, I had to laugh a bit about the "deep fat fryer" search example in the article.
“But to a data miner, tracking your click stream, this hunt could be read as a telltale signal of an unhealthy habit — a data-based prediction that could make its way to a health insurer or potential employer.”
If the search was taking place in the southern part of the US and there was concerned about people looking up topics like frying, they would be totally overwhelmed by the expectation of frying expertise. That doesn't mean that people fry every day, but knowing how (and possibly even being interested in the topic) is an expectation of life.
Being able to understand what is "normal" vs. aberrant behavior is a society issue. I am pretty sure what goes on in many of the big cities and considered normal behavior is defined as rude in rural areas (and vice versa). If insurance companies start using the information like this, it may turn out they turn into specialists for certain societies. If they do, they do – that’s why there is room for competition. This type of risk analysis is at the core of why insurance exists, to deny that insurance is based on the statistcal analysis of behavior is... interesting.
The use of big data techniques can definitely be used to classify and categorize (put structure upon) sets of “unstructured” information, including things like behavior. Let’s hope data scientists can get beyond coloring the analysis with their personal bias.
I was in a discussion today with some folks that are part of the Service Futures SIG of ISSIP. We were talking about the technology trends that will be shaping our approach to addressing business problems in our organizations. It seems that we may be at a pivot point for our perspective.