I am sure you have been hearing a lot about “big data” recently. You might be asking yourself “when did the data became big?”. “how big is big data”? and “what are characteristics of big data?” and many other questions like these. Don’t worry. Good answers to those good questions is a mouse click away!
I have a few questions for all of you who are still in universities and preparing yourself for the next chapter of your life. Are you getting ready for the age of big data? Do you know what courses and what projects well prepare you for the challenges and opportunities that “big data” is bringing to us?
Can we really build a smarter planet? It is hard for some to imagine where to start…
Types of Service Systems and Approximate Population Levels
(0) individual 1 person
(1) family (or dorm mates) ~10 people
(2) neighborhood ~100 people
(3) community ~1000 people
(4) urban-zone (district) ~10,000 people
(5) urban-center (city center) ~100,000 people
(6) metro-region (county) ~1,000,000 people
(7) state (province) ~10,000,000 people
(8) nation (country) ~100,000,000 people
(9) continent (union) ~1,000,000,000 people
(10) planet (world) ~10,000,000,000 people
As the world becomes more instrumented, interconnected, and intelligent, the flows of information, things, and people between these systems is growing, expanding opportunities and uncovering new challenges. When properly governed, these “service systems” interact with each other, seeking “win-win” outcomes, or more precisely mutual value-cocreation and capability-coelevation. Service is the application of capabilities for the benefit of another, including “the other” known as future-self.
In earlier times, global competition took place within a winner-take-all policy framework (the big get bigger, the successful become more successful). More recently, this is being put into balance, not discarded, but put into balance with improve-weakest-link policy framework. In an interdependent world, balancing these two policy frameworks is important to maintain innovativeness, equity, sustainability, and resiliency of systems. For example, consider sports.
In sports, improve-weakest-link policy is what keeps the games exciting and outcomes less predictable, so Colts (one of the weakest teams in US national football league) get Stanford’s quarterback (one of the top rated players in college league). When the best players on one level below are recruited to the weakest teams on the next level up – the games are more exciting and outcomes less predictable. The NFL Draft is designed explicitly to improve weakest link. Improve-weakest-link is also sometimes called a reverse-recruitment policy. The best are often attracted to the biggest challenges where they can personally make the most difference in the outcomes.
When properly governed, “holistic service systems” like cities provide high quality-of-life to their citizens by provisioning “whole service” to their citizens. Whole service relates to a society’s capabilities or knowledge burden with respect to three types of activities: flows, development, and governance. Flows include transportation, water & waste, food & products, energy & electricity, information & communications; Development includes buildings, retail & hospitality, finance & business, health, education; Governance at multiple levels includes security, law and order, peaceful dispute resolution, standards, and raising standards for a new generation.
Students look at your universities. Universities are like mini-cities and you are like the citizens of these mini-cities. The point is that universities are a key part of the system of systems, and they touch many lives because more and more hospitals are associated with their local research university. Many believe education is ready for a big change.
Students for a Smarter Planet (S4aSP)
More students are coming to understand better that they can work to make all levels of the system of systems smarter, if they work together to identify the right important challenges to work on…
Working on challenges along with lots of other students around the world is what makes a smarter planet…
“The best way to predict the future is to inspire the next generation of students to build it better”
“The future is already here at universities – it’s just not evenly distributed yet.”
Most students think of our company as a really old company (101 years old in fact) with mostly employees that their grandparents might know if they are interested in big business computers like the type of “big iron” that is used by the world’s banks and other big companies.
However, more and more students understand that our company is a global leader trying to create a Smarter Planet … with lots of amazingly enthusiastic IBMers all over the world (many thousands of employees are recent college graduates, especially in the emerging nations of the world) working with faculty and students solving grand challenge problems, small and large, to make the world a better place – innovativeness, equity, sustainability, resiliency, all matter on a smarter planet.
So start by re-imaging the role of students on a smarter planet – looking for local opportunities that can scale-up and make a global difference. For example, how much wind energy is your university harvesting? There are so many ways to make the planet smarter, one revolution at a time.
Over the last two years I’ve been concentrating on helping knowledge workers make intelligent decisions, and accomplishing this via analytic on various kinds of data.
I recently found 2 books on related subjects by Tom Davenport:
2 Stories of Big Decisions and the Teams that Got Them Right (with Brook Manville) in which “they share twelve stories of organizations that have successfully tapped their data assets, diverse perspectives, and deep knowledge to build an organizational decision-making capability.”
Analytics at Work: Smarter Decisions, Better Results, (with Jeanne Harris and Bob Morison) was published in February, 2010. “these Ideas were named one of the “management ideas of the millennium (so far)” by Harvard Business Review editors, and the HBR article I wrote on this topic in 2006 was named one of the “Ten Must Read” articles in HBR’s history.”
I feel pretty fortunate to work on such an important topic. What are your opinions about Big Data? Have these industry trends influenced what students learn at university today? How are today’s students prepared to take the emerging careers in big data analytics?
When you discuss efficiency, education isn’t the endeavor that first comes to mind. In fields such as manufacturing, one worker today is performing the equivalent of three or more workers fifty years ago. Even in services automation has significantly improved output per person. I know in my field of technology, responsible for many productivity gains, that the number of workers to output seems to have grown exponentially. Why has education, despite investments in technology, needed more people to educate per student than fifty years ago? And remain relatively stagnate in results?
In the 60′s it was not uncommon for class sizes to be well north of 30 students, in many districts approaching 40. Today class sizes in the low twenties are common, and teacher assistants are far more common than they were five decades ago. To clarify, I’m discussing the United States. Why has the technical revolution that allows manufacturing and services to increase productivity so much, not touched education?
When I talk to educators, and their spouses, I often hear how they work late into the night to perform all of their tasks. I believe there is something missing in the education establishment. We have thrown more bodies at students and shown some gains, billions have been spent on technology with limited results. What do we need to do, that will revolutionize education?
I think we have some basic steps to take to identify, acknowledge and start fixing the problem.
- Identify what educators do that doesn’t involve education, and automate it to minimize effort.
- Segregate the core business, education, and outsource the rest to appropriate organizations.
- Automate as much learning as possible. Allow educators to concentrate on remediation of weak students and encouragement of excelling pupils. Minimize the time required to teach, and enable motivated students to educate themselves.
It seems easy in a bullet list, but will require a great deal of effort, training and time to implement. Do educators think I’m on the correct path, or is education immune to productivity gains?