Following the post on the future of work, was thinking about what implications this would have for education, and the most obvious connection between work and education is about credentials. These are the signposts that tell (current or future) employers that a person has a certain set of characteristics. The most obvious example of credentials is the degree which your college/university has given you, telling the world that you meet a certain set of criteria. Often, this criteria is somewhat obscure, and may mean all things to all people, as we can see from the fact that the same credential from different universities mean different things, as seen from the value that people assign to them.
Today, a college degree has immense value for an employer, because the college degree tells the employer that the student has gone through a certain set of courses, and therefore is the right person to meet the requirements of the employers. From the employer’s perspective, the degree tells them that the prospective employee has the skills to be able to build a career. What employers look for is the assurance that the prospective employee has what it takes to fit into the grand scheme of things, to become a part of the larger picture that their organisation represents.
However, as the nature of work changes, as I said before, would such a credential of an ability to learn all things be as important? I believe that in such a scenario, where an individual would be contributing their specific quantum of work in a larger value chain as a ‘freelancer’ the skills of the individual in that particular space would become much more important than their generic ability. This means that organisations would naturally be more interesting in evidence of achievement in that specific area.
Such a shift in focus from organisations would necessarily mean that the ability to demonstrate ability in a particular area would become more valuable than the ability to demonstrate overall/generic ability. Hence, I feel, artefacts generated by individuals in the course of their learning, whether in the form of project reports, or papers authored, or creative work, would probably have a far greater impact than the degree. So, for instance, a paper written by a student on a particular topic, related to the work sphere of the student would likely have far more interest for employers than the degree or the grade would.
In other words, the evidence of achievement, in the form of artefacts, or in the form of eminence would become a far more valuable resource by which to evaluate prospective employees than simply the degree.
The way we work has been undergoing massive changes over the last decade or more, but today, I believe, we are at the cusp of a fundamental shift in the relations of work, facilitated by the developments in technology. By relations of work, I mean the role each individual plays in a ‘value chain’ and how the part contributes to the whole.
Before the advent of the modern corporation, people worked not for a corporation (they weren’t around, remember?). Rather, artisans, for instance, manufactured their final product, say a bicycle (if they were around …) as a single entity, and sold their products in a marketplace.
With the advent of the corporation came the concept of people working in jobs where they did specific work, which contributed (often in indefinable ways) to the overall value chain. In this way, the individual would do their part of the work, and pass on their output to someone else, who would do their part of the work (value add) and so on …
This aspect is changing, and, I believe, set to change in bigger ways. As we are seeing there is a trend towards organizations outsourcing their work to freelance contractors. As this grows (and we are seeing this happening more so in the technology sector) we would likely come to a state where instead of many individuals being brought together under the ambit of the organizations, people would work more in their capacity as individuals, being brought together under the ambit of the value chain. This value chain, by definition, would span organizations, which means that we can expect to see, more and more, the value chain being formed as a loose federation of individual freelance contributors, their output orchestrated by a set of organizations partnering together to create a certain set of products or services.
So in terms of work structures this could likely be a move towards towards ways of working the modern corporation replaced, though in ways which are very much the new millennium. This has massive implications on the aspirations of youngsters (I don’t quite rely on the generation nomenclature, partly because I don’t understand it …), in that they can probably no longer aspire to long term jobs and designations may lose their meaning, the content of work, and the satisfaction that generates being the main defining factors there.
In a way, going back in time, but in a 21st century way.
In todays L&D landscape, the way businesses determine who should participate in what training isnt far away from some sort of conjuring act. More often than not, the result of this is a mixed bag, and many of the L&D professionals I speak to tell me that the L1 scores (based on the Kirkpatrick model) are more often than not tending towards the lower end of the spectrum.
There are typically two ways a business determines training participation. One is based on mandated training (usually related to promotion/growth), while the other is nomination by the business manager. Both of these are based on picking up from a ‘menu’ of available programs, and neither really takes into consideration the actual learning needs of the individual.
This is where the idea of predictive learning comes in. The idea here is simple … today, with the technology available to us, especially in the Big Data/Analytics domains, the data about what has worked in the past in what context is available to the organization in a large scale. This data is available based on training, HR, and operations/business data. This rich data can be leveraged to determine what is the best training solution which would likely work in a particular employee context. Like Big Data, this neednt look at the reason (or connection) between cause and effect, rather, look at the linkages as they have been seen in the past.
An important aspect of this picture is that this shifts the focus from training and learning, and from L&D to the individual learner, and makes the entire process people-centric.
One concern with this, though, could be that the outcome of the requirements could be way too granular, and too tailored to individual needs, so as to be unviable from the delivery perspective. More about this later …
I am these days reading a book about Big Data, and going through some of the applications of the technology, I was thinking about some of the ways Big Data can be applied in people matters. I tried to google about usage of Big Data for Performance Management, and didnt quite find much (or maybe thats because the search terms show results for application performance management). One aspect of using technology in HR, I feel, is in the realm of Performance Management.
Today, appraisals are done in an objective manner, with ratings which try to capture achivements and performance. However, as we know, these are a sort of force-fit. What does a rating of “Exceeds Expectation” mean? Does this mean, for instance, that performance is high, or does this mean that expectations are low? Somehow, this seems to be like fitting a square peg in a round hole, or a round peg in a square hole, if you prefer it that way.
An alternative to this could be the usage of technologies like Big Data to handle this. To begin with, managers could have the option of writing their observations, along with specific examples or scenarios as part of the appraisal process. This kind of input gives us rich information about people performance. Instead of trying to fit performance into a quantitative scale, this has the possibility of giving us qualitative inputs into performance.
Add to this the fact that plenty of business-related data is available from finance, sales, and operations, and we have immense data, both quantitative and qualitative, with which to work. Using this data as the starting point, Big Data technologies could be used to build correlation between manager comments and business performance, and deriving employee performance based on this correlation. This has the benefit of giving a descriptive picture of performance, one which describes achievements in a more meaningful way which can be used to drive talent processes.
Theres much more that Big Data can be used for, as this post by @josh_bersin describes.
This is a topic which quite a few of us would have been thinking about … what are the implications of cloud for IT service providers? The reason this question gains importance is because with cloud paradigm, the levers of value for customers become different from what they have been. The days of mega implementations, for example, having 500 people teams working for 4 years to deliver a project are no longer to be seen. With cloud coming into the mainstream of technology, project profiles are changing further. Release cycles are much shorter, with larger number of releases coming out in quick succession. Project lifecycles are much shorter too, as is the scope of development or customization.
One is the fact that it is no longer possible for companies to differentiate themselves on the basis of IT as technology becomes commoditized. The paradox is that when IT was a specialized space, IT was almost an afterthought in organizational strategy, while today is becoming centre-stage in the strategy landscape.
As IT becomes more commoditized and more and more of the technology components in the organization, there is more reason for organizations to oursource more of their IT functions.
For enterprise apps, for instance, the cloud era seems to be one of short implementation lifecycles, far less customization, agile development, and accelerators. This means that for services organizations, this is a whole new paradigm, with the sales folks not keen on selling these engagements as the revenue potential from these is much less, and yet, organizations have more focus on cloud engagements. Services organizations would need to change the engagement model, probably with more shared-delivery in implementation projects, and reducing the distinction between implementation and support engagements from a delivery perspective.
For quite a while, I have been thinking that maybe I am the only one who doesnt understand what these words mean. I mean, with the buzz around these concepts (and here I mean the concepts, not the technology), these must be complex concepts to define, but the definitions that I was able to understand were all quite simple.
Big data is just that … BIG! There are essentially 3 things which define it:
1. Theres lots of it! Much more than we had imagined maybe even a couple of years ago.
2. The form of this data is too diverse. There text, images, videos, and what have you. Theres structured data and unstructured data, and data comes with its own context which makes it even more complex to handle.
3. Its being generated at a very fast pace. In fact, writing this blog is adding to this big data, as is your tweet, and those pictures you post on facebook, or those status updates that you like.
I was looking for whether this definition is correct or not, and I came across this video from Ericsson Research, which describes it quite simply with an example. If you want to get past the buzz and get to understand the concept, I would suggest you watch this.
So where does analytics come into the picture? Well, if theres so much of data, theres also the fact that its very difficult to build any coherent picture from this mass of data, and this problem is addressed by the analytics domain. Analytics helps us make sense of big data!
So what does Big Data Analytics need?
1. It requires infrastructure which is able to scale up or down based on the demands of the those who are generating this data, and those who are analyzing it. This means that the infrastructure needs to be flexible, and this can be handled much more easily with cloud solutions, and this is where cloud comes into the picture of big data and analytics.
2. It requires the applications which gather this data. A lot of this data is being generated by automated systems like sensors, and through mobile devices. With the scenario of equipment communicating with other equipment, the concept of the internet of things comes into the picture. Also, with the mobile device explosion, the importance of mobile applications and mobility solutions as an integral part of the picture also becomes apparent.
3. It requires the statistical and technology foundation which will help users or systems to make sense of this data. This is the analytics piece of the picture.
Heres a nice video about an IBM study on analytics.
This is how the picture gets a little clearer, and we can see how the cloud, internet of things, mobility, big data, and analytics are coming together to create a whole new technology paradigm.