The Future of Credentials?

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 Future of Work?

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.


Predictive Learning

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 …


Virtual Education

Over a period of time, I have written about how virtual education can be used to address some of the problems facing education today, namely, shortage of good faculty, and the high cost of getting high-quality education to a large number of students. Few days back (which is when I had made a note to write this, but didnt quite get round to it, maybe because of the New Year holidays, but then, that would be just an excuse) I read about virtual courses offered by IITs being attended by students from other colleges (please help with a link if you could find … have been searching, but not to much avail). In this, the college from which the participating students come from decides whether they want to give credit to students for the courses they attend virtually, but if they do, then this is a step in the direction I have written about. For the participating colleges, there is, of course, the additional aspect of building the infrastructure to support the learning around these courses, in the form of labs, Q&A sessions, tests, and so on, but there is a benefit too. This would arise in the form of the content being made available to students, around which the learning then takes place.

Taking this one step further, is the possibility of building Centres of Excellence, hosted by one or two colleges, and have courses around these topics being delivered virtually by Professors from these colleges. For instance, these centres could be like Material Science, Thermodynamics, Engines and Turbo-Machines, Operations Research, and so on. Based on mutual agreement, specific colleges could be identified as CoEs for each of these, and courses delivered on these areas by Professors from these colleges, with students participating from all participating colleges. This would give the benefit of standard, high-quality education being delivered to students across the country, without necessarily replicating faculty skills at multiple locations, and enhance access as well as address cost issues.

From here, the individual colleges could take over, in supporting the learning process around these courses. This would be in the form of Q&A, homework assignments, labs, case studies, projects, exams, and facilitating cross faculty-student collaboration. In this way, the participating colleges get access to world-class content, delivering colleges build a source of revenue, and there is a symbiotic relationship between the two. The participating colleges give credits for these courses, manage the learning process, and give degrees based on their own criteria.


Technology & Performance Management

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.


Blended Education

blog post I had written recently was what I was reminded of when I read this one about the Blossoms program, reason being that this was quite the type of delivery mechanism that I was talking about.

The idea here is simple … video recordings of lectures by a panel of expert teachers which form the backbone of education delivery across schools. This enables standard education delivery, while at the time making sure the best teachers are available to deliver classes to students across cities and villages, including in places where these top teachers would typically not want to go. Follow up this lecture with interactions at the classroom level, where the teacher running the class builds up on the video, and takes the students into interactions to discuss the content delivered, and ensure understanding of this content to all the students.

This logic of interactions can be extended to lab exercises too, as well as project reports, where the theoretical concepts are delivered in an electronic way, while the application of these concepts, including lab experiments, and the discussions among students are conducted face-to-face.

Needless to say, this method can also be extended to the L&D domain. The idea here being that with this mechanism, the L&D team can bring experts from across the world to the desktop of the learner, but the learning interaction doesnt necessarily end there (which it would in the classical e-learning method, which is still a dominant method in most organizations), and is extended with the learning interaction being extended to face-to-face (now face-to-face here could either be an actual face-to-face session, or a virtual face-to-face session), where learner interactions can happen, and learners can be presented with illustrations, case studies, and more detailed inputs, including, very importantly, inputs which are company-specific. These, of course, could be applicable both in traditional one-to-many training interventions, or in one-to-one coaching methods.


Social Eminence

A discussion I was having the other day with colleagues about eminence and the role of social media in creating the persona of people who are experts at things brought out some rather interesting thoughts. One of the ideas that came out was that social reputation is based on one’s willingness to share knowledge. While I completely agree with that, this viewpoint confuses knowledge with the act of sharing. One can actually share things on social media without really knowing much about them. One of the things I see, for instance, on twitter, is that the rate at which people share links must mean they are reading like probably a thousand words per minute. Quite a few people I know just glance through an article or blog, and share it on social media. This is why I say hat sometime knowledge can be confused with the act of sharing.

Another important thing to understand is that it is very easy to manufacture things on social media. You might have seen a number of quotes from Albert Einstein on the web, and I don’t know how many of them are attributable to him. Taking an instance of a talk show I was watching, the analyst on the show was quoting a long-departed leader as having said something. This didn’t quite sound logical to me, so I started searching. After much searching, I found a blog which told how a lie was fabricated and why, and how it was circulated all over the world over social media. The “fact” may find it’s way twice around the world before folks start finding out. Also, there will be a number of folks on social media who will have spread the word, and very few who would take the effort to validate. What this means is that social eminence can be manufactured, and while there are self-correcting mechanisms which are there in the social ecosystem, these methods may not always be effective in a world with a very short memory. By the time you figure out something is wrong, nobody’s really interested, and setting the record straight is a moot point.

The point I am trying to make is that we need to be selective in the sources we subscribe to, and that we need to do our research before publishing something, a thing which is seldom done.