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 …


Conversation Context

If you are on WhatsApp, you are probably on a host of groups, which you have, in all probability muted. Ever seen a pattern in the conversations in these groups? If someone started a group, and you connect with old friends after say 20 years, the first few days are about exchanging notes, and reminiscing about old days. After that the lull comes in the conversation, and this is when most groups become about forwarded messages or jokes.

Friends from college have a group, which is the place for college-style conversations. After a while, the conversations turn towards politics, and discussions about the world, pretty much the way it used to be back in college (boys don’t grow up, remember?!). However, due to a number of reasons, we decided to start a separate group about political/intellectual debates. Yes, intellectual indeed, even if I myself say so. Over time, the original group, which all of us are still members of has become a group for forwarded messages.

The reason is simple … context. No conversation can happen between two people without some modicum of a shared context. Take the context away, and the conversation can’t last. As college friends, we have gone different ways in our lives. However, there is a strong shared context of our time together at college, but beyond that, the shared context is that of the world around us. And hence, these are the two topics on which conversations can sustain.

In other words, context is key.


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.


TM & SCM – Contd

Continuing from this post, I was thinking about more details about this parallel between Talent Management and Supply Chain Management. The first principle, from which I am trying to derive things here is that in both cases, there is a demand (in one case for talent, and in the other case for products), which needs to be met, and frameworks or processes put into place to match supply with demand. With products, the source of demand is simple to visualize. Not so with talent. So lets begin by taking a look at that.

The need of strategies, processes, and practices in the organization is to meet the business vision of the organization. To meet this vision, some work needs to be done by some people, and therefore, there is a need for people, equipped with the talent to do this work. So, the demand for talent arises from the work to be done to meet the strategic goals of the organization. Add to this the fact that there is specific talent available within the organization, and from there, its a question of trying to match available talent to the demand for talent, and based on this, determine what talent is required (in which area) to meet this demand. The supply of demand comes from employees, contractors, applicants, and L&D. I say L&D because learning is one way for creating talent supply to meet the talent needs of the organization.

Having said this, the basic concept which is the core for SCM is the concept of the part number. This is the unique identifier which tells anyone across the supply chain which specific material or product is being talked about. There needs to be a concept similar to this, something which uniquely identifies the attributes of the talent required (somewhat like part number which uniquely identifies the specifications of the material being spoken about). Different organizations meet this requirement in different ways. As you will read here, IBM solved this with the concept of JRSS, the Job Role Skill-Set, which is a composite of the job role, the role that an individual performs, and the skill sets that the individual has. This is the common identifier which can uniquely define what talent is being spoken of in the talent planning process.


TM & SCM

A discussion I was having the other day got me to think about how Talent Management is based on principles which are analogous to other functions. And this brought me to the idea of the similarity between Talent Management and Supply Chain Management, in terms of principles. If we look at the essence of Talent Management to be about bringing the right people to the right roles at the right time, then we can, from there, start looking at the essence being to match the demand for talent with available supply, and building supply pipelines where there is a shortfall.

To begin with, one of the major conconers for organizations is uncertainty. If things were fairly certain, then there wouldnt be much to be gained by trying to manage talent, because things would be running pretty much the way they are running. The sources of uncertainty are many, but thats for another time. This uncertainty results in the need to identify, based on the organization strategy, and operating plans for the coming years, what the organization’s talent requirement is going to be. Given uncertainty, there is also the need to identify how good this estimate is. This is analogous to demand planning, where the need is to estimate how much demand the organization would need for which products, and the amount of uncertainty (sometimes measured in terms of probability) associated with that demand. Based on this estimate, one can arrive at the talent required to meet the strategic and operational plans.

With this forecast as the baseline, one can then look at the talent existing in the system. This may be the talent pool which is ready for the roles for which they are required. This is akin to the gross-to-net calculation which is common in all material planning (MRP) systems. At the same time, one also needs to try to identify how many people the organization wants to, or can, develop from within, to meet these talent requirements, and from here, derive what the hiring plan looks like. This is quite akin to make-or-buy decisions material planners regularly have to make, keeping in mind available resources to make. At the same time, this serves as the input to defining Development Plans, which is akin to creating Work Orders to meet the build requirements.

Traditional succession planning is about identifying which individual should be doing what role some period down the line, but this is problematic, given that after that period of time, either the person may not be with the organization, or the role envisaged may not be part of the role directory of the organization. So, instead of looking at an individual job and a particular person, one can look at a job family to be fulfilled by a talent pool. This is analogous to product-family or product-category level planning, because forecasting, and therefore planning at the aggregate level is more accurate.