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.
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.