Q. What is the power of context? And why is it important to understand it?
Let’s understand this with an illustrative example. When one teacher is teaching 60 students, there are a variety of student scores. When you get the kids to do the same thing again and again, you observe that each person learns differently, you learn based on the experience. Teaching, therefore, is not a function of the teacher, but the learner. This concept of experiential learning stems from the philosophy of constructive realism - according to which what we see as reality is what we have been taught to construct around us - we define our reality.
In the same way, organizations are also unique, just like the intellectual identity of an individual. That is the essence of why organizations need to use technology that understands their context. Most technologies that companies use today are generic. And there is a real opportunity for contextual intelligence. It is a failure to understand these insights that lead to a high performer in one company failing in a competitor’s business regardless of the industry and size.
Q. There has been a lot of buzz words in HR Tech like Big Data, Analytics, and Automation. Where does contextual intelligence fit in?
There are four parts to data science. The first is to do with getting/collating robust data. Second is what we call ‘data analytics’. The third part is what we call as the interpretation of analytics. The fourth part is the application of the interpretation; this is the stage where the real value comes in.
If Person ‘A’ was to search for the word Java, and Person ‘B’ was to search for the word Java, there can be multiple contexts that define what it actually is. And depending on the situation it can be coffee, motorcycle and even a computer language. Even when we are talking to each other, we tend to assume things because we share a single context. When you’re talking to machines, however, someone needs to teach machines this context and that is what most technologies have failed to do. We have not taught machines the power of context.
For SpireTalentSHIP, our focus is on analytics interpretation and application. As more devices are being connected to the internet, there is a lot of data being churned out. Most businesses today are doing analytics based entirely on numbers or structured data – SPSS, RoI and so on. However, this accounts for only 10 percent of data generated. The rest is unstructured data. We have taught machines to read English just like a human being would. This is what we call as unstructured data analytics. It involves searching, identifying an exact match or sub-matches, and mapping it in order of your preference. We bring the ability to comprehend unstructured data and to interpret it. When we start interpreting the unstructured data analytics, we interpret it contextually; we have the technology capability to that.
Q. Do you rely on the company to give you this data or is it data collected from the internet?
It is data that is collected from the internet. Let me give you another illustrative example, one of our prospective clients wanted to know what investment manager profiles looked like across geographies in the world. And the company does not have that data because it is not into investment banking. But it is possibly trying to pitch to investment bankers and hence they would need the data. Now, most financial statements of most companies are available on the internet since most companies are publically listed company. Secondly, most professionals today are registered on Social and Professional Media. There are these surrogates which are possible with the internet. Data itself is not the victory. It is the capability to generate meaningful intelligence out of it. Then use that intelligence for whichever function is required.
Q. How does the technology work on a practical level? And how do you authenticate this data?
Let’s say there is a need for an editor for an HR publishing house. First, the technology scans for editors of English publishing houses across the world to understand what their profiles look like. Out of these, it would narrow down to HR related profiles. It would also understand what the ideal profile is like.
Second, it goes into searches like consultants do with a list of say twenty to thirty people and matches relevant profiles. It doesn’t matter if it is 1.5 million candidates. It can analyze information at the press of a button, wherein, a human being will take two weeks or three weeks. A machine does this on a real time basis. Finally, you might just want to speak to two, three people. So the companies looking for the best fit and the candidates looking for a job are matched.
The fear of poaching is rooted in a lack of understanding, it’s because you don’t know if you have chosen the right person for the job and when the person doesn’t know if they are in the best possible job. This uncertainty is linked to people looking for a new job all the time. This is the inefficiency of the current system and not the fallout of the new system. The new system is based on a completely different philosophy. One does not need to be afraid of the competition anymore because their contexts are very different.
As for authentication, the problem is not just with the internet. How will a consultant verify information with them? They use a search engine, put in the keywords and select the candidates who they think they can sell the job to. It is far more difficult to fudge data on a public platform than in-person interviews. Technology can not only identify deviances but also reduce the human bias involved.
Q. What should business leaders be aware of?
Companies that work with us have a global mindset and have visionary leaders. While there is a lot of talk on automation and web 4.0, it is still going to take time to have a significant impact. The question in front of business leaders is this: Do you as a company have the ability to put your money where your mouth is? Are you convinced enough?
There is a real opportunity to bridge some fundamental problems in the ecosystem today. When two companies merge, they aren’t sure if their employees have the same skill sets. When an internet company plans to become a mobile company, they aren’t sure if their talent acquisition strategy is right. These are the kind of questions that we hope to answer with contextual intelligence.