Machine Learning in HR, reality or fiction?

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Artificial Intelligence promises to be the next Technological Holy Grail of our time - for some perhaps the last - and has unleashed an intense Innovation career. The giants of Technology (Apple, Google, Facebook, Microsoft or Amazon) spend millions of dollars to establish themselves and progress in this field, but the battle is even more bitter among states. China has drawn up a plan to position itself as a leader in global innovation in 2030 and presents astronomical investment figures, both public and private, in Artificial Intelligence. In front of them, of course, the United States, but also other actors such as France, where Macron promotes a national strategy to compete in this area, budgeting 1500 million Euros in the next 5 years to, at least, not be left behind in the race.

Under this scenario, the gurus move between three positions: the alarmist, championed by Elon Musk, which warns of the dangers that can involve in a few years the exponential development of AI and calls for regulation, the enthusiast, where characters like Marc Zuckerberg, and a third, which we could call the realist, who warns that much has been exaggerated and misinterpreted about the real capabilities of Artificial Intelligence, and that, at least in the short term, we do not have anything else on the table - nothing less-than more powerful and more accessible techniques of advanced information analysis.

Surely there is something true in each of these lines of thought, and each must seek its equidistance between these three poles, but it is clear that whatever the sector in which we work, this is the time to ask the question: Can the AI really contribute something to my business?

In the world of Human Resources there are already clear lines of work where techniques such as Machine Learning can be a breakthrough: the search and retention of talent, the improvement of the employee's experience in the company or the adaptation of people to teams or posts, are fields in which these new tools can help us get much more useful and accurate answers. And this brings us to the next question: where do we start?

Data, quality data, this is the first essential requirement. If we want to better understand, for example, what is the experience of the employees in our company, the first thing will be to make sure that we are storing the really relevant data about it: we need accurate and digitized information on the rotation, employee satisfaction, processes of feedback that we have implemented (evaluations, one to one, coaching ...), etc. And once we have correctly established the data sources, it will be necessary to ensure their quality through health information check processes, which allow us to discard repeated, inconsistent or outdated data. There are sources that ensure that only 25% of companies that are beginning to invest In these initiatives of information analysis they really improve their income, mainly because they base their analysis on low-quality data. IBM estimates that this is costing more than 3,000 million dollars annually, only to American companies.

Let's suppose that we already have a sufficiently good strategy of collecting and processing data, the following is obvious, financing. This type of projects involves time, investment in profiles that, with total security, we will not have on our staff, and tools, mainly Software. Introducing techniques such as Machine Learning or Deep Learning into the company's decision-making processes will not be successful if we try to manage it as just another project.

The next step is people. According to Glassdoor, the most valued and best-paid profession in the United States for the last 3 years has been that of Data Scientist. This is what we will need, but what exactly is a Data Scientist? Personally, I really like the definition provided by Josh Wills: "Data Scientist is a person who is better with statistics than any Software Engineer, and better with Software Engineering than any statistician."

This new profession arises from the intersection between Computing and Statistics, in fact, in Spain have appeared in recent years, double degrees in Computer Science and Mathematics or Computer Science and Statistics (Autonomous University of Madrid, Granada, Polytechnic of Madrid, Polytechnic of Catalonia, University of Valladolid ...), which are probably the most rigorous source of training for this new type of professionals. In addition to this, there are many postgraduate courses, online courses, etc. that can serve as specialization in the field, mainly for Software Engineers. For the moment what needs to be clear is that they are scarce and well-paid professionals, especially the good ones.

And finally, there are the Software tools with which to work, some Open Source and others terribly expensive. The emergence of these new tools has been one of the great recent advances in the popularization of Machine Learning: TensorFlow (Google), Amazon Machine Learning, Microsoft CNTK, Torch (Twitter, Facebook) and many others. The ecosystem of information processing tools has exploded literally in recent years: Frameworks, Algorithms, Libraries, integrations with all kinds of software ... Everything necessary to do industrially things that a few years ago belonged only to the field of research.

After all this, is it really worth investing all the effort and money necessary for a strategy to introduce Machine Learning in our processes? Is it so important to squeeze so sophisticated the information we have? Absolutely yes, Information is the great tool of our time, with the complexity of current environments, any decision we make not based on the right data is more a bet than a strategy. In the words of the engineer and statistician Deming: "In God we trust. All others must bring data. "

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