Companies across the world are aggressively moving towards using “people analytics” as a tool in recruiting. The face of Human Resources has constantly been evolving from being considered an iron-fisted, traditional department for rules, regulations and compliance to a true business partner in this new century.
Undeniably, the most competitive and valuable asset for any company is their Human Capital. Hiring these assets, retaining, promoting, encouraging, engaging and appreciating them costs America millions of dollars a year. Almost all decisions made in a company are made by individuals who are in a position of authority. These decisions can have consequences that severely impact not just the people performance but also the financial health and the final product.
People Analytics works on the basic premise that all people decisions are being made on data and analysis. In order to produce outstanding business results, managers need to be equipped to make accurate people decisions. People decisions can be at any level at any department. In Human Resources, this means to conclusively show evidence based result of a cause and effect relationship between the function of the Human Resource department and business outcome. Now, how many HR departments that you know of do that?
Are People Analytics and Big Data Right for Your Company?
Statistics show that around 60% of corporate variable costs are people costs. What?!? Yes. Can’t wrap your mind around it? Ask yourself what should my company be doing?
If your company has a significant people cost, then it makes absolute business sense to manage such a large chunk of that cost with intelligent data and analysis. An analysis of the past is often seen as a good predictor of future success. Hiring managers are relying more and more on data to make optimal hiring decisions. Reviewing the internal data of a company can reveal an infinite amount of information. Some of the basic grounds for the hiring team are:
- Where has the organization made the best hires from? Which companies? Geography?
- Which type of employees have been the top 25% of performers? Educational stream/ background?
- What kind of employees are the most loyal? How many organizations have they jumped over the last few years?
- What group of candidates are best for what kind of work? How may years of expertise? Additional language and skills?
- What job specifications can attract the best talent on board?
- Where has the best source of candidates come from? Job boards? Referrals?
- What are the learnings/ take aways/ risks from the data? Where do the bottom 10% come from? Source?
- What further processes can streamline the overall hiring process?
This kind of data eventually leads to predictive analysis and can manage attrition, performance, career planning/ succession and future hiring forecasts. Such level of information can enable HR to move from daily fire fighting to successful business partners in realizing and transforming a company’s full potential. Companies like Google and Apple have been highly successful and have risen to the forefront as the pioneers in this field.
Being data-driven does not mean that all hiring decisions need to be made only from data. What is required is to understand the nature of your company, the kind of product you are selling, and the kind of people you need on board to ensure the company stays successful. Does it require innovators? Entrepreneurs? Risk takers? Freshers? Or subject matter experts? Convert and use this information appropriately to make intelligent and intuitive hiring decisions.
Intelligence and intuition are perhaps the opposite sides to the same coin. While people analytics, data driven decision making, or algorithmic/ evidence based/ fact derived decision making are great tools to make smart decisions, it cannot and should not be the only system that HR relies on. Humans are still smarter than computers to understand the human mind and personality. Analytics can give the hiring manager insights. Use this as a tool to generate powerful and compelling hiring results.