People Analytics

What is People Analytics?

The domain of Human Resource has been moving since a while now towards People analytics and there in collecting and cleaning and making sense of the data you have about your people and using the same for key business decisions. Most common usage of People Analytics is Attrition, retention and turnover, organizational performance and sales productivity In an era of an economy with talent constrained, how are we keeping our  Talents, unless you find out why your people are unhappy, unproductive, misaligned, they don't know what they're supposed to be working on, you just can't be a good company. And so there's a huge demand for information about what is creating engagement, why are people leaving, what we can do to improve their levels of engagement, their productivity. The vast amount of data that we have access to in today's date and are constantly growing, orders of magnitude is more than it ever was before. . If you're an engineer or a sales person or you're a consultant in your company, and you're moving from place to place,  There's all this information that companies are collecting about their employees, similar to the data that we try to collect on customers that organizations are now beginning to realize, wow, this could be useful in understanding why this business unit is out-performing the other business units. How people are being hired, who gets promoted, who gets the new job, whose profile is to be enhanced and enlanged, who gets a higher increment, all those decisions are being made all the time in your company that were being made based on gut feel in the yesteryears and all going in the direction of data driven, well informed decisions. This is causing improvements in productivity . And today You have opportunities to improve all of those decisions that are being made all the time in the management side of your company with people analytics that are bringing in a competitive advantage that is helping organisations win the Competition games at an ever-increasing rate. Of course your competitors are doing the same thing so there is a little bit of a war going on here for data to become smarter about what makes your company successful. that's why it's such an exciting area to be affiliated with.

Where did it all Start?

When businesses were first being formed in the 1800s there was this idea of industrial engineering and one of the most famous industrial engineers - Frederick Taylor who actually studied the motion of iron workers in iron factory and he mathematically kept track of how much weight they were carrying as they were carrying around bolts of iron. And he found that there was an optimum weight that where if was too heavy they would carry more but they would hurt themselves, it was too light their productivity was lower. And so that was actually 200 years ago the beginnings of this idea of using data to optimize the workplace and from there we move to a world of employee surveys and collecting data in computers often times through applicant tracking systems. The companies started to develop richer data sets about their employees with analytics person in the training department, recruiting department and in the compensation department. Each of whom were independently looking at the data that they were managing to try to figure out what it meant. In today's date, all of that are coming together into one multifunctional area that can look at the relationship between all these different types of data and give beautiful business insights integrating to a big picture.

How does it compare to AI, machine learning, and big data?

The fundamental sort of building block is usually surveys. So one of the most common places where you have data about your people is the annual survey, the poll survey, the exit survey, the new candidate survey, the onboarding survey, so all that survey data. So the first thing you're going to do in people analytics is you're going to look at that data and make sense of it and get enough of it that it's reliable. One of the potential mistakes you can make as people analytics is looking at one survey and drawing a very broad conclusion without making sure that it's statistically valid. Now, beyond that, as more and more data becomes available in your HR department and you feel comfortable with the quality of the data, you're going to be looking at more sophisticated analysis. Does this answer to this survey correlate to sales productivity or turnover? Or some other business measure. For Example customer satisfaction in a hospital. Hospitals now know that data about employee happiness has a direct correlation to actually patient outcomes. And as that progresses and you get better and better at it, there's more sophisticated mathematical techniques which fall into the domain of AI and machine learning. Machine learning is a term that really refers to algorithms that can predict the future based on data that you already have. AI is really a broader phrase that really encompasses statistics, machine learning, cognitive software and bring in insights predicting the future results depending on the past data.

How people analytics drives impact

The most common, sort of easiest problem to look at is retention 'cause most companies have some level of turnover they're not happy with, and they would like to reduce that. And the benefit of reducing turnover is very high because not only do you save time and money on recruiting people and getting them back into the company, but when you lose somebody, you lose institutional knowledge, you lose relationships, you lose expertise. It's a big thing when you lose people. So number one probably project or application is look at the retention of your organization. Where is it high, where is it low, what can you correlate against it? Above that are issues of performance. If the correct correlations are found and studied and that can improve sales productivity by five percent, that's worth millions of dollars to most companies! Most sales organizations don't spend a lot of time statistically correlating what it is that's driving high performance and sales. Once you've gotten through sort of the core HR data, is what is actually correlated to great customer growth, great customer retention, great new deals, size of deals, and so forth. 

Some of the critical HR Matrices very commonly used in HR

• Time to hire - The number of days that it takes to post jobs and finalize the hiring of candidates. This metric is monitored over time and is compared to the desired organizational rate.
• Recruitment cost to hire - The total cost involved with recruiting and hiring candidates. This metric is monitored over time to track the typical costs involved with recruiting specific types of candidates.
• Employee Turnover - The rate at which employees quit their jobs after a given year of employment within the organization. This metric is monitored over time and is compared to the organization’s acceptable rate or goal.
• Absenteeism - The number of days and frequency that employees are away from their jobs. This metric is monitored over time and is compared to the organization’s acceptable rate or goal.
• Engagement rating - The measurement of employee productivity and employee satisfaction to gauge the level of engagement employees have in their job, measured through surveys, performance assessments or productivity measures.

Some other very important ones are improve sales, improve product quality, improve customer retention, improve margins and so on.

HR Analytics can through light into the Leadership. One of the major concerns to the CEOs - you know we don't have enough leaders, we don't have the right leaders, we're not driving enough innovation, we're not developing people fast enough. 

What do we know about our leaders that allow us to develop and incent our leaders in the best way? Lots of research you can do there. Are there productivity enhancers or detractors in the workplace? Can we look at email metadata or organizational network data or time data? All of these new data services that are now coming into HR that are, maybe were never really considered to be part of the core HR function are now part of another layer of organizational performance. 

Tools and platforms

Though one of the major and most widely used tool is still Excel, we cannot just do this alone with Excel. You're going to have to look for some core infrastructure that allows you to collect and analyze the data in the best possible way. The first place to look is look at your core human capital systems providers, your payroll provider, your core HRMS, your core learning platform, your core recruiting platform. Thereafter you're going to need some form of a statistical analysis tool. People do use Excel, SPSS, SAS and other fundamental statistical analysis tools. You as an HR person may not be the person who uses that tool. There may be the statistician who uses it, but you're going to need to have that type of technology. 

Another layer of tool that most companies need is visualization or presentation tools. This is where companies like Tableau and Power BI have been very, very successful because of the visual representation of Charts and Graphs and Heat Maps and with such a powerful tool like Tableau, you'll be able to build a library of successful projects that other people can learn from and you can use over and over again with business managers and draw insights. The field is evolving everyday and the next generation, is coming out with sophisticated tools - that might be survey tools that automatically compute the correlation and relationship between this survey versus that survey. They use powerful visual analytics technology to dive deeply into those insights. The result is faster analysis and better decision-making

Glint is one of such tools of LinkedIn. Cultural assessment, Sentiment analysis tools that look at organizational network data are some of the specialized tools. Though the idea of the application is not new and we all know how Siri and Alexa are using them so popularly. The tools are going ever so powerful and they now have intelligence within them that is often called narrative analytics, is the new phrase for it, that can look at the data in your company and tell you the stories that it finds.

To start off to use the power of People Analytics, the first and foremost step is to validate the quality of the data. Without a good repository of database, data mining will not be effective enough and wrong data will bring confusions and wrong conclusions which can be very frustrating. All these tools will help you manipulate and visualize and understand the data. But if the fundamentals are not right, the tools will not be of not much help.