The HR profession may have been slow to join the big data revolution, but now it’s available, and the insights provided by algorithms and machine learning will transform how decisions are managed and how people are engaged and managed
The HR profession may have been slow to join the big data revolution, but now it’s available, and the insights provided by algorithms and machine learning will transform how decisions are managed and how people are engaged and managed
“We’re just scratching the surface of where big data can take us.” Those are the confident words of someone who has been at the forefront of HR cloud technology for almost 20 years. As the senior vice president and general manager of Cornerstone OnDemand’s business in Asia-Pacific & Japan, Frank Ricciardi has watched HR’s
standing in the business evolve first-hand. A major driver of that evolution – especially for the past five years – has been the availability of HR data analytics and decision support.
While he concedes that HR has been one of the last of the business leaders in the executive suite to have true access to the technologies that allow for rich insights and more informed decision-making, Ricciardi says this is rapidly changing thanks to cloud and mobile technology which allow for the collection of immense amounts of data.
“Most companies have data, which has historically been retrospective, looking back at what has happened in the past,” says Ricciardi. “Today, however, thanks to cloud solutions like Cornerstone, companies not only have access to their own data but also access to data across many, many thousands of companies around the world encompassing many tens of millions of employees. We’ve been able to anonymise the data and build meaningful algorithms to not just look at what has happened in the past but also to predict what will happen in the future and prescribe what possible actions should be taken.”
This has major repercussions for HR.
Firstly, the way in which decisions are made about people will change. This doesn’t mean that those technology platforms will be making decisions; rather, it means that instead of the traditional slow decisionmaking process, executives, HR, managers and even employees are empowered with data insights at the point of decision-making.
In an instant, they can quickly visualise their data and make informed decisions. Secondly, it helps HR create ‘personalised’ services to employees. Ricciardi says that in much the same way that Google will return search results to a user based on the sites they have previously visited, HR can now provide more meaningful personalised employee experiences – and this, of course, leads to greater engagement.
Machine learning
It’s natural to want answers to problems. A good example might be the analysis of regional sales data within a company. If the data shows that Europe is not performing as well as Asia, then what is the reason? We could hypothesise that it might be economic variances in these regions or that it might be that the team is Asia is better trained and has a higher level of competency.
“In this case, you would need to look at the sales, training data and competency data to determine if a correlation exists and if the hypothesis is correct,” says Ricciardi.
However, this is all retrospective. Ricciardi says the next level is predictive and prescriptive analytics: taking what’s happened in the past and applying algorithms and ‘machine learning’ to make predictions of the future.
Some may be confused by the term ‘machine learning’ and how this plays into HR data. Ricciardi says the term covers two areas. One is the data and the infrastructure which sits behind the data: the databases, the processing power, the algorithms and the ‘data science’. Second is the ‘front end’ component, which encompasses the all-important user experience. This is perhaps the most critical component – data needs to be presented in a meaningful way that is easy to interpret and at the time at which it is needed.
IBM’s ‘Watson’ is considered a pioneer in machine learning. Watson combines the technology and the processing power, plus the framework to write algorithms that are applied to that data. “As you pump more data into the algorithm you can identify nuances and biases within your original hypothesis,” Ricciardi explains. “These nuances and biases are things you might not find meaningful if you were looking at a single data-point or a small data set. However, when you put millions, hundreds of millions or billions of data points together, ‘the machine’ becomes incredibly powerful. And when we work with the machine, humans will make better decisions and become more effective at performing the task at hand.”
Now, back to HR. When someone applies for a job, the machine can interpret data about them and help to make recommendations about whether that person should move through the screening process. Even before a company hires that person, the machine can make a prediction about the possible success of the hire – thus identifying risk of making a bad (and costly) hiring decision.
Ricciardi says the “highest level of data interaction” one can have is understanding what possible actions to take thanks to insight. “We also know what we could or should do because of these algorithms,” he says. “We know that by changing the parameters, certain things will happen.”
He cites an example everyone can relate to: weather forecasting. “We see that it rained today in Sydney. We can look at all the historical data and say, ‘generally it rains when the low-pressure system meets the high-pressure system’. Then we can say, ‘well that’s happening tomorrow, so it will probably rain’. Then we can prescribe to you: take an umbrella.”
So, in our context then, we may see that people who consume development, are meaningfully connected to other employees, who contribute to social dialogue within the organisation, and who have high performance scores are a possible future leader. Imagine getting some help from ‘the machine’ to more proactively identify these people, recommend career paths and prescrib further development.
Empowering employees
Back in the workplace, access to insights has one other key benefit: it empowers employees to make their own decisions.
A good example is plotting their own career path. Previously, if a product manager wanted to become a chief technology officer one day, their options might have been restricted to the direct manager’s direction about how to move from point A to point B. Or it may have been a collaborative effort – a conversation between employee and manager. However, the machine knows many thousands of people that have moved from A to B and, as such, can provide the various possible paths between these two points, which competencies and development are required, and how long each path may take.
“Before the cloud and before machine learning this would not have been impossible,” says Ricciardi. “Your manager may have said ‘you need to do this, and then do that’ – and that was a personal opinion and possibly interpreted as the only way to do it. Now the machine can present seven different options to you.”
Big, big (people) data
Cornerstone is 18 years old and has been in the cloud since day one. Being established effectively at the same time the internet was taking off means the company has a vast amount of employee data across over 3,000 clients and more than 30 million employees. Since starting in learning management back in 1999,
Cornerstone today provides a full suite of talent solutions for the employee lifecycle: from talent acquisition and onboarding, L&D, performance management, compensation management, career and succession planning and enterprise collaboration. The company was also the first to incorporate enterprise social networking into the talent suite – thus providing insights into how well connected people are within the organisation. All of this together, Ricciardi says, means that Cornerstone has what it calls “big, big people data”.
“We have more data on employees than probably than anyone else in the world,” he says. “If you take a simple equation like happy employees equals happy customers, data can put HR at the centre of the world. I believe the HR leaders who are the savviest supporters of technology and the most prolific investigators of data will be the most comfortable at that executive table.”
Cornerstone OnDemand
Cornerstone OnDemand is pioneering solutions to help organisations realise the potential of the modern workforce. As the global leader in cloud-based human capital management software, Cornerstone is designed to enable a lifetime of learning and development that is fundamental to the growth of employees and organisations.
“We’re just scratching the surface of where big data can take us.” Those are the confident words of someone who has been at the forefront of HR cloud technology for almost 20 years. As the senior vice president and general manager of Cornerstone OnDemand’s business in Asia-Pacific & Japan, Frank Ricciardi has watched HR’s
standing in the business evolve first-hand. A major driver of that evolution – especially for the past five years – has been the availability of HR data analytics and decision support.
While he concedes that HR has been one of the last of the business leaders in the executive suite to have true access to the technologies that allow for rich insights and more informed decision-making, Ricciardi says this is rapidly changing thanks to cloud and mobile technology which allow for the collection of immense amounts of data.
“Most companies have data, which has historically been retrospective, looking back at what has happened in the past,” says Ricciardi. “Today, however, thanks to cloud solutions like Cornerstone, companies not only have access to their own data but also access to data across many, many thousands of companies around the world encompassing many tens of millions of employees. We’ve been able to anonymise the data and build meaningful algorithms to not just look at what has happened in the past but also to predict what will happen in the future and prescribe what possible actions should be taken.”
This has major repercussions for HR.
Firstly, the way in which decisions are made about people will change. This doesn’t mean that those technology platforms will be making decisions; rather, it means that instead of the traditional slow decisionmaking process, executives, HR, managers and even employees are empowered with data insights at the point of decision-making.
In an instant, they can quickly visualise their data and make informed decisions. Secondly, it helps HR create ‘personalised’ services to employees. Ricciardi says that in much the same way that Google will return search results to a user based on the sites they have previously visited, HR can now provide more meaningful personalised employee experiences – and this, of course, leads to greater engagement.
Machine learning
It’s natural to want answers to problems. A good example might be the analysis of regional sales data within a company. If the data shows that Europe is not performing as well as Asia, then what is the reason? We could hypothesise that it might be economic variances in these regions or that it might be that the team is Asia is better trained and has a higher level of competency.
“In this case, you would need to look at the sales, training data and competency data to determine if a correlation exists and if the hypothesis is correct,” says Ricciardi.
However, this is all retrospective. Ricciardi says the next level is predictive and prescriptive analytics: taking what’s happened in the past and applying algorithms and ‘machine learning’ to make predictions of the future.
Some may be confused by the term ‘machine learning’ and how this plays into HR data. Ricciardi says the term covers two areas. One is the data and the infrastructure which sits behind the data: the databases, the processing power, the algorithms and the ‘data science’. Second is the ‘front end’ component, which encompasses the all-important user experience. This is perhaps the most critical component – data needs to be presented in a meaningful way that is easy to interpret and at the time at which it is needed.
IBM’s ‘Watson’ is considered a pioneer in machine learning. Watson combines the technology and the processing power, plus the framework to write algorithms that are applied to that data. “As you pump more data into the algorithm you can identify nuances and biases within your original hypothesis,” Ricciardi explains. “These nuances and biases are things you might not find meaningful if you were looking at a single data-point or a small data set. However, when you put millions, hundreds of millions or billions of data points together, ‘the machine’ becomes incredibly powerful. And when we work with the machine, humans will make better decisions and become more effective at performing the task at hand.”
Now, back to HR. When someone applies for a job, the machine can interpret data about them and help to make recommendations about whether that person should move through the screening process. Even before a company hires that person, the machine can make a prediction about the possible success of the hire – thus identifying risk of making a bad (and costly) hiring decision.
Ricciardi says the “highest level of data interaction” one can have is understanding what possible actions to take thanks to insight. “We also know what we could or should do because of these algorithms,” he says. “We know that by changing the parameters, certain things will happen.”
He cites an example everyone can relate to: weather forecasting. “We see that it rained today in Sydney. We can look at all the historical data and say, ‘generally it rains when the low-pressure system meets the high-pressure system’. Then we can say, ‘well that’s happening tomorrow, so it will probably rain’. Then we can prescribe to you: take an umbrella.”
So, in our context then, we may see that people who consume development, are meaningfully connected to other employees, who contribute to social dialogue within the organisation, and who have high performance scores are a possible future leader. Imagine getting some help from ‘the machine’ to more proactively identify these people, recommend career paths and prescrib further development.
HR AND PEOPLE DATA
Does HR have the capability/skills to truly leverage the people data? Frank Ricciardi responds: “There’s an element of oil and water with HR and data. It’s not because HR doesn’t want the data but because it has historically required advance support and skills usually only found within finance or IT. This was mostly only available to big companies, who have been doing this kind of work for almost 20 years. Today that’s not the case. Cornerstone has done this for you. It’s big data insights as a service delivered right through the application and available at the point of need – the point of need where decisions need to be made. We have made these insights not only available to HR, but to the executives, managers and employees to deliver maximum impact to the organisation. It’s all about empowering people to make effective decisions.”
Does HR have the capability/skills to truly leverage the people data? Frank Ricciardi responds: “There’s an element of oil and water with HR and data. It’s not because HR doesn’t want the data but because it has historically required advance support and skills usually only found within finance or IT. This was mostly only available to big companies, who have been doing this kind of work for almost 20 years. Today that’s not the case. Cornerstone has done this for you. It’s big data insights as a service delivered right through the application and available at the point of need – the point of need where decisions need to be made. We have made these insights not only available to HR, but to the executives, managers and employees to deliver maximum impact to the organisation. It’s all about empowering people to make effective decisions.”
Empowering employees
Back in the workplace, access to insights has one other key benefit: it empowers employees to make their own decisions.
A good example is plotting their own career path. Previously, if a product manager wanted to become a chief technology officer one day, their options might have been restricted to the direct manager’s direction about how to move from point A to point B. Or it may have been a collaborative effort – a conversation between employee and manager. However, the machine knows many thousands of people that have moved from A to B and, as such, can provide the various possible paths between these two points, which competencies and development are required, and how long each path may take.
“Before the cloud and before machine learning this would not have been impossible,” says Ricciardi. “Your manager may have said ‘you need to do this, and then do that’ – and that was a personal opinion and possibly interpreted as the only way to do it. Now the machine can present seven different options to you.”
Big, big (people) data
Cornerstone is 18 years old and has been in the cloud since day one. Being established effectively at the same time the internet was taking off means the company has a vast amount of employee data across over 3,000 clients and more than 30 million employees. Since starting in learning management back in 1999,
Cornerstone today provides a full suite of talent solutions for the employee lifecycle: from talent acquisition and onboarding, L&D, performance management, compensation management, career and succession planning and enterprise collaboration. The company was also the first to incorporate enterprise social networking into the talent suite – thus providing insights into how well connected people are within the organisation. All of this together, Ricciardi says, means that Cornerstone has what it calls “big, big people data”.
“We have more data on employees than probably than anyone else in the world,” he says. “If you take a simple equation like happy employees equals happy customers, data can put HR at the centre of the world. I believe the HR leaders who are the savviest supporters of technology and the most prolific investigators of data will be the most comfortable at that executive table.”
WHERE TO NEXT?
Frank Ricciardi highlights two emerging areas in HR big data:
1 Data insights will become the norm
“When I talk to HR people they are fascinated by the insights that we can actually provide and they want them. Imagine being able to predict the successful hires, flight risk, compliance, or successors. Imagine being able to prescribe development interventions, learning paths and career paths. And we’re only just getting started on how we can use this data to enhance productivity and employee engagement and experience.”
2 Benchmarking will be invaluable
“You’re an HRD or CHRO at a financial services company with 10,000 employees based in Australia. Imagine having a service that allows you to compare your company with similar companies – or even different companies in different industries – around the world with regards to staff retention. With Cornerstone Benchmark, you will be able to do just that. It will help clients make decisions that are not just about their own people but decisions about the business itself, including how to direct investment and better compete.” might be with the new software.”
Frank Ricciardi highlights two emerging areas in HR big data:
1 Data insights will become the norm
“When I talk to HR people they are fascinated by the insights that we can actually provide and they want them. Imagine being able to predict the successful hires, flight risk, compliance, or successors. Imagine being able to prescribe development interventions, learning paths and career paths. And we’re only just getting started on how we can use this data to enhance productivity and employee engagement and experience.”
2 Benchmarking will be invaluable
“You’re an HRD or CHRO at a financial services company with 10,000 employees based in Australia. Imagine having a service that allows you to compare your company with similar companies – or even different companies in different industries – around the world with regards to staff retention. With Cornerstone Benchmark, you will be able to do just that. It will help clients make decisions that are not just about their own people but decisions about the business itself, including how to direct investment and better compete.” might be with the new software.”
Cornerstone OnDemand
Cornerstone OnDemand is pioneering solutions to help organisations realise the potential of the modern workforce. As the global leader in cloud-based human capital management software, Cornerstone is designed to enable a lifetime of learning and development that is fundamental to the growth of employees and organisations.