“Often, when I’m seeing customers or prospects … I’ll say to them, ‘I can show you a system today that knows more about any of your people than any system you have within your organization,” Amar Dhaliwal, Saba’s chief evangelist, tells me. His tone is almost conspiratorial. You can imagine him looking over his shoulder and leaning across a desk to heighten the tension, as if about to divulge some dark secret in a political thriller.
Instead, he delivers a punchline: “We log on to LinkedIn, and the light goes on in people’s minds,” Dhaliwal says. “You’ll find thousands of their employees, and often this externally owned system knows more about their employees than any internal system.”
Dhaliwal holds LinkedIn up as an example of what enterprise technology should be like: this massive system knows everything about your employees simply because it invited your employees to tell it everything. And your employees responded. They gave LinkedIn all the information it wanted.
“Why would somebody make sure that their LinkedIn profile is complete?” Dhaliwal asks. “Because there’s personal utility that comes from that.”
People voluntarily give LinkedIn access to their information because doing so is beneficial to them. LinkedIn helps people build wider, stronger professional networks, in exchange for some personal data. By and large, people see this as a worthwhile trade, so they freely hand over the data.
According to Dhaliwal, organizations often take a more coercive approach to collecting their employees’ data: fill out this sheet; take this survey; HR needs to know these things. Employees don’t get much in return, so they only give the company a limited set of data — i.e., the data the company absolutely requires. Meanwhile, LinkedIn gets everything it asked for and more.
Dhaliwal wants to see enterprise technology act more like LinkedIn: “How do you find a way that encourages people to volunteer data? How do you then find ways that people engage with the system? If you have none of those things, you will not be successful.”
“Successful” in what capacity, exactly?
In predicting the future.
The Big Data Crystal Ball
Founded in 1997, Saba is “a talent management solution that basically provides a platform for the entirety of the employee lifecycle, from the hiring to the pre-boarding, the onboarding, then the development of the individual, their learning, their career path, then all of the performance support, performance management, goal-setting, succession,” according to Dhaliwal, who joined Saba after the 2005 acquisition of learning management systems (LMS) vendor Thinq. At the time, Dhaliwal was Thinq’s CTO, as well as one of the company’s founders.
“At Saba, I’ve actually done pretty much everything there is to be done on the product and operations side,” says Dhaliwal. He has run engineering, product management, and product strategy teams. He has worked on cloud operations, customer support, and professional services.
Dhaliwal’s lengthy, multi-faceted experience with Saba is probably the reason why the company named him “chief evangelist.” It’s a strange title, and even Dhaliwal admits that he struggles to define it. In essence, Dhaliwal’s job is to bring Saba’s brand and technology to the world. He is a missionary for a talent management system instead of a religion.
Dhaliwal’s vaguely mystical title fits well with Saba’s goal to harness the “power of predictive” — that is, to help organizations make informed choices about the future.
“Here’s how I think about it,” Dhaliwal says. “When all of these platforms that we have today — the recruiting platforms, the talent management platforms, the learning platforms — when we set out to build these, they were really very much transactional systems. They were really designed to capture transactions.”
Dhaliwal means that these systems are reactive — they respond to the organization by simply recording what happens in the organization. For example, an ATS merely keeps records of the candidates an organization feeds into it; a performance review platform documents a process that would occur with or without its help.
What Dhaliwal wants to see — and what he says Saba is working towards — is a system that can be proactive as well as reactive. “We’ve seen the injection of new technologies — machine learning, intelligence, natural language processing. We’ve been heading down a path that says that we believe that talent management systems — whether they are part of the recruiting process or the learning process or the performance process — can offer enough data that can be mined in order to make predictive and intelligent recommendations to an individual, a manager, an administrator.”
Essentially, Saba wants to use big data as a crystal ball to predict the future. “Let’s say that you have an opening,” Dhaliwal says. “We think that the system should be able to predict, out of the formal candidates, who is most likely to be the best fit for this position. It should be able to make predictions that say that this person is the best fit, or this pool of candidates is the best fit. And as we look at this pool of candidates, we can make intelligent recommendations based on their current set of skills, their aspirations, their own personal career planning. You can harness all of that information to make a certain type of prediction.”
But How Does Data Help Us Predict the Future?
According to Dhaliwal, vendors and HR departments have traditionally approached the employee’s lifecycle as a set of discrete processes. “Almost like, ‘Here’s a recruiting silo, here’s a learning silo, here’s an onboarding silo, here’s a performance review silo,’” he explains. “We don’t think that is the way that an employee grows through an organization.”
Instead, Dhaliwal says Saba views the employee lifecycle as an integrated whole. “We’re really focused on the notion of a lifecycle, starting with hire and going all the way to retire or exit from the organization,” he says. “Part of what we were really looking to do is, ‘How do you enmesh all of these things together?’”
For example: organizations often view recruiting and employee learning as two completely separate processes, and they treat those processes accordingly. Dhaliwal suggests that recruiting may have a learning component to it, and fostering that learning component could have good outcomes for employers: “As you go through a recruiting cycle, if you as an organization can be teaching people, providing them value through that hiring process, you’re likely to become much more attractive as a place where somebody can spend their time and energy.”
According to Dhaliwal, this integrated approach to the employee lifecycle is a crucial part of gathering the data necessary for making predictions. The second key is turning enterprise talent management systems into engaging, LinkedIn-esque platforms that invite employee engagement and participation.
Traditionally, recruiting has been focused on administration and completing forms. “It was really built for administrators. It was really built to track resumes,” Dhaliwal says. “[Saba does] something slightly different. Instead of focusing on administration and forms, we in turn have focused on usability of the application. Make it engaging. Make it something that is just a fantastic experience.”
Dhaliwal mentions the consumerization of IT — that is, the trend of employees bringing consumer technologies into businesses — as one of Saba’s guiding principles. “You have to design for the end user, and you have to design for the end user as a consumer,” Dhaliwal says.
“When we looked to build out the versions of the platform that we’re now delivering, we didn’t go and say ‘We’re going to be inspired by the products people have built in the past or some ATS system.’ We instead were inspired by LinkedIn. We were inspired by Facebook. We were inspired by Twitter.”
In general, people hate interacting with enterprise talent management applications. Dhaliwal points out that such technology is often hard to use, and employees generally have little-to-no motivation to work with these platforms — unlike, say, LinkedIn, which is easy to operate and offers personal benefits to users. This is why LinkedIn knows more about an organization’s employees than the organization does: employees willingly interact with LinkedIn and give it all their data, but they only give enterprise systems the data they absolutely have to give, because such systems are painful to use.
But organizations need this data to make the sort of predictions Dhaliwal believes Saba’s talent management systems can help them make. “Predictive depends upon a big enough data set for you to mine and for you to be able to make intelligent inferences,” he says.
Dhaliwal says Saba wants to ensure companies get the data they need by delivering a talent management system that is more like LinkedIn than any traditional ATS. Such a system would integrate all aspects of employee life into one lifecycle, and it would engage employees, driving them to use it and voluntarily give it the data it needs. “There’s a very big difference between volunteered data in an organization and coerced data in an organization, and most software in enterprises has elicited a set of coerced behaviors,” Dhaliwal says. “We better have people to volunteer enough data into the systems and the platforms, so that we can actually start taking advantage of predictive.”
Harnessing the “power of predictive” means more than just figuring out which candidates will be the best fits ahead of time — it also means predicting the support your employees are going to need and planning for the future before issues even arise. “When somebody comes into our platform, we’re able to predict what type of learning they would need. We’re able to predict, because of their interactions with the system and who they’re connected to, which people they should follow, who would be a good mentor for them, etc, etc,” Dhaliwal explains.
So, Can Saba Predict the Future?
Dhaliwal says that predictive intelligence is “a very powerful binding principle” for Saba’s operations. Indeed, Saba’s talent management system was designed around this principle. As Dhaliwal says, “We don’t think that you build recruiting systems to track resumes. We think that you do it to make smart, intelligent hires.”
Does this mean Saba will predict the future for your company?
Well, that depends on what sort of data your organization harnesses and how it uses that data. But Saba is certainly there to help you get the data you need. “We are building solutions that address these specific challenges — the challenges of engagement, the challenges of getting people to volunteer data, the challenges of connecting,” Dhaliwal says. “[We’re] making sure that we’re providing solutions such that HR professionals, talent management professionals, and learning professionals have a platform that allows them to create this type of engagement within their organizations.”
Saba also offers quite a bit of thought leadership on the subject of predictive intelligence for organizations. “We spend a lot of time sharing our opinions, in terms of the organization of yesteryear, and what their priorities were, and what we perceive to be the priorities of the HR organization of the future — or of today,” Dhaliwal says.
So Saba isn’t only concerned with predicting the future of your company — it’s also concerned with predicting the future of work in general. Dhaliwal points out that we’re currently in the middle of a generational shift as Millenials enter the workplace. A new world of work is on the way, with new characteristics, new types of workers, and new desires and aspirations.
“It’s very different from when my father entered the workforce — he worked with the same company for his entire life. My daughter just entered the workforce, and she’s going to have 14 jobs before she’s 35 or 38,” Dhaliwal says. “It’s just different. The worker is different, the technology is different. Unless you get people consumer-grade experiences inside of the enterprise, they will not engage with the technology.”