Cisco's got talent for analytics

Janine Milne Profile picture for user jmilne October 13, 2015
Talent analytics is beginning to make significant impact at Cisco, following Bersin’s four tier HR analytics maturity model.

Ian Bailie
Ian Bailie

Finding an area in China with a potential pool of one million graduates to fish from seemed an excellent spot to build a new Cisco facility.

But when Cisco’s talent acquisition team applied talent analytics to scrutinize those graduate numbers, they uncovered a very different story. Taking into account language and other skills, barely a 10th of that number of graduates would be available in the pool.

According to Ian Bailie, Cisco’s global head of talent acquisition operations:

It wasn’t a realistic pool to hire from and they actually changed and went for a different site. That just saved us years of trying to find talent that just wasn’t there.

This is just one business example where talent analytics is beginning to make significant impact at Cisco, but it’s taken time and the company is still very much on a journey, maintains Bailie. He believes that talent analytics journey is following Bersin’s four tier HR analytics maturity model.

Source: Bersin HR Analytics

During level one, companies get to grip with basic operational reporting. Bailie recalls:

When I joined Cisco eight years ago, this is where we were. We could get data out of the system and we had a few people who were very adept at matching the data together…but it was incredibly manual.

We were good at getting the data and starting to make sense of the data and were working on the one source of truth.

From there, moving into the second level of maturity, Cisco was able to use its good data sets to look at metrics and setting targets and establishing whether they were measuring the right things.

Bailie believes that Cisco is now at level 3 in the maturity stack, establishing why particular key performance indicators (KPIs) are going up or down and starting to do more in-depth and statistical analysis and establishing more correlations.

It is now starting to move into the most sophisticated level, looking at predictive analytics and moving away from cost and time metrics towards looking at quality.

The impetus to build up the talent analytics expertise is the war on talent and a shift in Cisco’s business model.
Cisco has been a big name in tech for 30 years plus, but the talent war has hotted up and the flames have been fanned higher by a shift in focus within the company from hardware to software. Bailie expands:

We’re moving from a traditional hardware model providing plumbing for the Internet to having to become far more agile and more software focused and moving towards having our workforce design solutions, not just boxes. And then we’re going into a lot of new streams such as security, cloud and the Internet of Things and with that brings a need for a completely different set of skills.

Competing in the software sphere takes them up against the likes of Facebook and start-ups in the talent war.

Bailie’s team, among other things, is using analytics to help find the best staff to meet these changing business demands. As a company with 72,000 employees worldwide and filling 12,000 to 15,000 positions a year from external and internal candidates, this is no trivial undertaking and a costly enterprise.

Talent analytics is a key way that Bailie can help reduce those costs and provide pertinent information to make it easier for its 200 recruiters globally to locate and hire high-quality staff.

While those early days were focused on getting the internal data set right, the company is now incorporating external data too.
To do this takes a team of analytics people. At the outset, analytics will probably be a side-project tackled by existing teams, but as things ramp up, Bailie believes that:

Investment in dedicated resources while hard to get, is critical.

At Cisco, Bailie says he was to find a couple recruiting execution members who were good at the numbers side as well as the people side. He adds:

Typically, recruiters are more about candidate engagement and candidate development and people skills and secondary is the numbers…so finding people with a mix of these skills was a great benefit.

Programming element

Bailie notes that his team of four is slightly unusual because it includes a dedicated programmer, taken on because Bailie could not find a suitable data visualization tool on the market that fitted Cisco’s requirements. Having a programmer on the team is cheaper and quicker than investing in huge software solution and being able to build stuff on the fly makes the team more agile.

On top of programming skills, Bailie states:

You still need that analytics capability to do the number crunching and you still need the strategic sourcer to make sense of the data and talk to recruiters and translate it.

Once you’ve got the people, you obviously need the data for them to work on. Cisco looks at a wealth of external data from a number of free and paid-for sites, such as LinkedIn, Google, Indeed, GitHub and many others, including some aggregator sites.

There needs to be a huge range of sites, because different locations and different types of jobs will have their own favorite hangouts. So if you are looking for sales people in the Bay area, then LinkedIn may be a great place to look for talent. But if you’re looking for engineers, they may have fled the obvious sites because they are sick of being targeted by emails.

And just because one channel is proved a good source of candidates doesn’t necessarily mean that it’s the best. There’s a huge level of complexity with this data, and as Bailie points out:

With external data you don’t have a single source of truth.

One area where Bailie and his team are combining external and internal data is creating talent maps that show where people with specific skills are located. A search for cloud Hadoop skills, for example, might have the biggest number of people in the Bay areas, there may be other clusters in Seattle, Texas or elsewhere in the world.

Cisco can also add in information about the competitor landscape: there may be plenty of people with the right skills in one area, but if they all work for Google, it’s going to be hard to prize them away.

Another interesting application for external data is that outside data on talent is often better than the internal data, because people are more likely to keep their LinkedIn entry up to date. Bailie notes:

If I want to know what skills my workforce has, I’m more likely to find that externally than internally. It’s not going to be perfect, but you get a much better picture.

By putting together that external information together with internal information such as job grade, talent teams will have a much richer picture of their workforce. This has been really useful for the business, according to Bailie, because:

As we start to look at transformation in our engineering teams and how we move from a workforce with hardware to software skills, we can start to look at how many of those skills do we need to have in the future.

Cisco’s journey with talent analytics is far from over, but it is making a real impact and improving communications, says Bailie:

It’s building their credibility and the credibility of the talent acquisition organization and enabling them to have a more powerful discussion with the business.

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