How HR data drives Cisco’s culture
Cisco has long been admired for its enviable culture and amazing employee experience. But as anyone there can tell you, building a great culture doesn’t just happen overnight. It requires a deliberate and strategic approach to cultural transformation. And for Cisco, Social Recognition® has been a key driver as they continue to deliver a positive employee experience.
I had the privilege of covering Cisco’s presentation during Workhuman® Live 2019 in Nashville, so I was particularly eager to hear the Workhuman Radio interview with Madison Beard, lead data scientist and researcher, leadership and team intelligence at Cisco, and Greg Stevens, Ph.D., analytics manager at the Workhuman® Analytics Research Institute (WARI).
In an engaging and far-ranging interview, they discussed some of the “behind-the-scenes” work being done as Cisco and WARI team up to continually drive a more human culture at the technology giant. As Greg observed, “It’s been a great chance for us to collaborate. I think one of the most interesting things about Cisco is the interdisciplinary nature of their analytics function, focused on leadership and team intelligence.”
Getting the most from recognition dollars
Describing her team’s role, Madison explained, “We study what makes teams great, and what makes leaders great at Cisco – and then we try to do more of that. We’re in charge of employee listening.”
Cisco has an entire team that does natural language processing as a supplement to their “quants” – those “focused on the number side of things.” She explained the benefits of combining quantitative and qualitative data:
“Not only can you see how people feel about what’s going on at Cisco – but also get into the mindset of what people are saying about it. So, you can combine these two sources of data to really try to understand the ‘why’ behind what’s going on with numbers.”
Her team is focused on working with various stakeholders within Cisco to answer a wide range of business problems, such as attrition: “Why are people leaving? Who is leaving? And at what rates?”
Partnering with program managers, they seek to understand “how to best use the Connected Recognition [Cisco’s Social Recognition program] dollars that we have, so we can have the biggest impact for the most people at Cisco. We want everyone to feel recognized for good work.”
One of the key findings? When it comes to social recognition, “frequency is important.”
Prioritizing analytics requests
Greg emphasized the importance of engaging with stakeholders early in the analytics process. That way, you can better understand and prioritize the burning questions they want to answer with data. As he noted, “We have a powerhouse team, and it’s easy to want to look at everything. You want to find answers to every possible question because you’ve got so much rich data.”
As he sees it, the challenge is about prioritizing analytics requests, and using the right talent to get the most from their abilities and specialties. This is especially true when you have quant and qualitative people. At WARI, they start with the “why” – “Why do we want to ask these questions?”
Madison echoed that same theme in describing the internal workings at Cisco. “There are always tons and tons of curiosities that we have. The question is, ‘How should we spend our finite resources of brain power on the team to make the biggest impact?’”
Fishing for salmon. Catching a flounder.
At the same time, the data teams at Cisco and WARI are primed for the unexpected findings they may come across. As Greg explained, “We have two modes of research on our team. One is hypothesis confirmation, which means we have a target and we want to see whether we hit the target or not. We also have a hypothesis generation mode that we go into when we don’t know which question to ask. That’s more of the exploratory nature of analytics.”
Madison used a clever analogy to illuminate this idea: “You’re fishing and you’re expecting a salmon, for example. And you pull up a flounder. And it’s a happy surprise. I mean, flounder is delicious, too. But you might serve the flounder to a different audience.
“It’s a way to be very curious. And when you find things that are surprising, you dig in and ask, ‘Who might care about this? Who in the business should know this information? All of those questions will lead you to present insights in a different way and to a different audience.”
The “democratization” of analytics
Looking beyond Cisco, Greg shared his perspective on the current state of HR analytics throughout different industries. As he put it, “Each company is in a different point in their journey toward analytics. Clearly, we have clients like Cisco that are very advanced and have teams in place that can answer the really interesting questions.”
He cited the positive impact of what he calls the “democratization” of analytics: “It’s easier than ever to have access to resources, whether it’s a through a partner – such as the Workhuman Analytics and Research Institute – or other means.”
So how can HR better leverage data?
According to Madison, it starts with data literacy, data fluency, and better understanding. She believes it’s vital that constituents understand the difference between good data and bad data. In her view, HR must take a more proactive role in helping business leaders use data.
“You need to understand the business context and how you can help leaders make the right decisions using the data.”
The power of small data – and small wins
All of this is well and good for a large organization that has the resources – and numbers – to leverage data for predictive analytics. But what about smaller organizations that might not have the scale of a Cisco?
According to Greg, “it gets down to the power of small data. Everyone likes to talk about big data and having millions upon millions of records to analyze. But often, there’s a tremendous amount of insight in the small data for small organizations.”
He suggests that smaller organizations can look for small wins – simple correlations that can be run in Excel® – that can provide insights and help them become more data-driven companies. In his view, it’s a mindset that’s independent of the resources a company has available.
As Madison sees it, it’s all about “making sure you have the data infrastructure to build and hold all the data that you are collecting. Because even though you’re small, you have to opportunity to collect a lot of data.”
“The resources will grow as you prove the value of analytics,” Greg observed. “And I think that’s been Workhuman’s journey – where we started with a small team and have grown as we’ve seen the value of our investment.”
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