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Why data beats intuition when organizing people

Luke Szyrmer March 2, 2021 274


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On today’s episode we chat with Nigel Dias who is a people analytics expert and managing director of 3N Strategy about organizing people and company culture. Company culture is a topic that everyone talks about. Often these conversations leave me feeling a little scattered and unclear if there is anything I can do about it.

I invited Nigel on the show because I was certain that he’d have useful insight into what company culture is and how it works from his practice. And he definitely delivered.

Nigel Dias on organizing people

On the episode we talk about:

  • The rise of data when measuring individuals and company performance
  • How to use technology to level the playing field and reduce unfair bias in company policies
  • Why companies with a data driven HR practice fared much better during the early days of Covid

About Nigel Dias

Nigel Dias is the Managing Director of 3n Strategy and Chair of the HR Analytics ThinkTank, and expert in people analytics who helps companies make better decisions about their workforces – by using evidence. His mission is to help organizations adopt a data-driven practices and behaviors to improve the way HR decisions, and therefore improve business performance and the way people experience their careers.

Takeaways (on organizing people)

  • The mindset around people and groups, that you can use data to get useful insight here too–it’s not all just an intuitive black box

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    Why data beats intuition when organizing people
    Luke Szyrmer

Transcript

[00:00:00.595]

I don’t think this was specific to HR, but effectively organizations which is faced with this obstacle and whether they wanted to or not, they had to get around it. And like I said earlier, what they had to do is they had to make some decisions, arguably some decisions which they had never made before. And maybe a little bit different from other crises that we’ve seen over the last few decades, people literally are at the core of covid like people are getting ill people.

[00:00:33.695]

It is a people related problem. You are listening to the Managing About Teams podcast, the show taking a kind of cool headed and fair look at remote teams. I’m the host, Luke Sherman, and I’ve participated in or run distributed teams for almost a decade as a practitioner. I’m speaking with experts on leadership, strategic alignment and about work to help you navigate the issues you start facing after you get your working from home gear sorted. Welcome, welcome, welcome back.

[00:01:13.975]

My name is Luke Schirmer, and if you’re new here, I’m the author of the book line Remotely. And this is the Managing Remote Teams podcast, newly rebranded from a line remotely.

[00:01:26.395]

I decided the podcast is going to have somewhat of a wider purview, hence the slightly wider name. Um, and what I do is I help teams thrive and achieve more together when everyone’s working remotely.

[00:01:40.705]

And you can find out more at aligned remotely dotcom, whereas the podcast has a website now of managing remote teams dot com. So on today’s episode, we chat with Nigel Diaz, who is a people analytics expert and a managing director of Three End Strategy. Now, company culture is a topic that everyone talks about. And personally, I often leave these conversations feeling a little scattered and unclear if there’s anything I can do about company culture. So I invited Nigel on the show because I was certain he’d have some kind of useful insight into what it actually is and how it works from his experience and practice.

[00:02:25.295]

And he definitely delivered. So on this episode, the first of two, we talk about the rise of data when measuring individuals and company performance.

[00:02:36.515]

We talk about how to use technology to level the playing field and reduce unfair bias in company policies. And we also cover wine companies with the data driven H.R. practice fared much better during the early days of covid. And let’s dig into the show. Nigel Diaz, welcome to the Managing Remote Teams podcast. Thanks very much. Happy to be here. Could you tell us a little bit about your story in terms of how you got into HRR Analytics?

[00:03:11.825]

I almost got into analytics by mistake, which is to say, when I applied for my very first job, I actually didn’t fully understand what the company did. It was just at the time that was before data was trendy and sexy. So let’s say maybe 12 or 13 years ago, I was just looking to get into an interesting consulting space. I happened to see an interesting job spec, which is backed up by an interesting website and a short interview process.

[00:03:41.375]

Later, I happened to be working in a company, learning what they did almost as I went.

[00:03:47.525]

OK, I’ve got the rain has been massive shifts in the industry.

[00:03:51.915]

I suppose it’s always been about the art of and what the practice and the science of using data as evidence to make better choices about not just people, but employees and the workforce.

[00:04:07.985]

I find I’ve always found organizing people quite a fascinating space. I’m naturally quite curious about people in psychology and things like that myself. I suppose my my university degree was in statistics and economics and the bits that I enjoyed most back then were in the practical side of statistics and the behavioral side of kind of economics and so on. Like how to think, how does the world work with a kind of numerical look on it and workforce analytics or analytics or people analytics?

[00:04:39.555]

There is a context within which. People work more specifically their context within which businesses make choices about their employees. So, you know, there’s no such thing as a business that isn’t trying to figure out how can we make people more productive? How can we drive performance? Maybe they’re making decisions around how they hire people, making them. Maybe they’re making decisions, how they retain people, or maybe they’re making decisions about how they train them or the things that are hot topics in the last few years around like diversity and so on.

[00:05:12.225]

All these things when it comes down to management level will ultimately be somebody will make a decision to try and solve them or address them or whatever it is they’re trying to do. And workforce analytics or any use of hard data, in my opinion, anyway, is use is providing those decision makers with evidence so that they can make better choices. And so sometimes those things are less sexy and it’s just like a pretty dashboard, still evidence, sometimes it’s super advanced statistics and nowadays is data science.

[00:05:45.435]

But in my it always comes down to helping organizations make better choices about their people.

[00:05:52.455]

And I’d say the main thing that’s changed has been how people perceive the use of it and the popularity of what we do as opposed to the value that it creates. Maybe why of companies gradually wanted to use more data to to make these kinds of decisions.

[00:06:10.845]

The data isn’t new, like you can’t pay people if you don’t collect this information about them from the very first time that a salary was paid.

[00:06:20.745]

And who knows how many thousand years ago that was in Babylon. Yeah, wherever it happened to be. I tried to find this for a conference recently, like the earliest recorded salary, and it went into the BCS. I think no one could quite decide. But the point is somebody had a salary by a regular payment and obviously somebody would have been tracking that now, even if it was just like some guy riding on a bit of papyrus or chiseling it into a tablet of stone or whatever it realistically was just in order to pay and employ people.

[00:06:50.625]

This data was being collected. If we take off the amusing side of that story, let’s just pretend we start this story in the 60s or the 50s. More recent times just employ people, especially on mass contracts existed. People needed to know when they started a job, when they ended the job, people started tracking performance. And yes, it might have been on a paper form and then maybe evolved into an Excel sheet and so on. But the data has always been there being collected.

[00:07:19.995]

I would argue it’s the same of like sales and stuff. Like people have always been collecting this information. I think it’s the awareness of what you can do with data that is shifting.

[00:07:30.765]

And I suppose in the at the time that we live, there’s just an awareness for data is almost being used for anything, maybe too much like people try to solve problems, which maybe data isn’t the answer, the place you should be going initially, but that rising awareness, especially, I’d say in the last seven years in the world in meteoric and there’s loads of evidence to show, like the increase in the Google Trends, searches for the phrases associated to the industry and the number of vendors building technology and the growth of people.

[00:08:03.975]

Analytics functions like 12 years ago. There were functions that did this, especially in big organizations, but now you’re seeing far more and you’re seeing smaller companies that attempt to do it, whereas maybe they would have felt they didn’t have the volume of data and stuff before.

[00:08:19.605]

So I think it’s more the awareness for what you can do with data not just specific to each other, but just in life that obviously is getting caught up in that and doing its bit. I go with the data driven decision making approach.

[00:08:35.145]

It seems like there’s also just more gathering now, too, because so much more happens digitally. It’s easier in the background by gathering data of different types and then of it too, I guess.

[00:08:47.895]

Yeah, yeah. And don’t get me wrong with those data points I was talking about before. Let’s say that your core bits of data nowadays, there’s like an explosion in new aspects of what you might want. Now, if you’re in a sexy world called the employee experience, just the employee lifecycle, there’s more tools that track more information more quickly, like bite size, some bigger things which you couldn’t have captured in systems even ten years ago, which is now where we can collect the data.

[00:09:18.105]

But then also because our data volumes are quite large, the tools that can actually cope with processing that data in the way that it needs to be processed because it’s a lot more complex than people recognize.

[00:09:30.225]

So going back to performance, this is one of my favorite topics. Obvious topics in this context. How is performance? Defined even first. That’s going to depend on who you have in mind when you are talking about defining performance. Ultimately, you would love performance to be related to revenue generation or some kind of product, something which you can measure that is a business outcome. Now, some companies or at least some roles within some companies, you can do that like obviously sales rose, operational production roles, things like that.

[00:10:15.405]

For other roles, it’s obviously harder in itself is quite hard to measure the performance of other and maybe there’s some metrics for marketing, so it’s not always possible to measure performance. So you get some companies which will have a harder metric to measure their people against for the general majority of. Performance reviews out there. It’s going to come down to a slightly subjective five point five scale rating, the label of good above average or exceeding expectations kind of label.

[00:10:51.785]

It’s probably not perfect. In fact, most organizations, it is less than perfect. There’s usually lots of flaws in the system. You do see some evolutions now in. Technologies that capture performance, which will help to identify high performance, help, at least to decide on the distribution of performance across the organization. But. It can be hard and I would say it’s still more often than not comes down to. Your manager’s opinion, maybe with some 360 degree ratings and stuff thrown in, maybe with some other pulse related surveys and things, but it is often still, I think, determined by managers and what they think, what they feel and maybe what they want to put on your performance.

[00:11:39.785]

Well, speaking of 360, other than the standard 360 tools, are there ways in which. Peers are more involved in that, particularly when it ties to compensation later or not really. And I think this is going to depend on the organization, just like everything else in business, there is this whole digitalization of Asia. Now that is a bit of a buzzword. But usually the organizations that are trying to be more digital are usually using more innovative ways of trying to find this information or be using systems to easily bring in people on your team, people that you’ve done projects with.

[00:12:25.125]

There’s more structured ways of capturing these these kind of.

[00:12:30.405]

That historically, it would just be too complex to do in a handwritten form or something a bit more old school, so I’d say it’s still probably the roots are still in the things you’re probably thinking of somebody else’s opinion being brought in. But I think the fact that these views now are that there’s more of a structure, both in terms of the process and the systems in the data that capture it and makes it easier to see these things. So your manager gets more evidence themselves where they’re fighting.

[00:13:03.395]

Are you a high performer or a low performer? For example, there are systems now which can almost help the manager validate what they’re doing now, especially in the fields of unconscious bias and stuff at the moment. Incredibly hot topic this year. If you’re a man, you’re more likely to rate a man as a high performer, not because you’re deliberately biased, but because it’s easier to identify potential. And people who look like you, talk like you think like you, tools can help expose your bias to you.

[00:13:31.785]

You write someone and then the tools. Wait a minute. Did you realize you just read it every what? Men on average, a whole grade higher than you rated women. Did you recognize in this hiring process that you made a certain action and there’s potential bias in it or things like that? So that’s where I think you’re seeing because I’m giving you an example, which is slightly wider than performance, but it’s a really important one.

[00:13:54.835]

And so you couldn’t you couldn’t have done that. And probably even five years ago. Well, you could have done it, but it would be a lot harder than it is to do now. So. In terms of. Using data to make decisions that say before the pandemic, what would be an example of the type of thing that we’re talking about? And I’m asking that so that we can then talk about. How it looks now during the pandemic, so with data driven, is any time anyone makes any decision about people.

[00:14:32.145]

There is an opportunity to use data and analytics to help make a better choice. And I think there is a very strong argument that organizations that have invested in allowing their people to do that, their organization to make better people choices over the years, they’re the ones who have great functions. For example, Google reputation only has one of the best functions in the world, but they didn’t do that by one day, clicking their fingers and they went from less good to great a job.

[00:15:05.175]

They let their H.R. people and their people, leaders and anyone, all line managers continue to make little choices just to make them well. And it’s almost like a savings account. You keep investing in making better choices. Those things add up, they compound, and that’s how you end up with great H.R. functions in the future. And so when people say what type of decisions, where do organizations make, I imagine intuitively anybody listening to this podcast knows at least one at least can imagine a people decision.

[00:15:36.915]

If you’re hiring someone, people are making choices about candidates. If you’re doing training, like what training will it produce, the skills that are required and so on. So what happens is and there’s interesting research to look at this is you can look at what I’ll describe as the decision architecture of how your whole H.R. function works across hundreds of processes. There are thousands of decisions being made. The trick to good politics is to identify, well, which decisions matter the most.

[00:16:06.285]

You can’t provide data analytics to help every decision. So which ones are you going to prioritize? Which ones are possible? Which ones matter the most? And so let’s say pre covid. I feel like everybody could have a gut feel for these type of decisions if you were in charge of a business. What are the skills that you need right now? What are the skills you need for the future? Are you losing your high performers? And what can we do to retain them?

[00:16:33.695]

Even the smaller business, these let’s call them scale ups that are now using people analytics to make decisions. They’re going through such rapid growth and their demand for talent is so high. Yes, you could hire an H.R. manager and just hire like crazy, but actually can the data bank that hiring a little bit more surgical, can the data from the evidence help you just be a little bit more successful at retaining your best performance before they leave all these types of information?

[00:17:03.875]

They exist within the data. And you combine that with that expertize and that people management expertize. And then that’s where you find. Where the best the most valuable decisions are being made. Mm hmm. So that’s. In a situation where it’s relatively stable, demand is stable, you know what you’re doing. So now the pandemic hit, suddenly everybody’s a startup. Everybody’s business model went out the window. How have you seen that reflected in our analytics at various companies.

[00:17:41.255]

So let’s say what really happened was covered, hit. Now there’s different interpretations of how to break the code phases down. But I think it was the CEO of Nike had a very convenient way of breaking it down into four stages, which were basically. I can’t remember the exact labels for them. The fourth one being getting back to business as usual. It’s been kind of adaptation phases, you know, I suppose, in that initial phase. And I don’t think this was specific to H.R., but effectively organizations which is faced with this obstacle and whether they wanted to or not, they had to get around it.

[00:18:19.535]

And like I said earlier, what they had to do is they had to make some decisions, arguably some decisions which they had never made before. And maybe a little bit different from other crises that we’ve seen over the last few decades, people literally are at the core of covid like people are getting ill people. It is a people related problem. So I suppose the decisions that were made very because you had all these leaders who are saying, well, I’ve got to make a decision about this, can we move everyone to remote working?

[00:18:50.685]

How likely is this part of the business to get sick, especially in big organizations, as different parts of our business, probably because of their geography, went through different phases of of the crisis at this time? How on earth do we even manage all these things now? Now, I suppose if we were to pretend there was no people, data and some organizations who didn’t who probably weren’t aware of the practices or who hadn’t built up the right function, they were basically saying, we’re going to make these choices and we’re just going to use our intuition, our experience.

[00:19:24.825]

But we don’t have as much evidence so as much insights based on how this organization’s work to make these choices. So I would argue you make those choices and some people could estimate them well, but let’s say there’s a higher margin for error. We conducted some research, so in addition to consulting the space, we work with lots of universities on just researching the evolution of the practice of data in fields. And so we did some specific projects that were saying, well, what are the decisions you’re making right now?

[00:19:58.225]

And it was on one hand, let’s call them working behaviors. So people who had to go into offices now couldn’t go into offices or the rapid decisions that go into finding out, can we work remotely all these different roles and how they work together, how will that actually play out? How can we manage people in this kind of remote? There were elements where, you know, some things where it was very positive conversations. How do we enable in some fields you did see trust related issues.

[00:20:30.145]

Can we trust our people to do these things that we’ve never let them do before? Again, every time they weren’t sure. There’s some data to help them do that, and then on the other side, you’ve got absence related in sickness related where people are getting sick. What’s the likelihood of more of our employees getting ill in this space? Some companies are saying in the. Milan office, obviously, Milan was particularly, but obviously. So what is the likelihood of being able to operate in that space?

[00:21:02.135]

When will people who are sick, when are they likely to come back? And I’d say most organizations split it into two very, in my opinion, positive aspects. One was obvious organizations needed to retain a level of productivity just doing work, but I also think there was a big rise in. Concern for employees, well, being related to more than more attention than I had, at least I had seen before. So are employees healthy? Are they stressed?

[00:21:35.365]

How can we help them adapt to these stressful environments and special changes? And so when they’re making decisions about that, again, yes, you can just use a gut feel, but the evidence can show you real time, like as we’re going through the crisis, are they coping? What can we do? How can we react? And so on. And a lot of organizations did that as well as they could. Which I thought was a very positive sign.

[00:22:04.795]

So these are the ones making database decisions, they already have systems in place for gathering data and they have a history of data. So therefore they could adjust better. Is that what you mean?

[00:22:15.175]

Like I referenced earlier, there are some data sets which all organizations have, and you can’t run a business without them. Mm hmm. And. You can use that to an extent to make certain decisions like those will probably include things around job roles, absence related systems and things like that. They might not have been real time systems and the data might not have been perfectly collected in them.

[00:22:40.545]

But I’d say sometimes they don’t need to be. I don’t think. Yeah, especially historically they haven’t needed to be. It’s probably a system which didn’t seem critical until this year, but you could just get by because absence wasn’t that much of an issue before March. Suddenly it is. And so I suppose this is slightly anecdotal, but I do think that the vendors, the people who can provide solutions that are a bit more agile, a bit easier to implement and deploy.

[00:23:11.895]

I think we have seen quite a few of those vendors doing quite well. People who collect data related to remote working people who could collect data related to how to improve an online learning experience, because suddenly everybody’s doing that as their way of learning. Those organizations probably done quite well and they probably capture more unique, useful data in the context of the covid decision making.

[00:23:39.675]

How do people get in touch with you to ask more if there’s a think tank website or two or three and strategy?

[00:23:46.995]

That’s the business, which is also a Korean strategy. Dot com on Twitter. My handle is Nigel de seven. So if you want to tweet at all, people are fairly responsive, growing family and anything like that aside. So I would respond if I can. And on LinkedIn. MySpace, Nigel Dyas I mean that. That was a great discussion so far. I mean, I think the the main thing that I’ve gotten out of this, unlike in previous episodes where it tended to be some very specific little tidbit, is that mindset, that mindset around people and groups of people, and that effectively you can use data to get useful insight here, too.

[00:24:36.515]

It’s not all just intuitive black box which you set and forget, and it just kind of happens to work.

[00:24:45.875]

You can actually be more structured about it using this kind of a quantitative approach.

[00:24:51.755]

So tune in next week where we continue on with this discussion and get more into culture related topics. Thanks for listening to this episode of the Managing Remote Teams podcast, if you enjoyed the show, please leave a review wherever it is that you listen to your podcasts and reach out to us on Twitter or LinkedIn with any feedback or thoughts that you have for a future show.

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