Episode Transcript
[00:00:06] All right, today I want to talk about the people analytics space and the big report that we published this week that we spent almost a year on.
[00:00:16] And for me personally, this is the fifth time that I've done a study of people analytics. So I've been doing this for a long time. I wrote a whole book on learning analytics back in the early 2000s. And it's been very frustrating, to be honest, that after 20 some odd years of studying this, we're still stuck in this weird backwards world where we have these groups of people in HR doing these analytics projects, and they're not really adding as much value as we would like. I'm not saying they're not adding value. They are, but it's still an odd to me domain where we have all this complex people data, we spend all this money and time and energy putting it together into an integrated system, and then we look for problems to solve.
[00:01:09] And that, to me, is still a little bit of a backwards approach.
[00:01:13] And so we coined the level four and the maturity model, Systemic Business Analytics. A little bit of a weird phrase, but the idea is that where we really want to go as a company, as a business, as an organization, is we want to look at all of the operational data that characterizes what's going on in the company now. And that's going on all the time, every week, every month, every quarter.
[00:01:44] And then we want to offer and deliver, even if it's not asked for, related and relevant people data that helps people make better decisions. In other words, when the CEO gets up there at the end of the quarter and says, you know, I had a really good quarter in North America, but we're 8% behind of our numbers in Asia and 2% in Europe.
[00:02:13] The next sentence he says is, and here's exactly why. And he talks about the hiring, the skills, the leadership, the engagement, whatever it is that has to do with the people. Because you know that the reasons for these inconsistencies in performance largely have to do with people.
[00:02:35] I mean, companies don't run without people.
[00:02:38] And if we could reach that point, and I'm not saying that many companies have, but we can, then all of a sudden, the investments we make in HR and human capital and leadership and development and all that are extremely relevant and extremely aligned with what the business needs.
[00:02:57] And that is what we mean by systemic people analytics now. You know, the problem that I think we have in hr, and I certainly reflect on this as a person in this domain, is that we think about the people data first.
[00:03:16] What we do is we look at all this data we have about people retention, engagement, leadership pipeline, labor unit activity, diversity, pay, sentiment, skills, et cetera. There's hundreds of data elements we have about people, tenure, retirement rate and so forth. And we say, okay, here's a bunch of really important information about our workforce. Let's show it to our business leaders, explain to them what's going on, and find ways for them to use this information to apply to different business situations where it where while in reality we should be doing this, the opposite.
[00:04:06] Imagine if you will, and this is going to be possible. And some companies do this. Imagine, if you will, every month, every quarter, every week, when a sales organization or a supply chain group or an inventory group or a manufacturing group looks at their numbers and says things are not trending in the right direction, whatever that may mean.
[00:04:29] They have related data about people at their fingertips that shows them what is going wrong.
[00:04:37] We have that data and it isn't a matter of us in HR taking the business data and putting it into our systems. It's the other way around. It's us taking the people data and putting it into the business systems where it belongs. Now, as I talked about in the article that I published this week, the reason companies buy Workday, Oracle, SAP and other ERP systems theoretically is to integrate the financial systems with the human capital systems. So this kind of information should track together. But of course, none of these ERP vendors really do that because their human capital product and their financial product are two different products and they weren't really designed to work together, with exception of maybe some new ones like Rippling or Hibob.
[00:05:30] So what that means is that in order for us to really deliver actionable, meaningful data to the business, we don't have to just analyze it, we have to integrate it and make it available in the flow of work of line leaders. Now, every now and then we run across a company that does this and they do it through their own tools in their own IT department. And in our study, we found that roughly nine and a half to 10% of companies are reasonably good at doing this. These are companies that have a lot of investment in data. They're data centric organizations in general, and they're not afraid to look at kind of oddball HR data and apply it to business problems. And they always discover amazing things. For example, sales organizations discover that it isn't necessarily the amount of sales training that drives results. It's the relationships that salespeople have with other people in the company that drives bigger deals because they can get deals done faster and easier. And they can get more accommodations from internal stakeholders to do large transactions. They also find out that labor union activity is not a result necessarily of poor management. It's a result of poor career opportunities for people in the stores or the plants. Because when people feel stultified by their careers, they're more amenable to join a union. I've seen that kind of study done.
[00:07:07] They oftentimes find out that the recruiting process where we've looked for skills or credentials or certain experiences are not relevant. And that we really need to look for people that have come from certain companies or certain industries or certain jobs that are proven to be high performing pipelines for high performing salespeople. And these things come up all the time. And you guys have done a lot of these studies yourselves, but we can't do them on an ad hoc basis. We should be doing them on a regular basis. So ultimately what should happen, and this is where AI is going to come in, is we take these, you know, beautiful tools like Vizier or One model or whatever analytics tool you have, and we connect them directly to the financial systems. We don't try to put financial systems into Vizier, financial data into Vizier, we connect Vizier to the financial systems so that the managers doing work in sales, manufacturing, marketing, whatever it may be, are getting people data directly correlated to the work they're doing.
[00:08:16] Now that does not say that the complex analytics we do in people analytics are going to go away. They're not. Because one of the fascinating things about the whole human capital side of organizations is it's very complex and it's very nuanced. And as we've talked about many times in our systemic HR study, all sorts of interesting things have an impact on the performance of your company. Well, being, culture, learning, growth, communication, style, flexibility, work, life, balance, execution, clarity, mission, purpose, all these weird things that don't have financial definitions, they're very sort of vague concepts, have a huge impact on people's ability to collaborate, innovate and execute at work. And we do, as HR professionals, have access to a lot of that data. So in addition to lining up our systemic business analytics into the business, we have insights of our own and we need to bring those forward and we need to show the company that our leadership confidence levels are dropping or our retention is decreasing, or our level of skill, or our level of skill development in this area is weak or whatever it may be. I mean, these are the things that we do all day, but that can't be the only thing we do. And we can't do people analytics as a bunch of PhD projects. I mean, one of the things I've always loved about working in the people analytics domain over the years is the types of projects people do.
[00:10:07] Because a lot of Companies do hire PhDs and data scientists into these jobs and they find fascinating things to study. Organizational network analysis, for example, is a big one. Studies of the role of leadership culture on different innovation, the impact of performance management models. I mean, these are really, really interesting things to do. Some of them are semi academic and sometimes they result in really groundbreaking new solutions in companies like, you know, I'll never remember that. You know, back when I was doing some work with American Express maybe seven or eight years ago, when they were redoing their TA function, the guy was running TA at the time, said to me, you know, we studied the service reps and sales reps that work with our clients over the phone, our members, and we looked at the really successful ones and their backgrounds and we found out that none of them had customer service and sales backgrounds. The really good ones had come from the hospitality industry. And of course that is the nature of what American Express is trying to do, is to be like as intimately supportive as a hospitality company. And they've effectively done that through their hiring. And they, and they did a study that understood, that unlocked that. And so there's lots, you know, lots of studies like the Project Oxygen stuff that was done at Google, which was an interesting PhD oriented study that came up with things that most people already knew. But I mean there's, you know, the role of psychological safety and all of those things that come out of academia are important, but they're not nearly as important as the most important thing of all, which is what are the people issues that are directly contributing to or blocking us from making progress in this particular domain. And that to me is a transformational magic implementation of science that we can do in hr. So that is what systemic analytics is all about.
[00:12:15] Now, you know, the other part of this whole area of research and organizational development in HR is the increasing role of AI. And based on my experience with AI and what I know about it, I think this is going to change very, very fast. Because the reason that people analytics is so messy is we've got 40 different payroll systems and 10 LMSs and 16 other time tracking systems and there's a lot of heterogeneous data, it's not integrated and it's kind of a big mess to clean it up. And nobody's really what the ROI is going to be because there's some other project that's more important. And then once we get it all organized, we've got to buy a bunch of tools and create a bunch of dashboards and give people access to Power Bi or Visier or whatever it may be. And pretty soon we spend a whole bunch of money on this and people don't use it.
[00:13:11] Well, AI is going to change that for two reasons. First of all, on the back end, AI technology, because of how it works, can integrate and make sense of heterogeneous data. Just thinking about our experience with Galileo alone, we have loaded into Galileo dozens and dozens of data sets of benchmark data skills, data maturity model data, soft skills models, data leadership models, data on and on and on. And you can ask Galileo any English or any question in any one of 130 languages, and it will find that data. It will make sense out of it if it's labeled correctly, and it will give you an answer that is a systemic answer that makes sense using the data that has access to that makes sense as an answer to the question you asked. And you don't have to do a bunch of SQL and create a bunch of pivot tables and create char and graphs and do crosstabs to figure out what's going on. It will tell you so it is an amazingly powerful data integration system. That means tools like Vee from Visier, Galileo from us, Joule from SAP, et cetera, are really going to be good in analytics when they have access to the data.
[00:14:41] The second reason that AI is going to make a big difference is that you can talk to it. You don't have to figure out how to use the tool. You don't have to figure out how to create a chart or how to create a pivot table or what button to push, because frankly, nobody really wants to do that anyway.
[00:14:58] Analysts do, but everybody else is too busy doing other things. And so if the system can't immediately answer your question or tell you proactively what you need to know, you're, you know, probably not going to use it very often. And it turns out AI can do that. Not only can you talk to it in a narrative form and iterate and ask more questions as to why this might happen and why that might happen. By the way, that's the reason we're integrating Galileo with V and Galileo with these other systems is that the data systems can give you the data, but you need a system like Galileo to tell you what to do about the data. And that's why this is such A, you know, sort of a magical combination of technologies, but all of a sudden we can democratize this information without massive amounts of training and change management. Now, I'm not saying this is all available out of the box yet, but I can pretty much guarantee you in the next year that the AI interfaces to people related data are going to be exceptionally valuable. And in the 100 use cases document we're publishing next month, that will give you examples of things you can do with Galileo and other similar tools, you're going to see a lot of them have to do with analyzing data in ways that you wouldn't have been able to spend the time to do it on your own, but you can do quite easily with these front end systems. And I think the third thing that's different about AI is the systems are learning systems. So once you start asking questions or getting reports on the relationship between tenure and sales, or source of hire and retention, or whatever it may be that the manager might ask, the system knows that this user is interested in that information and it has learned that that correlation is something of value and will prompt the user with additional questions to further understand that domain. And we see this with Galileo all the time. You ask Galileo a fairly detailed question and it answers it and then it comes back with another question that says, would you like to also look at this versus that? And sure enough, that second question was one that you kind of knew you needed to ask, but you weren't sure how to ask it. So these systems are going to become smarter and smarter to the point that we can set alerts and we could even say to the system, if this metric gets above or below this range, please let me know and send me a message. And they're going to become essentially AI analysts on our behalf.
[00:17:36] So two things as I wrap up. Number one, the main topic in systemic analytics is the C level and CHRO level issues.
[00:17:47] Think about your data in HR as not something to analyze on its own sake, but as a data feed that should be integrated into the business data of your company. And that means partnering with IT in the finance business to talk about how to do this effectively and not ask the finance department to give you extracts of their data so that you can do better reports. And that's a big sort of C level issue for the CHRO to think about in terms of how your systems architecture works and who's running the people analytics function in your company. Secondly, for those of you that are in the people analytics domain, you have a lot of exciting things to do. Number one, you can get involved in these cross functional projects. Number two, you should get your hands on these AI tools and learn how to use them and start implementing them. Number three, make sure your data is tagged and clear and labeled so that that when people start going after it with AI, they're getting the correct data and that will always be one of your jobs. And finally, take some time with the business watching what they do every day. Don't ask them for projects, don't ask them for problems to solve. They'll give you those. But spend time with them. And the whole focus of systemic HR which we've now been proselytizing for probably two and a half years, is for you to work with your business partners to be embedded into the business itself. So that when data related issues come up or you know business metrics are not going in the right direction, you have the information at your fingertips to show them and point out to them to teach and show and explain to leaders what they can be doing better in the human capital side of their operation to improve the productivity or performance of their own teams. That is the message in this research report. If you read it and if you're a client, you will get it. If you're a customer of Vizier, you'll get it because we did this in partner with Vizier or you can buy Galileo and you'll get all this plus much, much more.
[00:19:53] You will see that this is a whole new world and we're going to do the best we can to spend the next year or so explaining and training you more and more how to re this problem and get out of this mess of replacing all sorts of big fancy HR systems every time you think you need better data. Thanks everybody. Have a great weekend and I will talk to you next week.
