SPEAKERS

Noble Madu, Vin Vashishta

Vin Vashishta  00:02

I've been in data science for over 10 years. I worked in the people analytics HR technology space, off and on, for over seven years now. 25 years total in technology. Done way too much. And if I keep talking, I'm gonna sound older and older than I am. But one of my main areas of focus has been really on making data a strategic asset. And using the data science team leveraging us as strategic assets, because a lot of what's happened in the data science field hasn't been monetized very well. So that's been my area of focus for the last over seven years now, is trying to help businesses get value and prepare to monetize data science and machine learning.

Noble Madu  00:49

Very cool. A quick bio about me. I'm the director of expansion at Thinknum Alternative Data. We're a leading alternative data company that specializes in crawling external data. A lot of great companies right now are using external data to make decisions. And I work with corporations and investment groups and HR teams to understand where they are with their people analytics, and where they're trying to go. And how think them can help along the way. Prior to Thinknum, I was at Goldman Sachs and also Blackstone Launchpad, helping recruit leaders, my backgrounds in recruiting, my family has been recruiting for 30 years and staffing, and really enjoy just the science behind also recruiting too, as well in the process that has to be taken to kind of get to the right person. So really excited to be on the call here. We've been today. And he's definitely a data leader. So it's going to be a pretty interesting conversation.

Vin Vashishta  01:50

Alright, let's kick it off. Where do you want to start?

Noble Madu  01:53

Yeah, I think we should jump right into just talking rabout the points. You know, I think our title, is pretty self explanatory. You know, we, we didn't do any false advertising there. We just talk directly about, you know, four ways that people can leverage, you know, data. So I think let's talk about the first way, that'd be a really good way to start. If you're okay with that, then

Vin Vashishta  02:15

definitely just dive right in.

Noble Madu  02:17

Cool. Sounds great. So when people are pretty familiar right now with the war for talent, that's one of the biggest things I thought about as we prepped this actual webinar. And I kept thinking about something I read, actually, from McKinsey and Company. And McKinsey and Company talks about how people analytics is actually a top priority among 70% of executives across the board, 70%. And executives are really taking people to analytics very seriously. And right now, people have been driving a 25% increase in business productivity, and a 50% decrease in attrition, which definitely helps the bottom line, helps the HR departments and also kind of helps with teams cross functionally to as well. HR departments have very, very much been familiar with internal data and leveraging internal data. I think external data is very new for HR departments. And they haven't really spent a lot of time using external data. So right now, it's a very good time for a lot of companies to kind of tap into a secret weapon, so to speak, which would be external data. And I'm seeing that across the board, I've been talking to a lot of HR leaders from golf with phone with some great fortune 100 people, analytics leaders, too, as well, just earlier today. And we were talking about how HR teams are really using external sources. And that's a high priority for them too, as well. So before we jump into external data, let's talk about what it is. And then we can kind of talk about ways to leverage it. So external data is any type of data that can be captured, processed, and provided outside of your organization, I started your company. And it can come in a variety of ways. company websites, social media, marketplaces, government sources, and external data, something that's outside of your central internal bubble. One of the biggest ways that I've seen leaders leverage external data to drive HR mandates and goals would be within talent acquisition and recruiting actually, and then also in retention. So the number one way would be using a competitive intelligence to drive rapid recruiting. You know, recruiting is really important right now, there is a war for talent literally. And, you know, I didn't choose my words wisely, because of course, you know, we're watching what's going on with the news right now. So hopefully no one has any family and, you know, Ukraine or anywhere in Europe, hopefully they're okay and safe. But there is a there is a battle right now for talent going on. And a lot of people are really trying to figure out ways to attract the right talent and to attract the right talent. quickly too, as well. You know, there's a stat that says that some of the top talent, they're only available on the market for only 10 days, like, people are moving very quickly to hire people. And they're moving very quickly to attract them. And so I'll just say that talent landscape is really important. Companies across the globe right now are engaged in interviewing, retaining, and looking for the best talent and being able to use external sources to do that can be very helpful. So at Thinknum, we actually have a really cool tool, and it's our most popular data set. And our data set is a job listings data set. And so if you're not familiar with Thinknum, we are alternative data lead provider, we track 500,000, plus companies across 35 datasets. So we gather a bunch of external data, and we create a very easy to use dashboard, we also deliver the data through API. And it's a very simple way to be able to kind of see the competitive landscape and understand what's going on, you'd also use the data to look at yourself too, as well. And being able to track companies across those datasets very helpful. It gives you a holistic view, too, as well about some of the strategies. So if for way number one, being able to use talent and competitive intelligence to drive rapid recruiting, you can do that by looking at job listings. That's one of the biggest data sets that can kind of drive that. And we've seen that for a lot of different use cases, actually, specifically, when you're using fresh data sources to understand how the job listings are being boarded, how the job listings are actually being voiced, not just voice in terms of what the title is, but more so where the actual job is located. The actual salary by skill engineers versus salespeople, say for example, one company might be hiring a lot of engineers versus one company might be focusing in on hiring a lot of salespeople and kind of seeing exactly what their strategy will be based on the type of hiring trends. And for example, Apple has a lot of hiring shifts going on right now you can see kind of a different shift between how they're hiring people. And it's really interesting to kind of monitor that as you're trying to create your recruiting strategy. Me as a recruiter, I'm spending time trying to quickly decrease my time to fill decrease my time to hire, figure out ways to be able to get the right person in the right seat quickly. So I can use external data to wine, see what is going on in the marketplace. And how quickly are people filling these roles, first of all, and then looking at these job listings, to see exactly to what type of roles and by location to as well are people hiring for right. So as I can pull this information together, I get a better understanding of the actual job marketplace. And that can allow me to know better. So I can actually move quickly to put myself in a position to attract the talent that I want quicker to drag rapid recruiting. So that's a way that's really important for rapid recruiting, being able to kind of use external data to speed up the recruitment process. So you can know exactly who you're recruiting, hire, recruiting, where you're recruiting them, and and how to actually force that recruiting.

Vin Vashishta  08:13

I mean, you've hit on a ton of good points. And one of the big ones that you hit is region, because when you're looking at hard to source talent, and I hire a ton of data scientists, looking at just something as simple as salary by region, you begin to understand sort of the power of remote work, and allowing people to work remotely. But also, the fact that there's a lot of markets that are they're simply underpaid. And so if you look at from a state by state basis, if you're trying to recruit out of a state like Washington, California, New York, even Texas, you're looking at some of the highest paid people in fields like data science. But if you look at the Midwest, you look at the south, you're looking at people that are underpaid. And so if you're having trouble sourcing talent, like that little nugget, right, there is something that you can use to tell you recruiters look look in the states look for. Because if you look for a data science resume, you're gonna get the phone book, and not all of them are actually data scientists. And that's another side of this, you know, you have a more granular region, you know that you're going to have an easier time poaching, because only 1% or less than 1%, I think of data scientists are unemployed right now. So you're not going to, you're not going to be able to find people that are, you know, actively looking and don't have a job. So you're probably poaching and you're looking for people that are passively looking. And that's another thing you can do from a competitive competitive intelligence standpoint, is you can look for signs of those passive candidates, those people that aren't necessarily advertising it because they're in a job and they don't want to spoil that particular role. But there are signs and you can use external data to begin to understand things about a company that might indicate That company is vulnerable to poaching. And now you're you can hear me, I'm slicing this to the point where your search is so much more granular, it's going to take a lot less time and your results that the return that you're going to get on every outreach, it's so much more targeted. And now you're not doing sort of those spam emails, where you're, you feel like you're hitting, you know, 40,000 people, and getting two people to reply. You know, in doing that targeted, granular type of recruiting, you can't do that if all you're looking at is internal data, and you just you get an asymmetric view of the marketplace. So from a competitive intelligence standpoint, understanding, especially with return to work, understanding how a company saying, Look, everybody's coming back into the office, how is that going to increase the likelihood that you can reach out to people specifically in that company, and say, we allow remote work we are, you know, when you start pitching benefits, that are more specific to the person that you're trying to poach. And that's where, you know, this external data begins to give you a more efficient view of the marketplace so that the data that you're getting is actionable. And we talked about actionable insights, really loose, loosely defined, terrible term, but actionable is the small slices. So don't spend as much time searching through California, as you do searching through other markets, because all you have to do in those other markets is just offer, the normal salary that you have. And people in those markets, if you're allowed to remote work remotely, they're gonna say, wait a minute, I'm underpaid. And now you're not talking someone into leaving a company, you're saying, here's the salary. And it's, it's a different conversation, you hear shifting the use of data from this tactical, you know, want to stare at a number, what does that mean, to a strategic, where you are looking at building strategies for recruiting for retention, around a lot of the points that you make, now you have data driving strategy, at the top level, and all the way down to the individual, that frontline, whether it's a recruiter, whether it's a hiring manager, I mean, you can give managers have enough information where they can mine their own networks, and they know what to look for. What are the indicators of a passive candidate? What are they looking for in their feed? Where should they be going to begin to build relationships, and there's just so much strategy that can be introduced based on high quality data. And if all you have is an internal view, you're you're missing so much as a business. And when it comes to people analytics, we talked about 360, you want a 360 view? And that includes we sometimes forget this, that includes outside that includes going to external datasets to understand the macro factors affecting the market?

Noble Madu  13:06

Yeah, yeah, you hit on some really, really good points there, then, and being able to equip and really create a culture of data literacy. You know, I think you and I talked about that offline a little bit too, right. And equip your team. With that information. Everyone can move better. So it actually is cool that one of the points you touched on leads to my next point. And that next point would be way number two, that you can leverage alternative data to really beef up your talent acquisition and your retention. That'll be by uncovering strategic shifts, and also understanding what's going on with return at work. You know, a lot of HR leaders are trying to understand what's happening right now with the remote culture, and what companies are bringing people back into office, then I remember you and I were talking a few days ago, just about, you know, how humans are, you know, very, very people focus, like we need to be around people, we need to interact and people are returning back to the office. business continuity plans are shifting now. We're seeing a lot of cool things happen. But at the same time, the data is also giving us a lot of predictive information that we can use to understand what's happening there from a recruitment standpoint, too, as well. I know, one of the biggest things that we looked into would be how Walmart has been beefing up their advertising strategy by 240%, actually, and an analyst at Key Bank figured this out by scraping Walmart's job postings actually, and noticing an uptick in advertising for roles posted, right. And those rules are mostly advertising rules, right? And by seeing that they started to uncover that one more, it's really double looked down on the advertising business. And it's projected that Walmart's advertising could generate about 3 billion in EBIDA. Right. So that's really interesting. I think, being able to use that data to uncover insights like that, whether that's for a Walmart, that's where a competitor like Target or something that's very interesting for them. And staying ahead of those trends and seeing also, where are those people being hired, like, by location, by role type, by seniority level, is very helpful for HR team right to be able to move strategically, because at the end of the day, you are competing with Walmart for that talent, if I'm a target, I have to look at what they're doing to understand how can I win the best talent? Right? And how can I get the best people working for me. And so external data sources really help you to map out just kind of what that looks like, from a 360 view, as David was mentioning earlier,

Vin Vashishta  15:59

that I look at, you know, that there is an opportunity that is overlooked in most companies to use people analytics and to use sort of the external talent data, for competitive intelligence for actual strategy, business strategy planning, beyond just talent, you can look at, you know, in the Walmart example is a great, that's a great example where, and I used to do this too, in competitive intelligence, the amount of information that companies reveal in their hiring data, in their hiring trends, and even in their layoff and firing trends and their churn trends. You can learn so much, and as a people analytics group, being able to explain the trends, not just in how this impacts us trying to recruit, how is impacts supply and demand for talent that we're going to be looking for this year and into next year, but also explaining how, you know, a company like WalMart. Now all of a sudden, they're hiring advertisers, they're hiring people with ad backgrounds, what, why, and that reveals a new strategic focus for the business. And companies are trying to figure out, especially now, cross industry competition, companies like Google just show up. And suddenly you're competing with them. And you go from this very small, well known competitor landscape, to Google and Microsoft, you know, if you think about healthcare, Google and Microsoft just kind of showed up. But there were signs that they were coming, some companies were prepared. And talent is one of those signs that external data, talking about who's being hired, what capabilities are being requested inside of the individual job opportunities, look at the text, and do some sort of natural language and text analytics. And this is something that I've done in the past where you can classify a job and say, you know, based on this text, this is what they're asking for. So even if they're trying to hide a new focus in data science, you're going to see that delta change over time. And when you start talking about predictive, you know, what does that support? What type of changes in their competitive strategy? Does that change in focus? What about layoffs do they have and right now, not many people are doing layoffs, but a lot of the times even just churn? Remember an automotive company back in, I think it was 2016. I'm not gonna name the name. But they hired a very prominent data scientist, somebody with heavy data science expertise. And that was a shift, all of a sudden, this company was going from not so data focused to obviously if they're hiring someone with that caliber, the focus has changed. But then three years later, that person left. And so you have to say, okay, so that was a failed experiment. Look at the person who replaced them was that a higher quality was that a lower quality? And so you can hear, it's not just talking about talent strategy. This is business strategy. This is competitive intelligence, and it's a goldmine.

Noble Madu, 19:09

Very well said, be mindful of our time. So I'm going to move forward to the next point. And the next point that I would like to make would be how you can monitor a business's brand. And also listen to employee sentiment. Another thing we discussed too, as well. And you know, we talked about even earlier on this webinar, we're talking about just how some companies are really using boards like glassdoor edit to poach employees, you know, we're seeing that across the board. So before we even talk about that, let's talk about just how important you know your business's brand is when you're recruiting, and when you're actually out there looking for the best talent. Branding is a very important factor in attracting the best people to your doorsteps. The modern digital age has really agents weighing human opinion, for better or for worse, you know, talent leaders would be very proactive when it comes to building brand equity. And they're listening to what former employers and employees and the public are saying. And that is an important message to really understand and listen to. And I was looking at a few articles as I was doing some research. And research is really suggesting that, of course, you know, happy workers are good for profits. So they drive the bottom line, to sometimes HR is not directly tied to revenue. But we're kind of seeing that HR starting to become a little bit more tied to revenue do a lot of different ways, right. And happy employees are good for profits, right? That's something that I think we can agree on from a very high level. But I've received data to support that too, as well, which is really cool. And talent strategies are being very, very focused when it comes down to brand listening, right, one of the biggest things that we have here at Thinknum we have a really cool thing called a word cloud tool. And we're able to create a word cloud of employee reviews, a lot of times you look at an employee review one off, sometimes it looks negative, and it's kind of skewed, because sometimes people that leave reviews, or disgruntled employees, or people that don't really have a lot of good things to say about your company. So when you're looking at that externally on Glassdoor, indeed, it's very hard for you to kind of, I would say, derive very actionable insights and data from that. But the word cloud helps with that, because we can do with the word clouds, you can actually track certain words and see certain things that are very repetitive and reviews. And you can start getting ahead of the actual information, like say, for example, if a word start coming up over and over again, like harassment, or political or buzzwords that are leaning towards a certain way that would be very, very detrimental to the actual culture and the brand, you can start getting ahead of that and seeing what that means. Like, for example, we have a really cool use case, and one of our white papers that touches on one of the word clouds that we gathered together on how to words such as political, toxic, fake, overrated, racist, right, those are different things that pulled into that word cloud. And funny enough, but actually not funny for that company. The CEO actually left once after that word cloud was, you know, derived. And that's no surprise, right? You know, you have a lot of that happening across the board within reviews, and being able to understand employee sentiment, and looking at internal surveys and internal strategies, but also combining that internal focus with external data. That's index and kind of shown to you in a very simple way, because no one wants to go and search through a bunch of reviews and have to have, you know, for all and gather the data themselves, we create a very simple dashboard that pulls that information for you. So you can kind of compare that internal data with the external data and make better decisions because of that, and actionable insights that you can really step into when you understand exactly what people are saying about your brand. And also what employees are saying about your company can be very rewarding.

Vin Vashishta  23:21

And you've kind of nailed the, the biggest point is moving from understanding what's happening right now, to watching how what's happening changes, because a change indicates downstream impact. And that's where you can go from this being a descriptive data set to something that's more actionable. And by actionable I mean, you've established a link, just by analyzing prior data, doing experiments to understand how changes in your talent strategy or overall business culture, how some of the initiatives that HR is thinking about implementing or has implemented in the past, how have they changed? What impact was there? Is there a measurable impact? And when you use, you know, sort of the surveys and the internal data, you get one view, then you have to compare it to that external view. What are people saying when they're not being watched? What do people say when they think they're a little bit more anonymous than they are talking internally? So you begin to see, is it the same? Do we get the same picture? You have to watch out for bias? Like you said, an external data, you are hearing the extremes, you're hearing people that are at the top of I love this company, and at the bottom of get me out of here? And so there is a definite bias? And that's why I say comparing the two, what is the internal view look like versus what does the external view looks like? That'll give you an idea of how realistic your surveys are and how realistic that external data that external view is. Because now you have to fight a perception if you have a negative perception externally, in a positive perception internally, you have to then start working to change the narrative. Why? Why are all these people angry outside and happy inside. And so now you have a strategic function, you can actually use this data and begin to create relationships between outcomes that are important to the business, you can tie your data to KPIs and I talked about this a lot where you connect model metrics to business metrics. And now as a people analytics organization, you aren't just sort of a cost center that does, you know, something that the C suite doesn't quite understand, has strategic value and has revenue impacts, but talent can definitely have revenue impacts, and you can begin to quantify what the actual internal initiative and external impact or internal initiative and revenue generation cost savings. And so now you go from sort of this tactical cost center to more of a strategic partner, and senior leadership can see the connection between your activities, and what the business cares about. Because that's, that's critical. It's not enough to just throw data at people. It's really that thought process of okay, and now, what does this mean? What do we do? And so taking it from, I'm telling you what's happening, which is kind of obvious, you know, and I talked about Captain Obvious in one of my posts, that's kind of obvious, but we need more than obvious, we need more actionable more insight, we need to have, you know, past that, okay, now, what do we do? So it's really important to look outside to understand sentiment, but really to measure change, and to get access to the kind of data that allows you to perform talent experiments and culture experiments, to understand and get in front of potential negative changes, but also, to take the opportunity to identify positive to improve and identify improvement and say, well, we just figured something out. You know, and that's what I talk about when I say insight is like, Oh, I just figured something out. We did this over here. And look what happened, you can start doing additional experiments and saying, Well, what if we did this a little bit more? What did we really find a causal, or at least a strongly correlated signal, you begin to understand the drivers and that's the competitive advantage is you begin to be very targeted, instead of sort of the shotgun approach of this is what we're going to do from a, you know, from a planning and strategy perspective, you get very targeted, you say, these are our levers, anytime this change is detected in the marketplace, these are our levers to make that go into a more positive territory, or continue it in the positive direction that it's going.

Noble Madu  27:51

And it's really interesting how, you know, every company is different. And every company has different set of people, right, different personnel, and they digest the data in their own special way. You know, you and I talked a lot about, you know, descriptive analytics versus predictive and prescriptive modeling. Right. And, you know, predictive modeling, you know, shows what's most likely to happen. Descriptive talks about what's happening right now, looking, we actually quickly response. Right. At the end, we're talking about prescriptive models that explain probable implications of strategies that we're considering, you know, and so being able to decide, like, what does this data mean, and how can we use this data? And how can this data drive X insider why consigned right, and what are the actions that we're going to take, because a lot of times data is at our fingertips. And we're looking at data all the time, whether our phones are a full API Dashboard, or an Excel charts and sheets that we're combing through information. But it's hard for us to actually say, here's what we're going to use this data for. Here's how we're going to track change. And here's how we can show this to leadership too, as well. I think a really big reason that people champion certain data platforms is because some data platforms do a really good job creating very clear data, right, that we can actually put together and we can share this leadership, leadership understands it, leadership was like, Okay, I understand what this is. I understand how you're explaining it. And I can actually give you a green light or a yellow light on this. Right. And so that's one of the biggest things too, as well, that we focus in on here at Thinknum is  really working with teams and understanding how they're digesting data, you know, to what end to as well, where are they trying to go? And try to figure out how we can help along that way, because everyone has a different data journey, so to speak, right? And so trying to figure out exactly how we can play our part there too, as well. The last point that I wanted to talk on kind of as a recap, if you're just joining us, we're talking about four ways that a lot of great teams can lead average external data to drive talent acquisition and retention, we're on the fourth point. And the fourth point would be using external data for ESG. That's really big. And being able to, you know, uncover things by looking at job descriptions that definitely lean towards a lot of gender bias is definitely something that is important for a lot of HR leaders out there. I've had some great conversations with leaders that are looking at corporate job listings and seeing that there's a persistent problem. And researchers really, researchers are really talking about how, after analyzing balloons of different companies, they're seeing that a lot of companies are leaning towards a lot of masculine job descriptions. And that's something that is not okay, for obvious reasons. And, you know, being able to really look at that and be able to create a strategy behind that, you know, I think that's a really cool opportunity, where HR can actually work with marketing too, as well, to really create better messaging, because you are, you are marketing that role by trade a job description by talking about who you're trying to attract for that position. That is, you know, as much marketing as HR, and being able to understand what competitors are doing. And some of them are biased and with their advertising and attracting roles, and then seeing exactly how are you doing it? Can you benchmark the way that you are describing your roles with other competitors, too, as well? And what decisions can you make because of that, if I'm looking at a role that I'm trying to hire for, and I'm looking at my company versus for the competitors, and I'm seeing that, you know, I'm far left when it comes down to my gender bias. And all my competitors are far right. There's a clear issue there, right? You know, why is that? What am I seeing inside this job description. And so our gender decoder does a really good job of analyzing all that information. Because when you have a lot of job listings, it's really difficult to be able to gather all the insights and the natural language processing to figure out exactly what are the outliers, right? And what's the issue here. And so our analyzer does a really good job at doing that, being able to look at that information, gather the insights, spit back the data, and kind of show you exactly here, this is within a table or a chart that kind of shows you where there is bias, and by percentage, you know, bycategory too, as well, and gives you a lot of insights that you can actually use.

Vin Vashishta  32:35

You know, I go to the question of why I go to causal a lot when I talk about data science and machine learning, because you have to, when you start getting into something as complex as diversity, when you start getting into something that is really, businesses are facing complexity across the board. But diversity is really one of the most complex talent issues that we have, because companies don't truly understand why diversity improves, or why diversity goes in the opposite direction, until after something horrible has happened. And that's the power of data is you don't want to get to that point. You don't want to have one of those off the cliff moments before change happens. You want to know ahead of time, you want to begin to understand what policies what cultural changes actually have an impact, because that's the, that's the important pieces. It's good to talk. It's good to bring the brand forward and the messaging forward. And it's good to train and bring awareness. But at some point, you have to do something. And when it's not clear what works, businesses really again, go back to that shotgun approach. And so I talk about, you know, prescriptive, predictive. But what does that really mean? It means that you have higher levels of evidentiary support required, in order to say this can be used for describing because we all know about data quality, we know about data cleansing, we know about all the data wrangling and all the work that goes into kind of created the data engineering role. So now we have data that's clean, and it's reliable, to tell us what's happening right now. And then you move on to predictive, you want to be able to start saying and this is probably what's going to happen, but you can't use that data by itself without overextending. And so this is where you begin to talk about the importance of large datasets, more complete views of not only what's happening internally, but what's happening externally, what's happening in other companies. What's happening in the broader marketplace. Are you seeing a trend that's specific to your business? Are you seeing a trend that's specific to the marketplace? Are you seeing an impact of a policy or strategy that you're putting in place for diversity? Or are you just see In a marketplace impact playing out, you know, and that's now you can begin to say, Okay, we're going to do, we're going to change and we're going to measure the impact of and we're going to do this in a more rigorous way. And that's where you come to the point where you can say, I think we found a strong relationship between this strategy, this action that we took, and an outcome. That wasn't just an outcome that happened due to some other variable or some other event, we call it confounding. There's no confounding here, or at least we've eliminated it as much as possible, which is you can never get rid of it altogether. But you're able to do these more complete analyses of what really works. And this isn't just a diversity issue, when you talk about just the complexity of hiring across the board, the things that you can do to exclude talented people. Really, once you stop doing those, your diversity efforts will naturally benefit from that, because now you're looking more holistically at the problems that you face as a business. And again, this is where your HR group, your people analytics group, goes from being sort of siloed and tactical, limited to more of a strategic partner for the business as a whole. Because this sort of rigor, this sort of process, it can be taught to the rest of the organization, it can be adapted to the rest of the organization. And so that's the important piece here. And it all starts with data, it really does, we can't get to those really advanced sort of sort of applications, until we get that front end data clean, until it is reliable. And it has to meet these increasing levels of reliability and accuracy. Or you can't get to the next step, when you can't do all of these things that I'm talking about. You can't really understand what worked, what didn't. And that's the important piece of this is really to get to the point where you don't spend all your time cleaning data anymore. You don't spend all of your time and effort and budget, trying to get the data to the point where it tells you the truth about what happened two weeks ago, it's you have to get past that. And I think that's the gist, the important piece of data as in general across the board, internal external, whether you're sourcing it from a third party, you know, however you're getting it, you have to get past the point where you spend all your time worried about data quality. And so a lot of times, that means it's easier to source externally, it's easier to buy it because it is clean for you it is more reliable. In some cases, like with what you do you it's pre packaged, the insights are already built up a lot of the heavy lifting, that front end work is already done for you. And so there's a lot of benefits that come from just data quality in general. And that's really where you start. Because until you get there, everything has a question mark or an asterick behind it where you know, and the problem is the data. And senior leaders won't trust it until you've proven it out. And that means that your relationships really have to hold, they have to be accurate. And you just can't get there without the quality data

Noble Madu  38:23

Actually really makes me think about a conversation that I had with one of our clients or customers, Facebook. And we talked about Facebook's use case, another webinar. And we talked about how rich data is so important for them. And they have a team that spends a lot of time looking through data. Of course, they have a lot of data. And they don't want to spend a bunch of time cleaning data. They want to spend time using the data in a different way. And they have a team that can go and pull job listing data, they can go and pull information about a job listings by location, they can go pull information about how many roles are remote versus in office, they can do all that. But what they really appreciate is the fact that the data is pulled for them, its indexed, its cleaned. It's easy for them to digest. They can use it, they can apply it because they can allocate now their time towards other things that can really help drive the data strategy forward. Right. And so right now we're using our data to track job listings across five major tech companies actually. And they're looking through different metrics like by location, by job type, and they're looking at also, specifically, where is the future of work headed? Right. Like, I don't think we're coming back to office. But are we hiring people at the same pace as our competitor in Seattle? And are we hiring the same amount of people in the same category too, as well, like being able to be very granular on how you're looking at your hiring too, as well on your your talent acquisition, because if you're a company like Facebook, you want to know how you're competing with this new marketplace, this new talent, you know, marketplace of people coming back into the office and how they're getting hired, where they're going to be, how we're going to actually motivate them to be on our team. And so that's really important right now to be looked to be to look at as, as someone that's a data analyzer. The other big thing too, as well is filtering by keyword and job title. Facebook uses that a lot to be able to understand technical jobs versus sales jobs, right. And I talked about this earlier, and being able to look at the different types of roles that are being advertised, we actually pull the data directly from company websites and their ATS systems, right. So our data is very rich, we're not pulling the data from just anywhere, I've been able to pull the data directly from companies that are actually posting these jobs, and being able to track and look at the actual job title, the keywords, the location, by category, too, as well. And as we index this information, was very, very helpful for teams like Facebook to understand okay, what direction this company going, as we talked about earlier with Walmart, you know, you can kind of see through the job listings data, that Walmart is really doubling down on their advertising strategy, how do they know that because they saw a big uptick in the type of goals that are being posted, that are advertising focus, right, and it was a huge, huge change huge Delta, between where they were, you know, three or four months ago versus where they are now. So that month over month change, which is very, very aggressive, right? I think being able to kind of look at the data in that way. And use job listings data, use LinkedIn head count data, use Glassdoor data, use indeed data, use this data to be able to create better insights. And we have 35 data sets, which very, very great, some companies use certain datasets and they're more effective for them. Some companies don't really need certain datasets, because it's not very relevant. But I love to have a conversation with anyone on this call, just talk about data, talk about how to use data better, too, as well. But I can go on and on about this.

Vin Vashishta  42:24

Definitely, I think what's interesting about kind of where you're going, is, and I've done some work in this space, where you can begin to not only understand what your competitors doing what's different between you and them, you can start looking at how job descriptions change over time. And you can begin to understand where the marketplace is going, which is huge. You can begin to classify resumes, as sections of resumes, parts of job experience, you can begin to analyze career path you can look at, I mean, just just you can hear it, there's so many slices. And I've done a lot of these types of projects, where you begin to understand just this rich picture from doing some very basic text mining, some very basic classification, in the picture that you get of just fields in general and where they're headed, versus where your business is, it's, I can't tell you the amount of just valuable intelligence that you can get. And it changes the way of business hires, it changes the way the business approach approaches talent. It's important for companies to really understand, you know, just the power of moving beyond basic analytics, especially for people analytics. There's so much untapped ground.

Noble Madu  43:58

Today, I think we can open it up. Are we doing any questions then?

Vin Vashishta  44:04

Um, let me pull up the chat. Okay, if I, if I have both up at the same time, it ends up the echos not so good. I hear that. Let me pull this up and see what we got.

Noble Madu  44:17

While you're doing that, I wanted to comment again on your on your background. I like the whiteboard.

Vin Vashishta  44:28

Um, so far, I'm not seeing any new. I'm still scrolling. Looks like here we go. Sorry. I had to get the bottom. We got one from Paul: I was wondering if it's valid to ask for companies to pay for education like certificates and ask for paid work time for learning. And yeah, I think and I think this is something else that you can kind of look at from the perspective of number one Once you have access to greater data sources, that's what comes next. Now you have to train people up. Now you have to do a little bit of investment in the organization, to develop it towards not only doing the work that it's worth, the analytics work that it's doing right now for the talent organization, but also then, to look at that particular organization, that's gonna be doing your people analytics, and trying to grow that organization. And again, you're going to know what direction to grow it in, because now you're beginning to see strategic insights. And the team can justify the upskilling by the revenue by the cost savings. And so now, it's not just hey, train us up, because we have new technology that we could learn it is if you train us to do this, here are the kinds of insights we're going to be able to provide you. And there's this value proposition now. And you can apply that across the entire business, where you look at, in other companies, these are the types of training initiatives that are happening. And in companies with implemented this training initiative, here's the revenue impact, like you can see direct impacts where these companies as a whole have higher margins that are growth, you know, and you start measuring, okay, well, why? What's different about them? Who are achieving better results, the kind of results we want? versus us, what are they doing that we're not, and with talent data, you can actually follow, because people are gonna post, you know, hey, I got a new certificate, hey, just completed this training. And there's all of these data sources, you know, like you talked about from LinkedIn, when you look at somebody's profile, every time they get a certification, it's going up. And so you can look at the industry and start saying, Well, okay, so the biggest companies in our field, are doing this probably time to start investing in more training, or some of the most AI first companies or some of the most focused on marketing or effective marketers are doing this, and it becomes a justification. It becomes sort of an ROI, tangible number for training and internal development organizations. What do you think? Do you think about training and, you know, sort of using the just the data to justify new training and the expense of it?

Noble Madu  47:25

Yeah, I agree with you that, you know, brain is critical, very important. You're seeing a lot of startups actually, that just recently got well funded, that focus a lot on you know, corporate training, corporate development, especially in the HR space. And it's obviously very important. I want to wrap to two questions I've seen inside the the chats, I see one by Boris: How can companies use external data to speed up recruiting. And especially during this time of great resignation, and the shuffle, we shuffle, I think that's an important point that I want to touch on quickly, especially because, you know, I've been recruiting for so long. And external data is really important for that, because you can use external data in a very detailed way. Actually, I want to break those things down real quickly, or break those points down quickly. One, creating job posts, by looking at seeing what your competitors are doing with their job posts and how they're rewarding their job posts, you can create a better job posts, you can match passive candidates to open jobs, you can identify potential internal mobility, which is very, very important. You want to know when someone's about to leave when they're about to move. What's the headcount for that company, we got a cool data set, LinkedIn headcount, and facet data too, as well, LinkedIn, to be able to understand a little bit better, how's the internal mobility looking flag skill gaps across teams, right? That's really important too, as well, then you can improve the quality of hire too, as well, to be able to understand those things, those five points right there, that'll help you as a recruiter, be more equipped to be able to market the position better, right? know who to talk to the to reach out to passively to find those passive candidates are actively to find the passive candidates and be able to actually speed up that time for hire that time to fill. And when you do that, you can speed up your recruitment metrics and save time, save money, right. And that's pretty much the name of the game with external data, as you're watching what's going on, not just by tracking your company, but by tracking maybe five or four top competitors and seeing how they're doing through those metrics I just mentioned, again, job posts, internal mobility, skill gaps across teams and LinkedIn headcount. Employee sentiment data to as well as very relevant to as people are moving internally, be able to track those things and look at that external data before you make decisions on how you're going to recruit who you're going to recruit because no one wants us. All on dial day, just email blindly. You want to have a strategy that's going to get you in front of the right candidate quickly, and you'd have the right conversation with the right candidate when you get there. And by having that external data, you can not only find the right person would have the right conversation, that onboarding quicker,

Vin Vashishta  50:15

I think the big piece that you're kind of alluding to a little bit is, you know, you have these set of data points that you're offering, or that's the starting point. You know, when you say this is where you begin your efforts. So a lot of that front work is already done. And what's left for the company now and the people analytics team is to find new metrics, that's the opportunity to find new KPIs to focus on because as you said, every company is different. Every company has a different culture, a different talent pool, a different employee base, and a different direction, when it comes to where do they want to go? And how are they going to grow into the future. And so for each business, you start with the basics, and then using this large pool of data, it's internal data and external data, you can't forget about the internal data too. But the the power really is that now you are finding new insights are finding new metrics, you finding new KPIs that relate strongly to your business's specific objectives. And you begin to get more granular, more nuanced. And so your strategy isn't this. This is what works for everyone. You're beginning to narrow in on this is what works best for us as a company. That's, that's really powerful.

Noble Madu  51:38

Very good. Yeah, I think we can wrap it up. I know, I see one last question about where and how you acquire quality external data. And yeah, so great.

Vin Vashishta  51:54

Setting you up.

Noble Madu  51:55

Would love to have a conversation with you. And we'd love to talk to you on how to acquire external data. But then we'd like to save those to wrap it up. And I think it's been pretty, pretty.

Vin Vashishta  52:08

Good. Great. Thank you. Really appreciate it.

Noble Madu  52:11

Likewise, it's been good to hear your insights. As always, I learned a lot just talking to you. Definitely a leader in the data space. So look forward to having a conversation offline again, too, as well.

Vin Vashishta  52:20

All right.