Tim Freestone – Finding Data Analytics Talent

Connect with Tim Freestone:

• 

Connect with Dwight:

• 
• dwight.brown@salary.com

Connect with David:

• 
• 
• david.turetsky@salary.com

Resources:

• Alooba
• HR Data Labs Season 3 Episode 25
• HR Data Labs Season 3 Episode 23

• “We need to get to a point where we have a much more fair, objective way of making [hiring] decisions. I think in general, that's one of the big issues with hiring is even though it's such an important decision, people rely so much on their gut and intuition and heuristics, whereas really we need to make it a much more rational, objective decision.”
• “It's all about collecting the best data at each step—if that's the application step, the interview step, the testing step—and making the decisions based on that data. A rational objective decision, rather than the pure gut-feel intuition approach.”

Announcer  0:02

Here's an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what's happening in people analytics today. Give them the technology to connect, hit record, pour their discussions into a beaker. Mix thoroughly. And voila, you get the HR Data Labs Podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, but count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here's your host, David Turetsky.

David Turetsky  0:46

Hello, and welcome to the HR Data Labs Podcast. I'm your host, David Turetsky. Like always, we try and find fascinating people inside and outside the world of human resources to talk to you about the latest and what's happening with HR data, analytics and technology. Today, we have a special guest for us, but we always have a special guest in Dwight Brown. Hey, Dwight, how are you?

Dwight Brown  1:06

Hey David, I'm good. How you doing?

David Turetsky  1:08

Very good, Dwight is our co host. And our special guest today is Tim Freestone the founder of Alooba. I said that correctly? Right, Tim?

Tim Freestone  1:17

You showed that you know it.

David Turetsky  1:19

Okay, well, you'll have to tell me what Alooba is. And why don't you actually go into that as you tell us a little bit about your background?

Tim Freestone  1:27

Yeah, well, thanks for having me. That's great to be here. So yeah, Alooba is a word I sort of invented. So believe it or not Aloo, you guys might be fans of Indian food, Aloo means potato in Hindi. Okay, I grew up on potatoes. We grew up in a lot of aloo gobi. And so potato is such a versatile food. That's why one of my business it can do a bit of everything was humble, wasn't fancy, but just got on with the job. That's really where Alooba came from, I credit it on a long flight back from Vietnam a few years ago.

David Turetsky  1:54

That's awesome. That's great. Well tell us what do you do in Alooba.

Tim Freestone  1:58

So Alooba is basically a skills assessment platform for analytics, data science and data literacy skills. Businesses use it for two different reasons. One is to set the skills of the candidates, they might be hiring for roles, like data scientists, data analysts, bi analysts, and also to understand the capabilities of their teams and people.

David Turetsky  2:16

That's awesome. So is there an assessment that costs money? Or do we have to sign up for something? Or can we go up there and get a free assessment?

Tim Freestone  2:24

There's some free practice quizzes, which are also good for the candidate, before they're taking a real one and to get some feedback. And that should give you a sense of what the product does.

David Turetsky  2:32

Do us a favor and let us know what that URL is. So we can provide it to our listeners, and they might be able to or want to take that assessment as well.

Tim Freestone  2:40

Yeah, so that's, that's alooba.com, a,l,o,o,b,a.com.

David Turetsky  2:44

Outstanding. Okay, cool. But as we always do on every HR Data Labs Podcast, we ask you one fun thing that no one knows about you, Tim.

Tim Freestone  2:55

So I mean, no one's a stretch. My mother told me this, let's say, Paco, she knows it. But I have apparently a spare rib. I haven't verified this independently, but I have an extra rib somewhere in my body.

David Turetsky  3:08

You have an extra rib?

Tim Freestone  3:10

Yes.

David Turetsky  3:11

Okay.

Dwight Brown  3:11

And the best is that it's somewhere in your body. It's a mystery.

Tim Freestone  3:15

On my leg, I don't know.

David Turetsky  3:21

Okay, should I ask how you discovered this?

Tim Freestone  3:24

This is just something I remember getting told as a child and I never really went back to unpack that or have an x ray or anything. I don't think it really matters. But I googled it last night when I remembered I'm like, Yeah, this is the thing like this happens.

David Turetsky  3:35

And you know what the good news is, you haven't had to find out so you have one just in case. And for those of you who like eating spareribs please call Tim Freestone at Alooba. Just kidding. We do not condone cannibalism, sorry. We do not condone cannibalism on the HR Data Labs Podcast. So our topic for today is something really cool. And I think a lot of us have talked about how do we find good analytics folks, especially who have an HR background. But the topic for today is analytics hiring is a broken mess, and how do we actually fix it?

So Tim, our first question is, while wow, I didn't know that DaVinci was credited for the first CV back in 1482. First of all, that tells us when DaVinci was alive, which I didn't remember. I mean, it's second of all, it's about more than 500 years ago, and analytics really hasn't changed much. Sorry, the the resume hasn't really changed very much has it since then. That's pretty bad, isn't it?

Tim Freestone  4:49

Yeah, I think so. So the humble CV people probably don't think a lot about this, but almost every hiring process for any role in analytics or other roles as well, starts with basically someone looking at a CV, scanning through it and trying to say, Well, does this CV match the job description? And typically they're looking for keywords, does the person have this skill where this sort of experience and this is where typically based on the customers, we talk to 90 to 99% of candidates get excluded, so it'll get filtered out at that stage? So it says, I think a few fundamental problems. One is the CV if you think about it collects all this personal private information that is fundamentally irrelevant to the question, is this the best person for the role? So if I was trying to discriminate against someone, I basically get them to give me the CV, right? It tells you their gender, their ethnicity, where they went to school, but certainly economic status, their religion, in some countries, people put their marital status and a photo, there's like, all these points that we just shove in front of the decision maker and say, okay, by the way, remember that unconscious bias training you had? Just make sure you include all while you're trying to make your decision on whether or not you like this candidate? It's too much. Yeah, it's a bad start, when, when that's the start of the hiring process, I think.

David Turetsky  6:03

But we all I mean, especially those of us who've been managers, we've all had to deal with it. And it's even worse, because people don't really know how to write a good CV either. I've worked around the world I've hired around the world. And I think there are different cultures where a CV is, is an art form. And some were CVs are just thrown together for whatever reason. Somebody might want a job. They're not very, they're not very interested in getting one. So what does that say about the managers and the hiring process that we've had for so long about their aptitude to be able to utilize that tool?

Tim Freestone  6:44

Yeah, well, I think it's the CV itself is the problem in that if you think about it's basically someone's summary of themselves, their own subjective feelings about what skills they have. And the fact that there's so much effort put into like optimizing your CV to get a better chance of a callback shows you how flawed it is, because you as a person haven't changed, your skill sets and experience are the same. And if you can somehow tweak a CV and get a better callback rate, doesn't it show you how invalid a data set that is to be making that crucial decision, or whether it should hire them?

David Turetsky  7:16

Oh, it's so damaged. I mean, we've had so many people who've said recently, just take the job, the job posting, find the keywords in there, and just copy them in as like metadata into your resume. I mean, come on!

Dwight Brown  7:31

Very, very small, white font. That's what I've heard.

David Turetsky  7:34

Yeah, that's just well, I could use the word that I'd like, but we wouldn't be able to keep the non explicit rating. I mean, it's just awful. And so, you know, gaming the system is one thing, right? Dwight, I mean, you know, you could put it in the small white font? Or what I mean, what's the alternative?

Tim Freestone  7:52

I think the alternative is to collect a better quality data set, that's actually more predictive of whether or not that person is going to be the best person for the job. And to try to create a system that actively removes the noise. I think part of the issue with CVs, there's some information on there for sure. But there's so much noise, like right person's gender, their ethnicity, this, that, so we need to create a process that is basically remove that noise. So it cannot be, it cannot be in contention for that decision making process because it doesn't exist. And to focus on something is much more objective and measurable, that is predictive of whether or not that person is going to be the best fit for the job.

David Turetsky  8:29

But getting to that stage, though, of where you can do that means the ATS has to be trained in order to do that filter in order to be able to find the people to get to that moment. And unfortunately, what we found, especially recently is they are looking for those meditators that they're looking to be gamed to be able to find those people who've done that work. And so I don't know, what do you think I think I blame it more on the ATS than I do on the candidate.

Tim Freestone  8:59

Yeah, for sure. I mean, candidates, it's it's not a game they've created, they're playing the game set up by companies or by the technology and the processes generally used. So yeah, or more power to the candidates, if they found a way to kind of get their foot in the door. Even if that's completely bypassing normal hiring processes and just going to someone they know. Yeah, for sure. Like hats off to them. It's just we need to get to a point in the world where we have a much more fairer, objective way of making those decisions. I think, in general, that's one of the big issues with hiring is, even though it's such an important decision, people rely so much on their gut and intuition and heuristics. Whereas really willing to make a much more rational, objective decision, I think.

Dwight Brown  9:38

Well, I'm, definitely over the years, the, you know, you think about the way that resumes have changed and how you write a resume and how you do things. It's hard to keep up with that and especially in this age of, of ATS and AI and those kinds of things. You really have to move in and fast forward. So it's, it's it truly is a system that is set up to be gamed. And, you know, I guess you could call it optimized as opposed to gamed, exactly. However you want to call it. It's, it is what it is.

Tim Freestone  9:40

Yeah, and I think even if you just take the assumption of like, what are you? What are you doing? When you're looking at someone's CV, you're trying to make a guess, a quick guess in a minute or so are they a good match for this job. But you're assuming that writing a good CV is actually correlated with being good at that job? You know, what I mean? Like, why would a data analyst or a data scientist also be a good marketer, or a good designer or a good, you know, and amazing communicator on paper about their own skills versus those, those two things I think so weakly correlated to begin with.

David Turetsky  10:43

Because there might be a negative correlation, actually, especially for one for people to your point, if you're really good at the numbers, those people are typically introverts who are trying to find patterns, they're not really good at the marketing and the self aggrandizement that you need to be able to create the right CV.

Dwight Brown  11:01

And let's face it, we're taught to be humble.

David Turetsky  11:04

Exactly.

Tim Freestone  11:05

Yeah. And that is certainly a cultural thing as well. You know, like, depending on the country you're from, you're right, to talk yourself up or down, you know,

David Turetsky  11:15

Absolutely!

Announcer  11:17

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David Turetsky  11:27

And I think that gets us to the next question, which is getting to the stage where you've actually been accepted where you get past the ATS, you're actually going to be interviewing, getting to that stage? Is there a way to get the interview process from where it is today, which is very subjective, and even if their objective, I guess, you could say, tools used by the manager like checklists, tool used by the manager, by HR, by the recruiter, they're typically terrible. What are the, what are the other ways for us to be able to evaluate the appropriateness of these interviewees, candidates for the job?

Tim Freestone  12:12

Yeah, so I think interviews still have a really important place in the hiring process. But to your point, yeah, they absolutely are subjective. And I think from what I can see, a lot of that subjectivity comes about because of how unstructured the interviews often are, and how unstructured the hiring process is. So I generally recommend to companies when they're going to hire someone to really think in concrete detail, like what are the exact requirements you need for this role, and then setting up each stage of the hiring process to measure directly one of those things, and nothing else?

David Turetsky  12:43

Right.

Tim Freestone  12:43

So you know, anyone could design any interview process to make a candidate look silly, and to get them to answer poorly. So you start chucking in questions in there around, hey, like, how many balls are there in a 747? You know, if you're a dog, if you're a dog, what kind of dog would you be? You know, where do you see yourself in 50 years, so anyone can be made stupid if they're asked those questions.

David Turetsky  13:03

But those are the questions that people use to try and find problem solving, or, you know, future, you know, the, you know, the personality of the person. They're the stupidest questions like, Where do you want to be in 5 years, or what's your career aspiration? You know, to get this job? What kind of stupid freaking question is that you know, and actually, I've had candidates asked me those questions as a manager, you know, what's your goal for this job in five years? Dude, I'm not going to be around in five years. If you're in that job in five years, I screwed up. And so I guess turning back on, on the the question, there's gotta be something better. And, you know, I think, I know where you're gonna go with this, but there's got to be something better than asking stupid questions to try and find out the appropriateness of someone for a role.

Tim Freestone  13:56

Yeah, yeah, I think there's there's two parts to this. So one is just definitely honing in on the interviews and making them as objective as possible by having them as structured as possible. So defining exactly the questions you're going to ask and why. And making sure those questions are actually predictive of whether or not this person is going to be successful in the role, I think that can only come about by collecting high quality data through time and understanding the patterns and realizing, Okay, these questions are junk because they don't help us predict whether or a candidate is gonna be good. These questions are good, because they do. So that would be one piece of the interviews. But then I think you can't have an entire hiring processes that's just interviews, because even if you do that, it's still going to be reasonably subjective. So I think having some kind of purely objective test of someone's skills, I think, is generally a good way to go. And customizing that assessment to the role to make sure you're just assessing the skills that are absolutely needed for that role. And again, nothing else.

David Turetsky  14:47

So just to be clear, though, when we're talking about these assessments, we're talking about assessments that are focused on analytics candidates, people who have or should have certain not only math skills, but certain data interpretation skills, different scientific methods skills, something that, that would prove to be correlative to good outcomes when it comes to not only being able to find the right patterns in data, but also to be able to play them back right and be able to communicate them.

Tim Freestone  15:18

Yeah, absolutely, exactly. I think for any, any roles that includes, at their core, some fundamental knowledge base and fundamental skill set that can be measured, which is certainly anything in analytics, data science, anything like that, for sure. I think it's probably a little bit trickier. When you get into roles that are what you might call it pure, soft skill roles, like sales can be hard to test on sales, skills or knowledge, as can be more of a practical thing. But for any roles that have that fundamental technical skill set, I think, giving them this objective independent quiz is definitely a good way to give you that measure.

David Turetsky  15:51

We've actually had people on the HR Data Labs Podcast, which talk specifically to being able to provide assessments that do measure things that are correlative with with good skills, like sales. But I think what we're talking about here is the ability for us to judge the as we talked about those people who could be good fits for the analytical role. And I agree with you, they're not the same questions, you definitely be asking a salesperson. But, but so give us an example of what those might be, or how you would ask those questions, or how you would set up the interview for, for an analytics folk?

Tim Freestone  16:31

Yes. So I mean, in terms of the hiring process, you might have the interview, and then a test, which is separate. So the test would normally be asynchronous, someone's doing it in their own time, which I think is actually quite important. I don't know about you guys. But anytime I was at work, and someone would come and look over my shoulder, as I'm trying to write some SQL or something, I would lose the ability to use my fingers at all, yes, I just become like a sloppy mess. So I think giving people a chance to do those, those tests in their own time is really important. And so it's basically about creating those tests to match the role so for example, you might test the candidate and I don't know statistics, or visualizations, or SQL, or whatever it is, it's just about setting that up in a system and having that carried off.

David Turetsky  17:10

I remember being hazed by one of my early bosses, who as I was running PC, or no actual was mainframe focus tells you how long ago this was mainframe focus, where I was entering questionnaire data, and would watch as I entered data from a sheet that had been completed by a manager and put into a system. And I remember how awful that felt. So thank goodness, you're giving them the ability to do it on their own time. And, you know, do we care if they pick up a calculator?

Tim Freestone  17:42

Oh, no, go for it. And Google as well, right? I mean, like, every day, I Google Android things, there's absolutely no reason why you would want to prevent them doing that I think.

David Turetsky  17:51

I think this brings us back to, we're talking about specifically targeting assessments, and structured interviews for data analytics folks. The next question I have is focused on where to find good data analytics folks, because I gotta be honest with you, I've tried to find people. And unless you know, people like Dwight, who's a phenomenal data analytics person. The diamond in the rough....

Dwight Brown  18:27

Slightly exaggerated. Yeah, diamond in the rough.

David Turetsky  18:30

No, but seriously, it's hard to find people who have that analytical brain, that analytical mindset, and especially ones that you can relate to, in lots of different levels, and be able to find ways in which they solve the problem that you're trying to fulfill on, or that you're trying to at least talk about.

Tim Freestone  18:53

Yeah, so I think in this market, in particular, everyone's talking about, oh, it's a tough candidate market struggling to find candidates in analytics. That's the case in all the countries we operate in, I'd say a few quick wins. Firstly, thinking really carefully about the requirements you have for the role. Every additional requirement you have for the role filters, the candidate pool smaller and smaller and smaller. And I think really carefully about whether or not you need those exact things in the position. So I'll give you a good example. Business, I used to work at there was a hiring manager there, who was trying to hire like a BI developer, SQL engineer type of person. And they required they would had to know SQL Server specifically, rather than any other version of SQL. But any version of SQL is similar to any other version, and you could easily pick up a new instance of a new version. So that one decision cut down the candidate pool by like 80%. So you know, that didn't help our ability to hire quickly. Similarly, I think really carefully about the geographical constraints of where you're looking for candidates. The world has changed ridiculously in the last couple of years. Whether or not you have remote work or not, you can certainly source candidates and other locations. And then bring them into your office. And so a good metric to think about this is that 99.99% of all candidates for any position are not within commuting distance of your office. So think about that. The wall is very, very large.

David Turetsky  20:14

Yeah, yeah. Well, and I think there are a couple of other axes to worry about, which is, do I need someone full time? Can I do this with a gig worker? Right? Can I find someone to fulfill on let's just say that SQL server part of this, and then allow for someone who has the SQL capabilities be able to be on the other part time of this because, you know, as a manager, I have way I've been accused. And I probably was, in the past, of being way too focused on a full time hire rather than a part time slash gig

Dwight Brown  20:51

Like we've lost our capability to think creatively about these things. And, you know, there's more than one way to get to a resolution, we forget that.

Tim Freestone  21:01

Yep. And it's luck leaf. And within the team, like a portfolio of skills you already have. So maybe someone developing some an ultimate team to add those skills, rather than lumping it all on the new hire that you're looking for.

David Turetsky  21:12

And that's another critical area, which is sometimes your sourcing doesn't need to be external, right? Give people the opportunity to grow internally, and be able to see this as a position that you either can aspire to someday, or try making this a stretch assignment today, in order for them to be able to gain the necessary either insight skills, abilities, whatever, to be able to be that person that you need for them to be to be more rounded. Yep. I can't tell you, Tim, how many times as an internal person as a person inside an organization, a practitioner, where I asked the manager, I said, you know, why won't you let this person go? And they said, well, it'll start a domino effect of people wanting to go to different positions, and you know, grow their careers. And I said, Did you just hear what you said, right? Did you just hear what you said, That is awful. You need to let people go, I think the best quality of a manager is giving people the opportunity to grow and to and to leave, you know, the baby bird scenario, let them leave the nest, get them out of there, so that you can now mentor someone else to be able to grow and leave the nest at some other point.

Dwight Brown  22:31

I used to love it when my staff would come and say, Hey, I got I just got a promotion. And I mean, that's great. Because, you know, that's, that's what I strived for was to be able to give them those opportunities. And whether it was a whether it was a complete and total change, or whether it was a small change whatever it was, as long as they were growing. I was happy as could be.

David Turetsky  22:57

Yeah, I think Tim one of the things that we take for granted is that there are other skill sets that are synonymous, or close, at least, to the data analytics roles that we're utilizing today. Some of them have existed for decades inside our organization, whether it's been in supply chain, whether it's been in the financial organization, and I'm not just talking about analytics people, I'm talking about people who've used scientific methods or reporting and analytics methods, in other ways that we would have in the past gone, They probably can't do people analytics. And the answer, or the the question I'd ask right now is, why not? Why not give them a chance, at least they understand the organization and the structures in the company, that trying to teach people who come into the organization, you know, won't know, and you're gonna have a really hard time teaching them.

Tim Freestone  23:55

Yep. And I think this is where, particularly in really large organizations, having an existing map or measure of people's current skills, strengths and weaknesses, will actually give you this ability to say, Oh, hang on, like, here you go, here's this person who's only one extra skill away from being able to fill this position over here in this other function that we're currently trying to pay a recruiter to fill per month, like, hang on, if we just upskill this person and put them over here, they can get a bump. It's like a promotion for them. They're happy, right? They've gotten a new skill, we fill the role. So I think, yeah, having that kind of portfolio view of skills and having a measure of it, that's concrete, and you have available at any point in time, and it becomes really valuable for businesses.

David Turetsky  24:36

How do they actually capture those because a lot of the people and a lot of the clients that I deal with today don't really have a good handle on the skill inventory that exists inside their organization. So where did they find these? It can't be from the resumes or CVs, we talked about that they're crap.

Tim Freestone  24:51

Yeah, and likewise, even performance reviews are typically done in the same way quite subjective, often, informal, often Yeah, not not really that measurable. So that's where we do help some organizations and just explicitly going and collecting this data by getting other employees take mini quizzes through through time, and then identifying the strengths and weaknesses, which then gives them the ability to fill those gaps.

David Turetsky  25:14

Do you do I mean, just like we're talking about, do you do that assessment or that data gathering outside of just the analytics teams? Do you kind of go a little bit farther afield? Or is it are you just inviting certain roles to take those?

Tim Freestone  25:28

So typically, when we work with businesses internally, it's actually much broader. And so they're looking normally at data literacy holistically, which goes for a lot of companies right across the organization. And that's, I think, the recognition that, you know, anyone in any role these days needs to have the basic ability to understand data to interpret it. To understand what an outlier is to create a basic visualization, doesn't matter if you're a data scientist, or an accountant or a market or any anything else, you basically need those, those skills. And one nice byproduct of collecting all of that is they found these kind of hidden gems that are hiding away in some obscure Accounts Payable role or identify their next great data, data analyst.

David Turetsky  26:08

And it's those and you know, to go back to the comment before, it's those diamonds in the rough that you find that are just so precious, and that create the best stories to make sure that the next time someone goes to ask, Where can I find people like know, like, Dwight, that's another great reason to go further afield and to do that data literacy testing that that's wonderful.

So Tim, awesome conversation around trying to find people who have analytics capabilities, because today, you're absolutely right, today, it is a broken mess. So we've talked about the CV process, which is absolutely broken, because it's just totally subjective muck on there. And then trying to find people through an ATS with the filters they have on is awful, then getting them into the interview process, the interview process is currently broken to, and then talking about a way in which we might be able to find people by actually going and listening to what skills do they have? And what's their data literacy. To me, that's the way in the future, the way of the future is finding the right people in the organization or externally, by understanding what their literacy is, especially for analytics. What else did we forget Tim?

Tim Freestone  27:36

Well, I just say holistically, the best way I think about this is hiring is going to go in the same direction as every other industry. So think of like the Moneyball approach to baseball, that great movie with Brad Pitt, where they throw out all the heuristics, all the rules of thumb, all the subjective biases that they've been using forever. And they replaced it with cold, hard raw data and made decisions on the basis of that, and they immediately started winning. Same things happen in other sports. Now, subsequently, I think hiring is gonna go down the same route. So it's all about just collecting the best data at each step, if that's the application step, the interview step, the testing step, and making a decision based on that data, a rational objective decision, rather than the pure gut feel intuition approach.

David Turetsky  28:20

But, Tim, we've been doing this forever. This is the way it's always been done. No, I totally agree. It's cheese. No, exactly. That's where I was going. That's what I've heard from process owners for forever is, but this is the way it's always been done. And you gotta you just have to tell them. Look, trust me. Let me try. Let me show you let me experiment and try, right.

Tim Freestone  28:48

The proof is in the pudding, as they say, and once companies start doing this and winning, then every company has to do it to catch up, well, they'll they'll die.

David Turetsky  28:56

And that's the key, right, is that we're going to lose to our competition. If we don't learn new tricks, and this is a very good, very effective correlative with really good hire. So thank you very much, Tim. It's awesome. Great conversation.

Tim Freestone  29:15

My absolute pleasure. Thank you so much, gentlemen.

David Turetsky  29:17

Thank you, Dwight. As always.

Dwight Brown  29:19

Thank you. Thanks for being with us today.

David Turetsky  29:21

And thank you all for listening. Take care and stay safe.

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