Episode Thumbnail
Episode 17  |  30:39 min

S1E17: Data & Analytics: People, People, People-Who To Recruit, How To Find Them, And How To Keep Them

Episode 17  |  30:39 min  |  04.21.2021

S1E17: Data & Analytics: People, People, People-Who To Recruit, How To Find Them, And How To Keep Them

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This is a podcast episode titled, S1E17: Data & Analytics: People, People, People-Who To Recruit, How To Find Them, And How To Keep Them. The summary for this episode is: <p>This week, Shaun McAdams and Warren Sifre continue their discussion of the pillars of Data &amp; Analytics. This time their focus is on the people who fill the teams that address each of those pillars. Who to recruit, what to look for, and how important keeping those people is once you find them are all covered in this week's episode of ASCII Anything.</p>
Takeaway 1 | 01:05 MIN
Finding People For D&A Teams
Takeaway 2 | 00:36 MIN
Finding Storytellers
Takeaway 3 | 00:54 MIN
Finding The Right People
Takeaway 4 | 01:10 MIN
Know What You Need
Takeaway 5 | 02:06 MIN
Competencies and Behaviors For Hiring

This week, Shaun McAdams and Warren Sifre continue their discussion of the pillars of Data & Analytics. This time their focus is on the people who fill the teams that address each of those pillars. Who to recruit, what to look for, and how important keeping those people is once you find them are all covered in this week's episode of ASCII Anything.

Guest Thumbnail
Warren Sifre
Director of Strategy
Warren Sifre has been involved in the IT community since 1998 and has worked in a variety of industries.  Warren is Chapter Leader for a couple of User Groups and speaks at many conferences and user groups throughout the United States.  Warren has a passion for data architectures and solutions.  Serving as Director, he is helping advance the adoption of true Data Strategy in every engagement.
Guest Thumbnail
Shaun McAdams
Vice President of Data & Analytics
Shaun McAdams came to Moser in 2015 to help establish a division focused on Data & Analytics.  Serving as a Principal Consultant, Engagement Manager, Director and now Vice President, he has helped advance the Data & Analytics services to over 80 clients, 100 engagements, across many diverse platforms.

Angel Leon: Hello everyone and welcome to another episode of ASCII Anything presented by Moser Consulting. I'm your host Angel Leon, Moser's HR advisor. Today's episode continues a series of conversations between Sean McAdams and Warren Sifred, two of Moser's, top data analytics experts. This time, their focus is on the people who fill the teams that address each of those pillars, who to recruit? What to look for? And how important keeping those people is once you find them are all covered in this week's episode of asking anything. Without further ado, here are Sean McAdams, and Warren Sifred.

Warren Sifred: Hello Sean, we're here again on another podcast between the two of us. We'll see how much fun we can have this time.

Sean McAdams: I wish we could just do this.

Warren Sifred: I know this is-

Sean McAdams: Let's just do this.

Warren Sifred: We just could get paid to do this. We need people to subscribe. We need people to like, because if you do this, this could be our day jobs.

Sean McAdams: Yeah, just talking.

Warren Sifred: Exactly. So speaking of talking, right, we've been on this journey, the kind of talk about data analytics strategy and we've covered a couple of pillars, right? Sean, remind us what the pillars were.

Sean McAdams: Yeah, so we deliver data and analytic products through people, operating through a process and data functioning through tech. People process data tech.

Warren Sifred: That sounds great. And so far we've touched on process and I think it would behoove us to go down the path of what people are needed to make up this process. Right? So in the process conversation, we talked about delivery channels and these delivery channels were the insights engineering and platforms. And within there, we talked about this ideology of the uncover envision design, manage build, and all these different things that we do in those phases. We don't necessarily do them. We need people to do them and we need involvement of people and the channels are built with teams. So I think one of the things we'd like to talk about is what kind of individuals would we need to populate and be part of these different channels, right? So let's start with insights. What kind of individual would you say would be prime candidates or the type of individual that we would want in that kind of team?

Sean McAdams: Okay. I think immediately when you talk about insights, people start to think about the development of reports, but more importantly is going to be the people that are interfacing with the business, right? The people that can not worry about tech and worry about having these conversations about, well, that's not possible. Just the mentality and approach to how they interact with people. You need people who want to have communications, want to understand the desires of these individuals. I don't care what their role is in the organization, right? They want data to make better decisions. And so I think the first type of person you need is someone who is good at communicating, is very good at listening, is good at taking all of those notes down so that when it gets to uncover and either they are designing possible solutions or you have a team that comes in to do it, that they can also communicate those business needs. And talk about the tech a little bit with them. So I would say that's the first one. Now call out what you want to, we call that a product owner for our honeycomb team.

Warren Sifred: Other organizations, they've been known as business analysts, right? And they've been known as the individuals that interpret in what the business is asking for into a pseudo technical solution, so to speak so that it can go downstream. So anybody that has those characteristics, that has that passion desire and the capabilities I think work very well for that first phase, which ultimately is your uncover envision design. We should have discovery services piece, right?

Sean McAdams: Mm-hmm(affirmative) that's right.

Warren Sifred: It's trying to figure out what that is. What's another type of team member that would work?

Sean McAdams: For insights, if you have someone that's good doing the role pointing out those needs, you now need someone that I think is seasoned in communicating to particular users. So you don't need," Oh, this guy has some experience with this BI tool." There'll be a part of the delivery of that. But before you're building it, there needs to be a little bit more maturity and experience from someone who has used those to communicate and has some best practices or good practices to adopt and can push those out through their designs, to the organizations. Here's a better way of using this tool. This is going to be the right way to communicate to this user. So I think you need an experienced visualization engineer. If you want to call them that, that's done it for a while and they don't necessarily have to be the best at a particular tech stack. They should be very good at multiple tech stacks, but they have to be able to influence the fact that we need to ensure data literacy, ensure that we're communicating, ensure that these users understand those things.

Warren Sifred: I view this individual based on when describing early that it was a little chocolate because I was chocolate. And for those that are listening, because the first thing that popped in my mind is pie charts everywhere." Hey, look, I built you a dashboard. What do you think? You like it?" And we see a lot of that and it's just someone who's playing with the check trying to get familiar and working on it. I think what you're describing is what I would call a storyteller. Someone that can take the information that this BA has given and come up with a way to get people to that information faster. There's this statement that's out there in the space of BI, that if it takes you more than eight seconds to determine what is on this page or something that was mentioned in a previous podcast, if it doesn't make you happy or you off, we're probably not looking at the right thing or the thing is not being presented to you correctly.

Sean McAdams: Yeah. That's good. Turn it up here just for a second. Because we do say that if you analyze this and it doesn't make you happy or piss you off, I guess the third part of that is confusion. If you look at it and you're confused or you hesitate.

Warren Sifred: If it takes too long.

Sean McAdams: Something's not right, but we need to get it to the point where either makes you happy or makes you very upset.

Warren Sifred: The storytelling has to be a journey and the very first thing that you see has got to be those things that gravitate towards that capture your attention and drive you to the deeper analysis pages, where you get a bigger breakdown or see the raw record set. But someone has to come up with that storytelling and interact with the business to make sure that that's what's happening to make sure that's what they want. Right. I personally call that a storyteller.

Sean McAdams: Okay.

Warren Sifred: But they have that skill set of being able to take the BA goals, understand the tech stack that's currently employed and come up with a way to best communicate and achieve the goals of that emotional response and eliminate that confusion.

Sean McAdams: Yeah. I guess the other one, if we were just to do three, would be obviously the people that build this stuff. And here's the thing is the type of analytic product you're going to produce can vary. So it could be simple report writers, or it could be people doing visualization or dashboards. It could be marketing, creating infographics. You could follow the same process with that because they still usually presenting information. And obviously it can be more advanced. Where you can do more advanced analytics, and so people talk about maybe the role of a data scientist. I look at a data science as somebody is applying mathematics. So that's a lot of different takes on what that role is, but you do need someone that's going to ultimately produce and deliver that design.

Warren Sifred: And there are individuals out there that have all three of these skill sets.

Sean McAdams: Yeah. crosstalk

Warren Sifred: They're challenging to find, and when you find them, crosstalk figure them out.

Sean McAdams: Don't let them go.

Warren Sifred: Do recognize that some of those skillsets are easier to train than others. You can train somebody how to develop. You can't train them to listen. So when you find individuals with certain character traits that you think, you know what, I just need to show you how to do Tableau and you would be amazing as this. Find those individuals curate them, put a little investment, they will appreciate the challenges that this brings, assuming that it brings them happiness to be able to do that as a personal growth goal. But you as an organization will most definitely benefit from that ability of being able to translate the business, the tech, and be able to translate that requirement's, that was captured in that phase into a story and potentially develop it as well. Those individuals are challenging to find, easier to grow, but you need to figure out, make sure you get the right individuals.

Sean McAdams: Yeah. And I know you're going to talk about engineering, right? We're going to talk about platforms. I can tell that because of how you're leading the conversation. So for those that I know, like Warren or I just sitting and talking, it's not like we mapped out with these particular things, but I've talked with Warren long enough to know where his mindset is going. And I don't want to stop us having that conversation. I think what I would like to do though, based on what you just said is maybe in talking about our approach to hiring.

Warren Sifred: Oh, we almost definitely talk about that. Right?

Sean McAdams: Because I think that'll answer one of the things that you just brought up.

Warren Sifred: Exactly. Especially if you want to try to be consistent or trying to make sure you have the right individual with the right personality traits that drives them to want to do this. Right. And just because you're good at it doesn't mean you like it.

Sean McAdams: Yeah.

Warren Sifred: It just means you were born big and strong and you can lift through refrigerator system and you like doing that all day.

Sean McAdams: Yeah. So I don't know. Outside of the app, the methodology you apply, I think those are three types of people for the insights. So when I say methodology, I mean, maybe you're following some Agile, so you want like a scrum master or you want something like that with the team, but specifically delivering insights. If you can find people that have those three skills, whether it's one person or a set of team, I think you're doing pretty good.

Warren Sifred: Agreed. So let's go down with your forecast and go crosstalk right. Because no one could see that one coming. So again engineering coming in and we've got these individuals, right? We just got this ticket that says," Hey, I need this data." What kind of person, what kind of skills, what kind of mindset are we looking at for an individual that fits within that engineering team?

Sean McAdams: Yeah. I think you can become a little less business focused here. I will say, not all of the tickets that come in may come from insights, because if you run a data as a service model, then you're interfacing with people that have data needs and they're going to produce the insights, or you're just looking at how you organize data for that data as a service model, you obviously didn't date data engineers. Right?

Warren Sifred: Mm- hmm(affirmative).

Sean McAdams: You need folks that are going to follow your particular principles that have governance principles, data management practices, and-

Warren Sifred: What is a data engineer?

Sean McAdams: Oh, that's interesting. Okay.

Warren Sifred: Let's break that even further because you see that out there and people say, oh, data engineers and ETL specialist or data engineer is SQL guy or data engineers are big data guy that can do Python or a data scientist is a data engineer. What is a data engineer?

Sean McAdams: So if I was looking at that through the lens of Moser, then I think ETL, E L T, they do a part of that and that there are they're using tools or they are they using this particular paradigm under which to operate? It's essentially to me a person who is going to consume data from particular environments, they're going to use particular platforms in order to introduce certain data governance practices, validation, data quality, they're going to have to maybe ensure that things are represented within a catalog. So all of these things they have to do, which is far more than just here's your source, here's your target and map it.

Warren Sifred: Yeah. I mean, they're the enforcers of your data governance, right? They're the ones that are applying those principles that have been agreed upon by your governance council. The organization said, this is important to us. This needs to be there. They're the ones managing the security and applying the information security perspectives and the things that need to happen on that. They're the ones following the frameworks and pushing those frameworks that the platform's team designs or defined for them, right? They're the ones that are consistently pushing the envelope of what they can do with this tool. I would say this individual would need to have a passion for playing with technology to acquire data. I would say that they have to have an attention to these checkboxes or details of these check boxes or things that the organization said must exist. Because if you skip them, you just put the organization at risk, and depending on the impact, it could be financially costly, could cost them their job, could cost you a client. It could costs you market perception. I mean there's a lot out there that can happen with that. So I think these individuals, to your point, they don't need to have this polished,'I can talk to business users, make them feel warm and fuzzy. I can get them out. I'm the best listener.' No, they usually get the information a ticket. They're usually given this what's their, what they need to understand is how is this going to be used so they can apply the right principles of governance of security and everything else and understand the details surrounding what's the frequency of that data is necessary. What's the sensitivity, how do they need it, what's the shape. So they need to have a lot of ANSI SQL like skill sets to be able to shape the data. Whether we're talking about Snowflake, we're talking about any of these SQL- like technologies. They need to have a passion for wanting and loving those things.

Sean McAdams: Yeah and I think technically it's also steering toward the use of maybe Python for data manipulation and stuff like that transformation. So I think that's probably important as well, to your point. They don't need to have these soft skills. If they do have these soft skills, then I think that moves them into, for our organization, the ability to do the consultancy services where you're going to go in and help organizations, design data management principles and help data engineers apply that. So if you do have soft skills, then I think you can lead a team of engineers. You can also lead an organization and in the same line.

Warren Sifred: There you coaches. They're the ones that bring up the engineer potentials or new hires, or if you happen to be a consultant firm, then guess what, consultant services are right there. They're the ones that are advocating for this stuff and showcasing how this works. I think...

Sean McAdams: And we're making an assumption here that you have some type of management organization in place. We're not getting specific to those.

Warren Sifred: Exactly.

Sean McAdams: We're just looking at who's hands on keyboard, who's delivering in these types of things. That's the main one you're going to need in data engineering.

Warren Sifred: Yeah. And you know what, for the most part?

Sean McAdams: I'll go one for the data architect. A role of someone who's maybe doing the data warehousing and guiding.

Warren Sifred: A modeler maybe. Something like drawing-

Sean McAdams: Yeah. Guiding the organization of data and this refinement area. I know we haven't talked about our data engineering processes, but you have all these people that are going to use the data. And sometimes the people that are doing work in insight are really good about how to organize it for efficient means, but sometimes they're not, and they're dependent upon someone. So if a data engineer can do that, awesome. That is really good. But sometimes maybe a data engineer needs to know that target.

Warren Sifred: I think they need to understand the modeling principles. I think they need to follow the best practices or any of the governance guidelines that have been set. I think a data architect can live in either space, right? You can have someone live in the engineering space. So you're part of that delivery channel. Maybe this individual maybe also lives in platforms and coming up with the overarching, what is going to be the true data flow there things go when you're moving data from transient to trusted, refined, raw, these different stages and they come up with how the platform interfaces with that and how engineering works with that. So I think the data architect can straddle that piece. So having said that, what other individuals do you think we could use or are necessary for a platforms?

Sean McAdams: I think today you're obviously going to have some type of administration experience that's needed within platforms, whether you're on- prem with a particular data technology, or you're trying to get value out of the cloud technologies, you're going to be dependent upon someone who can administer those particular platforms. I also think that in order to have some enforcement of these data governance principles we talk about, the platform should have these higher level engineers that are creating paradigms and frameworks under which people that are doing engineering work, meaning they're actually going to get data from a source, they're bringing it through it, they can sort of leave because if you don't have that and you're not pushing those best practices up or providing some constraints, you're going to end up with a bunch of different ways and a lot of technical debt at some point, because someone or everyone's either going to do something that you have to redefine later, or someone is going to do a better than someone else, and they're like," Oh, well, let's go do it the way Warren said, because that's better." So someone on platform should also be thinking about the best ways to do engineering and I think someone in there the best ways to do the insights' development as well, because they're a user of the platform.

Warren Sifred: I would say that many platforms and individuals that work in the platform delivery channel, would also participate in the government council. They would participate in the architecture review boards. They would participate as part of the enterprise architecture teams. If you have one established, right. I think these are your high level individuals, these deep thinkers, these long viewers that are like," You know what, I understand we need something now, but let's take this three, five- year plan that we're going towards. Let's take our existing initiatives that we have and let's make sure we pick and do the right thing." They would be the individuals that would be instrumental in guiding how your insight's teams organize the delivery that they come up with. Maybe are they going to use some semantic model tool versus embedding it inside your presentation layer. They would help architect what that looks like and when do you use one or the other. You need to build a report. Do you build it in a paginated report, do you build in a visual report? It could be neither one. How do you make that determination? This team members would help in orchestrating what that looks like.

Sean McAdams: Yeah. And if you don't do that, then I think what you do is you isolate platform to this concept that it's only administration and you create this disconnect between how the tech is being used, the techniques that are being applied from the actual technology itself, and I don't think that that benefits the quality of the data and analytic products that you would create rather than meeting the paradigm you talk about, which is it's more than an administration. It's bringing everyone down so that you can follow. We talked about previously releasing the set of texts that you can go from an analytic as a service down into a data as a service, and maybe even down into a platform as a service if it makes sense for you.

Warren Sifred: Yeah. What we're talking about here is going to be very different for every one of the different organizations out there. Because depending on the maturity model where you're at in the state of your business, right? Are you a$ 10 million business, a hundred million dollar business, a hundred billion dollar business, you're going to have things that are already established, and if you're trying to figure out how to potentially adapt your existing team members with what you have in your different units of individuals, how they play in these different delivery channels and so forth, you're going to find out that you may have different makeups, right? And depending on where you're at in that scale and size of the organization, you may have one individual plan engineering and platforms, right?

Sean McAdams: Absolutely.

Warren Sifred: You may find yourself in a position where you may start off having an IT segment of the business, controlling all of the BI development or the report development with maybe a six month, 12 month plan of releasing some of that responsibility to the individual business units that have the skillset and the aptitude to adopt that and start migrating to that self- service concept that's been around for a while. A lot of organizations have tinkered with it. Some of them have discovered some things interesting and fun and scary and like,"Oh my gosh, what do we do?" As going through that journey. And I think this allows us to approach and lead our way to that by ensuring we have the right delivery channels, the right mindset and if we can get the right people, we convinced slowly make our way through, and I think as part of getting the right people, there's this piece that we do at Moser and I'll let you talk, it's a little bit more about that.

Sean McAdams: I'm glad you reminded me of that, but one of the things you talked about is at least in my mind, are the competencies and the behaviors of people, and competencies being things that you can teach, but their behaviors are a little bit harder to change. So how do you know what those things are before you're hiring someone or at least have some insight into it? So I think most organizations probably read resumes or looked at social media profiles like LinkedIn, if you're in the professional service area, probably do an interview, maybe do more interviews, but is there something else we can do in order to get some data to make a better decision? And so at Moser we implement benchmarks through our learning services practice. So there's a plug for learning service practice, which may be at some point that we'll have a podcast around that. We have essentially six main job descriptions. So data engineer, data visualization engineer, data architect, data scientist, DBA database administrator, or a platform engineer and you select a set of people in order to create a benchmark that when you go through the process of hiring, at some point in that process for us, it's after our technical interview. So it's before we do our cultural interview, and so the person that's doing that, which is usually one of our directors or something, has this input that breaks down, where do they exist from a disk perspective, but then also how do they align to our job profile and what gaps exist. So for every job, there's a certain amount of competencies that are more important. The other part that I think is important about that report is, it's very easy for somebody to use it to analyze a person and say, well, you're either going to be great at this job, or you're going to suck at it. And I think that that's not the entire way you should look at that report because everybody's going to have gaps.

Warren Sifred: Mm- hmm(affirmative).

Sean McAdams: I think it also is important for the individual applying to look at that report, because it does give you some insight into whether you're going to be happy in that role, because all of these role have these intangible rewards that you get more than monetary or compensation with the company. You're going to enjoy the type of work you do, because these are the type of rewards you would get out of that type of work. So I think it's a very good tool to use. We already have those benchmarks. So if organizations wanted to tie this into their process, they can reach out to Moser, to our learning services and get it tied in to where at a point in their hiring process, they would say," Hey, we really like to view this." Then we submit them this link. I think most of them are probably 150 to 190 questions somewhere in that. But you get a lot of metrics to look at in order to make a good decision.

Warren Sifred: And I think he had a point there that I think is really important and we'll use this as a bookend is happiness, right? We have these individuals they're stretching themselves. They're doing things that hopefully brings them satisfaction, right. People that are doing jobs that don't put a smile on their face, or they don't walk away feeling accomplished. They're probably in the wrong field, they're probably doing the wrong thing and they need an opportunity. And this goes back full circle to if you find an individual that has certain aptitudes, certain unteachable skill sets that they do naturally, they're probably lend themselves to be very good at this type of work that they align with as well as bring them happiness, re- energize them and make them a very complete individual for you as an organization, as well as it's going to transfer in their communication, anybody outbound, right? We've all talked to people that are angry, guess what? Even on their nicest day, you know they're pissed off. And then all of a sudden something changes in their life and they're doing something different and I'm like," Wow, you're a whole new man. What the heck happened to you? Why are you smiling? You're drinking nowadays, what's going on?" Right. Three sheets of the windows when I used to see this way, why am I seeing this way? It's kind of clock in the morning, right?

Sean McAdams: Yeah.

Warren Sifred: So it's really important to have that and I think that's something that we strive to emphasize as part of our consulting, and we give our clients is not only do you need these delivery channels, not only do you need this mindset, but you also need the right people.

Sean McAdams: Yeah. You have to have people to deliver these products. Even if you want to advance analytics into automated action, AI, that knowledge is still engineered off of people.

Warren Sifred: Exactly.

Sean McAdams: My mentality, which I won't say that I'm always successful in delivering is to provide meaningful work, and sometimes that's just in stressing the value of the work someone's doing to build relationships and to provide opportunity for growth. I feel like if I can hit all through those things, I'm doing a pretty good job. I think as a manager to people, if I can hit a couple of those things, I still think I'm doing a good job. If I'm not hitting those marks then something's misaligned.

Warren Sifred: I think that's great. I think one of the things we'll do is on our next podcast that follows this channel of Thought of Data Analytics Strategy are profoundly going to get some of the sexy stuff. Right. crosstalk We're going to talk about some technologies, we're going to talk about data and going to work through that and see how that works.

Sean McAdams: I'm excited for that because I mean, obviously we both are technologists, that's how we got into this space and I'm talking about our engineering processes and what we do in there, talking about our core components we want within a platform and there's a lot of things to talk about within data obviously because that's what we do. So it's going to be fun.

Warren Sifred: Yeah. It's going to be fun. Well, thank you for tuning in and until next time.

Sean McAdams: All right. Thanks.

Angel Leon: Thank you for listening in to this week's edition of ASCII Anything presented by Moser Consulting. We'd love it if you join us next week, when we continue to dive deeper with our resident experts and what they're currently working on in the meantime, please remember to give us a rating and subscribe to our feed wherever you get your podcasts until then, so long everybody.

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