Mining Your Own Business Podcast

Season 2 | Episode 5 - Inside Look at HR Data Science at Intel

Quote: “Understand that business problem and then be able to translate that to a data problem so that we can figure out what the value is for the company.”Sarah Kalicin from Intel shares insight on how data science can positively impact the way teams work on this episode of Mining Your Own Business. As an HR Data Scientist at Intel, Sarah brings a wealth of experience and knowledge to our discussion. Her journey is a fascinating blend of industrial statistics and diverse industry experience, including roles at Ford Motor Company, Nabisco Craft Foods, and medical device companies.

For nearly 20 years, she has been an asset at Intel, working across various departments. Her current role in HR data science showcases her ability to bridge the gap between business problems and data solutions. Join us as we explore Sarah’s insights into the ever-evolving landscape of data science and how it can transform organizations.

In this episode you will learn:

  • The importance of fully understanding a business problem before determining a data-driven solution
  • How effective training of team members can lead to tangible business results
  • Why data literacy and clear communication with stakeholders is crucial for successful adoption and impact of a data-driven solution
  • The importance of considering the user experience of new solutions that are implemented

Learn more about why we created the Mining Your Own Business podcast.

About This Episode's Participants

Sarah Kalicin HeadshotSarah Kalicin | Guest

Sarah is a skilled statistical leader with deep analytical expertise and a strong background in strategic thinking and business management. With almost twenty years at Intel Corporation, she now works as an HR Data Scientist.

Sarah loves helping cross-functional teams find ways to improve commercial profitability for their organizations. And as the founder of Achieve More with Data, she is focused on helping organizations better leverage data assets to achieve greater success.

Follow Sarah on LinkedIn

Photo of Evan WimpeyEvan Wimpey | Host

Evan Wimpey is the Director of Analytics Strategy at Elder Research where he works with organizations to transform deficient data into tangible business value that advances their mission.

He is uniquely suited for this challenge by pairing his professional experience in management and economics at high-functioning organizations like the Marine Corps and Goldman Sachs with his technical prowess in data science. His analytics skillset was strengthened while earning his MS in Analytics from the Institute for Advanced Analytics at NC State University.

Evan almost always has a smile on his face, which is at its widest when he is helping organizations use data in innovative ways to solve complex problems. He is also, in a strictly technical sense, a “professional” comedian.

Follow Evan on LinkedIn

Key Moments from This Episode

00:00 Introduction
00:26 Sarah’s diverse career background and journey to becoming an HR data scientist
03:23 Sarah’s love for helping organizations get more value from their data
04:52 An example of an HR data scientist project
06:11 The need for effective team training to achieve business goals
10:37 The balance of data analytics, privacy, and ethical considerations
12:19 Getting buy-in from key stakeholders
16:06 Data scientists at Intel and the importance of training teams based on their experience
20:22 The impact of AI at Intel
22:30 The importance of considering the user experience of a new application
24:56 Building a data-driven culture within an organization
29:02 Sarah’s desire to help organizations make strategic decisions using data analytics

Show Transcript

Evan: Hello and welcome to the Mining Your Own Business podcast. I’m your host, Evan Wimpey, and today I’m super excited to introduce our guest who is Sarah Kalicin. Sarah is an HR data scientist at Intel. Sarah, thank you so much for joining us on the show today.

Sarah: Thank you, Evan, for having me.

Evan: Alright, Sarah, to get started just a little bit, can you give us just a little bit about your background and, and how you got to your role, where you’re in today?

Sarah: Oh, my background, I have a very diverse background, so, I am actually an industrial statistician by training. I have a master’s degree from the University of Michigan. And with that I’ve worked in many different organizations and industries and so forth. So, you know, just kind of, you know, some of the industries are kind of automotive.

I worked at Ford Motor Company, Nabisco Craft Foods, so packaged goods and so forth, medical devices at BD. And then here at Intel I’ve been at Intel for about 17 years where I’ve worked in manufacturing sales and marketing R&D. And now I am working in HR. And one of the things I love about kind of my diverse background is really about being able to just have a business problem.

Understand that business problem and then be able to translate that to a data problem so that we can figure out kind of what the value is for the company. , you know, everything from like, you know, when I was at Nabisco Craft Foods, you know, some of my favorite things to do, it was check, you know, we would have like, Hey, here’s a, you know, a new sweetener that’s five pound, 5 cents per pound cheaper than what we were currently having.

And we would need to go and figure out kind of, how do we reformulate that? So doing designs of experiments and so forth and trying to figure out kind of what the right combination of pro of ingredients is so that you would maintain the, the texture or the consumer liking of that product.

You know, medical device companies going in and trying to figure out kind of, how do you make like a syringe less painful if you’re taking blood and so forth like that. So more designs of experiments or going into manufacturing and trying to figure out. How to remove waste from a, from the manufacturing.

Like, hey, you know, these cookies are a little bit bigger on this side versus that side, you know, you know, being able to go in and being able to tell that to the factory on that and being able to, you know, give something actionable that they could go back and fix and see immediate feedback. And then here with my current position in HR a lot of what I’m doing is just really just taking something that just seems really ordained, like kind of learning and trying to identify kind of how we can get business impact.

And we can talk a little bit more about that and some of the other questions. But, you know, I hopefully from this example you could see that my, my background’s pretty diverse and I, this is something that I really love doing and being able to help companies, you know, really be able to gain much more value than they would from kind of just basic numbers.

But really being able to translate that to a business value and being able to show them how that pathway of how to, how to gain that value from it. So really being able to think outside the box and bringing an organization along. That’s some of my passions about what I’ve done from my background. Hopefully that helps.

Evan: Yeah Sarah, that’s great. And really I think that’s sort of a, a common theme that we sort of like to highlight on the show. We try to get at that, you know, it’s maybe. There is some avenue, there is some specific use case and some specific industry that is your avenue for this.

But really when you zoom out, it is, it is exactly what you just described. It’s finding some challenge or some problem, and yet maybe data and statistics and analytics is the means, but you’re really just focusing on a business problem and how folks can try to make better decisions or how folks can try to do better with that.

And you’ve touched a lot of spaces, which is, which is very cool. I often taught that as the—one of the perks or benefits of being in consulting is you get to get, to try a lot of things, but I think that you’ve absolutely gotten to touch a lot of very cool things, and I want to talk about a lot of them.

But I also do want to spend some time on the HR part, your, your current role, because, you know, we’ve interviewed a lot of guests here. We’ve talked to marketing folks to operations, supply chain folks. I don’t think we’ve talked to anybody specifically in HR. So can you give us just a little bit of flavor of what an HR data scientist does and may maybe if, if you want to, or if you’re able to paint an example of, of a, of a project that you’ve worked on.

Sarah: Yeah, so I mean, I work at Intel, so we have a lot of data scientists within our HR department that kind of deal with different domains within HR. So because we’re dealing with people and so forth, there’s a lot of different things that we can be able to measure. But in my domain, I am responsible for the learning and development aspects of Intel. And I’m responsible for that data and being able to translate that into some kind of in business impact. And to give you an idea of, of something that we’re doing is, and this isn’t, you know, this is something that’s kind of public, is Intel’s been struggling for the last few years on execution.

We’ve missed a lot of our deadlines and so forth and losing our competitive advantage. So, you know, there’s a big push for ex, you know, being able to execute much more efficiently. So, you know, part of that is training really is really getting, you know, getting training out to our employees and really understanding, you know, what does it mean to execute as an enterprise or even within an organization.

So we’ve been putting together a lot of cultural transformational training and my responsibility is really just to go out and try to figure out how do we know that we’ve moved the needle on our organization? Did our training really help, you know, push the, or the, the company forward toward being able to execute.

So, you know, as you probably know, like, you know, the easy things to do are to measure kind of the, what is called in Kirkpatrick Model, the level one, which is like the reaction, but just because you took a class doesn’t necessarily, and then you like, the class doesn’t necessarily mean it translates into skills or behaviors or business impact.

And the further you go down that line of skills, does that transit into behaviors and business? It becomes much, much harder to quantify. And I know a lot of organizations are struggling with that. So, you know, my job is really trying to figure out how do we do that? How do we break that down? What kind of measurements do we need to do in order to be able to showcase that, you know, these classes are having impact and that.

Folks are gaining the right skills and then getting those behaviors so that as a collective we can move the company forward. So that’s kind of my role. It’s really exciting to be able to really have that challenge. And like, that’s like, you know, you know, as soon as you get up to business impact, how do you know that, how much of that is really from the training versus So being able to break that apart is kind of like the thing that I’m trying to figure out.

Evan: Yeah, that, that sounds like a super challenging probably. I would imagine it’s very noisy data, the business impact as whatever multitudes of factors that are going into it, and where training comes in along with the time lag of training before you see business impact. W you mentioned in, in your background, some of the things you’ve done at previous roles has been able to do design of experiments.

Is that something that you can do here, where some folks can get some training, some get other? Is there, is it, is it okay to, to not train some folks?

Sarah: Well, I mean, some of this, you know, I mean, yeah, having a control and so forth would be nice and so forth, but we can do, you know, there, I mean that’s the beauty of having a really, you know, like a Fortune 50 company is it’s really big and we can do some experimentation or causal analysis and so forth to be able to look at that.

And that, that’s some of the things that, you know, we’re kind of, you know, looking at how, you know, how do we tease out some of that noise and to be able to have, be able to showcase kind of the impact. So, yeah.

Evan: Yeah. Very cool. Yeah, and you’re right, you know, the data scientists certainly always claiming for more data.

More data. And I would, I would imagine HR is a place where generally, you know, you sort of limited based on how, how big your organization is. You know, in a lot of, a lot of fields you think, well, we shipped this many products, or we market to this many people, and you can get a lot of that data. But with HR I would imagine that’s tends to be more of—is it more of a constraint, more of something that you have to be cognizant about sort of small data.

Sarah: Well, I mean, it is small data versus kind of like a, like our manufacturing, you know, where we have lots of tools and so forth and you know, really we’re just dealing with kind of the employee base, which is around 130,000. So, you know, and there’s a lot of different classes and so forth that are out there.

So, I mean, from a big data point of view, we don’t really have, I wouldn’t say this really qualifies as big data on it. The big struggle, the big things that we have to think about and stuff are kind of around more around privacy and use of data and really make, because we’re dealing with people, we also have to think about kind of like disclosures and privacy awareness and, you know, and, and, you know, making sure we’re, we’re giving folks the right disclosures of how we’re using the data, , and being ethical with that because—I mean Intel obviously is a very ethical company—so we want to make sure that we’re, we’re following that we’re living those, those values. But we also want to be able to make sure that, you know, we’re, we’re evaluating our programs as well, so, you know, how do you position that so that you get behind that?

I mean I guess also gets in the other thing and another aspect is, you know, you don’t want to be using this to penalize. This is really more for discover and kind of how to make improvements within the organization. So being able to understand that and be able to communicate that, that, that’s really important.

So, especially with our stakeholders.

Evan: Yeah, that, that’s, that’s a great point. And, and certainly highlighted in, in the HR realm more, more so than some of the other business functions. So let’s say, you know, you’ve found some insight, you’ve found some that, you know, this type of learning or this learning provider, something is, is useful.

We’ve, we’ve done the stats, we’ve been able to measure something. We have good confidence. Can you talk about what it looks like to try to, IM impart some change. Like who I—who are the stakeholders that are going to say, okay, great work, Sarah, let’s make this change. Or who are the folks that are, Nope, you don’t, we, we don’t believe any of this.

This is different from what our gut tells us or different from the way things have usually been.

Sarah: Oh, you, you totally understand the kind of the dynamics. Yeah. So I mean, one of the things that, you know, I mean, once you kind of get into being a data scientist or a data professional, You, you go up to your stakeholders and you, you find out that you have those that are really, really excited about data.

And then there’s others who are like, why do I need to do this? Fortunately, the organization I have is really, really, they really do want, have data. They’re really data driven, so I’m really fortunate about that they’re really eager about it. The, the part that, that you have to keep in mind is that, while they may be very data centric or what I call number centric, sometimes they get overwhelmed if you give them too much complexity on it.

So you have to really, , be careful and have a lot of stakeholder management around what they’re willing to absorb or adopt. , so, you know, like for example, I’ll just give you kind of. , like at a toy example, like, you know, folks are very comfortable with level one like reaction on their training products, right?

Yeah. Getting them to think kind of a little bit further down can be a challenge sometimes of thinking about, because it becomes noisy. Like, how do I know that? You know, they think that they’re going to be accountable for kind of the. The kind of the skill development or the behavioral development and they’re like, Hey, that’s a little bit out of my scope.

And I’m like, yeah, but it helps the company going forward. So trying to get them kind of thinking further down into more from a descriptive like, Hey, how did my product go to more kind of like, what could be some of the things that are holding the organization back from absorbing this or reinforcing this.

You know, get, getting the, getting people to kind of think a little broader about it and being open to that, , tends to be, some of the cha can be some of the challenges, , on it because if they aren’t bought into those metrics or they don’t feel like they can really push that or be accountable to, it can be a struggle for the adoption.

So you have to think about kind of how is it beneficial to them. And position positioning those metrics and analysis in that way. So

Evany: Yeah, certainly, yeah. Stakeholder management. Yeah, certainly is, is, is key here. And one of the things that, that you touch that I really like is how much they’re able to absorb.

And maybe that’s how much content information they’re able to absorb or how much really change you’re able to absorb. Like if we do these 11 things, we’re going to see these great results with great confidence. Well, I, we can’t change 11 things right now and, you know, flip the bear on its head here.

We can do, we can make, make some incremental pieces and you know, in my mind, Intel was this high-tech company, I’m sure the folks, the a hundred plus thousand folks that work there, there’s a very wide variety of things that folks do. You mentioned there’s a lot of data scientists. I would imagine this training applies to the analytics data science type folks as well.

Do they get, do they get any special, special treatment or are there any other ways, like presumably when you’re dealing with a, an analytics manager who’s training their, their data scientists, their analytics, Personnel professionals, you know, where, where they’ve got some background and they can speak more to the technical aspects of, of the type of work that you’re doing.

Is that a different conversation?

Sarah: Yeah. I mean I don’t know if it’s a different conversation, but in your stakeholder management, you have to, you have to build that into your, into that plan because it, because. It’s one thing to say, Hey, you know, let’s go do like some machine learning, da, da, dah, dah, dah.

And here, here’s some training over here about, you know, what, you know, a clustering algorithm does, or, you know, a regression or, you know, name your favorite analysis. It’s one thing to go and, you know, tell them to go take, you know, a generic training that showcases that, but it’s a different set—it’s a different perspective when they have to actually I either absorb it as for here’s the results and being able to trust it or giving them data so that they can do their own analysis and so forth.

And what I’ve found is if you just give them generic training, they’re not going to necessarily know how to bring that into their everyday work. They really need, because I mean, essentially you, they then need to figure out how to get that data. They need to know how to structure that data. They need to know, be able to trust the data is even clean.

So if you’re going to do training, the most effective way that I’ve seen it done is really kind of. Following the 24-hour rule, which is give them training on, you know, on their data with, with systems that they can basically go and access at any time to, to be able to use their, be able to get the answers that they need.

So you really need to be thinking about. Okay, this group of stakeholders need this data. Alright, let’s go build that system and give them the data and train them on their data on how to use it and give them a way of like, okay, this is, you know, system 1.0. Give us feedback on how we can improve this, but at least getting them in so that they can get their hands dirty, be able to use it. You know, start getting trust on it, getting some intuition around it so that they can go further because it’s a huge gap between giving them training of here’s kind of an algorithm versus here’s how, here’s the, here’s the data and the results and, you know, let’s talk about how you want to use that.

Is this useful? Is this going to help you? And, and that actually brings the organization much further along, and you’re also managing that stakeholder expectations. So

Evan: Yeah, I think that’s great. And maybe this isn’t eating you up, but I certainly have seen it. You know, AI was a nebulous term for some numbers geeks, you know, eight months ago.

And now it’s, every leader is exposed and has some access and can even toy around and tinker with things and can see, you know, the, the abstract level of here’s what is specifically large language models, but I think broadly AI can do. Do you, do you see any, do you see that at Intel at all or generally over your career?

Have you seen, you know, as the hype of AI has, has fluctuated. How’s that? How’s that changed the appetite for the types of things you do or the way, the way folks, your stakeholders ask things from you?

Sarah: You know, I, I haven’t necessarily seen it per se with the work that I’m doing, but I am seeing conversations of how do we bring AI into our business so that we can move much faster.

So, you know, a lot of. A lot of what AI applications are, are really around, you know, how do we make the training much more effective? Can we reduce the amount of, you know, the time that we put something together? So could we put a script together and have, have an AI person instead of having an actor have, have somebody come in have an AI person or an AI system, you know, deliver that, that training.

You know, how can we kind of get the sentiment analysis with using kind of maybe some already example of sentiment from folks and so forth different situations. So those are kind of the things that I’m seeing. You know, it’s more kind of a you, I mean, I heard somebody say that, you know, really.

Today’s AI is really around just a UX use cases. So a lot of the applications are really just prepackaged algorithms that have a really sweet UX platform around it. And I think that’s what we’re going to end up seeing going forward now. That’s going to be on the commercial side, but for folks to actually, you know, implement, I mean a lot of the competitive advantage for AI is not going to necessarily, is going to be part of, it’s going to be, you know, how can you use these commercially UX designed applications are already, and how can you use that in a unique way?

But that, I think that’s only going to be like maybe 40%, but really where the competitive advantage is. Is really trying to figure out what data you have and how can you get more value out of that, and then being able to put that UX application on top of it so that, you know, it makes it easier for stakeholders.

Just like what I just explained to you, kind of like. You really need to train your folks on their data and with, with the algorithms that they need to make the decisions. That’s kind of the, you know, I think that’s the next thing that needs, that companies need to be investing in is really getting the data in a form to them in a UX with some UX thought around it in order to, you know, speed, the value that they’re going to get from their data. And I think that’s kind of what’s missing right now, is that combination of getting the data to the right people and making it user friendly.

Evan: Yeah, I, I think that’s a, that’s a really measured way to look at it. And it’s, it’s, you know, if, if you can be positive about it, the, the chat GPTs, it’s, it’s not, you know, it’s not putting some false sense of well why do we need Sarah to do all this? Why can’t we just ask chat g p t to measure our learning and then design learning? But it really is, maybe, maybe the brighter side is it’s giving sort of this UX experience where, hey, this is a way that is now becoming easier to deliver. If you’ve done this work, you’ve gotten the data, you’ve asked the right questions, this UX can help stakeholders sort of absorb some of the things that, that you’re able to do.

So I think that’s a, a great way to look at it. So you’ve zoomed out a little bit from your role here. You’ve given us a lot of things with a, a lot of your background varied. A lot of the work you do now, it, it, you, we’ve heard it here, focus on stakeholder management and actually helping to solve some problems.

Maybe I, I know it’s hard to distill it down, but do you have any like, quick takeaway, do you have any skills that you think tend to be the most important or maybe the most overlooked for other leaders? You know, the, the folks listening to the show, leaders in the data space that are trying to implement these changes.

Make better decisions, what’s, what’s a key piece of advice that, that you think you could give?

Sarah: So I created this framework called the da, the data-driven cultural spiral, which is really thinking about, you know, how do you bring the whole team, like a team together to be able to implement? And the first thing you have to do is really think about the analytic maturity of the, of the current group.

So, you know, the current group is not just your data scientists. Those are, those guys probably are, you know, are up here on the analytic maturity where, you know, you have to think about who, who’s going to be, , , consuming that, the, the, , analytics, but also the domain owners too, the ones who kind of understand the data or kind of the dynamics or the subject matter experts along with your leadership.

Management and leadership and so forth, the sponsorships, , they need to come all together and try to figure out kind of where are we on this analytic maturity and try to figure out what are the business problems we need to have and start from as, as low as you know, of the lowest group. , because you don’t really want to go faster than any, you know, your domain experts or your stakeholders or your management.

And really it’s really about getting, identifying that data that you need and then trying to figure out how do you get that data to them in a way that they can consume it, and then start measuring success. And from that, you’re going to go into this kind of spiral, like this growth spiral where you’re going to start asking more sophisticated questions, you’re going to go get, need to get more data.

You’re going to need to get that data back to the folks. And the faster that organizations can actually do that and measure their success and be able to iterate, they’re going to be the ones that are going to have the competitive advantage of analytics. And it’s really starting with a small tiger team to figure this out.

And start getting that business process, get those data processes together and being able to iterate and the, and that’s kind of the typical theme that I’ve seen really be able to work with organizations. You know, a lot of organizations like, I have to go get my data together. Well, no, you have to start with the Business Pro problems first.

Figure out kind of what data, figure out how to discipline that, get discipline around that, that particular data, get that to the people so that they can trust the data, they understand it, and then try to figure out is this making some kind of business impact? And then start, you know, figuring out how do you improve on it.

It’s an iterative cycle. So that’s kind of where I would say, you know, where to start. You know, if you’re a data leader struggling, because a lot of organizations are struggling with that. That’s, that’s kind of the framework that I see has, has worked for me and then for other, within the organizations that have been really successful in their data adoption.

Evan: And that’s great. Yeah, I think it’s great. Yeah. I think it, it, it is an intuitive, an intuitive framework, and. I think that the thing probably that’s, that’s the caution of slow down, go as fast as people are able to absorb and implement is challenging. It, it feels like there’s a lot of pressure and a lot of probably external pressure to go fast, go fast, just throw the coolest, newest thing at it quick before something else new comes up.

So I think that’s, that’s a very deliberate and, and useful framework. So I do want to ask you one more question. This is sort of we can, we can throw a useful framework to the wind here and just sort of pie in the sky. We’ve got an easy button to press now and everybody at Intel is aligned to your vision and they all are at your speed and they’re ready to go.

And they say, Sarah, if you let us know where, where should we point our analytic efforts? We’re all on the same page. We’ve got. All of the data we’re ready to, to spend and, and allocate whatever team time, resources, stakeholders you need, where is the analytic effort going to go?

Sarah: I would love, I mean this is kind of like, you know, I geek out like what would be my like ideal, like my ultimate goal is to really digitalize in an entire corporation, right?

So really thinking about kind of from a tops-down approach and kind of looking at the business model, and let’s just take something like a manufacturing company. You know, some of the things that you want to, you know, be able to look at it are like from a manufacturing is okay. I want to be able to tell, be able to determine how many widgets we’re going to, I’m just going to say widgets, how many widgets we’re going to need to manufacture.

Right. So if you actually break that in that problem, you know, and go through and so forth, you’re going to have to start thinking about kind of what’s the, what are the components of widgets that you need to, so you need to know how many widgets to make. Which means you need to know, understand kind of what your yield or yield loss is, because you need to be able to make up for that.

So you need to be able to predict that. You also need to understand kind of what, how many maybe be in the distribution, so you don’t want to make too much so that you’re not having, you know, a burnout rate, a burn rate of product to sell. You also need to look at the consumer demand or the demand from your customers and so forth.

So, you know, right there, you already have a formula, right? So how do you go back? You have this formula that may be the number of widgets, which is basically, you know, the number that you can make minus the plus the loss that you have versus minus the mountain and distribution and so forth.

And then start thinking about, okay, when do we need to have an idea of when we need to have those values? Well, you might find out like the, one of the key components is really hard to source. So you have to give them a three-month order to be able to get something, get product raw materials to be able to make that.

So now you have to do a forecast at least three months out to be able to get the product that you can get to fulfill. So being able to, you know, go in and try to identify all the dynamics and to be able to model that in a, in a way would be kind of really awesome to be able to go solve that problem.

Evan: Yeah, that, that’s very cool. Yeah, you, you listed like four or five common analytic things, which too often I think standalone and stand independently and great, now we’ve got this demand forecast. Now let’s plug it into our, whatever, our shipment data so that we can, we can balance our inventory, but

Sarah: Right.

And you’re also dealing with maybe three different, you know, organizations. So, you know, logistics, manufacturing, sales and marketing. And probably even, you know, raw materials. So, procurement. Procurement. So yeah, I mean, and I mean that, that right there could be a, a political nightmare just being able to get all that together.

But I mean, that would be kind of cool just to be able to go, you know, one figure out and get that whole team alignment together to figure out how, how that would happen. And yeah, so that’s kind of some of the things that. I love doing and trying to figure out kind of, you know, how, how would you, so if I hit all the resources that you know, that that’s the type of project I would love to work on.

Evan: Awesome. Well that is very cool. Sarah. It has been a pleasure talking to you today if this framework seemed helpful for you. Check out Sarah. She’s also got achieve more with data. Is that right? Do you want to say a quick word about achieve more with data?

Sarah: So I have my own website. I’m going to be putting more content up on there.

In fact, if you want to learn a little bit more about the, a data-driven cultural transformation, I have a blog up there that kind of describes that. I plan on putting more and more up there over the next. You know, a couple months and so forth on, on, on that. So, yeah, so check me out on, , achieve More, achieve More with

Evan: Okay, perfect. And we will link to that in today’s show notes. So wherever you’re listening or watching this, you can check out the notes, you can click there for Achieve More with Data. Learn more about Sarah. Sarah, thank you so much for, for joining us on the show today.

Sarah Kalicin: Thank you, Evan, for the opportunity, and thank you everyone for listening.