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In this episode of the Customer Service Secrets Podcast, Gabe Larsen is joined by Joshua Moskovitz to learn the secrets of optimizing data for an exceptional customer experience. If you’re looking to revamp the way your company utilizes data, this episode is for you! Tune in to learn more.
Where is Data Found?
Joshua has a rich background in tech and CX as a Customer Success Engineer at Google. Adding this fresh perspective to CX is super valuable in this space because it allows teams to think outside the box for the best ways to help customers. When different perspectives intersect (like UX and CX or marketing and CX) companies are able to have better relationships with their customers because they are understood on so many different levels.
Using data can be really overwhelming, especially if you don’t know where to find it or how to collect it. According to Joshua, data is everywhere and the first step to successfully utilizing it is to centralize all the data into a comprehensive stack. From there, it’s much easier to organize and pick the right tools moving forward. Data can be found in customer chat logs and calls. If your company uses a CRM or other tools for support tickets, data can be found there as well. “Typically what you’ll find is that there’s just a lot of silos of data, different places where there’s a record of customers or marketing spend or different tools and systems you might be using, applications, software, that all have rich, rich data sets.”
Organizing Data Through Prioritization
Once data is collected and centralized, the next step is to set priorities. It’s important for leaders to visualize their perfect customer and then organize data in a way that attracts those kinds of customers to the brand. While this may seem like a complex way of going about it, Joshua explains that it isn’t as difficult as it seems and once leaders start to gain an understanding of their collected data, it then shapes their view of the common customer.
“It’s really just about, well, where can you prioritize? What do you want to bring together?… It’s more managing the life cycle of data and less about trying to land on the optimal, single way of doing it.”
Being Data Obsessed for Success
Understanding data is key to having a 360 view of your customers. The more information you collect and organize, the better you can predict future customer actions and resolve recurring issues. Luckily, data collection is fairly cost-effective and is absolutely worth the investment, as you’ll save money in the long run by utilizing data now. As Joshua says, “I think there are so many different tools and services out there. If you go to any of these companies that work with ETL and get into a data warehouse, that’s cheaper than it ever has been to store data.” Joshua hopes that leaders will consider using this to their advantage as soon as possible, especially now that there are so many options for storing and collecting data that don’t hurt the wallet.
“So I think the barrier’s really low today, it’s just a matter of what are you trying to measure? Where are those data sets? And then where do people need to see it?”
To learn more about data and how it gives your company an edge, check out the Customer Service Secrets podcast episode below, and be sure to subscribe for new episodes each Thursday.
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Full Episode Transcript:
The Time to Prioritize Data is Now with Joshua Moskovitz.
Intro Voice: (00:04)
You’re listening to the Customer Service Secrets Podcast by Kustomer.
Gabe Larsen: (00:11)
All right. Welcome everybody to today’s show. We’re super, super excited to get going today. We’re going to be talking about engineering customer success, really dive into using data to drive some of those success initiatives in your business. To do that, we brought on Joshua Moskovitz. I’ve been wanting to get Joshua on for a while. Real fun background, previous Customer Engineering Leader at Google. Google Looker was there for a little while, and he’ll talk about more in just a minute, but as of now, actually an Angel Investor and still staying in kind of the same area, AI, data, business intelligence, but some really interesting background with this idea of customer engineering. And we’re going to be talking a little bit about that today. So Joshua, thanks for coming, and how the heck are you?
Joshua Moskovitz: (01:01)
Hey, there, I’m doing really well. Super excited to be on the show today. Thanks for having me, Gabe.
Gabe Larsen: (01:06)
Yeah, this is going to be a fun one. Like I said, we’ve been thinking about you and your title at Google is so cool that we couldn’t help but not reach out. So if you can maybe tell us a little more about some of the things you did at Google Looker and now, what’s on the horizon as an investor?
Joshua Moskovitz: (01:23)
Yeah, certainly. I had quite the journey at Looker. One of the things that drew me to the company early was that they built a fantastic product for exploring data and making it accessible within an organization. I joined pretty early. We were about 15 people, and for those of the folks that are doing startups or have been a part of startups, that point in time, you’re really just doing everything you can to keep the ship afloat, get people excited about the product, show them how it works, support them, and scale quickly. And I had a number of different roles throughout my time there that had been really impactful on my ability to understand customers, understand their data, and also how to use data to support them. I was our first sales engineer, so that was working in the field, helping customers initially connect the product and solution to their databases, modeling the data, and starting to make it accessible to their stakeholders, non-technical folks, technical folks, and really kind of what we were doing and how we were doing it differently.
Joshua Moskovitz: (02:16)
As we were acquiring customers, I was then asked to build and scale out our customer success engineering. And so really the meat of what we’ll be talking about today, and that was a really great role. It involved building a team globally, hiring folks that we’re engineers, but that would work with our customers in the field on a lot of the thorny, more scalable issues that they were facing, things related to how to use their data, how to scale their use of Looker as a platform, but it also had a big component of building the internal tools and systems around data and how the rest of the company would use data related to our customers. So this meant things like building customer health scores, integrating data into things like Salesforce and Slack as they were looking for information around customers or what they were doing or how they were using Looker and the product. We could make that readily available to them. So sharing some tips and tricks on those topics today.
Gabe Larsen: (03:06)
I love it. Great overview and again, kudos, because it sounds like it was a fun run and certainly what you guys did over at Looker, now Google, I think such an important part of, to get that intelligence, the data, and it sounds like you were pretty fundamental in that. And then just real quick, you’ve obviously moved into investing. I mean, how’s it going? Any, you passionate about, has it been pretty fun? Any quick insights there?
Joshua Moskovitz: (03:31)
Yeah. Absolutely. I mean, it’s definitely an incredible time, to be working probably as a founder and thinking about starting companies. There’s just an abundance of capital out there and a lot of great people like the folks at Looker and other folks that have been super successful and have had this great operational experience. And that’s really what we’re looking to do is look for those next founders and those next companies that are trying to be disruptive with data, with business applications, with software, and using technology to solve these problems in these areas. And really, how can we then take this experience that we’ve had building a company and really just pay that forward and be mentors and be supportive not only with our capital and our money but really mostly our experience? We’ve been through something that was incredible and a lot of folks hope for and dream for, we just want to help keep the dream alive for other people as well. So it has been a great experience. Definitely learned a lot. I like to put myself into new roles that are challenging and give me a lot of access to some interesting things. And Angel Investing and working with founders has definitely been fantastic and a great way to just, again, challenge the assumptions, reiterate the things that we’ve learned, and be able to be supportive to the next class of companies and disruptive technology.
Gabe Larsen: (04:43)
Man. I’m jealous, but kudos. Again, if you haven’t checked out Joshua’s background, just a really cool story. So thanks for giving us a little bit of insight into that. Before we dive into some of these ideas around engineering, customer success, et cetera, always just like to bug people. If there’s anything outside of work, just to make you a little more real, hobbies, crazy things you’ve done, anything come to mind that you’d like to share with the audience?
Joshua Moskovitz: (05:13)
Yeah, absolutely. Certainly. Yeah, I’m not just a customer engineer. There’s a lot more that I do outside of helping customers be successful with their data and how to use it. I think there are a couple of things that come to mind. Firstly, I just had a new baby boy as of October 24th, so eight weeks with our new son, and that’s been, wow. I mean, think about all the things you learn in starting a company, when you start a life, that’s something else.
Gabe Larsen: (05:40)
Buckle up!
Joshua Moskovitz: (05:40)
That’s definitely been an adventure. I’m learning how to be a CEO at home and then I’d also say we have a great protector in the family as well, a seven-pound Shitzu that we named after a techno DJ named Solomon. I DJ for fun as well. So that’s kind of my, how do I keep myself sane when I’m not just looking at data all day? I’d say music’s a big passion and try to see shows or see music and with COVID probably bring a little bit of that home. [Inaudible].
Gabe Larsen: (06:10)
Yeah, that’s true. DJing probably isn’t as hot as it once was but it’s coming back, but –
Joshua Moskovitz: (06:13)
Not quite.
Gabe Larsen: (06:14)
Never know with all these new variants coming on. Well, hey man, congrats. So, you said October 24th for the son, is that right?
Joshua Moskovitz: (06:23)
Yeah. Little Luca. So it’ll be two months here in just a couple of days.
Gabe Larsen: (06:26)
Crazy. Yeah. Well, hey. Welcome for the, to each their own, but if you’re going to go the kid route, I got a couple myself, and yeah. It’s just so different. So buckle up, it’s going to be a fun ride for the next, at least you got the holidays and stuff, but I’m sure that first year, in fact, the first like ten years is pretty tough.
Joshua Moskovitz: (06:48)
Well good that I’ve got my years ahead of me.
Gabe Larsen: (06:50)
No, I’m teasing. Kudos. It’ll be fun. Right. Let’s jump into the topic at hand. So I wanted to maybe just start the big picture. Obviously, you’ve been in the space. You’ve been doing this, you kind of went from small to big in your background. A lot of lessons were learned in doing this the right way, using data to drive customer success. Curious where you’d kind of start if you were coaching future leaders on trying to tackle some of those same problems you’ve dealt with.
Joshua Moskovitz: (07:19)
Certainly. Yeah. Well, one of the first things that always comes up, and it kind of makes sense, right? You want to start to use data around your customers and be able to better manage them. It’s really well, how do we find out where all that data is? And typically what you’ll find is that there’s just a lot of silos of data, different places where there’s a record of customers or marketing spend or different tools and systems you might be using, applications, software, that all has rich, rich data sets, but they all tend to be decoupled. So the first thing we talk about with customers is how do we take inventory of where all these different data sources are? And what’s our process and our methodology of bringing it together reliably to get that single view or that centralized view of all your customer’s data in one place? Because that really does unlock a lot more that you can start to do with it.
Gabe Larsen: (08:08)
Where do you, I mean, the holy grail is to kind of have that, right? That 360 view of the customer, but it seems like we’ve been talking about it for a while and just can’t really get there. When you’ve kind of tackled some of these, where are people going? Well, maybe you could jump into some examples. Like where is the data? Is it really in different systems? Is it one, what are some of those ideas?
Joshua Moskovitz: (08:36)
Yeah. It’s really, it’s all over the place and the reason for that is a lot of different reasons. Firstly, different database systems and different tools have their own systems of record, and that’s just a byproduct of using things like a CRM like Salesforce or Zendesk for support tickets, for example, or if you built an application or eCommerce store, you’re using a Shopify but have your own transactional history and system. And all those different systems are meant to support and function separately from one another. One of the things that have happened in the industry is this idea of databases and data warehouses being much faster and easier to work with. And there’s also the rise of a lot of different tools that help you make and move these different systems and record into a centralized place. And so what I’m talking about here is ETL or ETL tools.
Joshua Moskovitz: (09:19)
And so a lot of the time, it’s talking with customers and the folks that have these different data systems and finding out, well, what systems are you using? And a lot of the time, there’s some commonality there. They’re using Salesforce, they’re using Zendesk. They’re all using these same systems and you’re seeing a rise of other tools like Stitch or Fivetran that are built specifically to help move these data sets into a centralized repository. So you can actually start to operate on them so you can build different reports, alerts, and things on top of it. And in the case of Looker, build full semantic models that allow you to join the data together and start to actually look at it through a single pane of glass.
Gabe Larsen: (09:55)
Maybe that’s where some people get stuck then. Right? Because it’s like, hey, I have Salesforce. I have my CRM, so I have the data, but yet isn’t that kind of like the, isn’t that the myth? I’m not mocking Salesforce, I mean, but just because they’re easy to mock. They’re a big company. Right? But all jokes aside, it’s like, but no, because your account, your order data, isn’t actually in Salesforce only like your [inaudible]. So now you don’t know anything about it. Isn’t it like that?
Joshua Moskovitz: (10:27)
You don’t have the full picture. Exactly. You have silos and you have a piece of the picture and you can only report on a piece of the picture at any given time. And I think one quick thing that anyone can do if they’ve got these different systems is just starting pulling some data. Pull different spreadsheets or different CSVs or flat files down from a Salesforce and from your ticket system and just start putting it and matching it up into a single Excel file and start to try to get a sense of, well, if I was building this magical table or this magical central source of truth, like what would want in there and what can I start to see and understand by doing some pivoting or some summing or averaging of some values to get essentially this idea of customer facts, right?
Joshua Moskovitz: (11:06)
So who are my all-time high spenders? Or when I look at my marketing spend data, that’s in another system, but I look at that alongside customer lifetime value, I can see something interesting like, oh, wow. Everyone that I acquire through this podcast is spending way more. They maybe have much more intent. And you can really start to do more with that just by seeing everything in the single place. And I think when it comes time to justifying the cost or trying to get investment into this system, you now have something to point to and say, well, this is what I did. It’s thought it’s not that easy to do today. I’ve gotta go and hit the download button and I’ve gotta pull these different systems together. And it creates yet another tangled web of data sets. But now there are tools that make it really easy to store this stuff and move it very, very cheaply and quickly and regularly, daily, maybe even every minute or in real-time. And so just by starting there is a good place to kind of get a sense of, well, what can we look at? But maybe there’s the technology that makes it a little bit more scalable once we start trying to use it.
Gabe Larsen: (12:05)
Maybe one more double click on that before we go to the next. But you know, because I do feel like, one gentleman had used this on the podcast and I stole it from him. He’s like, look, I feel like I’ve got like a Frankenstack where I got all these different technologies and none of them talk together. We’ve tried to stitch them all together, just like Frankenstein. We got one nail sticking out here and another thumb from somebody, whatever it might be. And he’s like, it just feels like it’s so, like sometimes I just want to start over because it feels like it’s such a heavy lift. Having done this before, we’re now pretty in a place where APIs and getting stuff in and out, like it’s not, this isn’t a 10 year, $2 million projects. Like you should figure this out. Is that kind of advice you’d give?
Joshua Moskovitz: (12:55)
Yeah, I definitely would say so. I think the Frankenstack, that’s a real thing that people definitely have to struggle with and it only gets worse as the business scales and a lot of the time it’s not about trying to have the perfect stack and trying to remove all the extra redundancy or issues that you’ve had before, but it’s really just about, well, where can you prioritize? What do you want to bring together? And what do you start to depreciate over time? And it’s more about managing the life cycle of data and less about trying to land on the optimal, single way of doing it, because you do need to bank on changed management and flexibility in the process. Maybe you want to change the database at some point in the future because you’re not getting the same mileage or maybe there’s a better offering or program somewhere else or maybe you’re switching your marketing system over to something different. And so always anticipating and knowing that these changes will happen is kind of part of that, right? It’s part of that ownership and that maintenance of it over time. But having that flexibility and being able to take advantage of the technologies that move the data, get it in and make it accessible to the rest of the business is definitely a good place to focus.
Gabe Larsen: (13:52)
Okay. So the first point is just understanding what you have and trying to start, maybe piecing it together. What do we have? How can I consolidate it? Okay. Where do you go next as far as kind of tackling using this data?
Joshua Moskovitz: (14:07)
Certainly. The next thing we typically talk a lot with customers about is the difference between clarity metrics and vanity metrics.
Gabe Larsen: (14:14)
Interesting.
Joshua Moskovitz: (14:15)
And at the end of the day, as you’re building a business, you’re going to need both, right? You’re gonna need to know from a vanity metric perspective, how many daily active users you have or how many new signups you have or just how quickly you might be growing over time. But it’s really the idea of clarity metrics, the ones that you want to be tracking because they drive the right behavior or they measure the right experience for your customers. Those are the ones that you definitely want to build out, understand, and be able to iterate on.
Gabe Larsen: (14:40)
I think so. So give me a quick definition for those. What would be clarity metrics versus vanity metrics?
Joshua Moskovitz: (14:47)
Certainly. Yeah. So clarity metrics are really the ones that act as operational guideposts for your business. So for example, if you’re looking at a business that you serve, you sell like a service or you’re a product business and everything is important around engagement. Obviously, daily active users is a great way to look at that from a vanity metric perspective, you’re going to see, okay, how much we’re growing. But as a product, want to really look at well, how often are people using this product, right? What’s the actual engagement looking like, are there a number or percentage of users that are logging in every day? Do we want them to log in every day? For example, with Looker, we had this concept of get committing and pushing out code. That was a core activity within our product. And if we saw that our customers weren’t taking that action or doing that every day or there wasn’t more than one user doing that, we saw that as a pretty big risk to that customer’s health.
Joshua Moskovitz: (15:42)
And therefore we tracked it and we actually started to bucket our customers and segment them based on just how much they were doing that core action and how they were doing it over time. So that’s something around product and engagement. You might see another clarity metric if you’re selling products from an eCommerce perspective or just products over the web. You might be looking for repeat purchases or certain products that folks when they buy it for the first time, they never return and they never buy again and these are what we call poison products. Or we might look at things like how long it takes for the products to even arrive to our customers. And can we measure their satisfaction based on them receiving things in real-time or as close to as real-time as they’d like to get those items and not getting caught up in the supply chain issues that we’re seeing?
Joshua Moskovitz: (16:25)
So it’s really just those big differences between a vanity metric, looking good and you wanting to see it increase, but you can’t really make a decision off of it. Like what happens if my daily active users is up a hundred percent today versus yesterday? Can I manage the business any differently? Not quite, but I can definitely do something if I see that there’s a certain product when people buy that they never return, right? I might, it’s not merchandising or maybe not hold that product in inventory anymore. Or I might see that if there are certain products that are when they’re bought to together, they increase the customer lifetime value, or there’s a certain channel when folks come in that they’re doing the behaviors that I’m looking for. Those become really great indicators. And I think another place where you can start to dig in further and ask for more context when you see those outliers when you’re seeing behavior that you want to track and that’s going the way that you want to track. The next thing I like to do is drill in and see, well, who are these people, what makes them unique, and actually go have a conversation.
Joshua Moskovitz: (17:17)
So use the data to provide where to go and talk, but have a conversation that helps you pick up the context of really who they are and what’s making them unique. And ultimately, how do you find more people like that? Because they’re doing the behaviors that you want to see.
Gabe Larsen: (17:33)
Yeah. I mean the metrics, it’s just in this CX space, it’s been difficult. A couple of things we’ve heard that I think to align a little bit with what you’re talking about. It’s, we had a couple of people when we reached out about the podcast and wanting different topics, they said, “Man, it’s so hard because we focus on certain metrics that the E-Team, or executive team, doesn’t really care about or they don’t seem to, we speak in a different language.” We want maybe use your term, sometimes like vanity metrics that are like our average handle time is up or down or something. And our C-SAT is up and down and the executive team is like, I just want to know what the top and the bottom line is. So there’s sometimes this like, what metrics really work and in our space, oftentimes you’ve found that customer satisfaction, for example, has zero correlation or negative correlation with repeat purchase. And you’re like, “Oh, that’s so frustrating because I focus on these metrics.” So that’s interesting. Really trying to get clarity on what metrics matter. Vanity versus clarity. I like that.
Joshua Moskovitz: (18:41)
And you’re probably going to have both to be honest, right? We all need to have our executive dashboards to see the business at the top level. But when it comes to the teams that are in the field and them managing the customers on the day to day, what are the operational metrics that they’re using to make sure that when the vanity metrics are going in a certain direction, we know operationally, well, what could be driving that? How could these be correlated? But ultimately, this is my cockpit and this is my speedometer. But I also need to know, well, how fast am I revving? Am I shifting gears correctly? And what are the other signals that are happening out there that again, helped me manage the plane to a successful landing?
Gabe Larsen: (19:15)
Yeah. That’s deep. That is probably the right way to think about it. Man, I’d like to spend a little more time on that, but let’s continue. So you got to then understand, I like this clarity versus vanity metrics. So first you take inventory, then you start to make sense of some of the metrics. Where do you go for your last point here?
Joshua Moskovitz: (19:36)
Yeah, I think after you’ve got the data, you have the metrics that you want to be tracking or that are operational. I think the last thing you have to think about is our end users, the folks that are going to need to see this data to be able to act upon it. And where are they going to be able to do that? I think you mentioned this earlier in the podcast, but one of the things we’ve seen with technology these days is that a lot of them do have API interfaces. They have interoperability with one another, although they’ve been designed in a siloed or decentralized way, there are ways to interact with them and to make data accessible within those tools. And that’s what I’d be looking for. I’d be asking myself, where is product making design decisions around customers? Where do we have our CX teams, our customer success teams, our sales teams, trying to dig in and understand their folks that they want to go support, that they want to go sell to, and give them a little bit more clarity when they’re making those decisions and looking for those insights?
Gabe Larsen: (20:34)
That is so true because it does seem like people, getting them actionable…maybe that’s the right word. People start to get data, but, and maybe that also gets a little bit to the vanity/clarity metrics, but it’s like, okay, well what does this mean? And then how do I get in a way that I can actually do something with it? Because there are, I’ve had this happen where I get a dashboard, there is a dashboard let’s say, and it’s got 70 tiles on it. And I don’t know what to do with this thing. I’m like, no. I can’t. Help me interpret this Chinese, this language I cannot read. And that often feels like it takes some iteration or, I mean, any thoughts on making it actually because it does seem like it, right? It doesn’t, you don’t just pop it on a dashboard, and then it’s like, okay, it’s done some massaging.
Joshua Moskovitz: (21:34)
Certainly. And I think that’s one of the biggest things we talk about with our customers. A lot of them would say, well, Looker, you’re just a BI tool. You’re visualization, right? Like you’re no different than Tableau and we’d have to spend a lot of time coaching and working with them on just what was different. And I think one of the key things that was different about Looker was we asked folks to build data applications and we asked them to understand, well, let’s not think about a dashboard for a second. Let’s just think about the data and the decision and where it needs to be made and what we’d ultimately land on is that a lot of the time we might not even be building a dashboard. We might be building in the learning system, that whenever there are orders that are past a certain threshold for delivery dates, whether there’s a customer that submitted a support ticket that didn’t meet an SLA expectation or maybe wasn’t able to be resolved with documentation that’s been created, what are those situations that we wanna make known?
Joshua Moskovitz: (22:26)
And then where do we wanna make them known? And these could be places like in Slack or in teams, they could be sent out through email distribution. They could be alerted to an individual. And when they come back in or they click that link or they look at the report, it’s not just a visualization. It could literally be line items. They could be clicking buttons and clicking links and opening up the CRM and changing the close date because they realized their close date is after today’s date or whatever it is, those anomalies or those inconsistencies that need to be addressed, but go a step further. Don’t just show it on the screen, give them a button to click, right? Allow them to go and actually take that next action. And I think that’s just, again, back to the fact that a lot of these systems can be interconnected or can point to one another. Things tend to behave as links in SAAS applications. So we can open a link or update a product page or go make some edits somewhere. So it really comes down to, again, what are these systems and tools that we’re already using today and how can we go and interact with them? And if we can interact with them, then what is it that we want to do and how often do we want to do it? And let’s create that experience as what we give folks in their ability to go and manage their business and manage their workflows a little bit better with data.
Gabe Larsen: (23:39)
Yeah. Yeah. You got to go with like the end in mind. I think we all need like the course on how to get data, visible and actionable because it, well, maybe we need a course on all this stuff, but that point, I just, I feel like so many people, you think you’re to the finish line because you’ve got it, but you just can’t really like make sense of it.
Joshua Moskovitz: (24:01)
Sometimes it’s also just making it available too. I think one of the biggest things we built at Looker was the idea of a customer lookup dashboard. I was just talking to an engineer the other day, who was early with me and we’d built that dashboard and shipped it to the company. We didn’t quite know everything that it was going to be used for. We didn’t even know if we had all the data on it, but it had a lot of great things. It had the customer information, the contact information, and those customers coming from our CRM. It had product usage information and how that was changing over time. It had licensed information. So how many user seats were they contracted for how many were they actually using and was there upsell capability there? It had information related to their hosting environment for operations engineers.
Joshua Moskovitz: (24:41)
And this just became a really great tool that the business could use that anytime they had a question about a customer, it allowed them to go in without needing to contact someone or ask for all these different data polls or write their own sequel, which who knows what they’d ultimately end up writing. And if it was going to be accurate or consistent. So we had a way to govern the entire company’s ability to go in and ask a question about a customer and just see everything there in one place. And from there they could go and draw their own conclusions. They might be able to go and take a different action or think about something a little bit differently, or maybe they just had a bunch of new questions, but very insightful questions because they were able to see this rich data set around the customer that quite frankly if you had to go and pull that together, as I was describing earlier, without proper ETL and proper tools, bringing it into a warehouse and building an interface on top of it, like a Looker, it would be really cumbersome and difficult. And just the regular person sitting in the other department that doesn’t think about data all day, they’re not going to do that, or they’re going to wait a really long time with many requests out to many different systems on how to go pull that and collate all that information together. So it’s just things like that. How do you make it visible? Sometimes it’s just pushing it, shipping it, getting some feedback, letting people use it being surprised by all the great stuff that they’re able to accomplish, and then iterating from there.
Gabe Larsen: (26:00)
I think that’s, it’s, sometimes it’s, you’re not, what are they…there’s some saying, don’t let them, something about perfection and –
Joshua Moskovitz: (26:15)
Yeah. Don’t let perfect be the enemy of done.
Gabe Larsen: (26:18)
That’s it. Thank you. You know when it’s on the tip of your tongue and you can’t get it out? Yeah, you just got to kind of go like, get me one out and get cracking. It sounds like you’ve maybe not perfected it, but certainly live by that principle many times.
Joshua Moskovitz: (26:31)
And I’m a big fan of teaching a person to fish, right? I think with these dashboards and these reports, you can spend a lot of time baking and cooking the perfect fish, but if I can give someone a fishing rod and allow them to go fish, they could probably eat a lot more data that way. And that keeps our team happy and feeling fulfilled and allows us to measure success just based on how well the business is using data.
Gabe Larsen: (26:54)
No, I love it. I think that makes a lot of sense. So fun talk track Joshua, especially around obviously using data visualization, et cetera. Summarizing that for leaders that are struggling to try to kind of get this going, getting the data and getting it there, what would you kind of leave them within summary?
Joshua Moskovitz: (27:17)
Yeah, absolutely. I think ultimately again, where are all the data sets today? Just go broad and go big, right? Describe your perfect view of customers and all the things you’d want to know about them. Put it into a document somewhere and then just start looking for the deltas. Right? Do you have the data? Do you need a way to get it? I think there are so many different tools and services out there. If you go to any of these companies that work with ETL and get into a data warehouse, that’s cheaper than it ever has been to store data. There are definitely great pricing models around consuming and only paying for data that you’re consuming. So I think the barrier’s really low today, it’s just a matter of what are you trying to measure? Where are those data sets? And then where do people need to see it? And I think there are plenty of tools and solutions out there that make these systems interoperate with one another and make it easy to pull the data out and do it in a reliable way that you can start to build business processes on them and make sure that they’re scalable and they can keep working into the future.
Gabe Larsen: (28:16)
Okay. All right, man. So, as we wrap, if someone wants to touch base or learn a little more about this, what’s the best way to get in touch with you?
Joshua Moskovitz: (28:23)
Yeah, certainly. So either my LinkedIn, which is just Joshua Moskovitz, M-O-S-K-O-V-I-T-Z, or my email, which is moskovitz.joshua@gmail.com.
Gabe Larsen: (28:34)
Perfect. Well, hey. Really appreciate you taking the time. Fun talk track, and for the audience, have a fantastic day.
Exit Voice: (28:46)
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