Unlock Actionable Insights With Fully Managed Data Analytics

Here’s a glimpse of what you’ll learn:

• How organizations like schools are using analytics data
• AI’s role in data analytics
• What is human-centered data?
• How Luke Komiskey got involved in data analytics
• Is AI changing how we leverage data?
• Luke explains “life KPIs”
• Why you need a foundational data strategy
• Building out analytics capability

In this episode…

Data analytics is more than just a numbers game. Businesses possess considerable data they can only utilize if they have coherent insights. How can AI streamline your analytics?

While many businesses miss out on opportunities due to data overflow and fear that AI technology will take over jobs, data enthusiast Luke Komiskey believes humans will always be necessary to guide AI. AI can gather valuable insights to enhance your marketing story, but people must create the narrative. That’s where a data specialist comes in; however, hiring a data professional is not a one-size-fits-all solution. To effectively leverage AI, you need all the appropriate data professionals on your team. Often, data scientists are hired first, but they are only one part of the data team required to leverage AI fully. A complete end-to-end platform requires data analysts, report developers, data engineers, and data product managers working together.

In this episode of the Proof Point podcast, Stacie Porter Bilger sits down with Luke Komiskey, CEO and Founder of DataDrive, to explore the value of fully managed data analytics. Luke discusses how organizations are using data, the meaning of human-centered data, and why a foundational data strategy is necessary.

Resources mentioned in this episode:

• Proof Digital
• Stacie Porter Bilger on LinkedIn
• DataDrive
• Luke Komiskey on LinkedIn
• Rise25

Sponsor for this episode…

This episode is brought to you by Proof Digital.

We are a strategic and creative performance marketing agency partnering with organizations to create data-fueled marketing engines that drive growth and deliver a tangible ROI.

Founded by Stacie Porter Bilger in 2012, Proof Digital employs a strategic marketing approach by blending today’s marketing tools like SEO, PPC, and paid social ads with traditional sales funnel processes.

Ready to get results? Visit https://proofdigital.com/ to learn more.

Interview Transcription – Unlock Actionable Insights With Fully Managed Data Analytics

(0:00 – 0:16)

Welcome to the Proof Point Podcast, where we decode digital success one click at a time. We share key takeaways fueled by data and insights that your team can implement today to drive growth. Now, let’s get started.

(0:20 – 0:49)

Hi, it’s Stacie Porter Bilger, your host for the Proof Point Podcast, where I feature B2B and D2C businesses and thought leaders, and share marketing, data tactics, and sales strategies to kickstart growth in a rapidly changing digital space. This podcast is brought to you by Proof Digital. Proof Digital is a strategic and creative performance marketing agency partnering with companies to create data-fueled marketing campaigns and tactics that drive growth and deliver results with a tangible ROI.

(0:50 – 1:18)

Ready to get results? Visit proofdigital.com to learn more. Before I jump into our guest today, I do want to thank our friends at Rise25 for connecting me with our guest. Go check out their website at rise25.com to learn more about how they help B2B businesses connect to their Dream 200 clients and referral partners and get ROI using their podcast process. 

(1:19 – 1:32)

Our guest today is Luke Komiskey. Luke, thanks for coming. Luke is a founder and CEO of Data Drive, a data consultancy providing managed analytics services. 

(1:32 – 2:02)

With an ongoing partnership, Data Drive helps growing midsize organizations transform their messy data into insight, providing both the reporting platform and data team for delivering faster-informed decisions. Over the past seven years, Luke has grown Data Drive into a worldwide team of highly skilled data professionals serving over 150 organizations, including media agencies, public school systems, manufacturers, and more. Go to godatadrive.com to learn more.

(2:03 – 2:07)

Luke, thanks for joining me today. I really appreciate it. Thanks for having me on.

(2:07 – 2:18)

Excited to be here. Tell me, first thing, tell me a little bit about your company. Just kind of give a little bit of highlight of what you do and how you’re helping schools, companies, etc. 

(2:19 – 2:39)

Yeah. So as you mentioned, Data Drive provides managed analytics services. And really what the challenge is, is that organizations of all shapes and sizes, and it really doesn’t matter what industry you’re even in, are often drowning in data, not knowing how to put together coherent insights that are going to help drive more informed or faster decisions for them.

(2:40 – 3:24)

So what managed analytics services really provides is for those organizations that need help with figuring out what that first step is, often traditional approaches are having to go out and hire a very niche skill set around a data professional, maybe a specialized data scientist to come in and assess the situation, figure out what systems are being brought in. And what managed analytics provides is both the ongoing team and the support, but also the entire end-to-end integrated platform so that all of the various business software and operational software that your organization has in place can now start speaking to each other in a way that can bring insights together that all levels of the organization may have never been exposed to before. That is amazing.

(3:24 – 3:33)

You’re speaking to a data nerd as well. So this is music to my ears. And what I do know is data tells a story.

(3:34 – 4:14)

I mean, and if you can put the story in front of you, you can become more efficient, effective, and it’s really endless. So tell me a couple, maybe how do companies use this data, or what are you seeing from a standpoint of the companies or organizations like schools are using this data? Yeah, let’s dig into very specific examples because organizations can have a lot of different use cases that they can go after. So from a school district perspective, if you think about how a school has to operate, let’s pick on K-12 school districts.

(4:14 – 4:34)

They’ve got a lot of different stakeholders up and down the organization. You’ve got superintendents and district staff that are setting goals around what kind of positive student outcomes we want to achieve over the course of however many years. And that might look like increased testing scores, better attendance rates, and overall just better student outcomes.

(4:34 – 5:13)

And then if you dive down into the individual school levels, you’ve got more principal level staff, you’ve got teachers that are working with a lot of different students that have their own unique story that can be seen in the data. But for school districts in particular, and much like other organizations too, they tend to bring in a lot of different technologies or applications to help serve a very pointed solution. And while that works well from operationally trying to tackle whatever problems that might arise, the challenge comes from being able to tell that coherent data-driven story about what’s happening with that student over the course of their school career with a school district.

(5:13 – 6:13)

And so where data can really play a role and data integration plays a role is, what if we were able to tap into all of those various student information systems and attendance systems and state assessment scoring systems to be able to provide a near real-time view about how students are performing on any given day, and to be able to tell that whole journey over the course of their time with the school, and ultimately provide that visibility for both like teachers to take immediate action in the classroom, but then district level staff to understand where can we make are the best investments to try to drive a better student outcome for what we’re trying to do as a district as a whole. It seems like this would just really accelerate individual learning across the board. I mean, because if you have that information right in front of you, you’re going to be able to, as a teacher or school system, understand really quickly about where, you know, instead of doing a broad approach to a classroom, do a really individual approach to the education system if data was used in this way.

(6:14 – 6:39)

Yeah, it is. And some of the most powerful views that we build as part of our offering to school districts are these views called Class 360 and Student 360, and that 360 is meant as that element to understand every different component about who they are as a person. And it’s super powerful from a granular analytics perspective because you can imagine looking up a student’s information and be able to understand how they’ve done on all of these testing scores. 

(6:39 – 7:08)

Have they been attending class? Have they been missing certain classes along their journey? And that’s invaluable for a teacher that’s already incredibly crunched for time to be able to see all of that in one view, one click, all of these various applications that they’re entering all of this important data into. We need to get people out of the manual spreadsheets and into more of an interactive environment where they can focus more on the story and like the insights and less about the data work that has to be done behind the scenes. Right.

(7:08 – 7:24)

I’m going to kind of pull out maybe a couple other case studies out of you because we work with manufacturers. We also work with clients in the non-profit space or education space as well. But this is just take them.

(7:24 – 7:30)

And then also I’m kind of curious about how you work with organizations like ours. Right. We’re data-focused.

(7:31 – 7:49)

So the big thing from my team standpoint is to look at all those data points to try to that competitive intelligence, that information to understand consumer behavior. This is growing and changing actually rapidly, especially with AI. And maybe you can throw a little bit of AI information in that point.

(7:50 – 8:15)

But talk about say take an organization like ours that’s really data-focused. How do you help organizations like us help our clients make better decisions with data? Yeah, absolutely. So when it comes to media marketing agencies, I think what’s really cool about that space, in particular, is there is a lot of digitally native data that’s sitting in various applications.

(8:15 – 8:51)

But then there’s also more traditional media spend that can be spent that may not sit very nicely in platforms. But a challenge that a media marketing agency will always have is being able to share with their customers, and their community that if you give a dollar of marketing spend that you’re hopefully getting more than a dollar of value back. Being able to share that story means that you need to not only tap into these various systems like LinkedIn ads, Facebook ads, and Google ads but then also capture the story around business outcomes.

(8:52 – 9:12)

And so what are ways that we are proving that we can drive leads, drive conversions? Every marketing story is going to be quite different depending on the organization. But the value for media marketing agencies is to, again, get out of all of the spreadsheet wrangling that has to happen. CSV downloading to bring together that coherent picture together.

(9:12 – 9:49)

But then also where we help agencies is from this whole play towards data monetization, where they recognize that the data that they’re sitting on is actually a valuable asset within their company that they can in turn give back insights to internal marketing teams at their customers to be able to provide insights to help them even share that marketing story with their senior leadership. Yeah, as you mentioned at the beginning, I think data has such an interesting opportunity for a story that can be shared. And that’s why when we get into this era of AI and automation that we think that we’re going to be losing jobs to AI.

(9:50 – 10:05)

But really, the reality of it is that, of course, there are things that can be automated and things that can be driven by AI. But there’s often still that human component of some kind of story needs to be crafted out of that. There’s always nuance in the data that will never be captured just by pure robots working on it.

(10:05 – 10:31)

We look at it from an AI standpoint and the data pieces, and dashboards are critical to our work. And again, the complexities of digital space, you have ads in multiple platforms, multiple places. You also have data points where you’re pulling all the way ranking site rankings, which is an SEO tactic to ads on LinkedIn or Google and pulling those into, again, clients want to understand what’s my ROI.

(10:32 – 10:52)

If I had a dollar, where’s the best place to spend it? And so those are important pieces. But back to the AI point, it’s actually just another clear data point. And it’s a way to really get a little bit more intelligence from the behaviors online, more intelligence from the data.

(10:53 – 11:09)

But you still have to tell the story. You still have to communicate to whoever you want to communicate from a marketing standpoint. What problem do I solve? How am I going to make your life better? And how do you get in touch with me? That’s a human emotional piece that’s the storytelling part.

(11:09 – 11:42)

But I’m going to know how to make your life better because I know what your problem is through the data. So I think your company has a great opportunity to help get to that point quicker so that people can be more efficient and effective when they’re using their marketing dollars. What about an example or maybe a manufacturer or maybe that type of company from efficiency standpoint with data or an e-commerce site if you have one? It’s okay if you don’t, but somewhere around those, we work with a lot of companies in that space.

(11:42 – 12:04)

Yeah, absolutely. I think a really, really cool use case in the manufacturing space that I’ve had an opportunity to work with is early on and in data drives growth. We worked with a manufacturer that was actually creating a lot of more just like a wedding invitations and pamphlets and manuals.

(12:04 – 12:19)

And there was what I love about manufacturers is that there’s just a very real process that can be witnessed. You know, when you talk about e-commerce everything just kind of exists on the interwebs. But with manufacturing, there’s a very real process that gets to be created.

(12:19 – 12:52)

And as a data guy, when I get on these manufacturing floors to be able to see all these data points of there are humans working on a machine, there is a machine that has a laser that’s saying when sheets are passing through there. And there’s a lot of interesting stats and constant data that’s being created on these machines and by humans on time cards, on sales data. And one of the coolest examples we had for data is for this manufacturer that actually sold a lot through e-commerce was looking at when bulk orders came in.

(12:52 – 13:05)

As you can imagine, they have lots of big wedding invitation orders that hit. And because, you know, I’m sure many people are like me and they tend to be really delayed on actually making that order. Everything is an urgent shipment that needs to get sent out.

(13:05 – 13:46)

Right. And so we were working with them to do analysis around when order dates come in, what can we guarantee for a shipping date? How long does it take for that process to actually create a given, you know, seconds per wedding invite type KPI and then be able to use all of that data and aggregate to make a smarter delivery system to help prioritize the work for factory workers to figure out what is the order I should actually start working on now because of all the downstream dependencies. And then for management to take a look at how can we better allocate our scheduling of our employees to make sure that we’ve got effective shifts that are going to help us achieve our goals.

(13:47 – 14:01)

Right. Again, all of these interesting questions that at face value are just like how quickly can we get an order out the door has so many different data components. And with manufacturers specifically, there are so many different applications and machines and people that are involved in them.

(14:01 – 14:32)

It’s just a really powerful example of how service-level data questions can be a lot more sophisticated. But often for these organizations, it’s just a matter of starting down that data journey and understanding that you are creating a lot of great insights, but likely your team isn’t taking advantage of that because of data access issues. And this basically maybe data access and then centralizing it in a place that you can actually look at from a standpoint of to make sense of it because folks can get overwhelmed by the data.

(14:32 – 14:43)

Right. And if you can’t put it in a consumable fashion, it’s hard to understand of how I can use the data. So that is great. 

(14:43 – 15:00)

But if you don’t know how to use it or what it says, it’s not helpful. So it sounds like what you’re trying to do is solve that problem. Yeah, a big part of our offering is that ongoing support and more of the self-service analytics environment on the front end.

(15:00 – 15:56)

I mean, this is ultimately the sandbox we want to create for people to dream big and ask different questions of the data and, of course, get them comfortable in a way that meets them where they’re at with their level of comfort working with data. But more than anything, a lot of what we get involved with on the front end, too, is not just landing millions of rows of data and say, good luck finding insights, but more of building data products that are going to actually resonate with these end users. It’s so much of our practice that we’ve actually trademarked the term human-centered data because of how we run design sessions and help connect an executive’s vision of how they want to view their business with what the data could be telling them and build a data product that’s going to make that easily resonate and give them ability to explore down to a deep enough level for their comfort and then give their team space to really dig deep if they need to go down to the granular details underneath.

(15:57 – 16:23)

And I’m sure that shows them about almost to the percentage of what kind of lift they will get by making tweaks or shifts on the data, which is what CEOs and leaders of businesses or schools want to see. It’s amazing how the smallest efficiencies that can be gained from these insights can lead to exponential gains. And you won’t you won’t see it by looking through individual spreadsheet rows.

(16:24 – 16:42)

You see it from the pattern that arises from data visualization and being able to anchor that up and be able to understand a little bit more of the pattern within the noise. Right. We geek out of making those shifts on a site or ad campaign by use of words or other or different platforms.

(16:43 – 17:08)

And we see the ROI and the money rise really quickly. And it’s just like it’s an addiction, frankly, from my standpoint, when I look at data and make a tweak and then see the difference. So from a, you know, a data geek myself, tell me a little bit about how you got down this road a little bit, because, you know, we must have went to similar experiences to to love data like we do.

(17:09 – 17:34)

Yeah, if we go way back to when I was a farm boy in Wisconsin. As I was going through high school and thinking about where I wanted to go in my career, I always had a fascination with numbers and working with data. I got into a little bit of lightweight programming and ultimately pursued a degree in computer science and coming out of coming out of university with my degree in computer science. 

(17:34 – 17:56)

I graduated in a time where Angry Birds was kind of the big thing in the market. So I had a choice of right, either going off to create the next mobile app or I ultimately got a job offer to just do full-time data engineering-type work. And for those unfamiliar with, you know, these job titles in this space, there’s quite a lot of you can do within data.

(17:56 – 18:59)

But data engineers are the people that are essentially laying the data pipelines that move data from point A to point B. And from there, I think that’s where I really got exposed to the power of the technology that was coming up and has continued to innovate like crazy over the last decade-plus within analytics. But what I also really loved about my journey and what keeps me around in the data slash consulting spaces, it’s an amazing opportunity to work with other business leaders, and organizational leaders, be able to peek into their years of experience and how they think about their business and how it operates and really be able to take their gut decisions, their intuition and break that down into what is actually being driven by that in the data. For me, it’s the ultimate puzzle of trying to decode the things that they feel versus what is actually being seen and at times confirm that assumption, but also even challenge their intuition as we continue to evolve and business always changes.

(18:59 – 19:34)

Long story short is the data piece for me, it continues to be such an amazing middle ground for me to be able to work on business problems and solve data puzzles in a space that is just super fun and happens to be growing. And I think we’re only scratching the surface as I get into more managed analytic services and helping organizations. Really what I’m focused on is helping organizations that would otherwise not have access to full-time data professionals get access to some of these amazing technologies that can take their organization to the next level.

(19:34 – 19:57)

And those are just fun, fun journeys with owners. And in the last year, we touched on a little bit earlier, anything else you can say about AI and how it’s changing the way we leverage data? I mean, we’re looking at it on a daily basis and it’s changing rapidly. And the big four are kind of moving quickly and you’re seeing things come out more and more.

(19:57 – 20:13)

But from your perspective, what are you seeing? Yeah, it’s been really cool to see the AI innovation in the data space. What has really happened over once, let’s call it chat GPT hit, everyone started using it. Media started talking about it a lot more.

(20:13 – 20:50)

All of the big vendors in this space have really spent all of 2023 advertising about this new AI capability that’s coming out for them. And 2024 is where moving to full production and moving to beta like this is where the rubber is going to meet the road of all these great things that we’ve been hyping up from marketing talk. Is it actually going to move the needle for what organizations are going to be capable of doing? Fundamentally, I think it is an amazing advancement because if you think about what people in organizations are doing, they’ve got their spreadsheets, they’ve got pivot tables, and they’re constantly pivoting by different data dimensions to try to find patterns.

(20:50 – 21:27)

What AI provides for data analysis is, imagine somebody doing that, but a million times faster to maybe try to find that little bit of signal in the noise faster than a human would be able to do it in a pivot table. But ultimately, AI is not going to really replace anything within data analysis. It is people who can lean in on these AI applications to do that initial data mining work, to be able to tell that story a lot faster and apply nuance to, well, of course, these are going to be correlated because in our business when revenue goes up, expenses go up. 

(21:27 – 21:35)

So that’s not really an interesting insight for me. It’s a tool in the toolbox. And it’s a significant tool in the toolbox.

(21:35 – 21:56)

You still have to understand from a standpoint of what are your company schools, and what are your target markets. Chat’s not going to be able to tell you that, okay, based on what we see in the marketplace, this is where we should position ourselves necessarily. It might give you some data points to help you back that up or maybe add some different industry focuses, but you still have to understand who you are as a company and where you want to go.

(21:57 – 22:19)

Yeah. And even more from a data foundation perspective for AI to actually be useful and meaningful for you is that organizations need to get their data in a common spot, in a well-defined spot. Many organizations, I think, are underappreciating how much foundational work that still needs to be done, and that your data can’t be a mess.

(22:19 – 22:32)

It can’t be all over in disparate locations. AI is kind of like this giant machine that you’re going to feed in clean insights and it will feed back hopefully good insights back. Yeah.

(22:32 – 22:39)

The problem with AI is that it can confidently lie to you if you’re giving it faulty data. It is. You have no way of seeing that coming.

(22:40 – 22:48)

It’s all about inputs. I mean, you’re going to have, it’s all about inputs with chat. I mean, if you put the wrong inputs, you’re going to get the wrong answer.

(22:48 – 22:53)

That’s a good point. That’s a really good point. So that’s a really good point.

(22:54 – 23:01)

Tell me a little bit about yourself personally. I mean, you’ve had some adventures, I understand. So tell me a little bit about that, if you don’t mind. 

(23:03 – 23:25)

So one big thing that I live my life on is a mantra around living a great story. I don’t know how it got injected into my life, but it is something I truly think about at every stage of every milestone that I live through. I want to make sure that I can look back at a life that has many different chapters and just some incredible stories along the way, whether good or bad.

(23:25 – 23:47)

I want to look back and say that I’ve experienced life. And for me, one of the big, we’ll call it life KPIs because I’m a data guy, is I want to always travel or have traveled to more countries than my age. And by the time I hit my mid-20s, I’d been married for a few years, no family or no kids, no house.

(23:49 – 24:27)

And ultimately, my wife and I decided to actually quit our jobs, sell everything, and go on something that we would later rebrand as a quarter-life retirement. And in 2016, we left the US on a one-way ticket to Tokyo and spent all of 2016 traveling to 28 countries around the world before we ultimately ran out of money and needed to start getting back to civilization, more or less. But as you can imagine, I’m a data guy that had the spreadsheet behind the scenes that was tracking the $50 a day per person KPI that I was trying to go after.

(24:27 – 25:04)

But more importantly, taking that step in life to, I guess, walk away from that normal, I don’t know, just American life of going to school, going to college, get a job, buy a house. We decided to short-circuit that a little bit or at least inject a little bit of a different path in there. And for me, I look back on those types of experiences and why it’s even further amplified my perspective that the more you can just plan out what are the life events you want to do, whether it’s traveling the world, whether it’s doing certain activities with the people that are important in your life, it’s something that I really advocate for.

(25:04 – 25:15)

And for me, travel represents a lot of that freedom that I’m always trying to inject more of in my life. Well, a couple of things. I’m going to have to chat with you offline to figure out where the places I need to visit.

(25:15 – 25:29)

But here in a few weeks, I’m monitoring a women’s conference and a panel. And it’s the road less, you know, talking about the road less traveled. And I definitely think it’s important for all of us to think about doing that.

(25:30 – 25:40)

And you obviously did that with that trip. How long were you gone? It was from February till right before Thanksgiving. So roughly about 11 months.

(25:40 – 26:04)

Okay. And did you stay to your spreadsheet? We did. We did.

I did. I did my homework in advance and I knew that in places like Japan, Australia, and Western Europe, it would be next to impossible to survive on $50 a day per person. But if you blend that in with some cheaper places like Eastern Europe and Southeast Asia, the math works out.

(26:04 – 26:30)

And it was amazing. Okay. I’m jealous. I’ll talk to you about that offline a little bit and get some tips along the way. But that is that is definitely the road less traveled and quite, quite exciting. Tell me anything else about your company that would be helpful for our listeners to hear that can help them grow their company or find efficiencies within their organization so they can serve like schools, their students better.

(26:30 – 26:51)

Anything else you would like to kind of hit on? Yeah, the data drive, like our big focus is helping organizations just understand what they could even be doing with data. And oftentimes what I do in my role is at least just show people the art of the possible. What can be done, and how it should be thought about.

(26:52 – 27:45)

I think often people when they approach something technical like data analytics, they see much like AI is just kind of this fuzzy technology that I throw out my business and magically it’s going to create positive outcomes for us. And oftentimes I think what many business leaders need help on is understanding what goes into a full data strategy, that it’s more than just a technology investment, but it’s truly around aligning people in process that you’re bringing in applications. You’re asking people to fill out certain things.

They’re going through the motions. For me, I feel like it is a missed opportunity if an organization is spending all this money on their business software and asking people to do things and not really taking advantage of all of this amazing insights that are being created every day. I think more than anything with now, when I started a business, I had no idea that a global pandemic was going to hit.

(27:45 – 28:05)

And all of these other recessions and the bumps and bumps and bruises that go along with anyone’s business journey. More than anything, I think it’s important for business owners to understand what’s working in their business. And what can I double down on? Much like when you do a marketing play, you want to understand what channels are working, and what motions are working.

(28:05 – 29:08)

And you can’t confidently tell that unless either you’re in their day-to-day as an overarching business owner looking at your business or a smarter, more efficient, and scalable way is to let data drive where you are trying to find those patterns and unlock them. And I think it’s important for people just to understand that if you want to be doing a lot of these cool investments around AI and helping unlock it for your business, it has to start with a foundational data strategy that I think too many people are relying on manual spreadsheets and heroic efforts by their staff to pull off. Right.

And like when I introduced you, I mean, if the data is messy, the data is not good. And so I mean, from a standpoint, from that standpoint, I mean, your company and helping solve that problem so that they are making good data decisions and good decisions for a company because it’s good data and not messy. So I think that’s an addition to the other points that you made.

(29:08 – 29:33)

You know, obviously, the efficiencies and time saving of the spreadsheets, that’s a given. But if that data is not in a good place, it’s not good data. Yeah, and it’s a very, like, underappreciated element of what goes into a data strategy is that there’s actually probably more time spent with business users and leaders to help identify what processes and even behavioral changes need to take place in the past.

(29:33 – 29:58)

That data analytics isn’t this magic bandaid that’s going to fix years of bad data entry. It has to foundationally start with a well-defined process and people following that process to get good insights out of that. But it’s like planting a tree.

The best time to do it was yesterday. The next best time is right now. And I or any other other system is not going to fix it, to be truthful.

(29:58 – 30:10)

So you have to have that foundation. In addition to that foundation, I know you all have teams in place to help put this in, you know, in play. And speaking from experience, especially working with e-commerce folks.

(30:10 – 30:26)

And, you know, when you have ERPs or if you have various systems that exist, it’s overwhelming for those who don’t know how to. And it’s probably not advisable to have. It’s probably better to bring in some folks who actually understand how to put those systems in place.

(30:27 – 30:53)

You probably save yourself a whole lot of time by having folks who actually understand how to put those systems. And you want to talk a little bit about that because I think that’s an important piece that I don’t think we’ve covered because there are the best practices on how you put those systems in place. Yeah, a big part of building out an analytics capability is that you have to have the right often niche skill sets in place to pull off an end-to-end system. 

(30:54 – 31:17)

I think when most people think about data professionals, what they hear from the big magazines is that everyone needs to hire a data scientist. And a data scientist is only a fraction of what goes into the full skill set that needs to be put in place. I would argue that if the first hire is a data scientist, they are probably not going to work out because it’s not the right skill set for somebody who’s starting from scratch.

(31:17 – 31:39)

That’s more of a sophisticated type of hire. And so my role, my evangelism with what I help small business leaders work through is just understanding that you know, there’s there’s data analysts, there’s report developers, there’s data engineers, there’s data scientists, there’s data product managers. There are a lot of very niche skill sets that need to be put in place to be able to build an entire end-to-end platform.

(31:39 – 32:20)

Of course, managed analytics services like it provide that whole slew of all of these skills because we know that most organizations, while they absolutely want to be making an investment in data and analytics and create more informed decisions, it’s not feasible for them to go out and hire often highly compensated data professionals, three or four at a time. That’s a lot of risk to put on an investment that they’re even still unsure of. And so I think I like to think that managed analytics services provide a more of a jumping off point of like, let’s see how data can actually work in your business without the risk of having to take on a lot of upfront capital and hiring risk that most organizations would have traditionally thought about.

(32:20 – 32:35)

Yeah, that’s a really good point. And we see that a lot because we’re we’re we’re kind of the consumer of that data. I mean, that we’re hired to put sites together to put e-commerce sites together, working on pricing products and those types of pieces.

(32:35 – 34:13)

And if there’s you know, if that data doesn’t have I mean, all the way from the data is not inputted correctly from, you know, apostrophes and all different things of how that how things data can be put in there. Consistency in that data pulling that consumer facing requires us to build things out to accommodate for that data not being in place, which costs them money to do so because we have to build out things to accommodate for bad data. And so it’s important to kind of address those issues.

Well, one last question before we I mean, this is proof point. This is what we want to take and help our listeners and our clients, you know, take something away. What is one point that you would like our folks to take away from our discussions? Can you add to that? Yeah.

The biggest thing that I can have people take away from today’s discussion is that data analytics today should not feel like a scary investment to make. The technical innovations, the types of ways that you can actually bring data as a strategic asset for your company. It’s never been easier to get started.

And more than anything, it’s just about the act of getting started and asking about what can data provide for us versus sitting back and being worried about what is data and AI going to do to my business if I don’t make that investment. Right. And a solid data strategy will help grow your company, either talking about marketing or operations.

(34:14 – 34:41)

They all kind of work together. So I mean, that’s the that’s the big takeaway from my perspective. Well, thank you. 

Thanks so much, Luke, for joining us today. We’ve been talking to Luke Kaminsky, founder and CEO of Data Drive. Luke, where can we find you? Where can listeners find you? Yeah, you can learn more about Data Drive services and reach out to me via our website at godatadrive.com. And I’m also incredibly active on LinkedIn and would love to connect with you there.

(34:42 – 34:47)

Awesome. Thank you for joining The Proof Podcast. We’ll see you next time.

(34:47 – 34:56)

Thanks for having me. Thanks for listening to The Proof Point Podcast. We’ll see you again next time. 

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