The Sidekick of Business: Understanding Data's Role

In this episode, Joe Crist interviews Solomon Kahn, a data leader and founder of two data-focused companies. They discuss the evolving role of data in business, the challenges organizations face in leveraging data effectively, and the importance of data literacy among executives. Solomon emphasizes the need for data teams to understand the business context to drive value and shares insights on the future of data in an AI-driven world. He concludes with advice on enjoying one's career in data, highlighting the importance of passion in professional success.

Transcript

Joe Crist (00:01)
Hey everybody, welcome to another episode of Thriving in the Digital Age. I'm your host, Joe Crist. Joining me today is Solomon Kahn. Solomon, thank you so much for joining us today. Could you tell the audience a little bit about yourself?

Solomon Kahn (00:14)
Sure. And Joe, thank you so much for having me. A little bit of background. I am a long time data leader. I fell into data, you know, 13 or 14 years ago when data science started to become a thing. I've led data teams, built, ran data businesses at both startups and big companies. And I am currently the founder of two different companies in the data space. The first is called Delivery Layer, where

We build customer facing data applications, so dashboards and APIs for data companies who need a simple access layer so that they can offer a product to their customers. And it's all around data sharing. And we can talk about how different companies and organizations share data with each other. And then the second company is called Top Data People. And this

was sort of an evolution of the marketing that I've done for delivery layer. But I built up a lot of content about how to be really great at driving impact with data. You know, so much of the data world is focused on the tools as opposed to actually being successful using the tools to drive some sort of impact. And I have a lot of experience with that. And I sort of built out.

a program to help people. So it's a six month development program right now focused on senior and mid-level individual contributor data people to help them get really good at the business side of data work. And so we can talk about data and new education nowadays and, and a whole bunch of fun conversations there. So that's, that's my background.

Joe Crist (01:58)
That's very cool. You know, it's interesting. So I've been, you know, in the consulting world for nearly a decade now, and especially in the realm of digital transformation. And one of the biggest challenges I find for doing this stuff revolves around data, right? Data is, I mean, our world sits on it, right? Decisions are made from it. The things we take, a lot of things we take for granted are all ran by data.

So obviously in your experience though, like what challenges do you see out there? Like what are, you know, companies and the industry is facing when it comes to just getting data and leveraging data.

Solomon Kahn (02:40)
Sure. So that's a big question, right? Because there's the, there's, to me, I see data most importantly as a tool that's supporting something in the business, right? Like, you know, data has been positioned in the world as like this hero, right? Like data and the data people are going to come and save everybody. And I like to think about data way more as the sidekick because data doesn't

Data itself doesn't do anything. In order for data to have some sort of impact, somebody else needs to take that data and then make a different decision than they would have made without it. Otherwise, nothing is happening. And so as a data person or as a data industry, I see it less as that we are doing something and much more that we are the sidekick helping the heroes who are actually making the decisions.

And so when you look at it like that, whatever problems there are in data is how to help the different heroes. But there are so many different types of people doing so many different types of things that it's tough to say that there's one, one different problem. mean, at Delivery Layer, certainly we're focused on a very narrow problem, which is for companies that need to build insights products or for organizations that need to share data effectively.

outside of your company, right? There's a lot of tools for doing that inside your company. Most data is used to support internal decision-making and then it kind of falls apart when you need to get outside your company. This is an underserved market because it's tinier compared to like all of the big company projects that are being used for internal decision-making. So that's where delivery layer plays, where it's sort of tiny bootstrapped startup. And then the...

Data education space or like how to use data effectively, that's probably a bigger market because I find that, and this is probably more true specifically focused on like data for business decision making is that data started when

Like the, the prototypical data people were sort of business people who really liked the data side of their business work. And so they had all this business context coming in over the last 15 years. What we've seen is that a lot of the education in the space has trained data people on the technical side. And then they go into data jobs, but don't have that same business background that the original data people used to have. And as a result, if you think about data.

needing a combination of the technical and the business side, think the industry has gotten behind where it needs to be when it comes to that sort of business effectiveness to actually use the data to get some sort of value.

Joe Crist (05:39)
So it's interesting. it's, there's, it sounds like a couple of different problems that are aggregating together, right? Where you have the, I guess the culture of data professionals, right? Really steered where we are today and that has left us behind and something I've noticed myself and I'm not really sure if you've seen the same thing, but a lot of people love data, right? Especially executives where you have, you're able to see, hey, we have,

the numbers we care about going the direction we want them to go or going the direction we don't want them to go, right? And they take that feedback. what I see is a lot of things are measured that don't need to be measured. with that though, there's still a lot of things that need to be measured that aren't even looked at, right? I mean, is that something you're seeing as well?

Solomon Kahn (06:26)
Yes, and that's going to happen forever. You'll have people on all sides of the spectrum. You're going to have people who really need to be measuring things that they're not measuring, and as a result, they're just flying blind. And they are making what, once you see the data, they will recognize as obviously bad decisions. And then you've got other people on the opposite side of that spectrum who are trying to

turn every decision into something that is obvious based on data. And unfortunately, that is also not possible with data. In many cases, even with the same facts, people can come to different conclusions. And the complexity of data for many big strategic decisions means that you're never going to have perfect and complete data.

Even if you did, it wouldn't necessarily automatically mean you know exactly how to answer the question. But even that is, an impossible standard to meet. And so to me, one of the most important things that we can do with data is, is to say, okay, data can give us all of these facts, right? It puts us in a much better position to make decisions than if we didn't have facts, right? It can get you 80 % of the way there. So if you

It can make a lot of things that you might have thought were smart, obviously not, right? You can exclude a lot of things, but it doesn't automatically get you to 100%. And so that's sort of how I think about this is how to approach it where you're getting a lot of value from it, but you're not overly relying on it. It's like a balance. It's not the answer.

Joe Crist (08:14)
Right, the way I've always seen data, right, when it comes to decision making, I'm a big fan of data-driven decision making. I think it's one of the best ways of approaching things and eliminating a lot of that bias that we have. But in my eyes, I've always seen data as it's evidence for context.

If we're telling a story of what's going on, the data supports the context of what we actually are trying to explain. But the thing is, too, it's easy to tell multiple stories of the same data, because we don't know. And that's the unfortunate reality. We see these numbers, and then we can make a jump from what the evidence states to what the actual fact is.

And by doing that, we are miscommunicating a lot. I've seen that plenty of times when you're looking at either revenue or throughput or things like that. And throughput's a great example. I've worked with companies that, because they think they're doing a lot of really good work and they're very focused on the output.

they're putting out and that's their main driving factor, right? It's like develop, develop, develop and innovate, which is a great thing to do. But if you're innovating things that don't actually aren't really valued by people, then it doesn't really matter how fast you output. That's the wild part about it. Data is incredibly useful, right? And it helps to make decisions, but there's the reality that we need to understand our biases as well.

towards that.

Solomon Kahn (09:47)
a hundred percent. it's like, can't, if you don't have the data, I mean, the data is just another way of saying the reality, like what's happening. That's all it is. If you have a sales team and you don't know which salesperson closed which deals, then how are you?

Joe Crist (10:15)
Yeah.

Solomon Kahn (10:16)
going to manage your business. It's going to be really, really challenging, right? And so there's many categories of data where the value is just so obvious and it's so ingrained that you couldn't even imagine operating a business where you didn't track which salesperson closed which deal. And then you get to where it is less.

less clear and then you're like, well, which, marketing effort influenced those deals? And then it's like a lot trickier to answer that question. And so you have data, but it doesn't automatically give you the answer. If someone saw a whole bunch of your, your, marketing campaigns and then met somebody in a booth at a conference and then, and then, you know, engaged with social and then a salesperson reached out like,

what was it that led that person to decide to buy? And it's a combination of a lot of those things.

Joe Crist (11:21)
So obviously the data world is tricky, right? I mean, it's just definitely something we need and something we need to get better at. What are some of the solutions or opportunities that businesses or even individuals can start really focusing on today to get better when it comes to data?

Solomon Kahn (11:34)
Yeah. So I, so I, so on this, for this topic, I'll sort of put delivery layer aside and focus on a lot of the work that, I do with my top data person professional development program. because there's, are things for businesses to do, but I actually think that it is not fair to put the onus on the business people to like,

be able to navigate what can often be incredibly complex data when that's why you hire data teams to help you with that, right? Like there's a level of literacy that I think every executive needs. And in my experience, every executive has nobody, I've worked with many, many people over the years and I probably disproportionately have worked with more sophisticated organizations, but still.

Pretty much everybody understands the data when you put it in front of them. Executives are not stupid. But what is challenging is when you get into things that are very complex. And that's why I think data teams are important. Because I think that data teams are necessary in order to effectively organize all that data from the technical side and then get it to a usable form for executives.

That's why I think that the skill of those data teams is one of the most important factors because this is not, this is not, I don't, some people want to have the data business be a business of like cogs and widgets where one person is completely replaceable for another person. And there's no difference in output between, between.

any other data people, but I don't, I don't, haven't seen that. And that's not been my experience. My experience is that it is an art in a skill to be able to take a very complicated, chaotic world and then a whole bunch of data and then mash those together and then interpret that in ways that can help drive clarity for your executives and impact for your business. And I think that that is a skill that can be trained. And I think that there are.

things that data people can do to be substantially better at that. So that's kind of how I see it.

Joe Crist (14:04)
Interesting. So let's make sure I understand this. So really good starting point is it's not just the data literacy, but also having the data team. a big thing, something I'm a fan of, right, is cross training across different teams, right? When you have, we'll say, you know,

marketing explaining why they're actually marketing or how they're marketing products to the product team. Right? And the product team saying, well, well, well, that's not realistic. We can't say that. You know, when you have that communication and people are actually able to really like essentially build off one another, like would you recommend that for data teams as well? Like actually having the data teams come in and like talking to executives and explaining to them and talking to the team, like they're.

I guess the teams they support and that support them saying like, here is what we're actually doing. Kind of like a more like agile approach when we're working like internal things, right? Where it's just like, hey, like what do you need? Like here's what you need to know, but we need to know what you need from us. I know it's a fairly complex question.

Solomon Kahn (15:12)
yeah, I I think it's less of a nice to have and more of a must have, but because data people, if you don't understand the business, you can't do effective data work. And that's been one of my major points that even compelled me to start this, which is that many data people

don't have a good understanding of the businesses that they're supporting and it is holding us back as an industry. But for even the examples that you gave, I don't think this is limited to data. think that business functions are connected in pursuit of a common goal. And so the sales team that sells

product features that don't exist because they don't talk to the product team, the marketing, the marketing teams that are describing things that don't exist. The product team that doesn't know from the sales team or the marketing team that it's these three features that everybody is asking for and they're easy to build. And if you just built them, our whole go-to-market would be substantially easier. Like I don't see any team being successful in a vacuum, but I think, and I think data.

is a level above what you would expect from typical teams there. Because for me as a data person, if I'm supporting marketing, how could I possibly support marketing if I don't even know what marketing is doing or what their goals are? How would you expect that you would be effective in helping them? You don't even know what they're trying to do. And I find this way too often is

that data teams are marginalized because they don't even know the major goals that their business units that they're supporting are working on. And so they're working on their own data thing. It's like, yeah, we're building this new data warehouse. Meanwhile, the business is moving along without them. And I think that that is a big missed opportunity for data, big mistake to be operating that way for the company.

Joe Crist (17:27)
Yeah, you know, it's, I was thinking about it as we were talking, just trying to imagine in my head, like a business, we have this like data team just building things, right? And they're doing what they do best. They're trying to, you know, essentially build the platform with all this integrates, but they're really not understanding the business case behind it. Right. Meanwhile, the business, you have all these people like, you know, in the marketing team, sales team, product development engineers, even IT, right? All just like,

Not knowing what's going on, right? They're, they're existing. And I'm sure because that a lot of people, let me realize they have the capability of a data team. Like I can't tell you the amount of companies I've worked with that don't even know certain teams that even exist. That can really make a huge impact on what they're doing. All I have to do is reach out and ask.

Solomon Kahn (18:10)
yeah.

And let me also give some more context because when you explain it this way, it sounds obviously stupid. So why would any smart person operate this way? I think that it plays out in a way that is more reasonable than you expect, which is why it happens so often, because it doesn't seem bad as it's happening. Because what will occur is that the CEO and C-suite and all the business leaders

will come in and say that we need to get substantially better at using data. And then they'll hire a data team and their mandate to the data team will be build out a data capability for us. And the executives, the data leader will rightly look at a very bad data situation and say, okay, I need to build out infrastructure because once we have a stable foundational infrastructure,

then we can get kick in and everybody can have the data that they need and the data is gonna look great. And they set themselves out oftentimes to sort of an impossible standard. And then they focus a lot on backend data infrastructure stuff. they're spending 75 % of the time, 80 % of the time doing exactly what they were told, which is build out this data capability. The business was...

Is sitting there and saying, OK, we need this data capability. It's taking a lot of time, but this is what we need. This is what we need to invest in, and I guess it's just not ready to help me with these big initiatives, but I'll sit back and I'll wait because we all know we obviously need data, so they're happy and they're happy until a couple of years go by and suddenly everybody goes. We've been spending a lot of money for a lot of years now and now I'm not so happy, but you've got this culture where.

The business leaders haven't had the right expectations of the team. And so the team has executed to these wrong expectations. And now they just like sort of flip a switch and aren't happy. And the data leader does exactly what they were hired to do. And they were told to do meanwhile, that probably wasn't what they should have done. And, and then the team culture is set up in a bad direction because the culture was we're focusing on this and building out this data infrastructure. And then.

That was what they've been told. That was what everybody has done. And then changing that culture, as you know, from, you know, any transformation is tough. And that's how data teams, that's how smart, reasonable people get into a bad situation. And just so as to give some practical advice to anybody listening to this for how to avoid this, I try to think about this. I call it my a third, a third, a third, like think about it in thirds. And so, cause you do need data infrastructure investments, right?

But that should be one third of what you do as a data person. And then the, should have one third as like high level strategic initiatives. Like what are the big questions that we have for the quarter that we really need to answer this quarter that are going to move the business substantially if we answer them or have the potential to move the business substantially. These are like your bigger research projects, your churn models, your lead scoring, your...

recommendation, like all of those types of things would fall into that bucket. And then you've got a third ad hoc work where you're sort of in the trenches tactically helping your business teams. And I think if you kind of split that to where you're focusing two thirds on the business and one third on the backend, instead of 75, 80 % of the backend and maybe 20 % on the business, it sets you up to be far more effective in actually.

driving more business value. Yes, your data infrastructure is slow and yes, things are messy, but frankly, even a couple of years from now, things are still going to be messy on the data infrastructure side. Data infrastructure is always messy. So I hope that was, that was, it was a long rant, but I hope it was beneficial.

Joe Crist (22:15)
No, that was good. I like that.

Yeah. You know, it's, I think about that a lot too, when it comes to the data. And I've seen that myself, right? Where it's, and this is not just a cultural issue when it comes to like data team. This is every team I've seen where it's the expectations aren't really clearly communicated. I like what the business goals are. It's like, we need this, this, and this. here's why, right? Because you have, there's always, but through,

industries right in the more in the more like complex ones that I see this even more where it's you have somebody with very little understanding of actual business objectives and business strategy who are

highly technical people and very competent in their field are giving directions to other technical people in very technical terms saying we need this, this and this, but they're really eluding the business value. Whereas saying, hey, we need to do this because of that. And we are trying to accomplish this goal here. And instead of saying, hey, we need you to help us figure out how to get here, it's go do this. This is what the bosses want. And when you have a miscommunication,

that alignment and that clear vision. It's easy. It's very easy to do that. And then you start noticing this shift and it gets further and further and further away as you were saying before where it's, well, this isn't what we wanted. Well, this is what you have for.

Solomon Kahn (23:42)
Yeah. And if you think about the

analogy between software engineering and data, software engineers typically have product managers specifically for this reason, which is that everybody recognizes that the default is to go into technical work and you need somebody with a strong vision to make sure that that technical work actually ends up with some sort of business value.

people are overwhelmingly doing acting as their own product managers. And they're the ones that need to have those business conversations with the stakeholders that are coming to them. When they came from the business originally, those were far easier conversations because you could interpret what the execs like if you worked in marketing for 10 years and now you're the marketing data.

person, you know exactly what the marketing leader is talking about and that you have a lot of context to interpret that. If you've just come from technical data work, you don't necessarily have that context and that's something that is important to develop if you want to be successful.

Joe Crist (24:56)
Yeah, no, absolutely. Yeah, it's definitely challenging to work with anybody when you don't actually have the domain knowledge. If you don't speak the same language as they do, right, and you don't know what they're talking about, especially when you don't say anything about it, right? Is that we've all been in that position where we don't look stupid in front of someone. You know, when they were talking about all these really complex things, and I'm sure there's plenty of non-technical people out there listening to data people, and they're just like...

All right, man, whatever you say, go do it. You know, and really that disconnect is pretty detrimental. And it's, like I said before, it's one of those things as the time goes on the gap widens, where you have that greater disconnect and it becomes harder to get this recentered, right? Because it's...

Solomon Kahn (25:37)
Yeah, totally.

Joe Crist (25:43)
people aren't asking those questions because either they feel awkward because they don't know, or they're just not enough, I guess, cross-domain knowledge of, here's what we're doing. And especially for data education, data literacy, everyone needs to that.

Right, even at a small level where can at least be a part of the conversation to have that and ask the questions that matter to the data people and the data people need to have the domain knowledge for who they're supporting so they can ask the right questions. Because if you don't know, you just don't know. But if it's really challenging for people if they don't even know where to start. A great example of that is when I was first learning how to code.

I knew so little about coding, I didn't even know how to ask for help because I couldn't even define the problems I was having. I just said, code didn't work. I don't know why it doesn't work. Right? So it's one of those things where you really do need to be able to create that context and actually have those conversations that do drive business value.

Yeah. So I do have one last question for you though. I'll let you know, I do have two questions for you. So the first one, right? So obviously the world of data is changing rapidly. The world with AI, the amount of information just out there, right? It's overwhelming, right? Where do you see the future of data? know, as with the creation of everything from

AI to IoT to everything in between. Like how do you really see data impacting and changing the landscape?

Solomon Kahn (27:33)
Yeah, I I, I see it as continuing to be extremely important. And I think, you know, over the last 15, 20 years, we've seen so many ways in which data is just built into the fabric of things. I mean, even in, even in this recording, there are data algorithms that are doing noise cancellation in the background and just

like helping us have a better conversation in a totally invisible way. I don't think that changes. And I think that with AI and GenAI, we're going to see data become even more ingrained into our lives and in good ways and also coming with some drawbacks.

and, but I think data is just going to continue to be extremely important and, the, the sort of impact of data stuff in our everyday life is going to continue to grow.

Joe Crist (28:44)
All right, no, absolutely. I mean, it's nice to be forward to like data being the sidekick. I think it's a really amazing analogy, right? Where it's the partner that really helps us make those like good decisions and data does control, it does impact everything we do.

Right from traffic lights to the navigation or cars to the way businesses make decisions. Data is everywhere and it's always gonna be everywhere. It's one of those things that I think we've gotten to the point where we can't exist without it.

Yeah, so my one last question for you, right? It's definitely my favorite question. Obviously, you've been in the game for a while, right? You know data, you work a lot of different companies. If you could give the audience any piece of advice to walk away with today, what would it be?

Solomon Kahn (29:39)
yeah. So you, you, you give me some heads up on this question. So I will give what, is probably the most, unexpressed. Like I have a module in my professional development program right at the beginning. It's called, enjoy the ride and, or enjoying your career. And that's probably the thing that people don't expect to get in it. But my, my experience has.

been that it's tough to really know what's going to happen in the future. And frankly, it's tough even to know if the decisions that you're making now will end up being good or bad. I always see career stuff and job stuff as like two things. It's like, number one, as long as you can keep learning and keep trying, then eventually you will figure it out. And so

And the one danger for data people is that they get so frustrated and burned out that you just leave. So my advice is don't be afraid to do things because you enjoy them. Like that's a totally legitimate reason to make even career decisions that impact your career. It's not always about the highest paying job. It's not always about the most optimized everything. If you

If you enjoy what you're doing, then you're probably going to be better at it. But even if you're not, it gets rid of this more existential risk that you kind of burn out and blow up. that's my, my, maybe unexpected advice, which is yes, I'm a data leader. Yes, I care about the facts and the statistics about things, but it is

Also data and a fact, whether you enjoy something or not. So use that as part of your decisions.

Joe Crist (31:38)
No, I love that man. That's good. I think that's the lesson that we all really need to hear, Just gotta enjoy it. Yeah, enjoy what you can, as you never know.

Solomon Kahn (31:48)
Yeah, you know. And listen,

if it was easy, they wouldn't need you. So.

Joe Crist (31:54)
Yeah, right.

Absolutely. Well, everybody. was Solomon Khan. Solomon, thank you so much for joining us today. I really appreciate it. I definitely learned a lot. I'm sure the audience did as well. And once again, everyone, thank you for joining us listening. Have a good one, everyone.

Solomon Kahn (32:12)
All right, thanks Joe.