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Despite heavy investments in sales teams and data infrastructure, many organizations struggle with stagnating sales productivity. The core issue often lies not in a lack of data, but in failing to make critical insights immediately actionable for frontline sales and marketing professionals. Without a direct connection between data intelligence and operational tools, businesses miss opportunities to target high-value customers, personalize outreach, and accurately measure campaign impact.
Sam Arnold, Head of Product at Census, brings a unique perspective as a sales professional leading product development for a data activation platform. He shares how Census’s reverse ETL approach directly injects data warehouse intelligence into operational tools, enabling sales reps to make better decisions faster.
The conversation explores how data activation platforms bridge the gap between data insights and sales execution, the dramatic productivity gains AI tools offer individual reps, and the broader implications for transforming business operations and product development.
Key Takeaways
Direct Data Activation Drives Sales & Marketing ROI
Data stored in a warehouse provides limited value until it powers operational tools. Platforms like Census enable sales and marketing teams to directly access segmented customer data for targeted campaigns, lead scoring, and personalized outreach without needing data team intervention. This closes the loop on data investment, improving efficiency and tangible business outcomes.
AI Tools Multiply Individual Productivity
AI-powered large language models significantly reduce the time spent on research, summarization, and content generation for sales professionals. This efficiency gain—ranging from 30% to over 500% depending on the task—allows B and C-level performers to achieve the output and insight quality previously exclusive to A-players. Adopting these tools is becoming crucial for competitive advantage.
From Insight to Action: The Data’s Last Mile
The primary bottleneck for data teams is often not the generation of complex insights, but their practical application by business users. True data value emerges when data science outputs—like customer churn predictions or fit scores—are automated and integrated into daily workflows, turning dormant intelligence into tangible business results. Data leaders must prioritize this “last mile” activation.
AI Accelerates R&D and Prototyping
Beyond immediate sales efficiency, AI is reshaping core business functions like manufacturing and drug discovery. By enabling rapid simulation and virtual prototyping (e.g., of molecules or construction methods), AI drastically cuts down on expensive physical R&D cycles, lowering development costs and speeding innovation. This signals a fundamental shift in how physical products and services will be developed.
Related: CorrDyn helps organizations build robust data engineering foundations that make data actionable, and we specialize in marketing analytics to drive tangible business outcomes. Our data assessments identify critical gaps in data activation, and we guide clients on data cost optimization when adopting new AI tooling.
Full Transcript
Jason: Welcome to Data BS, a show dedicated to tackling the big questions impacting world data and ML AI without any of the BS. My name is James Winegar. Each week I sit down with guests from across the data ecosystem to unpack how they’re shaping their businesses or the businesses of others through the real-world application of data engineering, ML AI, infrastructure, analytics and more. No fluff, unfiltered, but slightly edited to remove noise, this is Data BS, let’s get into it.
James Winegar: Welcome to the Data BS podcast. We’ve got Sam Arnold here with us. I think he knows a little bit more about himself than I do, so I’m gonna hand it off to him to introduce himself.
Sam Arnold: Thanks James, happy to be here, thanks for having me. A little bit about myself, I’m an enterprise account executive at a data tech startup called Census. Series B, Sequoia Andreessen backed, just about 100 employees at this point. But I digress. My background is that I’ve been a sales professional in a few different industries in tech, including Martech and developer tools and Census is my first foray into data. I’ve had a lot of conversations at this point in the last year with companies big and small about everything they’re going through with data. It’s been a crash course, learned a lot and happy to talk about some of this with you today.
James Winegar: You work at Census right now and we work a lot with y’all on certain projects and Census operates in a pretty competitive space in Reverse ETL slash customer data platform slash etc. How do you think that Census is differentiated from these other data activation platforms and what parts really stand out to you?
Sam Arnold: Maybe for folks who are listening who don’t quite know what some of these concepts are or what Census is, I can give a quick explanation. Like you mentioned, Census is in the data activation. We are a reverse ETL platform. Actually, my founder Boris and his co-founders really coined the term ‘reverse ETL’ all the way back in 2018 when syncing data out from your warehouse into operational SaaS tools was a foreign concept, nobody really did it and from what I’ve heard, he was laughed at at the time. But now fast forward to 2024 and it’s a staple in the modern data tech stack. What we do is the opposite, some people might be familiar with Fivetran and other ETL tools that take data from various sources and get it into a data warehouse in a usable state. What we do is the opposite, we take data from the data warehouse and we sync it out to any of hundreds of different tools. It’s a little bit different because we have to both serve data teams who have pretty rigorous requirements around how to handle their models and govern data and observability on these data pipelines and so on. That side’s important, we serve data engineers and technical folks. But then because we are solving a couple of problems for them, not least of which is making data more self-service for their business teams, we’re talking marketing teams, finance, sales, you name it, there needs to be a good user-friendly front end so that once they have the data connected, they can hand the keys to some of these business stakeholders and they don’t need to know SQL or any technical concepts to be able to stand up a pipeline of data into their own tools. To your point, it is a very competitive space. There are a lot of tools out there that are trying to solve this problem in different ways. How we are differentiating ourselves is we are getting away from a particular category that everybody else has cozied up close to, which is the CDP, right? Customer data platform, everyone’s calling themselves a customer data platform, I think it’s lost its meaning. We’re looking to build bridges between data teams and the entire rest of the business, not just a marketing team, for example, and that’s really where we’re differentiating.
James Winegar: Just in my experience dealing with Census, we’re mostly trying to enable product and marketing teams. Sometimes there’s other use cases, but that’s been our primary. I have a customer right now where we’re walking through some pretty complex segmentation use cases across multiple different entities, users, events, booked meetings. It’s about four or five different entities. What the real goal is for us to provide them a tool, Census in this case, that gives them the ability to without calling a data engineer or our team or whatever to say, ‘Okay, give me this really funky segment so that I can do very targeted transactional type use cases, not even marketing use cases’ that were actually really complicated in their platform just because across so many different entities. In enabling that, it’s going to allow a product team to understand these really niche customers that exist because you’re grouping across several different entities. Then also for the marketing team or the sales team or whatever, it’s going to enable them to be very specific on targeting. They have pretty high average customer value, so it’s worth it to them. It’s really interesting to hear about computed fields, there’s a lot of use cases for computed fields, but we’re really trying to enable the non-technically savvy user. GPT or prompt-based aggregations or roll-ups seems really interesting. Is there any type of use cases you could talk to that you’ve seen success with for the prompt-based?
Sam Arnold: Really common use case. Why are data use cases so big in marketing and sales? It’s pretty obvious. It’s because that’s the straightest line between using data and making money. Whereas other things like helping the finance team move faster and helping an operations team handle tickets quicker and stuff like that, all that’s good, but usually the tip of the spear with adopting any new technology is often the marketing and sales team because they’re trying to use whatever edge they can to stay ahead of competition. With that in mind, what we do is try to give the sales team, like myself, insights to use to help us know who to target with our outreach, who to go after. We have hundreds and hundreds, if not thousands, of potential companies out there that could all use our product, but who should I reach out to right now? There’s a lot of fads around signal-based marketing and sales and enriching from various sources and so on. What we do is we have a lot of data that gets ingested and enriched in our own warehouse. We will ingest some of that are signals like job postings, for example, for different companies. We have calculations that run as well as a prompt that generates a couple of things for me and my coworkers which we call a ‘fit score’. What that does is it’s a numeric score that is an attempt to put a number on how likely a given account is to get a lot of value out of Census. Things like what’s their tech stack? Oh, they use Snowflake, they use Salesforce, they use these different apps that we integrate really well with, they get a lot of points for that. Do they have any open roles for data engineer? Do they have a lot of employees that work there with ‘data’ in the title? There’s a whole list of different criteria. But I don’t really have to think about that, when I’m going in and doing my job, I can sort a bunch of accounts and select who is going to be really good fit. That’s really helpful and a lot of companies are interested in that use case because especially with the changing economics and the macro environment being what it is, the bar for sales productivity is really high and very few companies out there really hitting it. They’re spending a lot of money on salespeople that are just not productive and they’re looking for anything they can take advantage of to move the needle.
James Winegar: In terms of the utilization of AI tools, I’m super bullish on the efficiency gains for people. I’m not bullish on artificial general intelligence or AGI, but I’m super bullish on how do we make Sam and James two to five times more effective with their time because they’re not spending as much time on wording details, but instead ‘What’s the message I’m trying to send and why I’m trying to send it?’ Really think that in terms of our team, summarization has been a major time-savings for our project managers and things like that. Just take some notes in Google Docs, use Gemini or ChatGPT to summarize it, generate action items and then just do a quick review to make sure, ‘Hey, it missed one action item because it was locked into some other stuff.’ Okay, cool. What used to take 30 minutes now takes three or four minutes for somebody.
Sam Arnold: Even better. For me, I use tools every day. My favorite right now is Claude. I love Claude, Anthropic’s GPT tool and I do use ChatGPT as well. I even use Grok sometimes which is cool. But the benefits are huge. It stuns me that not everybody takes advantage of this. I was on a call yesterday preparing for this meeting with a very technical company that sells biotech stuff that I cannot even begin to fathom. I dropped Bio 101 in college a million years ago. I like biology, I’ll read pop science books about it, but when you start talking about the stuff that’s actually commercialized out there, no idea. Their website did not help at all when I was trying to research. What I did and it took me all of 10 minutes, I took information that I found, copied and pasted from their site, copied and pasted from their earnings call transcript, which I love doing, and then gave it to Claude and asked a series of questions. Within about 10 minutes I fully understood what they sell, who they sell to, what’s going well there, what’s not going well there. I summarized this stuff into a slide that I showed at the very beginning of the conversation with this group and they were very impressed. They were like, ‘Wow, no other sales rep we’ve spoken with has outlined clearly what we do in the business context for this discussion.’ They were like, ‘Did you use some kind of ChatGPT for this?’ I said, ‘Oh, it’s my little secret.’ But of course I did. You’re right because it takes-I mean, it’s like in college. I did philosophy in college and all of the work that you’re doing is reading large volumes of text, trying to understand it and then giving a summary and a perspective about it. It’s such a huge consumption of mental resources and we’re able to make that so much better.
James Winegar: I think we become selective about our information consumption. When I think about AI tooling and things like that, I really think the user experience is the key. How do you get Sam to be effective in the utilization of the tool? That’s really what ChatGPT when it first came out, that was the secret sauce is it was good enough that anybody could use it and understand what was happening and it gave good enough results that it had utility. Because before all this stuff has existed but it wasn’t good enough to give you good results and have that user experience that everybody’s like, ‘Oh yeah, I can use this.’ Another interesting thing is when me and you have worked together on a few different projects or deals or whatever and the outline of ‘Hey, why do you care about talking to me?’ is something, I deal with a lot of salespeople trying to hit me up all the time, too. I’m a sales guy practically by trade because I’m running the company. Talking to the business, what does the business actually care about and what’s the use case and not just ‘Hey, you need my tool. Here’s why you need my tool.’ really sets the ground for ‘Okay, we’re establishing a relationship built on trust that you want, that I understand what you’re looking for or might potentially need and that I have that understanding so we’re on common ground.’ And then now we’re going to have this discussion about why I think in your case Census makes sense for y’all to use.
Sam Arnold: In the context of data, what’s interesting is we’re taking a lot of the hard work off the table. There’s even other companies, for example, I talked to one of the executive directors at the city hall of New York City, here where I’m based, this morning and we talked about Census a little bit and that was cool, but I also told him about a startup that a friend of mine made which is a different use case of the same technology, it’s called Hamlet. It’s a really cool tech company here he just spun up and it’s getting a lot of traction. What they’re doing is they are ingesting large amounts of public data for local cities. Things like city council meetings, the minutes, the videos, different stats and so on, things that are happening in the town. They are producing high-quality summaries of these for their citizens. If you live in, I think Saratoga is one of the ones that have adopted this, then you can go in a portal now and within minutes see everything that is going on in the city. What are they approving? What are they talking about? What are they debating? It’s a way of furthering civic engagement. Because it’s such a huge task to go wade through these data and it really prohibits people from having any kind of influence at all. But now with this technology, these cities are able to open it up and we’ll see if New York ends up going that way. He was very interested in this where you don’t need to be a well-read person with a bunch of time on your hands to understand what’s important and then to decide if and when to get involved. I thought that was a really cool use case as well.
James Winegar: It’s crazy interesting to me because the local government is the government that people are least engaged with and it’s probably the most important in their actual day-to-day lives in terms of what’s going on with my school systems? Me and you both have young kids. We care about they’re going to get a good education, it’s safe and all that other stuff. What’s going on with the business in the area chamber of commerce type meetings and things like that? I don’t have time to go to every chamber of commerce meeting in my suburb area. Being able to get just ‘Hey, here’s the meeting minutes summarized’ which is basically what our project manager is doing for all of our meetings to say ‘Here’s the one-minute bites that you need to consume about what happened and why it happened.’ That’s super interesting. I hope that takes off and he gets a lot of cities signed up for that.
Sam Arnold: It’s going to get even more intense, I’m sure. There’s this concept also emerging, I think Apple might even be promoting this, of local AI agents that will be running on our own local hardware, your own phone. It will have access to your own little private ecosystem of data. What are you buying? How are you spending your time? What restaurants do you go to? What movies you watch? Think about everything that’s in just your Gmail inbox, for example, how much you could learn about that. I think what we’re going to end up with is a situation where we’re going to all have our own AI agents, they’re going to know us even better than we know ourselves. You’re going to be able to ask questions like, ‘I want to move somewhere. Where should I go?’ It’s going to know, it’s going to say, ‘You’re for sure going to be way happier living in Boston’ or ‘You should think about the benefits of living in Zimbabwe’. Stuff that you’ve maybe not even thought about. I think it’s going to be interesting.
James Winegar: It’s like the internet coming in. When the internet existed suddenly, nobody knew what to do with it. I think the GenAI use cases in particular opening up that just because the ability for every person to interact with it, not just ‘Hey, the thing that was running on Google that gives you better search results’, that was most people’s interactions with AI until a couple of years ago. Now they are actively probing that system. I do just want to remind you that the podcast is called Data BS, so we’re allowed to deviate a little bit. I’m sure Census would really love us to stay on topic, but more than happy to keep exploring. If we look back at history, humanity’s always come out on the other side typically in a better place. Industrial Revolution, a lot of people had big problems back then because it really changed the dynamic, centralized a lot of power in-I don’t remember the word off the top of my head but oligarchs basically and there was a bunch of political turmoil during that time. But if you ask yourself, what’s the quality of life pre-Industrial Revolution to 30 years after the Industrial Revolution started? Night and day. Airplanes, the ability to travel across the world in less than a day. What did that enable? It enabled globalization to even be reasonably possible. Information sharing was key to being able to do globalization. People have been trying to-the Silk Road and things like that, people been trying to do that for thousands of years but just the technology-traveling 3,000 miles in a wagon through mountains and deserts and things like that, it’s very painful. Now we have our logistics system with gigantic boats, barges, etc. and airplane, we can really see the evolution how that’s impacted our overall quality of life. Medical advancements, polio and all those types of things. My grandparents were scared of polio, we don’t even talk about it anymore because it’s basically nonexistent in the US. It exists in Africa and some places, but there’s people working on that as well. Tuberculosis pretty similar. Medical advancements and then internet comes along, personal computers, mobile phones. Now we’re in this AI craze and I think that AI, there’s hype cycle around it, but there’s also just truly use cases that were impossible prior.
Sam Arnold: I think the hype is overhyped. I know a lot of people, there was articles that came out recently from Goldman being like, ‘Ah, when are we going to see the money?’ That’s of course going to be their perspective until the money comes in. But I don’t think that people get it. I think the people that are poo-pooing AI and saying it’s just a fad, I think they’re going to feel pretty silly in a few years because I think the potential is even bigger than what we can currently fathom. I don’t think it’s smaller. I also think that you’re right in that what’s going to happen with a lot of these applications of AI is that the work of even coming up with ideas for how to use it will start to be used by AI. Once we hit this point where there’s this self-reinforcing virtuous cycle of AI development, another thing that’s going to be happening is that a lot of things that are-I was just talking to somebody else in the manufacturing industry about this. Manufacturing has not really advanced too much in recent years. I remember when I was in college I spent an internship at a Goodyear tire factory. On one side of the factory was the brand new shiny machines, on the other side of the factory was all this stuff that’s been there since the 40s. It wasn’t really that different. The new stuff was slicker, it was white instead of dirty green, but it was doing the same basic stuff just slightly better. There’s not really been a leap forward. I think what’s going to happen is that a lot of things that require physical material to develop is going to become more virtual. You use the medicine example. They’re using AI now to basically simulate and generate what different kinds of molecules can do, which is a big part of medicine, it’s coming up with a molecule, trying to predict what it does based on the shape of it and the properties of it. Within minutes they can generate millions of these things now and figure out with AI which ones are likely to have what effects and then they can take that and run with it. I think we’re going to see the same thing in the physical space in all kinds of places. What’s the best way to build a house? Is it really pulling up with a truck full of lumber in the way that we’re doing it now? I think a lot of these things are going to be simulated, they’re going to be done just like we’re simulating molecules. We’re going to simulate all kinds of things in the virtual world to prove it out before we take it into the physical world to actually do it. That’s not really happening too much yet, maybe a little bit, but I think it’s going to explode.
James Winegar: When you’re talking about manufacturing, drug discovery type use cases with medicine, a lot of that’s how do we reduce the prototyping time? Prototyping is the most expensive part of most endeavors in terms of R&D and things like that. Once you get-
Sam Arnold: That’s a big part why drugs are so expensive. They spend billions of dollars to develop a drug and they need to recoup those costs. If you can reduce that, we’re in a much better state.
James Winegar: Exactly. I have a few different thoughts here and then I want to bring it back to the more-let’s talk about you and the sales team and then we’ll close it out. On the manufacturing side, what I was thinking about is reinforcement learning, and this concept of a digital twin, so you create this digital simulation environment which is basically equivalent to a physical space what you were talking about. Then we got reinforcement learning. When we think about these large language models and how good they are given the amount of information they’ve consumed, there’s a person I don’t remember how to say his name correctly, LeCun at Facebook, he’s the director of AI at Facebook, he has this thing about the amount of information that these LLMs have consumed is tens of thousands of years of consumption but the intellectual level is roughly equivalent to a four-year-old. We can do things to make it better. I think he believes that that way of approaching generative AI through LLMs is not the end-all-be-all, he thinks there’s other more effective ways we’ll find in the future. But it’s interesting to compare intelligence levels based off the amount of information consumed. How do people operate within the learning cycle relative to how machine learning operates? Reinforcement learning is this do a thing, update your information, you play Mario Brothers or something like that. When you do that, it takes a really long time for these AI algorithms to be able to beat Mario. I think there’s something to be said there too about just overall we’re not even close to human-level ability to learn. All we’re doing is throwing tens of thousands of years of equivalent time at them. I just thought that was an interesting idea to think about.
Sam Arnold: But at the end of the day our intelligence developed over however many millions of years, some say a lot more quickly too, so we get to the point where we can simulate the evolution of a fruit fly brain and we can speed that up through compute to where it can run about a billion years in an afternoon, who knows what we turn out with.
James Winegar: One of the things I was thinking about bringing it back to business and really your profession is sales, is that there’s a high skew in sales reps. At a given company there’s a few people really bringing it home and then most of the people aren’t making their number for the most part. When I think about sales profession or really just where you have to execute at a high level, the utilization of these tools is the difference between making it and not making it. I believe that for most people we’re seeing a 30 percent to 500 percent increase in productivity by leveraging these tools, just more based off their role. I think sales is very much outsized in terms of the productivity gain because it’s about your information consumption. Before, five years ago you’re reading 10-Ks, you’re looking at job descriptions. You’re basically trying to bring all this information in, but maybe you read the job title but not the description. Then when you read the description it’s really not what the job title is. We summarized all this information ourselves historically. You spent 20, 30 minutes on it, but then it wasn’t as effective and then that impacts your ability to have a really powerful conversation with stakeholder that you’re talking with. Now like we talked about earlier, 15 minutes you’ve really digested the business even if it’s complicated and understand what they’re doing, why they’re doing it, how they’re doing it, what the problems they’re facing are, etc. That’s leading to the ability really to establish a relationship because you understand what’s going on and then by proxy is the ability to close a deal or to say, ‘Hey, this doesn’t make as much sense’ and divert your time to where it does make sense. I think that these efficiency gains are really powerful within the sales rep environment.
Sam Arnold: I would agree. A lot of sales is really like gambling. You don’t have chips, you have time. Your ability to be effective as a salesperson is directly linearly correlated with your ability to place good bets on your time. Who should I be reaching out to, what should I say, how should I do it, let’s say I have 200 different accounts I could reach out to, I only have however much time in a day, who’s gonna get my time today? Of the deals that I’m working, who’s gonna get my time? I think that the best salespeople are always the ones who are the best users of their time. What we’re able to do with these AI tools and bringing it back to what we were talking about at the beginning, is putting insights into the hands of sales reps. That means that some of your sales reps who may not be that good, frankly, at going out, researching, understanding, maybe they don’t have very good business acumen or whatever, you really can’t scale a sales team of pure A-players for very long. You just can’t. You can hire A-player only sales team for maybe your first 10 hires, maybe if you’re lucky. At a certain point, you’ve got to be able to have a good profitable growing business with B-players. And maybe eventually C-players. I think AI tools are going to allow C and B-players to do the kinds of things that A-players have been doing for decades. That’s how I see it. I agree. Another thing to bring it back to the business data thing is that data scientists and these teams, people like yourself, you’re spending a lot of time on producing these valuable insights in whatever ways. At the most basic level, maybe people are listening to this and they’re like, ‘Oh, this is too fancy, this is too complex, I can’t do any of this.’ At the most basic level, you could just take the data you have and produce very simple metrics that are still extremely useful. Good, bad. Is this a good customer, bad customer? Are they likely to spend more, are they likely to churn? A lot of these things get out of the box and you can find models to help you work through things like lifetime value and whatever else. But then you have to do something with it. It’s not enough to produce the insight, you have to actually put it into action. There are tools out there and that’s a big bottleneck that I work at with people. You have data scientists who will come in from Harvard, MIT data science program and they’re doing really cool fancy things. But then people aren’t really using it. They’re not really getting it into the hands of the people like the sales team, the marketing team, whoever, and actually getting value out of it. They end up spending inordinate amounts of time doing that. That’s what Census does, that’s what a lot of people are spending time on is going not just from producing the cool shiny interesting thing, but actually producing results with it. That’s what I spend a lot of my time talking about with companies. I think we’re just scratching the surface here of how far we’re going to take this. But every executive out there is paying attention to it. I think it’s pretty big results, sales you’re right sales is a beginning, marketing is a beginning, eventually it’s going to show up everywhere.
James Winegar: There’s a lot of opportunity in the space to continue to grow our approaches. One of the things that happens in a lot of sales orgs is you have your BDR, your SDR and then your actual account exec. It’s the same idea. You’re pushing down B and C. Who can just generate a meeting versus who can figure out whether this makes sense versus actually trying to establish is there a need, is there a way that we can make this work? One of the things that the VCs are pitching is this idea of inverting the org chart a little bit. You don’t need as many SDR, BDR type folks. Instead you can with the utilization of these tools get your own SDR, BDR. Some of that’s really annoying because it’s robocall type of stuff and people are really turned off by that. There’s still a human element to a lot of the approaches that we need to take to everything.
Sam Arnold: There’s always good and bad ways to use and abuse technology. You see it everywhere, when email automation tools came out, what happened? People just loaded up the spam cannon and started blasting people with drip sequences. I remember 10 years ago I would get five emails a day that’d be like, ‘You haven’t responded. Were you being eaten by an alligator?’ You get the same dumb emails that nobody personalized or cared about. But then some people used it really well, really effectively. AI’s the same way. You have some people who are spamming LinkedIn right now with the worst comments ever, just complete waste of time. You have people selling e-books within five seconds you can tell they just generated it. There’s people abusing this stuff, there always is. But then there’s people that are using it really intelligently. I tell my BDR, I have a BDR I work with and I’m working with her on using Claude effectively and I told her, and I’m serious about this, ‘For us to stay employed in the long term, we must take advantage of these tools, we must learn them, become experts at them because the folks that are ignoring them, the folks that five years from now are still trying to figure it out, they’re going to get left behind.’
James Winegar: That’s a good point to close off on. Where can people learn more about you, Sam, and about Census?
Sam Arnold: Sure. Again I’m Sam Arnold, you can find me on LinkedIn. Please feel free to add me. You can find Census at getcensus.com. You can sign up for a trial if you’re curious, we have a pretty generous perpetual free plan. Of course you can get in touch with me too if you want to see some of the fancier stuff, you want a demo. That’s me.
James Winegar: Thanks for joining us, Sam. Hope you have a great weekend.
Sam Arnold: Thanks James.
Jason: That’s it for this episode of Data BS. If you enjoyed this episode, make sure you subscribe wherever you listen to your podcasts and not miss the next one. This episode was sponsored by CorrDyn, a data consultancy that helps organizations unlock the power of their data. If you have a data challenge, we can help. Visit corrdyn.com, C-O-R-R-D-Y-N dot com to learn more. See you next time.
