All data is not the same. While quality data is essential for an effective direct sourcing strategy, Macrobond’s Chief Data & Growth Officer, Rachael Tucker, reveals that it’s still possible to have a strong data advantage. “It’ll be a long time before we all really have access to the same data.” In this latest episode of Sourcescrub’s Inside the Source, Rachael goes on to explain, “People might say it's the same data because it's a list of companies, but you don't necessarily have the know-how to understand and assess what [the data] means for those companies... There's some level of domain expertise that I think we still need as a society in looking at the data.” The secret sauce is in the ability to contextualize proprietary data and put it to work in a way competitors can’t.
Rachael dives in further to emphasize the importance of differentiating data usage to create a competitive moat in the age of AI — not just as a way to stand out from other businesses. “You have your internal operational data, and then you have what data you might provide out to your customers... There's a lot of talk about large language models in AI, and those tools have been around for a very long time. So, one of the things to think about is your competitive moat. What really makes you unique and continually asking the question: Where could I be displaced?” Realizing there isn’t a one-size-fits-all solution can prevent firms from getting overlooked.
The answers to those questions are what can position companies to tap into setting themselves apart, from partnering with complementary organizations to leveraging AI and automation. Rachael highlights the importance of humans in the loop for relationship building, making sense of the data, and handling what machines can’t.
Watch the full episode to hear Rachael Tucker’s thoughts on the future, promise, and pitfalls of data.
Transcription:
Sourcescrub: Can you share a little bit about your current role and background in the data & analytics space?
Rachael Tucker (RT): So my name is Rachel Tucker. I'm currently chief data and growth officer for a company called Macrobond and Macrobond is a macroeconomic data and software business that really specializes in collecting a bunch of macroeconomic data, but also streamlining the research process and getting people out of Excel and into a more streamlined, analytic workflow where they can share that data across to their different constituents.
So it's a pretty cool business. I am required by my social media policy to make sure that I recognize that these opinions are my own opinions and that, and that they're not the opinions of Macrobond in this conversation, but that's my current role. I came to Macrobond because I worked for a very long time at Moody's Corporation, which is a publicly-traded business, in a variety of roles there.
And one of the roles that I was in was the research business and really thinking about, you know, how do you monetize the research from the credit rating agency, but also working closely with that team to think about how they produce that research content and Macrobonds stood out to me when I was looking at different companies. It's a really interesting combination of that data business, as well as the software that enables the research.
Another role that I had at Moody's is I was effectively the chief operating officer for a company called Bureau van Djik, which is a private company data business that got its start in Europe and is definitely a big global business now, and really a core part of Moody's Analytics offerings these days.
Sourcescrub: How do you believe high-growth companies are currently using data to differentiate themselves and get ahead?
RT: If you think about the role of data in any growth business, I think I think of it in two ways, right? You have your internal operational data, and then you have what data you might provide out to your customers. And, you know, I'm really thinking about companies like a Macrobond or like a Sourcescrub that have a very large proprietary data set within their business.
You know, I think first, looking at those types of businesses and thinking about, you know, what is your competitive edge? You know, there's a lot of talk about, you know, large language models in AI. And those tools have been around for a very long time. And so one of the things to think about is what is your competitive moat?
You know, what really makes you unique and continually asking the question of where could I be displaced? You know, from AI and other competitors and what I've found in my experience and working with a variety of data and analytics businesses is for years people have been talking about, you know, this ability to automate and scrape data off the websites, etc., etc. but when you really get down to domain areas like, you know, private company data like macroeconomic information, there is an element of domain expertise that you really need in order to make those automation tools work.
So they work at the 30,000ft level. It's really great if I want to, you know, edit a note to my customer or I want to, draft a note, you know, my team. But when you really need to get into the details and you need the level of quality that you need in order to really make decisions as a business owner, then you know that human in the loop is vitally important.
And I think it's really recognizing, that and the other facet of it is from an internal decision-making perspective. And, you know, here where I found a lot of challenges is, is if you're good at the first, right, you're really great at collecting the data, organizing the data as a business that you tend to also collect and organize a lot of operational data.
And so it's almost the reverse there that you can get in your own way, because you have so much data that you really lose track of what really matters for you in your business. And so one of the things that, you know, we've talked a lot about at Macrobond is really making sure, you know, what are the what's the data that matters?
What are the questions that we really need to answer in order to best address the needs of our market and our customers? And then, you know, how do we make sure we have that available rather than starting with, well, what's the data we have? And therefore what questions can we answer from that, right? And I think that's a distinction between how you look at data kind of externally for your customers versus internally for you as a business.
Sourcescrub: How have you seen data value and usage shift and evolve most recently?
RT: I think data goes through phases, right? There are different kind of fads that happen with data, different areas of focus depending on what's going on in the world. So you think five years ago, right? April of 2020. And that was right in the height of COVID. There were a lot of concerns about supply chain. There was a lot of concerns about, you know, health risks, travel, etc..
Now you have another crisis, but it's really trade- and tariff-driven, right? And so the trends of that data, I think change over time depend on what's happening kind of geopolitically and geographically. But the big theme I would extract out of all of that is that this increasingly interconnected world and increasing availability of data, is really what's changed over time.
And people recognize that it's not good enough to just look at, you know, from a supply chain perspective, you know, who are you doing business with and what is a credit risk as an example. You also need to look at their supply chain. You need to look at the geopolitical risk of the locations where they are. The climate risk, and the weather risk of where they're located.
So just this recognition of the interconnectivity of the information that we have and, you know, the different things that are driving risk and opportunity within the businesses today.
Sourcescrub: Companies today have access to plenty of data, and the technology to make sense of it. What do you think is the next major data challenge that needs to be solved, particularly as it relates to AI?
RT: I think one problem to be solved is intellectual property, right? So Google's done a great job with, you know, Gemini. You've got ChatGPT. You have all those big vendors now who've developed these very large language models that are basically taking everything that they could find off the internet that was ever written or created and created these like big models.
I think now a lot of information providers and content, you know, providers like a Moody's, like a Macrobond, like a Sourcescrub, like a lot of other companies are really thinking about how do you protect your IP? And so I think there's been a bit of a barrier at this moment, because people are really trying to figure out how to take advantage of that new technology without giving away what's really like high-value content.
And I mentioned earlier that expertise that those individual data providers bring to the context of that content, into the information that they're collecting. I think the other challenges, you know, being able to answer the question of, is this a good response or is this good information? And so even doing things like you see these, you know, helpbots, I think are a good way that people are starting to bring that information in.
But how do you assess what a good response is, and how do you make sure that it doesn't just become generic, right? If everybody's using the same types of models in the same way, it's all going to start to kind of sound the same. And even now I don't I don't know if I could feel it, but I hear, you know, other friends and colleagues who said, oh, I know that was written by ChatGPT.
You know, there's just this, this tone and formality that comes out of some of these models that, you know, over time, I think it makes us dumber as a society, to be frank. And I think that's a real risk for us of, you know, who's going to be identifying those next areas of opportunity and, you know, getting that detailed expertise that you really need in order to really understand and provide context around the data that you're providing.
Sourcescrub: What are most data & analytics companies getting wrong?
RT: I think a lot of companies I've seen and worked with over time, they think, oh, I've got this data set. Well, you know, this, this other group could use it within the business. But it's a completely different sales motion. It's a completely different kind of context that you need to put that data into that kind of renders it less valuable in that market and harder to really go after, even if you have that core data.
And so I think that's what people are getting wrong, is that assumption of, hey, anybody could use this. But I think what people are starting to get right and righter is this idea of partnership right, and not thinking that you have to be the one data provider that by partnering with other related vendors who might be in adjacent spaces, isn't a competitive threat, but really an opportunity.
So really being able to knit together, what do you do well, and then where are those partners that, that you can, you know, sell through, sell to, combine the data with, in a new way that really gives you opportunity to sell into new markets. So as an example, Macrobond has a well-established relationship with Bloomberg.
Macrobond is really good at timely, accurate macroeconomic data and tools to analyze that data on a time series basis. Well, Bloomberg has a lot of data about individual securities, about indexes, about market volatility that you can bring into Macrobond's platform as well as vice versa. So I think that's a really good example of how we've been able to extend our market reach by having those partnerships in place.
Sourcescrub: Sometimes it feels like everyone has access to the same information. What advice do you have for businesses looking to develop a strategic, competitive advantage based on data?
RT: I think that's a big assumption, assuming everybody has access to the same data. And again, I go back to my point earlier, which is I think we're still a long time before we really have access to the same data, right? It kind of people might say, oh, it's the same data because, you know, it's a list of companies, but you don't necessarily have the know how in order to understand and assess, what does it mean for those companies?
What's the related information for those companies that you really need to start to pull together? So there's some level of just domain expertise that I think we still need as a society and looking at the data. And I think computers will evolve towards that, but I think they're only going to evolve so far and so fast, at least in our lifetimes.
And so I do think that competitive advantage is kind of sticking to your guns and thinking about what is that unique data set that you can continue to capture and aggregate. And then also, how do you use it, apply that data and really understanding, you know, what are the tools that you could provide to your customers in order to access that information, right?
Be it analytic models that you can help them with, be it software that helps them, use that content more efficiently and effectively. I think there are a variety of ways that you can add value in addition to the content itself. I think we're far from the day where we just shout out like, tell me, tell me who I should go sell, you know, this new product or capability to.
And it gives me a list of people and calls them for me, right? I mean, I think we're far away from that world, so it's not a competitive differentiator anymore. I'm a big believer in that large language models are not a competitive differentiator. It's like having search on your website, right? So there is more that we've been able to do since we helped people find the data through search, right? There's still more questions to be answered, more information to be gathered.
Sourcescrub: Are there any data & analytics developments you expect to see within the next couple years? Anything that might surprise people?
RT: Once you solve one problem as a business, there's always new problems that get unearthed by that, right? That then you give them the, you know, occupancy information for the building and then they want to know, well, what does that mean for, you know, air conditioner usage or what does that mean for, you know, food supply. And so the more data you have, it's like this flywheel of more data that you need in order to feed that beast and to answer those questions.
I think people will be surprised by how much they still need people to do work and how, you know, there'll be a lot that that AI will displace. But it doesn't take away from the need for individuals and humans to actually do a lot of the core work, and to really bring that expert judgment into the business.
And it might change. But even some of the things, even if you can automate them, you still need kind of people who understand foundationally how it works and how it all comes together. And to be able to be there when the machines break, right? And so really thinking about that and how you develop your staff now and how you develop your teams, not necessarily thinking, you know, it's all going to go away, but how do you make sure you've got that kind of human backup for when the computers fail?
I don't think like you're going to see dramatic reductions in staffing that that would be might take, right? Because you're always creating new problems and you always still need people there to make sure that the machines behave themselves.
