I keep thinking about those Matthew McConaughey Salesforce commercials where he says “data is the new gold.” It’s hilarious, but it’s also exactly the case. If that really is the new gold—and we’re not always talking about compromising people’s ethics or their personal information in the net neutrality debate—then the question becomes: how are people getting their data? How are they aggregating it, and how do they know that it’s fresh, reliable, something they can actually rely on?
We’re making more data-driven decisions than ever because people don’t have to cross-reference millions of data points manually like we can nowadays. What we do with that data is more impactful and insightful than ever. It’s just about how we aggregate it, use it, and have quality control that knows it’s reliable.
The Citation Problem Nobody Talks About
Here’s my litmus test, and it starts with anything that’s straightforward: I’m not going to look at any data without citations. It’s as simple as that. If there’s no citations, there’s no narrative on how the data is gathered and no real reason to look at it. This is where most agencies fail. They’ll present you with insights, trends, and recommendations built on data that has no methodology disclosure, sample sizes, or explanation of how they arrived at their conclusions. Just numbers on a slide deck that you’re supposed to trust because, well, they said so.
The bucket-and-ocean problem is real. Agencies often misrepresent third-party data as proprietary. They’re claiming they own the ocean because they filled a bucket. That’s not proprietary data infrastructure. That’s repackaged API calls with a markup. Understanding why transparency matters in SEO and digital marketing progress means demanding citation and methodology disclosure from every partner you work with.
What Actual Proprietary Data Infrastructure Looks Like
Let’s talk about what separates real data assets from marketing spin. There’s a ridiculous amount of confirmation bias that happens with agencies. They just want to find a couple sources that support their ideology without doing anything objective or to counter that, and then they’re just going to run with it. I’ve seen this as long as I’ve been in this industry. The sample size is too low.
When I talk about peer-reviewed methodology sections and the consistency and freshness of data, this can be data all alone. But what’s most important is the fact that people will do reports and analysis without realizing how few practitioners actually know how to properly store data, aggregate it, cull it, and cross-reference it in ways that produce meaningful insights—not just for individual clients, but for their entire agency.
Why? Because it’s complicated. It’s analytical and mathematical. Let’s say it’s just hard. It’s as simple as that. So people would rather look for self-validation to be able to fit the narrative or just rely on tools alone to cover everything.
The Third-Party Tool Problem
But what we don’t get with the tools is transparency. We don’t know where it came from. A lot of times, even for something like data for SEO which is becoming more prevalent, they just have APIs to places like Google Ads, which provides very little information and transparency about how they got search volume or how they got the CPCs.
You would think that, well, it comes from Google. Google knows all this stuff. Of course that’s the source of truth. But in reality, why would they even allocate that many resources to it? Being able to process this information multiple times a day, once a day, across millions and millions of keywords with all these metrics, storing it, curating it? It’s expensive. It takes resources.
Google’s incentive is just to say: buy keywords and pay for them. Here’s just a wide range. They don’t have a huge incentive to get so specific, especially if you can just extrapolate that information and then sell it as if it’s your own. We’re also looking at this from the third-party tool standpoint. What’s their incentive for even being the middleman for information? They’re taking Google’s API data, adding their own margins, and selling it back to you as “insights.”
Building Minimum Viable Data Infrastructure
Let’s start with the simpler methods. Surveys and focus groups seem straightforward, but they’re deceptively complex. Focus groups with five to ten people get expensive fast. Surveys with 200 to 400 responses require tight questioning—five to seven questions maximum—or response rates plummet.
Then there’s the representativeness problem. You need to account for age brackets, geography, urban versus rural demographics, gender splits, and ideological differences. Getting a sample that’s actually representative of your target market isn’t just difficult—it’s expensive and time-consuming, especially now that AI-generated survey responses make data quality even harder to verify.
When you get to that point and you’re obsessing about things that maybe at scale just don’t matter as much, maybe this is the kind of conversation you have if you’re selling a soft drink where you have to go-to-market strategy for a new brand of sneakers. For legal or dental, you’re already going to have an ICP that can be representative. You’re going to know, at least hopefully internally, that your clients that pay the most are 35 to 44 within a certain income bracket within certain zip codes.
That information is relatively easy to get for the most part, as long as you have a CRM or call tracking that can pull it out. And if you don’t, I recommend that you start working on that because that affects your messaging, it affects your brand and your marketing channels. Understanding how to take the guessing game out of defining your ideal customer profile is foundational to building meaningful data infrastructure.
If your average client is 55 and older, I don’t think it’s a no-brainer that you’re probably not going to be too worried about TikTok right now, among other things.
The Scale and Architecture Required
To achieve meaningful sample sizes with Analytics, we’re pulling millions of data points: every client, every page on their website, every week, and potentially every search query. The computational intensity is substantial. You can imagine this is why we have to reel it in to an extent, because once you start introducing AI into the fold, there is a resource and environmental impact to do all that cross-referencing.
But a lot of information needs to get stored and referenced later. One website times 100 pages times 52 weeks in a year and you’re already at 5,200 data points. If they even have 100 search queries, then you’re at half a million. Then, you multiply that by hundreds of clients and you can see how there’s an architecture and responsibility and also a dedicated craft to making this happen.
But ultimately what you get in these situations is information that’s reliable because it’s based off of your metrics and your analysis. Frankly, nothing that you will ever see in these tools because of what I mentioned really from the outset about Google Ads. At some point there’s just always going to be limitations to what you can do for everyone all at once.
What you care about is what you can do for your subset of clients and their specific scenario. That allows for the granularity and the drill-down and the ability to get reliable data that you can actually make actions with. Our approach to tracking content traffic, engagement, and leads demonstrates how proprietary infrastructure creates actionable insights that third-party tools simply can’t match.
The Debate Around Data Ownership and Privacy
There’s significant tension in our industry right now around data collection and usage. Regulatory bodies are increasingly emphasizing data accessibility while promoting data literacy and applying necessity and proportionality frameworks when acquiring data. Compliance obligations relating to “important data” remain a major challenge for businesses, largely due to the lack of clear and comprehensive rules.
Copyright and the use of copyrighted works for training generative AI models has emerged as one of the most debated topics in recent consultation periods. Some practitioners argue that the regulatory burden makes proprietary data collection too risky. Others suggest that third-party tools provide sufficient coverage without the liability concerns.
In my experience, agencies willing to invest in proper data infrastructure, with transparent methodologies and ethical collection practices, create significantly more value for clients than those relying solely on repackaged third-party data. The key difference is control and specificity. When you own your data collection methodology, you can drill down to the exact questions that matter for your specific clients in their specific markets.
Third-party tools will always optimize for the broadest possible use case, which means they’ll never be as precise for your particular needs.
Why This Matters More Than Ever
AI transparency is becoming non-negotiable for brands. Regulators across the world are starting to hard-wire AI transparency into law. The EU, China, and five US states already have measures in place. This regulatory environment makes proprietary data infrastructure even more valuable. When you control your data sources and can document your methodology, you’re in a much stronger position to demonstrate compliance.
When you’re just reselling someone else’s API data, you’re at the mercy of their compliance practices and their transparency, or lack thereof. We’ve had a complete crisis with terrible content for a year and a half now. AI makes it trivially easy to generate volume, but that doesn’t mean the insights are actionable or even accurate.
The agencies that will thrive are those that can point to their own verified, documented, consistently maintained data sources and say, “Here’s exactly how we know this, here’s our sample size, here’s our methodology, and here’s why you can trust it.” Understanding how the AI play represents the death of transparency that digital agencies have been waiting for reveals why proprietary infrastructure matters more than ever.
The Path Forward
If you’re an agency looking to build this capability in-house, start simple. Begin with basic data points that are relatively easy to aggregate: your own client CRM data, call tracking analytics, and website performance metrics across your portfolio. Document your methodology from day one. Be transparent about sample sizes and limitations.
As you scale, invest in the architecture. That means storage solutions that can handle millions of data points. It means people who understand how to cross-reference and analyze at scale. It means quality control processes that ensure freshness and reliability. Most importantly, it means being willing to say “we don’t have data on that yet” instead of reaching for a third-party tool that gives you a false sense of authority.
That honesty, combined with a genuine proprietary data infrastructure where you do have coverage, is what separates agencies that will be effective in this new landscape from those that are just repackaging the same APIs everyone else has access to. Our commitment to responsible integration of AI in SEO while upholding ethical standards demonstrates how transparency and proprietary infrastructure work together.
Partner with Market My Market for Data-Driven Certainty
At Market My Market, we’ve invested years building proprietary data infrastructure that aggregates millions of data points across our client portfolio. We don’t repackage third-party API calls and claim them as insights. We document our methodology, disclose our sample sizes, and show you exactly how we arrived at our conclusions. Our team understands that meaningful analysis requires computational architecture, quality control processes, and the analytical expertise to cross-reference data in ways that produce actionable insights for your specific market and practice areas.
When we make recommendations about your marketing strategy, we can point to verified data sources, representative sample sizes, and transparent methodologies that stand up to scrutiny. We’re willing to say “we don’t have data on that yet” rather than reaching for convenient narratives that sound good but lack foundation. If you’re ready to work with a team that treats data as the strategic asset it actually is, contact our office to discuss how our proprietary infrastructure can inform smarter decisions for your firm’s growth.
Frequently Asked Questions
What makes proprietary data different from third-party data?
Proprietary data is collected, stored, and analyzed by your agency using your own methodology. Third-party data comes from external APIs and tools where you don’t control the collection method, sample size, or freshness. Proprietary data allows for granular analysis specific to your clients’ needs, while third-party tools optimize for the broadest possible use case.
How much data do I need to build a proprietary data infrastructure?
Start with what you have: client CRM data, call tracking analytics, and website performance metrics across your portfolio. For meaningful analytics, you’re looking at millions of data points (one website with 100 pages tracked weekly over a year equals 5,200 data points; add 100 search queries and you’re at 500,000). Scale matters, but quality methodology matters more.
Why can’t I trust Google Ads data for my analysis?
Google provides very little transparency about how they calculate search volume or CPCs. Processing that information multiple times daily across millions of keywords is resource-intensive and expensive. Google’s incentive is to get you to buy keywords and pay for them, not to provide hyper-specific data. They use wide ranges because precise data doesn’t serve their business model.
What’s the minimum requirement for trusting agency data?
Citations. If there’s no narrative on how the data was gathered, no methodology disclosure, no sample sizes, there’s no reason to look at it. The data needs documented collection methods, clear sample sizes, and transparency about limitations. Without these, you’re just looking at numbers someone wants you to trust without verification.
How do I handle client confidentiality with proprietary data?
You can share aggregated insights and methodologies without revealing client-specific details. Document your sample sizes, collection methods, and analysis frameworks. You don’t need to name clients to demonstrate that you’re working with real data across a meaningful portfolio. Transparency about process doesn’t require compromising individual client confidentiality.