Adam Cardarelli is Chief Product Officer at Automated Data Inc. (ADI), an AI-enabled data connectivity platform to match and join data. He began his career as a research analyst at Multex.com, Inc. before transitioning to product management at Thomas Reuters (and later Refinitiv), where he spent the next 15 years managing the company’s data products, analytics, and enterprise software. Before his current role at ADI, he served as VP of Product Management and, later, CPO at LightBox.
In our conversation, Adam highlights one of the most essential elements of leading product management teams — deeply understanding the customer and creating awe-inspiring products that truly delight them. He discusses interplay of master data, customer data, and product data, and how the three work together to create holistic insights. Adam also shares how he fosters an environment for growth within organizations so PMs can truly thrive.
I got into product management because I love transforming conceptual ideas into tangible products and services that make a meaningful impact for a user and, more broadly, the market. I’m absolutely passionate about creating value and solving complex problems through innovation. I also love the chameleon nature of product management. As a PM, you could be on a sales call, iterating on a design spec with your UX team, going through sprint reviews with your tech team, or leading a coffee chat with your go-to-market team.
Most of my experience is in data integration and creating data products and analytical platforms. I have led several teams across startups and large enterprises and continue to find that businesses often operate within data silos limiting opportunities to collaborate, connect and generate insights.
In today’s knowledge economy, having big data isn’t a cool asset anymore unless you can generate insights and know how to utilize it. It doesn’t matter what industry you are in; customers want to focus their resources on value creation and workflows that give them a competitive advantage.
ADI’s connectivity platform transforms messy, siloed data into a trusted, unified data resource for the enterprise that can drive any AI, business, or operational process. If you can structure your data, i.e., make it easy to understand and join it together, then you can really generate real-time knowledge and make decisions faster and more intuitively.
Product management at small or early-stage startups looks a little different from that of larger or more mature organizations — the processes and the speed at which we operate are very different. With that said, the core principles are the same. Product management is all about guiding the development and lifecycle of a product to ensure it meets customer needs and drives real business value. It involves understanding the market, customers, and competition to define a product vision that aligns with the company goals.
Our approach is leaner, but we really emphasize a focus on the customer. A significant part of my day involves spending time with customers and design partners to learn how they do things — what they value, what they do not like and what challenges they face.
I have found that even after a lifetime of exposure to a specific industry, it’s still imperative to dedicate a ton of time and energy to customers because their mindsets, preferences, and habits change over time.
The other key aspect of product management is collaborating closely with cross-functional teams like sales, marketing, development, and support. I believe it’s crucial for product management to unify all stakeholders and ensure everyone in the organization understands customer feedback, market trends, and how the product is evolving to stay ahead of the market.
Yes, I’ve done it all throughout my career — the individual contributor, the manager of a large global team, and everything in between.
I believe the most important aspect, whether you’re a team of one or 150, is the ability to identify common issues across a range of customers and develop an elegant solution that addresses these seemingly disparate challenges. You must be tight with your feature prioritization, how you execute the sequences, how you iterate, and how the organization is structured to bring that vision to life.
Out of the gate, know the market and the audience that you’re building for this way you avoid building toward an idea that’s not really what they wanted. This involves deeply researching their needs, pain points, and how they interact with existing solutions. By truly empathizing with the customer, you can create a product that not only meets their needs but also delights them in ways they didn’t expect.
I invest a significant amount of time understanding why a problem hasn’t been fully solved before or, if it has, why users are still searching for alternative solutions. This is especially crucial when building a new product. It’s easy to get caught up in why an idea is great, why it will succeed, and why everyone will love it. What’s more challenging is recognizing all the reasons it might not work or anticipating how the market will evolve over the next 3, 6, or 9 months.
This is the approach I’ve always encouraged my teams to take — you know that you’re going to build a great product, but always think through all the things that can go wrong at every step of the process.
I believe in pushing research and the data that you’re collecting to the limit. This helps you to avoid bias and look for certain customer data points that prove that bias.
When launching a new product specifically, I start by identifying ICPs, working closely with design partners, and iterating rapidly with a small group of users. For SaaS offerings, I like to focus on engagement metrics — session length, feature usage, user frequency, path analysis, scroll depth, click-through rate, and stickiness.
For data products, the metric that matters most to me is how long does it take for a user to start generating insights. Can they easily find, understand, and use the data to start making decisions? If you can measure how long that takes end-to-end, starting from the moment that users hit your website, all the other metrics fall into place — especially your revenue metrics.
Before starting any analysis, I want to make sure the data is clean and connected. Once prepared, I segment the data to uncover trends within specific groups such as premium versus non-premium users or, say, user verticals. This helps drive some of my marketing and commercial strategy. I am generally using visual tools to identify and predict certain patterns. What features are gaining the most and least traction? At what point in the customer journey is usage changing, and why? Are they getting stuck? Is something not clear?
I also look a lot at sales cycles, which means thinking more traditionally than your product defined spec-and-execute roadmap. Are there particular personas or verticals that are seeing less or more traction? At ADI, we’re seeing a lot of traction within financial services, insurance, and real estate, and that’s ranging from large knowledge-based industries to lesser digitized verticals. I try to incorporate all that into my strategy.
In general, I think businesses tend to overly communicate at the top of the sales cycle as opposed to after a client has converted. If I am seeing different trends within usage, we’re going to look into the reasons behind them. What’s happening within the industry? Is there relevant news or emerging macro themes? Has the search paradigm shifted? Are users asking different questions? Or is the information they need buried too deep in the product—five clicks in instead of two?
Once we’ve identified a theme in customer pain points, we are going to focus on proactive communication to keep them informed on how the product is going to evolve or how we’re addressing their concerns. I think it is super critical to build partnerships with all of your customers — not just beta testers, but everyday users.
I like to leverage in-app notifications, newsletters, product blogs, roadmaps, etc. but am a huge fan of release notes, especially outlining known bugs. There’s no reason for a customer to report them when we already know what they are, so it’s good to be transparent. Transparency and executing on what you say is ultimately going to drive more usage towards the product — and more importantly, build trust and long-lasting relationships.
Some people say that master data is everything, while others disagree. When I say master data, I’m talking about data that’s typically less transactional in nature, such as customer and product data. This includes things like accounts, contacts, or information about accounts and contacts, such as addresses, product codes, prices, descriptions, employees, suppliers, etc. Master data is typically your golden record in a centralized system that everything can tie to.
Throughout my career, I continue to see the negative impact of messy, siloed data — it’s costly and can lead to any product or analysis to fail. At ADI, we have been redefining data matching by focusing on all data attributes, not just those traditionally classified as master data. Our goal is to create a trusted, unified data resource for the enterprise that users can build on top of. That can be data products, model training, business intelligence or data virtualization.
I’d say customer obsession and the ability to synthesize information, form an opinion, and communicate really well are crucial. It’s very easy to hear five different client issues and think that means five different solutions, but a good product manager listens, synthesizes, and creates a solution that addresses all those needs.
In terms of the day-to-day responsibilities, a PM should be researching the market, reviewing metrics, prioritizing features, executing on the roadmap, and keeping everyone informed. Good product managers are always aware of the issues around them.
Another key responsibility is nurturing productive and balanced relationships. While we hear a lot of people talk about the relationship between product and development, a product manager needs to nurture relationships across the entire organization, including go-to-market and support teams.
For development-focused PMs, it’s critical that they deliver specs that are clear and complete enough to ensure that engineers do not have to fill in important gaps themselves, but that are high level enough that engineering has the flexibility it needs to make implementation tradeoffs without breaking the spec. Product needs to stay closely involved throughout the project to make sure that things stay on track with the spec, be able to react quickly to feasibility issues, and to ensure a customer view of quality.
It’s about creating the right environment. That can mean many things, but success comes from the success of the team, so I aim to create a space where everyone is learning from each other, feels trusted to lead initiatives and takes responsibility for outcomes — without fear of making mistakes.
Everyone makes mistakes, and at some point, experiences a failed product launch. What matters is owning it and learning from it. The ability to discuss what went well, what didn’t go well, what would be done differently, and the lessons learned is crucial growth.
I highly encourage, if not push, every product manager to present their own work, regardless of the level of the organization. Presenting your product, getting people to rally behind it, hearing questions firsthand, and responding to both praise and criticism are all vital for growth.
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