The panel, moderated by Josh Dahl (SVP, Product + General Manager, ScholarOne at Silverchair), featured Christian Grubak (CEO & Founder of ChronosHub), Colleen Scollans (Practice Lead for Marketing & Customer Experience at Clarke & Esposito), Beth Windsor (Senior Business Analyst at the American Chemical Society), and Michael Crumsho (VP, Technology & Product Delivery at McGraw Hill Professional).
The discussion yielded a wealth of insights, which we recap below and in the forthcoming Part 2.
Defining Data in the Modern Publishing Era
What exactly do we mean when we talk about "data"? As Colleen Scollans explained: "When I think about data, I think of it on two axes. One is the type of data and the second is how businesses utilize it."
Data exists on two crucial axes --- the types you collect and how your business utilizes it:
- Customer and audience intelligence: Understanding not just who engages with your content, but how they behave
- Content intelligence: Sophisticated classification and analysis of the products you create
- Financial performance data: Revenue streams and economic indicators that drive strategy
- Campaign performance: Evidence-based insights into what resonates with audiences
- Operational workflow data: Process efficiencies and system performance metrics
Scollans further elaborated on the data types: "We could be talking about customer and audience data. What do we know about the people that engage with our products and services, read our journals, our authors, et cetera. And that includes know information that we know about them, but also behavioral data. We could be talking about content intelligence and how we classify and better understand the content we publish, the products we create. We could be talking about financial data, revenue, P&L, all of that kind of good stuff."
The critical insight from our panel: having data isn't the same as having data competency. Real value lies in transforming diverse data streams into actionable business intelligence spanning marketing optimization, sales enablement, product strategy, and operational excellence.
The Competency Challenge: More Than Just Technology
Beth Windsor stated: "Data is no one's part-time job." She emphasized that "It really is that dedicated collaboration across multiple teams. And the only way to make this truly successful is for the organization to have a shared vision for what they're doing."
Building data competency requires dedicated collaboration across teams, unified leadership vision, and cultural transformation beyond implementing new tools.
As stated by the panelists, many organizations may face these common challenges:
- Normalization complexity: Managing diverse product ecosystems with different analytics capabilities
- Business context education: Technical teams need to understand industry-specific metrics
- Executive buy-in: Foundational data work often struggles for resources compared to flashy AI projects
Michael Crumsho highlighted this challenge: "Data projects-- everybody likes the cool AI project that has a nice widget that you can put on your site or that looks sexy in a press release and saying, hey, we've now made sure that a click is a click is a click across our entire ecosystem. It's not really something that a marketer is going to be super... excited to send a message about to clients because that's just table stakes and that's expected."
The Cultural Foundation: Making Data Everyone's Priority
The most successful data transformations start with culture, not technology. As Beth Windsor explained: "I truly believe that [culture] is 51% of your problem. And we have found an effective way to shift our culture."
ACS's approach offers a proven blueprint: embedding data professionals directly within business units to collaborate on real problems. Windsor described their method: "We've done it by taking those data professionals and having them collaborate directly with those business units to understand the problem and understand how those business units are doing the work. We don't make any assumptions that we know how to do their job better than them. We work alongside them and we create those solutions alongside them."
This collaborative model achieves multiple objectives:
- Business units gain immediate value from data-driven insights
- Data professionals develop deep domain expertise
- Organizations naturally build cross-functional competency
- Teams approach new projects with data considerations from the outset
Windsor noted the transformative effect: "This is really not one of those build it and they will come moments. It's really about making their job easier, allowing them to have better results with what they do, and then they start coming to you naturally in the early stages when they're kicking off a project or they have a problem to solve."
The result? A shift from "build it and they will come" to proactive data-informed decision making.
Practical Steps for Building Competency
Before diving into technical solutions:
- Audit your opportunities: What business problems could data help solve?
- Define your KPIs: What performance metrics truly matter for success?
- Identify ownership: Who will govern data standards and ensure consistency?
- Prioritize use cases: Focus on initiatives that will move your business forward
Colleen Scollans advised: "I would say it starts with building an inventory of what you want to do. That could be some KPIs, that could be some use cases. And then being really disciplined and prioritizing what's going to move your business forward."
Maximize What You Already Have
Many organizations haven't properly configured basic tools like Google Analytics. Scollans observed: "The number of organizations that we encounter that don't even have Google Analytics set up correctly. So for me, it's look at what you have, what it can do, and make sure you're fully maximizing that."
Before investing in advanced solutions:
- Ensure existing analytics tools are correctly implemented
- Establish standard tracking parameters for marketing campaigns
- Build accessible dashboards for key stakeholders
- Train teams on interpreting and acting on available data
Think Integration, Not Perfection
Christian Grubak emphasized: "Data doesn't have to be perfect to be good." He elaborated: "There are many, many, many data sources out there, which will not give us 100% in terms of precision rate but they're good enough for purpose. Because if we do, if we cross-reference it enough, we're going to find the anomalies."
Organizations often delay action waiting for complete solutions, but value comes from:
- Establishing baselines with available data
- Cross-referencing multiple sources to identify patterns
- Building analytical habits before investing in comprehensive platforms
- Starting with targeted use cases rather than enterprise-wide transformation
Avoiding Common Pitfalls
Privacy regulations and operational efficiency both point toward "minimal viable data." Collect only what you need for defined purposes. Colleen Scollans advocated for this approach: "I preach minimal viable data. What is the data you need to hold for those use cases? You don't want to be overwhelmed with data. You also, privacy legislation indicates we shouldn't be holding data that we're not using."
Ask yourself:
- What specific business decisions will this data support?
- How will this data improve customer experience or efficiency?
- Do we have analytical capacity to act on these insights?
Michael Crumsho shared his practical approach: "A lot of times when I encounter a request for collecting a specific piece of data that isn't attached to any utility for the data, I kick it back right away. You should be collecting this. OK, well, what do you want to do with it. I don't know yet, but maybe someday we'll want to do something with it."
The Metrics Context Challenge
Data competency isn't just tracking numbers---it's understanding what they mean in context. Christian Grubak provided a compelling example: "If we look at something as simple as retention rates, so if a retention rate goes up, is that good or bad? Actually it should be good. But if your user count doesn't follow, it actually means you're attracting less business."
A rising retention rate might seem positive, but if user acquisition is declining, it could signal market challenges. True competency comes from understanding how metrics interact and what combinations indicate healthy performance.
Building Your Path Forward
Successful data competency initiatives share key characteristics:
- Executive sponsorship that makes data initiatives part of team objectives
- Cross-functional collaboration between business units, IT, and data professionals
- Quick wins that demonstrate value and build momentum
- Iterative approach that builds analytical muscle through regular practice
Beth Windsor described how they started: "This literally started with two people that had-- one person had a problem and one person had data. They literally used a spreadsheet to solve this problem. And it was a pretty significant problem that saw a nice return. And that's what got everyone's attention."
Christian Grubak reinforced the importance of starting small: "Starting with the small projects first because what we often see is organizations go from the first level of maturity to the second, and now they want to be able to analyze everything... that change management process of keep looking at your analytics, Google Analytics data, even though it's not first class. It's better than what you had a minute ago."
Organizations at every maturity stage can begin building stronger data competency. Whether you're just realizing data's importance or ready to optimize existing capabilities, the path forward starts with clarity about business objectives and commitment to cultural changes that make data-driven decision making the norm.
The publishing industry's data revolution isn't coming - it's here. Organizations that build true data competency now will thrive in an increasingly competitive and data-rich environment. The question isn't whether your organization needs stronger data capabilities, but how quickly you can develop them.
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