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 Part 1.
Poll Results: Data Maturity & Focus Areas
Before diving into the key insights, our webinar poll results revealed some interesting trends about where publishing organizations currently stand with their data initiatives.
Data Maturity Levels
When asked to rate their level of data maturity, participants responded:
- Managed (45%): The importance of data in the organization is realized
- Defined (29%): Data regulation and management guidelines are defined better and are integrated with the company processes
- Optimizing (12%): Data governance is an enterprise-wide effort that improves productivity and efficacy
- Initial (8%): There is little to no awareness of the importance of data and there are no set standards for managing data
- Quantitatively Managed (6%): Measurable quality goals are set for each project, data process, and maintenance
Main Areas of Focus with Data
Our respondents identified their primary data focus areas (multiple choice):
- Workflow optimization: 29 responses (62%)
- Product development: 28 responses (60%)
- Marketing/digital experience/personalization: 27 responses (57%)
- Business intelligence: 22 responses (47%)
- Business development & sales enablement: 16 responses (34%)
- Data monetization: 8 responses (17%)
- Other: 0 responses (0%)
These results highlight that most organizations recognize data's importance but are still working toward fully optimized, enterprise-wide data governance.
Start with the Business, Not the Data
Organizations often fall into the trap of collecting everything and using nothing. Instead, flip the model: start with clear business objectives and work backward to identify the data that matters.
As Colleen Scollans emphasized: "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."
Whether it's increasing retention, identifying market expansion opportunities, or improving editorial workflows, the most successful organizations define specific use cases---and only then determine what data is needed to support them.
Michael Crumsho reinforced this approach: "It's always starting from the top and understanding what is the problem that you're trying to solve and how can data play a role in that."
Pro tip: Build a data inventory mapped to key business KPIs. It's the first step in shifting from reactive data collection to proactive insight generation.
Cultivate a Shared Data Culture
"Data is no one's part-time job." That simple truth, shared by ACS's Beth Windsor, underscores the cultural shift required to move data programs forward.
Cross-functional collaboration is essential. Data professionals must work hand-in-hand with product owners, marketers, and sales teams---not to dictate solutions, but to co-create them. When everyone shares a common data language and a mutual understanding of use cases, silos dissolve, and strategy accelerates.
Beth Windsor elaborated on this collaborative approach: "We've been doing actually a pretty good job here at ACS of shifting into a data centric culture. And 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."
Action step: Form a data working group with representatives from business units, IT, and analytics. Embed shared KPIs and ensure everyone understands the "why" behind the data.
Don't Wait for Perfect---Good Enough is Powerful
Organizations often hold off on data projects until everything is perfectly standardized. That's a mistake. As ChronosHub's Christian Grubak puts it: "You don't need Google Translate before you start speaking."
Christian emphasized this point further: "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."
Yes, data governance and normalization are important, but they shouldn't be a barrier to action. If your existing systems aren't built for modern analytics, you can still extract value with creative workarounds and focused integrations.
Quick win: Set up and fully utilize basic tools like Google Analytics. Many organizations aren't even maximizing what they already have.
Colleen Scollans noted: "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."
Incorporate External Data for Market-Wide Visibility
Third-party datasets aren't just nice-to-haves---they're insight amplifiers. At ACS, external feeds support competitive intelligence, customer growth, and even editorial strategy. From understanding research funding flows to tracking user affiliations, external data is helping publishers build fuller, smarter pictures of their audiences and markets.
Beth Windsor shared ACS's experience: "Third party data sets are a huge opportunity to create insight and opportunity outside of your organization. You're no longer just looking within. You're able to keep tabs on the greater market space. We're using a number of external feeds. I actually started tallying them up in my head the other day, and I think I stopped at 10."
How to start:
Identify low-cost or sample feeds to test value. Pilot integration with a single business use case. Measure success through KPIs before scaling up.
The key? Be agile. Prove value early and iterate from there.
Beth provided practical advice: "If you can find a data product out there that you think has value, try to get a sample of their data, or they might have a web tool available that you can subscribe to and develop your use cases around that."
Prioritize Interoperability Over Perfection
Forget chasing the mythical "one standard to rule them all." Instead, focus on making your systems talk to each other---cleanly, clearly, and consistently.
Christian Grubak captured this sentiment: "Standardization should not be the end goal. To make sure that we have interoperability, accessibility of high fidelity data is much more powerful than a common standard, which everybody struggles to keep up with."
Standardization enables plug-and-play integrations, especially when bringing in external data. But it's not about adopting an industry-wide schema; it's about having your house in order so you can move fast when opportunity knocks.
Action step: Implement a lightweight data dictionary and governance framework to ensure consistency in key fields across systems.
Link Data Directly to Revenue Opportunities
Data's impact becomes crystal clear when it's tied to outcomes. Publishers are using data to:
Power sales enablement with evidence-based insights. Improve product development by identifying underperforming assets. Support sponsorship and advertising models with audience intelligence. Measure and optimize marketing and user engagement in real time.
Colleen Scollans outlined these diverse applications: "Another really big category of data is sales enablement. Business development as sales teams are selling into institutions organizations. Increasingly that's selling needs to be evidence-based, data-backed."
And here's the key: Start small. Identify one high-value use case. Solve it well. Then let that success fund your next initiative.
Beth Windsor shared how ACS proved value: "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."
Final Thought: Build Muscle, Not Just Metrics
Data maturity isn't about perfection---it's about progression. Start with what you have. Build culture and competency. Layer in external intelligence. And above all, focus on business impact, not dashboard aesthetics.
As Christian Grubak reminded us: "It's just getting the whole organization used to that decision process is very important, seen from my perspective, because there's so many products out there just idle because they're not being used."
At Silverchair, we believe in practical progress. We meet partners where they are, and we help them grow---one insight, one integration, one opportunity at a time.
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