In a recent blog from Clarke & Esposito, Colleen Scollans, Michael Clarke, and Pam Harley powerfully articulated why data sovereignty matters for associations and societies working with commercial publishers. 

They write: “Organizations often face multiple challenges including fragmented data across departments, outdated technology, limited analytics, and lack of modern marketing skills. For associations that outsource their journal publishing to commercial publishers, the challenge of data availability can often be added to this list.” 

Hum has seen firsthand how access to comprehensive data can transform an organization's ability to serve its community and drive innovation. Here, we provide practical guidance on building your first-party data strategy and securing your data rights.

Access to Your First-Party Data Matters More Than Ever

The rise of AI and machine learning has made owning AND having access to your data more critical than ever. Your publication data isn't just about tracking usage anymore. It's the foundation for many things, including:

  • Training specialized AI models that understand your field's unique terminology and concepts
  • Creating personalized recommendation engines that connect your readers with your relevant content
  • Identifying emerging research trends and potential breakthrough areas
  • Developing predictive analytics for submission patterns and peer review management
  • Building automated content summarization tools tailored to different audience segments
  • Linking up engagement data from your publishing platforms with data from your other platforms to drive things like event programming, educational programs, and society member engagement

Without direct access to your data, you're not just missing out on current opportunities – you're ceding future AI-driven innovations to your commercial publishing partners.

How to Build Your First-Party Data Strategy

1. Audit Your Current Data Landscape

Start by mapping out what data you currently have access to, what data exists but you can't access, what data you need but isn't being collected, and where your data lives and how it flows between systems. 

Work with membership, publications, events, and education teams to understand what data they collect, use, and need. Review historical data access requests to understand what information your teams most frequently need but struggle to obtain.

Create a detailed inventory of data sources, including commercial publisher platforms, association management systems, learning management systems, and conference platforms. Document data access methods and limitations for each system, noting any technical or contractual barriers; and map data flows between systems to identify redundancies, gaps, and potential integration points.

2. Define Your Use Cases

Prioritize specific use cases that align with your mission:

  • Track member journey patterns across touchpoints to identify engagement opportunities and churn risks.
  • Analyzing content consumption patterns to better understand member interests and needs. 
  • Analyzing search patterns and content usage to identify emerging research trends and gaps in your portfolio.
  • Analyzing usage patterns to understand content impact, identify upsell opportunities, and at-risk subscriptions. 
  • Monitoring member purchasing trends to develop new content bundles or pricing strategies. 
  • Using engagement data to identify new products or services, such as specialized research tools, training programs, or collaboration platforms. 
  • Building training datasets for field-specific AI models that can assist with content tagging, recommendation engines, or automated quality checks. 

3. Assess Your Technical Readiness

Before implementing your data strategy, make sure you have a clear understanding of your organization's current technical capabilities.

Start by examining your existing data storage and processing infrastructure to ensure it can handle increased data volumes and complex analytics. Review gaps in your ability to derive meaningful insights from your data, and assess your systems' integration capabilities to understand how effectively you can connect different data sources and platforms.

Don’t forget to pay special attention to your privacy and security protocols, as these are crucial for protecting sensitive data and maintaining compliance. 

Finally, evaluate your team's skills and identify any training needs to ensure your staff can effectively manage and utilize your data infrastructure.

4. Create Your Data Architecture Plan

Your data architecture should be designed as a comprehensive system that addresses multiple critical functions:

  • Ingest data from multiple sources
  • Clean and standardize data
  • Store data securely
  • Make data accessible to authorized users

Finally, the architecture should be forward-looking, with the capability to support advanced analytics and AI applications, enabling your organization to leverage emerging technologies and derive deeper insights from your data.

Looking Ahead

Data sovereignty isn't just about owning and having access to your data - it’s about controlling your future. As AI continues to transform scholarly publishing, organizations that have full access to their data will be best positioned to innovate and serve their communities effectively.

Don’t Just Get Your Data. Get Your Data.

Interested in learning more about building a stronger data strategy and using your data to drive results? Gain inspiration from societies, associations, and publishers who are putting their data to work and getting real results, or schedule a demo to see Hum in action.