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 the value from 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 easily access, and what data you need but isn't being collected (tip: this is often behavioral data). Then, for the first two, determine where your data lives and how it flows between systems.
- Determine all the places you collect data, in particular data about people and content. For example, if you’re a society, work with your 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.
- Map data flows between systems to identify redundancies, gaps, and potential integration points.
2. Define Your Business Use Cases
Define and prioritize specific use cases that align with your business goals. For example:
- Track member journey patterns across touchpoints to identify engagement opportunities and churn risks.
- Analyzing content consumption patterns to better understand member interests and needs. Use this to drive content recommendations and for journey orchestration.
- 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 uncover at-risk subscriptions.
- Monitoring member purchasing trends to develop new content bundles or pricing strategies.
- Using engagement data to identify potential 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.