Anatomy of a modern data-driven content product

1. Anatomy of a modern data-driven content product

2. Sam Herbert Client Services Director 67 Bricks www.67bricks.com sam.herbert@67bricks.com @67bricks

3. 67 Bricks • We help publishers build data-driven content platforms • We use modern content technologies to increase automation, develop new revenue streams and deliver more value to content consumers • We are based in Oxford, our clients include:

4. Information products must evolve how they use data to stay relevant and competitive

5. There are multiple drivers for change

6. Information product evolution User value Knowledge / workflow solution Information product Documents online Print Data maturity

7. Data maturity • Your ability to store, manage, create and use data about your content and users • We have found it useful to develop a ‘data maturity model’

8. Level 1: Document data Data maturity

9. Level 2: Improved granularity Data maturity

10. Level 3: Smart content Data maturity

11. Level 4: Personalisation Data maturity

12. Level 5: Knowledge Data maturity

13. Product development data maturity model

14. Predicting high impact research Improving marketing communications Delivering customised / personalised experiences Helping researchers discover content The future for data-driven information products Delivering information services rather than documents Selling data to machine learning companies Automated or semi-automated content creation Understand the links between research objects Augmenting / automating peer review Predicting emerging subject areas Identifying peer reviewers Adaptive learning products Cross selling across content domains Unlocking value in legacy content Automated creation of marketing materials Improve internal content discovery and research

15. Information companies need to plot a course for improved data maturity in their content products to deliver more value to end users…