1. Anatomy of a modern data-driven content product

2. Client Services Director 67 Bricks www.67bricks.com
Sam Herbert
Client Services Director
67 Bricks
@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. We have found it useful to develop a ‘data maturity model’
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 Store and manage
Extract and create
Data use
Product value
Level 1
Document level metadata
User access rights data
Usage data
Manual metadata creation during editorial processes
Access control
Document collections
Basic faceted search
Offline usage analytics
Find and access documents online

9. Level 2: Improved granularity
Data maturity
Store and manage
Extract and create
Data use
Product value
Level 2
Granular content ‘chunk’ level metadata
Granular usage data
Document structure relationships
Inherited document level metadata
Granular search
More precise usage analytics
Find and access relevant sections of content
Content production efficiencies

10. Level 3: Smart content Data maturity Store and manage
Extract and create
Data use
Product value
Level 3
Semantic fingerprints for content items
Extract content key phrases and entities
Content classification
Proximity matching
Improved faceted search
Suggested search
Usage trending
Enhanced search and discovery
Slice and dice content products
Support sales

11. Level 4: Personalisation
Data maturity
Store and manage
Extract and create
Data use
Product value
Level 4
Semantic fingerprints for users
‘Integrated’ usage data
User interest metadata
User classification
Personalised proximity matching
User type analytics
Personalised discovery experience
Improved content marketing

12. Level 5: Knowledge Data maturity Store and manage Extract and create
Data use
Product value
Level 5
Data relationships
Knowledge extraction - as relationships
Targeted search
Knowledge query capability
Knowledge solutions
Questions and answers

13. Product development data maturity model
1: Document data
2: Improved granularity
3: Smart content
4: Personalisation
5: Knowledge
Store and manage
Document level metadata
User access rights data
Usage data
Granular content ‘chunk’ level metadata
Granular usage data
Semantic fingerprints for content items
Semantic fingerprints for users
‘Integrated’ usage data
Data relationships (e.g. as triples)
Extract and create
Manual metadata creation during editorial processes
Document structure relationships
Extract content key phrases and entities
Content classification
User interest metadata
User classification
Knowledge extraction - as relationships
Data use
Access control
Document collections
Offline usage analytics
Granular search
Basic faceted search
More precise usage analytics
Proximity matching
Improved faceted search
Suggested search
Usage trending
Personalised proximity matching
User type analytics
Targeted search
Knowledge query capability
Product value
Find and access documents online
Find and access relevant sections of content
Content production efficiencies
Enhanced search and discovery
Slice and dice content products
Support sales
Personalised discovery experience
Improved content marketing
Questions and answers

14. The future for data-driven information products
Predicting high impact research
Improving marketing communications
Delivering customised / personalised experiences
Helping researchers discover content
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…