The value of structured data
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Our slides from the presentation I held at the Gilbane Boston Conference 2012. Topic: The value of structured data in Content Management Systems.
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The value of structured data
- 1. The value of Structured Data in Content Management Systems Ole Gulbrandsen CTO Webnodes ole@webnodes.com GILBANE CONFERENCE Boston - 2012 www.webnodes.com
- 2. The key goals of your website 1. Capture new customers 2. Engage your customers 3. Retain visitors and inspire loyalty
- 4. John works lives in the UK. He is an engineer and works for Nike. He was 42 years old in 2011. Margareth works in Spain. She likes rowing. She works for Nike too and is a financial analyst. Ronald works for Nike too, but i Norway. He is a shop assistant. His hobby is cycling. Bert is a cleaner from United Kingdom. He loves painting, and works in Hewlet Packard. Sofie likes antiques and lives in Australia where she works for the same company as Bert. Margareth is five years older than John. Ronald is 30 and 3 years older than Bert. John works lives in the UK. He is an engineer and works for Nike. He was 42 years old in 2011. Margareth works in Spain. She likes rowing. She works for Nike too and is a financial analyst. Ronald works for Nike too, but i Norway. He is a shop assistant. His hobby is cycling. Bert is a cleaner from United Kingdom. He loves painting, and works in Hewlet Packard. Sofie likes antiques and lives in Australia where she works for the same company as Bert. Margareth is five years older than John. Ronald is 30 and 3 years older than Bert. John works lives in the UK. He is an engineer and works for Nike. He was 42 years old in 2011. Margareth works in Spain. She likes rowing. She works for Nike too and is a financial analyst. Ronald works for Nike too, but i Norway. He is a shop assistant. His hobby is cycling. Bert is a cleaner from United Kingdom. He loves painting, and works in Hewlet Packard. Sofie likes antiques and lives in Australia where she works for the same company as Bert. Margareth is five years older than John. Ronald is 30 and 3 years older than Bert. John works lives in the UK. He is an engineer and works for Nike. He was 42 years old in 2011. Margareth works in Spain. She likes rowing. She works for Nike too and is a financial analyst. Ronald works for Nike too, but i Norway. He is a shop assistant. His hobby is cycling. Bert is a cleaner from United Kingdom. He loves painting, and works in Hewlet Packard. Sofie likes antiques and lives in Australia where she works for the same company as Bert. Margareth is five years older than John. Ronald is 30 and 3 years older than Bert. John works lives in the UK. He is an engineer and works for Nike. He was 42 years old in 2011. Margareth works in Spain. She likes rowing. She works for Nike too and is a financial analyst. Ronald works for Nike too, but i Norway. He is a
- 5. John Engineer 42 years Cycling US Nike Margaret Analyst Born 1969 Rowing Spain Nike Ronald Engineer 34 years Cycling UK HP Bert Cleaner 28 years Painting Australia HP Sofia Accountant Born 12/79 Antiques US Nike John Engineer 42 years Cycling US Nike Margaret Analyst Born 1969 Rowing Spain Nike Ronald Engineer 34 years Cycling UK HP Bert Cleaner 28 years Painting Australia HP Sofia Accountant Born 12/79 Antiques US Nike John Engineer 42 years Cycling US Nike Margaret Analyst Born 1969 Rowing Spain Nike Ronald Engineer 34 years Cycling UK HP Bert Cleaner 28 years Painting Australia HP Sofia Accountant Born 12/79 Antiques US Nike
- 6. Name Position Age Interest Country Company John Engineer 42 years Cycling US Nike Margaret Analyst Born 1969 Rowing Spain Nike Ronald Engineer 34 years Cycling UK HP Bert Cleaner 28 years Painting Australia HP Sofia Accountant Born 12/79 Antiques US Nike John Engineer 42 years Cycling US Nike Margaret Analyst Born 1969 Rowing Spain Nike Ronald Engineer 34 years Cycling UK HP Bert Cleaner 28 years Painting Australia HP Sofia Accountant Born 12/79 Antiques US Nike John Engineer 42 years Cycling US Nike Margaret Analyst Born 1969 Rowing Spain Nike Ronald Engineer 34 years Cycling UK HP
- 7. Name Position Birth Interest Country Company John Engineer 01.02.1969 Cycling US Nike Margaret Analyst 03.02.1969 Rowing Spain Nike Ronald Engineer 02.12.1975 Cycling UK HP Bert Cleaner 02.12.1971 Painting Australia HP Sofia Accountant 02.12.1979 Antiques US Nike John Engineer 01.02.1969 Cycling US Nike Margaret Analyst 03.02.1969 Rowing Spain Nike Ronald Engineer 02.12.1975 Cycling UK HP Bert Cleaner 02.12.1971 Painting Australia HP Sofia Accountant 02.12.1979 Antiques US Nike John Engineer 01.02.1969 Cycling US Nike Margaret Analyst 03.02.1969 Rowing Spain Nike Ronald Engineer 02.12.1975 Cycling UK HP
- 8. Name Position Birth Interest Country Company John Engineer 01.02.1969 Cycling US Nike Margaret Analyst 03.02.1969 Rowing Spain Nike Ronald Engineer 02.12.1975 Cycling UK HP Bert Cleaner 02.12.1971 Painting Australia HP Sofia Accountant 02.12.1979 Antiques US Nike John Engineer 01.02.1969 Cycling US Nike Margaret Analyst 03.02.1969 Rowing Spain Nike Ronald Engineer 02.12.1975 Cycling UK HP Bert Cleaner 02.12.1971 Painting Australia HP Sofia Accountant 02.12.1979 Antiques US Nike John Engineer 01.02.1969 Cycling US Nike Margaret Analyst 03.02.1969 Rowing Spain Nike Ronald Engineer 02.12.1975 Cycling UK HP
- 9. PEOPLE COUNTRY Name Position Birth Company Interest Country ID Name John 1 01.02.1969 1 2 2 1 Australia Margaret 2 01.02.1969 1 2 3 2 UK Ronald 2 01.02.1969 2 4 4 3 US Bert 4 01.02.1969 2 3 2 4 Painting Sofia 3 01.02.1969 1 1 1 POSITION COMPANY INTEREST ID Name ID Name ID Name Sport 1 Engineering 1 Hewlett Packard Cycling True 1 2 Analytics 2 Nike 2 Antiques False 3 Accounting 3 Rowing True 4 Cleaning 4 Painting False
- 10. «Structured data» in CMS systems?
- 11. TAG TAG TAG TAG TAG TAG Pages are Generated Views of the content “Manage content, not pages”
- 12. How can Structured Data improve your CMS?
- 13. Topics • Navigation • Multichannel publishing • Social collaboration • E-commerce & BI • Personalized content • Web Applications • SEO & Schema.org • Integration and data sharing • Semantic Web Ole Gulbrandsen – ole@webnodes.com www.webnodes.com
- 14. Tree-based navigation Region City Activity West Rafting Rafting Oslo West East Skiing Skiing Oslo Explore Hamar East Norway.com North Biking Biking Hamar Biking in Hamar North South Hiking Hiking South
- 15. Relation based navigation DEMO
- 16. Multichannel publication CMS Different HTML Layouts for devices «One system» & «One data source» for all devices Different and all formats Data Formats for App frameworks in tablets & mobiles
- 17. DEMO
- 19. Social collaboration • Social data is a network of relations. • If your Data Model support relations, you can model social graphs directly in your CMS and integrate it with your content.
- 20. Social collaboration Communicate, Collaborate, Connect • Collaboration platform for – 2 500 schools + 4 mill users • All data and functionality in one CMS • Seamless integration of content and social data • E-Commerce • Unified access system on all content • Multiple devices, Multiple formats
- 22. Without semantic tags [H1] [Blue] text text [Red] text [Image] 240x130px [Black] text [Bold] text
- 23. With semantic tags Product Phone Name Color Price Product Image Currency Stock Status
- 25. Engage you customers TREND 1: Customers land directly on one of your product pages after searching for it in one of the search engines TREND 2: Customers use your search for navigating, not your menus and links Consequence for your website: • Relation-based navigation • Product recommendations • Accurate and faceted search • Seamless transition between menus and searching
- 27. Web Applications • Business processes moves to the web • Websites are becoming Web Applications • Increased need for data integration and sharing -> All points to the need for Structured Data
- 28. web av data
- 31. Integration and Data Sharing through Data endpoints
- 32. «A protocol for sharing and updating structured data between applications.»
- 34. Ecobox project database NORWEGIAN STATE HOUSING BANK OData Endpoint FURTHER CONNECTING TO 13 OTHER WEBSITES GOVERMENTAL INITIATIVE ABOUT ENERGY EFFICIENT HEATING
- 35. Topics Navigation Multichannel publishing Social collaboration E-commerce & BI Personalized content Web Applications SEO & Schema.org Integration and data sharing Semantic Web Ole Gulbrandsen – ole@webnodes.com www.webnodes.com
- 36. Capture SEO / Rich snippets / Data sharing Engage Navigation / Richer clients / Search Retain Social collaboration / Personalized content / BI Ole Gulbrandsen – ole@webnodes.com www.webnodes.com
Editor's Notes
- Ok, so why do I want to talk about Structured Data.I want to talk about Structured Data because it is directly linked to the key goals of most websites:
- Unstructured data in this context, is just plain text.Computers are not good at understanding text and language. They are super fast, but has little value use as long as they do not understand the data.To help them we add structure.
- We organize data into columns
- Tables where each column containing specific data
- We introduce datatypes and rules for the dataformat
- We normalize the data to ensure consitency and avoid repeating the same data over and over again, like the company name for instands
- We split the data into tables and introduce entity types and link them with releations. All in the purpose of making the computer understand the data, so it can use the data effectively, analyse, manipulate it, and generate real value from contents of data.
- So, what about structured data in CMS systems?
- DocumentsHiererchyDocument typesDocument fieldsMeta Tags Tags provide additional meta data, and can be used to indirectly relate content by tagging both witn the same tag.But tags do not give the relation a specific meaning. It does not specify that the relation between a person and a company is employment.To to that you need to introduce direct and typed relations, where each type of relation has a spesific meaning.
- Men du harogså data om de besøkende. Sådeterikkenok å bare bringedataenetiloverflaten. Du måpersonaliseredetogtilpassedet den individuellebrukeren. Systemetmåkunnebrukebrukrensprofilogpreferansertil å presentereinnholdet I riktigcontekst. Ogdetspennerfra å tilpassesegtilbrukrensspesifikkebrukerenhet, entendetermobil, tablet ellertvogsettesammeninnholdsomer relevant for deresprofil. There is also data about the user. So it is notenough to just bring it to the surface. You need to personalize it and adapt it to the user. Your systems must be able react on the identity of the user, adapt to the users preferences and present content for the right context. It ranges from adapting to the specific device she or he is using, to composing data that is relevant to their profile.
- We among the first to implement this auto
- I