Part 2 of a 2 part presentation that I did in 2009, this presentation covers more about unstructured data, and operational data vault components. YES, even then I was commenting on how this market will evolve. IF you want to use these slides, please let me know, and add: "(C) Dan Linstedt, all rights reserved, http://LearnDataVault.com" in a VISIBLE fashion on your slides.

1. Data Vault ModelingWhat’s Next? Part 2 © Dan Linstedt 2009-2012 This was PART 2 of a presentation I gave at an Array Conference In the Netherlands, in 2009.

2. A bit about me… Author, Inventor, Speaker – and part time photographer… 25+ years in the IT industry Worked in DoD, US Gov’t, Fortune 50, and so on… Find out more about the Data Vault: http://www.youtube.com/LearnDataVault http://LearnDataVault.com Full profile on http://www.LinkedIn.com/dlinstedt LearnDataVault.com

3. Where are We Today? IF you are using Data Vault… Auto Generation of Staging Loads Auto Generation of Data Vault Loads Auto Generation of Data Vault Reconciliation Routines Auto Generation of RAW Star Schemas Rapid Build out of Star Schemas If you are lucky… Auto Generation of the Data Vault Model Auto Consolidation of Source System Data Models Auto Generation of the Staging Data Model LearnDataVault.com

4. Where do all these pieces fit? DW2.0 Framework! LearnDataVault.com

6. Plain Text

7. Word Docs

8. ImagesTemp HOT SSD! (Cloud RAM) MediuM Cloud Storage Warm COLD METADATA Enterprise Data Warehouse

9. How do we get there? LearnDataVault.com

10. Virtual Marts: What are they? They Are: RAM based data marts, or SSD drive based Data Marts OLAP cubes (most of the time) built on the fly by new queries “hot-data” that are continually accessed by the BI tool the result sets of the most frequently used queries built dynamically, are accessed regularly, and are destroyed after “idle” for a specific time FAST only a subset of data from the EDW NOTE: They have WRITE-BACK capabilities!! LearnDataVault.com

12. Write Back to BDV

13. Dynamic create/destroy capability

15. Virtual Marts Affect The BDV Write Back Capability: from Virtual Marts affect business decisions New Business transactions/changed transactions will be fed back to operational systems Changes will be sent on the bus to notify other systems of business decisions User security and control will have to be in place to authorize WHO can change WHAT in which parts of the marts. Tracking of each change will become a required standard Eventually the Virtual Marts will become a MIXED BI Application with an operational front end! LearnDataVault.com

16. Unstructured Data: What is it? It is: Information that resides on your desktop, on your servers, on the web, is multi-lingual, and conceptually based. Technically: Documents, E-Mails, Transcripts, Videos, Images, Sound Files. It is 80% of the data yet un-used by EDW/BI operations around the world It is 10x harder to deal with than structured data due to privacy concerns, ownership issues, and ethical concerns. Data Governance, and Data Stewardship play a HUGE role in the success/failure of working with Unstructured Data Sets LearnDataVault.com

18. Pointers to data sets

19. Use of & Loading of Ontologies

21. Write Back to BDV

22. Dynamic create/destroy capability

24. U-Data & Data Vault Unstructured Data – Loaded To Database Ontology, Loaded to Database Dynamic Links Built from Analyzing Queries And Ontologies Used to Load Cubes! Structured RAW Data Vault LearnDataVault.com

25. U-Data & Ontologies Ontologies describe term relationships Ontologies house term hierarchies Ontologies can correlate terms across languages Ontologies can provide synonyms, homonyms, and antonyms Ontologies are the key piece of Metadataneeded to cross unstructured mining results to structured data sets in source systems Ontologies define the manner in which natural language ties together concepts Ontologies (or pieces of them) are required for success within the understanding of Unstructured Data & Structured Data Combinations LearnDataVault.com

26. Ontologies and BI Applications Business Users will shift their BI applications to include managing data sets THROUGH ontology specifications Business Users will assign governance to ontologies and manage changes to ontologies as their metadata definitions Tomorrows BI tool set will provide visualizations of Ontologies cross-mapped to analytical data sets Ontologies ARE the metadata of tomorrow LearnDataVault.com

28. Applications for metadata management on top of the EDV

29. Applications for Ontology Management on top of the EDV

30. Applications to edit/maintain Operational Data

31. Virtual Data Marts

34. The Experts Say… “The Data Vault is the optimal choice for modeling the EDW in the DW 2.0 framework.” Bill Inmon “The Data Vault is foundationally strong and exceptionally scalable architecture.” Stephen Brobst “The Data Vault is a technique which some industry experts have predicted may spark a revolution as the next big thing in data modeling for enterprise warehousing....” Doug Laney LearnDataVault.com

35. More Notables… “This enables organizations to take control of their data warehousing destiny, supporting better and more relevant data warehouses in less time than before.” Howard Dresner “[The Data Vault] captures a practical body of knowledge for data warehouse development which both agile and traditional practitioners will benefit from..” Scott Ambler LearnDataVault.com

36. Where To Learn More The Technical Modeling Book: http://LearnDataVault.com The Discussion Forums: & eventshttp://LinkedIn.com – Data Vault Discussions Contact me:http://DanLinstedt.com - web siteDanLinstedt@gmail.com - email World wide User Group (Free)http://dvusergroup.com LearnDataVault.com