Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Idq summit2014 ronald damhof - it's all about the data

1,693 views

Published on

"It's all about the data, a managerial perspective" - these are the slides of the presentations I gave at Data Modeling Zone 2014 in Hamburg and at the International Data Quality Summit in Richmond (VA) 2014.

Published in: Data & Analytics

Idq summit2014 ronald damhof - it's all about the data

  1. 1. R.D.Damhof – October 2014 – IDQ Summit 2014 It’s all about the data ! A managerial perspective By Ronald Damhof
  2. 2. I am an opinionated kind a guy…. R.D.Damhof R.D.Damhof –– OPrcutdoebnezra 2B0V1 -4 C –o IpDyrQigh Stu -m 22m mit e2i 021041 4 !
  3. 3. Who am I - My Data Manifesto The X commandments of data management ! I. Context is leading ! II. Data is the ultimate proprietary asset, it is to be managed and governed in line with morals & ethics, internal and external rules and legislation ! III. Stop center apps and process over data; data first, facts first ! IV. It is all about the quality of our product; the data. Get clean,Stay clean, Get access ! V. Thou shall abstract and separate concerns rigorously ! R.D.Damhof R.D.Damhof –– OPrcutdoebnezra 2B0V1 -4 C –o IpDyrQigh Stu -m 22m mit e2i 021041 4 !
  4. 4. Who am I - My Data Manifesto The X commandments of data management ! ! VI. a) Thou shall make a fundamentalistic distinction between Fact and Context b) Thou shall not forsake ‘Time’ ! VII.Data architecture is not the same as technology architecture ! VIII.The science and practice of Information & Data Modeling needs to be uphold, improved and taught ! IX. Specify, Standardise, Automate & Productise ! X. Thou can not buy your way out of the data misery you are in R.D.Damhof R.D.Damhof –– OPrcutdoebnezra 2B0V1 -4 C –o IpDyrQigh Stu -m 22m mit e2i 021041 4 !
  5. 5. Who am I - My Data Manifesto The X commandments of data management R.D.Damhof R.D.Damhof –– OPrcutdoebnezra 2B0V1 -4 C –o IpDyrQigh Stu -m 22m mit e2i 021041 4 XI There is a new saviour in town. Its name is Hadoop and it calls to us from its mountain: ! ‘we got a lake and thou shall throw all your data in it. The water will be clean so you can drink it, the water will flow so it will irrigate your lands, grow your stock, feed your kids and of course bring you world peace…..’ ! nah, kidding ;-) !
  6. 6. R.D.Damhof – September 2014 – Data Modeling Zone
  7. 7. R.D.Damhof R.D.Damhof –– OPrcutdoebnezra 2B0V1 -4 C –o IpDyrQigh Stu -m 22m mit e2i 021041 4
  8. 8. R.D.Damhof – October 2014 – IDQ Summit 2014
  9. 9. Logistics & Manufacturing R.D.Damhof – October 2014 – IDQ Summit 2014
  10. 10. R.D.Damhof – October 2014 – IDQ Summit 2014
  11. 11. The Data Push Pull Point Push/Supply/Source driven Pull/Demand/Product driven ▪ Mass deployment ▪ Control > Agility! ▪ Validation of “ingredients” ▪ Repeatable & predictable processes ▪ Standardized processes ▪ High level of automation ▪ Relatively high IT/Data expertise R.D.Damhof – October 2014 – IDQ Summit 2014 ▪ Piece deployment ▪ Agility > Control! ▪ Plausibility ▪ User-friendliness ▪ Relatively low IT expertise ▪ Domain expertise essential All facts, fully temporal Truth, Interpretation, Context Business Rules Downstream
  12. 12. The Development Style Systematic ▪ User and developer are separated ▪ Defensive Governance; focus on control and compliance ▪ Strong focus on non-functionals; auditability, robustness, traceability, …. ▪ Centralised and organisation-wide information domain ▪ Configured and controlled deployment environment (dev/tst/acc/prod) ▪ User and developer are the same person or closely related ▪ Offensive governance; focus on adaptability & agility ▪ Decentralised,personal/workgroup/department/theme information domain ▪ All deployment is done in production Opportunistic R.D.Damhof – October 2014 – IDQ Summit 2014
  13. 13. A Data Deployment Quadrant Systematic Development Style Opportunistic Push/Supply/Source driven Pull/Demand/Product driven R.D.Damhof – October 2014 – IDQ Summit 2014 I II III IV Research, Innovation & Design “Shadow IT, Incubation, Ad-hoc, Once off” Data Push/Pull Point Facts Context
  14. 14. 7 Applications of the Quadrant R.D.Damhof – October 2014 – IDQ Summit 2014
  15. 15. (1) How we produce R.D.Damhof – October 2014 – IDQ Summit 2014
  16. 16. How we produce, process variants R.D.Damhof – October 2014 – IDQ Summit 2014
  17. 17. How we produce, automation Rephrased - somewhat more nerdy:! • Model-driven, metadata driven! • Declarative instead of imperative ! ! Rephrased - somewhat more popular: ! “In Data, the developer is the data modeller” R.D.Damhof – October 2014 – IDQ Summit 2014
  18. 18. How we produce, production lines Production-line: Data orientation R.D.Damhof – October 2014 – IDQ Summit 2014 Data Products Information Products Access to data Analytical tools Processing Power Production-line: Forms orientation Eg. XBRL
  19. 19. (2) How we organize R.D.Damhof – October 2014 – IDQ Summit 2014
  20. 20. To centralize or to decentralize R.D.Damhof – October 2014 – IDQ Summit 2014
  21. 21. (3) How we govern R.D.Damhof – October 2014 – IDQ Summit 2014
  22. 22. How we govern, products R.D.Damhof – October 2014 – IDQ Summit 2014
  23. 23. How we govern, accountability Never, never, never ‘ownership’ R.D.Damhof – October 2014 – IDQ Summit 2014 In- en outbound Data Delivery Agreements I II III IV Deliverant is Accountable Demandee is Accountable Data scientist/Analyst/Researcher responsible
  24. 24. (4) How do people excel R.D.Damhof – October 2014 – IDQ Summit 2014
  25. 25. (5) How to use technology R.D.Damhof – October 2014 – IDQ Summit 2014
  26. 26. (6) How about Technology Storage: (R)DBMS Processing: Automation Software Data Quality: Validation, Profiling Development: Data Modeling Accessibility: Data Virtualization R.D.Damhof – October 2014 – IDQ Summit 2014 Storage: Pattern based Processing: Automation/limited ETL Data Quality: DQ rules/dashboards User tooling: Reporting, dashboards, Data Visualization Storage: Analytical Processing: Preptools for Data Analyst User tooling: Advanced Analytics, Data Visualization
  27. 27. (7) Business-,Information- or Data Modeling is key The Logical Model drives the technical data architecture, design and implementation R.D.Damhof – October 2014 – IDQ Summit 2014 Conceptual Logical e.g Data Vault, Anchor Model Ontology Facts Relational e.g. Dimensional, hierarchical,flat
  28. 28. Oh…data warehouse? The classic distinction between ‘operational data environment’ and ‘informational data environment’ is fading. " ! Modern day data warehouses have been split up. Where the ‘fact’-part (Q1) moved into the operational side." ! Although data warehouses have evolved, operational applications have not, at least not in terms of data architecture. They should though….. R.D.Damhof – October 2014 – IDQ Summit 2014
  29. 29. Email: ronald.damhof@prudenza.nl Linkedin: nl.linkedin.com/in/ronalddamhof/ Twitter: RonaldDamhof Blog: prudenza.typepad.com Website: www.prudenza.nl R.D.Damhof – October 2014 – IDQ Summit 2014

×