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INTO THE BOX 2024
THE NEW ERA OF
MODERN DEVELOPMENT
Eberly Hall Side A
PRESENTED BY
Curt Gratz
TAMING THE DATA SPRAWL
Curt Gratz
SPEAKER EN ITB 2024
‣ Less of a Werido then Luis
‣ Husband
‣ Dad
‣ Coach (CC and Track)
‣ Co-Owner of CKH Consulting
‣ Runner
‣ BoxLang developer
‣ Polyglot
‣ "Data is the new oil. Like oil, data can bring tremendous wealth, but if
uncontrolled, it can also be very messy." - Clive Humby, British data scientist
MadLibs
‣ Give me a Company Name
‣ Give me a thing
‣ Give me a programming language
‣ Give me a database
‣ Give me a noun
‣ Give me accounting software
‣ Give me a cloud provider
‣ Give me a noun
MadLibs
‣ Give me another databases
‣ Give me a noun
‣ and another noun
‣ Give me another databases
‣ Give me a logging platform
‣ Give me a programming language
‣ Give me another databases
‣ Give me a storage technology
MadLibs
‣ Give me another company name
‣ Give me a thing
‣ Give me a search solution
‣ Give me another database
‣ Give me another accounting software
‣ Give me another storage solution
‣ Give me an programming language
‣ Give me a cache
‣ Give me an analytics platform
Taming the Data Sprawl
So, why does this suck
Taming the Data Sprawl
Step 1) Identify the problem
Taming the Data Sprawl
Step 2) Fix it
Taming the Data Sprawl
Taming the Data Sprawl
REPEAT
Taming the Data Sprawl
The End, thank you for coming
to my TED talk
Taming the Data Sprawl
Taming the Data Sprawl
Fix it, Step 1
‣ Eliminate
‣ Remove duplicate
sources
‣ Consolidate
‣ Remove any unused
data
Taming the Data Sprawl
Fix it, Step 2
‣ Govern It
‣ Know
‣ Validate
‣ Own
‣ Secure
‣ Clean
Taming the Data Sprawl
Fix it, Step 3
‣ De-Duplicate
‣ What’s a match
‣ Victim/Survivorship
‣ Merging
Taming the Data Sprawl
Fix it, Step 4
Taming the Data Sprawl
Fix it, Step 5
REPEAT
Taming the Data Sprawl
Taming the Data Sprawl
Data Governance
“Right Information at the right time to the right people to make the right
decisions”
Taming the Data Sprawl
Taming the Data Sprawl
Taming the Data Sprawl
Challenges
Best Practices
Taming the Data Sprawl
Multiple versions of truth
Single version of truth
Taming the Data Sprawl
Time wasted on data gathering
Time for analysis
Taming the Data Sprawl
Project-driven approach to data
Common data definitions leveraged across
applications
Taming the Data Sprawl
Unclear data ownership rules
Well defined data ownership/stewardship rules
Taming the Data Sprawl
Inconsistent and incomplete information (poor
quality)
Well defined data with robust data quality
processes
Taming the Data Sprawl
“Work-arounds” from early implementations
Thought out data architecture and wholistic
approach (lower TCO)
Taming the Data Sprawl
Challenging to correct data history/errors
Detailed data repository and “single source”
Taming the Data Sprawl
Development focus on providing detail data
Development focus on providing quality data
Taming the Data Sprawl
THE DATA USER’S BILL OF
RIGHTS
Data users have the right to know what the data means
1. The right to know the de
fi
nition of the data.
2. The right to know where the data came from.
3. The right to know how the data was calculated or manipulated.
Taming the Data Sprawl
THE DATA USER’S BILL OF
RIGHTS
Data users have the right to know how risks to the data have (or have not) been
managed
4. The right to know what Security risks weren't eliminated.
5. The right to know what Quality risks weren't eliminated.
6. The right to know what Privacy risks weren't eliminated.
7. The right to know what Compliance requirements in
fl
uenced data
processing and usage.
Taming the Data Sprawl
THE DATA USER’S BILL OF
RIGHTS
Data users have the right to know who made decisions about managing the data,
according to what rules
8. The right to know who made data-related decisions.
9. The right to know what decision-making checks-and-balances were in place.
10. The right to know how issues have been and will be resolved.
Taming the Data Sprawl
Taming the Data Sprawl
Taming the Data Sprawl
Data Quality
”Quality is never an accident; it is
always the result of high intention,
sincere effort, intelligent direction and
skillful execution" - John Ruskin
Taming the Data Sprawl
Data Quality
‣ Inaccurate Data
‣ Incomplete Data
‣ Inconsistant Data
‣ Outdated Data
‣ Invalid Data
Taming the Data Sprawl
Inaccurate Data
‣ 3rd Party data validation
‣ Avoid Freeform
fi
elds
‣ Regular Data Audits
Taming the Data Sprawl
Incomplete Data
‣ Checks for completeness before submission
‣ Data audits to capture missing data
‣ Processes to stop actions without needed data
Taming the Data Sprawl
Inconsistant Data
‣ Avoid freeform data
‣ Require formats
‣ Audit and adapt
Taming the Data Sprawl
Outdated Data
‣ Checks against 3rd party systems
‣ Data retention policies
‣ Procedures to force rechecking stale data
Taming the Data Sprawl
Invalid Data
‣ Data validation rules on entry
‣ Avoid freeform data
‣ Audit and clean
‣ Process for identifying
Taming the Data Sprawl
Source of Truth
“We find the truth only when are searching for it.”
Taming the Data Sprawl
Data Management Association (DAMA)
www.dama.org
The Data Administrator Newsletter
www.tdan.com
The Data Governance Institute (DGI)
www.datagovernance.com
Taming the Data Sprawl
“Thank you.”
–hold up applause sign here
- First talk in given in 2024 with no AI reference
Taming the Data Sprawl
‣ Email - gratzc@compknowhow.com
‣ Blog - https://ckhconsulting.com/blog/
‣ Twitter - gratzc
‣ LinkedIn - gratzc
‣ League of Legends - gratzc
Contact Info
INTO THE BOX 2024
THANK YOU TO OUR
SPONSORS
INTO THE BOX 2024

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Managing and Controlling Data Proliferation.pdf

  • 1. INTO THE BOX 2024 THE NEW ERA OF MODERN DEVELOPMENT
  • 2. Eberly Hall Side A PRESENTED BY Curt Gratz TAMING THE DATA SPRAWL
  • 3. Curt Gratz SPEAKER EN ITB 2024 ‣ Less of a Werido then Luis ‣ Husband ‣ Dad ‣ Coach (CC and Track) ‣ Co-Owner of CKH Consulting ‣ Runner ‣ BoxLang developer ‣ Polyglot
  • 4. ‣ "Data is the new oil. Like oil, data can bring tremendous wealth, but if uncontrolled, it can also be very messy." - Clive Humby, British data scientist
  • 5. MadLibs ‣ Give me a Company Name ‣ Give me a thing ‣ Give me a programming language ‣ Give me a database ‣ Give me a noun ‣ Give me accounting software ‣ Give me a cloud provider ‣ Give me a noun
  • 6. MadLibs ‣ Give me another databases ‣ Give me a noun ‣ and another noun ‣ Give me another databases ‣ Give me a logging platform ‣ Give me a programming language ‣ Give me another databases ‣ Give me a storage technology
  • 7. MadLibs ‣ Give me another company name ‣ Give me a thing ‣ Give me a search solution ‣ Give me another database ‣ Give me another accounting software ‣ Give me another storage solution ‣ Give me an programming language ‣ Give me a cache ‣ Give me an analytics platform
  • 9. So, why does this suck Taming the Data Sprawl
  • 10. Step 1) Identify the problem Taming the Data Sprawl
  • 11. Step 2) Fix it Taming the Data Sprawl
  • 12. Taming the Data Sprawl REPEAT
  • 13. Taming the Data Sprawl
  • 14. The End, thank you for coming to my TED talk Taming the Data Sprawl
  • 15. Taming the Data Sprawl Fix it, Step 1 ‣ Eliminate ‣ Remove duplicate sources ‣ Consolidate ‣ Remove any unused data
  • 16. Taming the Data Sprawl Fix it, Step 2 ‣ Govern It ‣ Know ‣ Validate ‣ Own ‣ Secure ‣ Clean
  • 17. Taming the Data Sprawl Fix it, Step 3 ‣ De-Duplicate ‣ What’s a match ‣ Victim/Survivorship ‣ Merging
  • 18. Taming the Data Sprawl Fix it, Step 4
  • 19. Taming the Data Sprawl Fix it, Step 5 REPEAT
  • 20. Taming the Data Sprawl
  • 21. Taming the Data Sprawl Data Governance “Right Information at the right time to the right people to make the right decisions”
  • 22. Taming the Data Sprawl
  • 23. Taming the Data Sprawl
  • 24. Taming the Data Sprawl Challenges Best Practices
  • 25. Taming the Data Sprawl Multiple versions of truth Single version of truth
  • 26. Taming the Data Sprawl Time wasted on data gathering Time for analysis
  • 27. Taming the Data Sprawl Project-driven approach to data Common data definitions leveraged across applications
  • 28. Taming the Data Sprawl Unclear data ownership rules Well defined data ownership/stewardship rules
  • 29. Taming the Data Sprawl Inconsistent and incomplete information (poor quality) Well defined data with robust data quality processes
  • 30. Taming the Data Sprawl “Work-arounds” from early implementations Thought out data architecture and wholistic approach (lower TCO)
  • 31. Taming the Data Sprawl Challenging to correct data history/errors Detailed data repository and “single source”
  • 32. Taming the Data Sprawl Development focus on providing detail data Development focus on providing quality data
  • 33. Taming the Data Sprawl THE DATA USER’S BILL OF RIGHTS Data users have the right to know what the data means 1. The right to know the de fi nition of the data. 2. The right to know where the data came from. 3. The right to know how the data was calculated or manipulated.
  • 34. Taming the Data Sprawl THE DATA USER’S BILL OF RIGHTS Data users have the right to know how risks to the data have (or have not) been managed 4. The right to know what Security risks weren't eliminated. 5. The right to know what Quality risks weren't eliminated. 6. The right to know what Privacy risks weren't eliminated. 7. The right to know what Compliance requirements in fl uenced data processing and usage.
  • 35. Taming the Data Sprawl THE DATA USER’S BILL OF RIGHTS Data users have the right to know who made decisions about managing the data, according to what rules 8. The right to know who made data-related decisions. 9. The right to know what decision-making checks-and-balances were in place. 10. The right to know how issues have been and will be resolved.
  • 36. Taming the Data Sprawl
  • 37. Taming the Data Sprawl
  • 38. Taming the Data Sprawl Data Quality ”Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction and skillful execution" - John Ruskin
  • 39. Taming the Data Sprawl Data Quality ‣ Inaccurate Data ‣ Incomplete Data ‣ Inconsistant Data ‣ Outdated Data ‣ Invalid Data
  • 40. Taming the Data Sprawl Inaccurate Data ‣ 3rd Party data validation ‣ Avoid Freeform fi elds ‣ Regular Data Audits
  • 41. Taming the Data Sprawl Incomplete Data ‣ Checks for completeness before submission ‣ Data audits to capture missing data ‣ Processes to stop actions without needed data
  • 42. Taming the Data Sprawl Inconsistant Data ‣ Avoid freeform data ‣ Require formats ‣ Audit and adapt
  • 43. Taming the Data Sprawl Outdated Data ‣ Checks against 3rd party systems ‣ Data retention policies ‣ Procedures to force rechecking stale data
  • 44. Taming the Data Sprawl Invalid Data ‣ Data validation rules on entry ‣ Avoid freeform data ‣ Audit and clean ‣ Process for identifying
  • 45. Taming the Data Sprawl Source of Truth “We find the truth only when are searching for it.”
  • 46. Taming the Data Sprawl Data Management Association (DAMA) www.dama.org The Data Administrator Newsletter www.tdan.com The Data Governance Institute (DGI) www.datagovernance.com
  • 47. Taming the Data Sprawl “Thank you.” –hold up applause sign here - First talk in given in 2024 with no AI reference
  • 48. Taming the Data Sprawl ‣ Email - gratzc@compknowhow.com ‣ Blog - https://ckhconsulting.com/blog/ ‣ Twitter - gratzc ‣ LinkedIn - gratzc ‣ League of Legends - gratzc Contact Info
  • 49. INTO THE BOX 2024 THANK YOU TO OUR SPONSORS INTO THE BOX 2024