30 Reasons You Still Need to Stage Your Data

November 29, 2016     Business Strategy & Perspectives

If you sit in on Senturus team meetings, you’ll hear impassioned discussions about how folks need to properly stage their data. And we’re not talking about merely replicating tables in a separate database offline and calling that a data warehouse. We mean a high performance, business-centric, subject-area-organized system, aligned with the company’s strategic goals. One with validated and secure metrics that is easy to pull data from and as a result enjoys high adoption rates across the organization.

As technologies advance and data types grow, the more we see the need for solidly architected data warehouses. Our CEO and co-founder John Peterson can wax poetic on the subject. He recently pulled together this quick list of real-life, practical reasons why a data warehouse plays such an important role in any data driven organization. (We’d bet John’s got another good 30 reasons in his back pocket!)

To make data truly usable and valuable, it needs to be transformed into business centric terms and metrics that support organizational decision-making and enriched to provide roll up and drill down access into information. This transformation needs to take place somewhere between the raw source and the final report/analysis. It becomes a question of where and how you handle that transformation, upstream or downstream.

Addressing the transformation downstream simply forces business end-users to tackle it themselves, which leads to the horribly time consuming process of what we call “Excel hell.” It also results in complex, slow and inaccurate self-service dashboards and reports and data “acquisition aggravation.”

Senturus recommends you do the work upstream by properly staging your data to avoid “Excel hell”… and for at least 30 other good reasons.

30 Reasons You Still Need to Stage Data

  1. Consolidates multiple sources of data
  2. Retains history when changing/upgrading systems
  3. Captures snapshots and realigns data
  4. Consolidates data from cloud and on-premise
  5. Provides persistent storage of critical data
  6. Increases efficiency by storing data only once
  7. Cleanses data
  8. Handles and fixes NULL values
  9. Applies universal, one-time filters
  10. Improves performance (end-user reports, etc.)
  11. Eliminates “expensive” and incorrect joins
  12. Transforms complex source data into usable facts
  13. Captures strategic business-centric metrics
  14. Consolidates and simplifies disparate attribute data
  15. Adds mandatory business “dimensional richness”
  16. Simplifies complex data relationships
  17. Applies logic to complete and align data
  18. Facilitates allocation and attribution
  19. Provides “insulating layer” from source systems
  20. Eliminates complex logic needed in BA layer(s)
  21. Allows for “slow-changing dimensions”
  22. Captures “slow-changing facts”
  23. Eliminates live connections to source data
  24. Eliminates spreadmarts and local databases
  25. Reduces challenges associated when key person(s) leave
  26. Eliminates incorrect calculations by end-users
  27. Provides security and controlled access
  28. Helps reduce software license fees
  29. Enables consolidated dashboards and aligned metrics
  30. Enables better dashboards thru context

Benefits of Properly Staged Data

Properly staged data offer numerous strategic advantages to an organization. Benefits include:

  • Better decisions
  • Faster actions
  • Unified strategic direction: what gets measured, gets managed
  • Greater efficiency: less time in "Excel hell"
  • Less redundancy and waste
  • Fewer errors: some can cost $millions
  • Happier business users
  • Greater user adoption

For more on this topic, you can hear direct from John in his recorded webinar Death of the Data Warehouse?