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TDWI Blog: Data 360

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Data Warehouse Modernization: An Overview in 30 Tweets

By Philip Russom, Senior Research Director for Data Management, TDWI

To help you better understand what data warehouse (DW) modernization is, what variations it takes, who’s doing it, and why, I’d like to share with you the series of 30 tweets I recently issued on the topic. I think you’ll find the tweets interesting, because they provide an overview of data warehouse modernization in a form that’s compact, yet amazingly comprehensive.

Each tweet below is a short sound bite or stat bite drawn from the recent TDWI report “Data Warehouse Modernization in the Age of Big Data and Analytics,” which I researched and wrote. Many of the tweets focus on a statistic cited in the report, while other tweets are definitions stated in the report.

I left in the arcane acronyms, abbreviations, and incomplete sentences typical of tweets, because I think that all of you already know them or can figure them out. Even so, I deleted a few tiny URLs, hashtags, and repetitive phrases. I issued the tweets in groups, on related topics; so I’ve added some headings to this blog to show that organization. Otherwise, these are raw tweets. Enjoy!

Introduction to Data Warehouse Modernization
1. #DataWarehouse #Modernization ranges widely: upgrades; new subject areas; more platforms etc.
2. #DataWarehouse #Modernization is real. 76% of DWs are evolving dramatically or moderately.
3. 89% of #TDWI survey respondents say #DataWarehouse #Modernization is opp for innovation.

State of Data Warehouse Modernization
4. 91% of users surveyed find #DataWarehouse #Modernization extremely or moderately important.
5. Half of users surveyed say #DataWarehouse is up-to-date. Other half is behind. Both need modernizing.
6. 88% of users surveyed say #DataWarehouse still relevant to how mgt runs biz.

Drivers of Data Warehouse Modernization
7. #DWE #Modernization drivers = aligning DW w/biz; scaling to #BigData; new analytic apps; new tools & data types.
8. #DataWarehouse #Modernization fixes problems w/ DW focus, design, architecture, platform.
9. Modernize DW to leverage new types of data (unstruc,sensors,GPS) & tools (#Hadoop,CEP, cloud,SaaS).

Types of Data Warehouse Modernization
10. Continuous #Modernization is about regular recurring updates & extensions of a #DataWarehouse.
11. Disruptive #DataWarehouse #Modernization is about rip-&-replace of major datasets, platforms, tools.
12. Optimization #Modernization is about remodeling data, interfaces, processing for DW performance.

Benefits and Barriers for Data Warehouse Modernization
13. Leading beneficiaries of DW #Modernization = analytics; biz mgt; #RealTime operations.
14. Leading barriers to DW mod = problems w/ governance, staffing, funding, designs & platforms.
15. #Modernization also needed for systems DW integrates with = reporting, #analytics, #DataIntegration.

Trends in Data Warehouse Modernization
16. No.1 #Modernization trend is toward #DataWarehouse Environments (#DWEs) with multiple standalone data platforms.
17. Improving DW system arch (adding/replacing data platforms) is most common DW #modernization.
18. Platforms added to #DWE are based on column, appliance, event proc, adv’d analytics, # Hadoop.

User Plans for Data Warehouse Modernization
19. Half of org’s surveyed plan to leave current DW platform in place & add complementary platforms.
20. Half of org’s surveyed plan to rip out current DW platform & replace it within 3 to 4 years.
21. Very few users surveyed lack a plan or strategy for #DataWarehouse #Modernization.

Data Warehouse Modernization’s Effect on Architecture
22. #Modernization has reduced the number of single-DBMS-instance #DataWarehouses. Down to 19%.
23. Multi-platform #DataWarehouse Environment (#DWE) is norm for DW sys arch; 34% today.
24. Extreme #DataWarehouse Environment (#DWE) with LOTS of platforms will become sys arch norm in 3 yrs.

Hadoop’s Role in Data Warehouse Modernization, Part 1
25. #Hadoop is often deployed to modernize a DW or #DWE. Orgs w/#Hadoop in #DWE will double in 3yrs.
26. For early adaptors, #Hadoop is DW/#DWE complement, not replacement.
27. #Modernization via #Hadoop helps address “exotic” data: non-relational, unstruc, social, sensors.

Hadoop’s Role in Data Warehouse Modernization, Part 2
28. Modern DW of future will still have relational DBMS at core. But probably integrate w/#Hadoop too.
29. #Hadoop’s relational functions will improve greatly; more likely as DW replacement in 3 to 5 yrs.
30. A few users surveyed think #Hadoop will grow larger than DW but not replace it. 2% now; 14% in 3yrs.

Want to learn more about Data Warehouse Modernization?
For a more detailed discussion – in a traditional publication! – get the TDWI Best Practices Report, titled “Data Warehouse Modernization in the Age of Big Data and Analytics,” which is available in a PDF file via a free download.

You can also register for and replay the TDWI Webinar, where I discussed the findings of the TDWI report.

Posted on June 8, 2016


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