Data warehouse teams are under increasing pressure to prototype sooner, deploy solutions faster, create designs that more flexibly adapt as the business changes, and achieve better alignment with business goals. Watch this recorded webcast to hear how data warehousing teams are getting the most out of their data warehouses by modernizing the tools and methods they use through an Automation First approach.

1. WEBINAR
Automation First as a Strategy for
Data Warehouse Modernization
Achieving productivity and flexibility via modern tools and methods
Philip Russom, Ph.D.
September 19, 2019

2. SPONSOR
2

3. PHILIP RUSSOM
Senior Research Director
for Data Management,
TDWI

4. AGENDA
• Definitions
– Data warehouse modernization
– Data warehouse automation
• Modernization Best Practices
– Why is modernization important?
– What are the strategies for it?
– Automation First strategies
• Use Cases for DW Mod/Automation
– Benefits of Automation First
• Recommendations
Automation First
for DW Mods
@prussom, #TDWI,
@WhereScape

5. DEFINITION OF
DW Modernization
• “Modern” means “up to date” or “recent”
• We modernize to keep pace with evolving biz & tech requirements
– To realign the DW with current business goals
– To provision data for existing and future business use cases
– To leverage new data platforms and data-driven tools
– To adopt new data mgt best practices and to adjust DW teams & skills
– To remediate limitations of scale, speed, functionality, agility
• Synonyms for “data warehouse modernization”
– Data warehouse augmentation, automation, optimization

6. MANIFESTATIONS OF
Data Warehouse Modernization
• Replatforming is the most hyped right now
– Deploying new data platforms: cloud, Hadoop, NoSQL
– Rip-and-Replace: New platform replaces old one
– Augmentation: Old & new platforms coexist & integrate
• Many warehouses need to be redesigned to…
– Improve data models, data quality, metadata
– Improve architecture for multi-platform & logical DWs
• Modernizing the DW to support related disciplines
– Advanced analytics – This is leading driver of DW mod
– New data structures, sources. New biz use cases

7. Related disciplines need modernization, too
• Analytics
• Reporting
• Self-service data
• Data integration
• Data quality
• Metadata mgt

8. DEFINITION OF
DW Automation
• Data warehouse automation is a strategy for DW modernization
– It’s mostly about modern tooling for DW design, development, admin
• The point of data warehouse automation is…
– To give data and operations professionals modern productivity,
flexibility, innovative designs, and warehouse-to-biz alignment
– To support agile, rapid prototyping, and collaborative methods
• Data warehouse automation achieves these goals via…
– Tools with high ease of use, graphical user interfaces, wizard driven
– Modern pipelining, modeling, dataset creation, cloud support, etc.
– Metadata-backed automation. Smart tool algorithms.

9. Automation First
As a strategy for DW modernization
• In the context of DW modernization,
Automation First means…
– Give priority or preference to automation
– Project should start with tools for automation
• Multiple DW mod strategies can be done in series
– Automation First improves data schema and quality
before migrating the data to a new platform
– When you introduce a new data platform into a DW,
that’s a “green field” opportunity to introduce new
DW development methods

10. Why Automation First?
• Because DW automation enables DW mod tasks
– Modern data modeling and modern metadata mgt
– Automatic documentation generation and update
• Because DW automation fixes DW problems
– Outmoded development methods and processes
• With automation, agile typically becomes the norm
– Limited productivity, flexibility, reuse, standards
• Leading driver for adoption of DW automation
• Because DW automation accelerates later DW mod tasks
– Get a productivity boost that applies to whole mod project

11. Metadata is more relevant than ever
• All data-driven action goes thru metadata
– Browsing data, running query, inserting data,
making transactions, updating records, modeling
data, making virtual views, refreshing rpt, etc.
• Still mission-critical for data-driven biz
– Operations, analytics, compliance, discovery
– Enables modern info capture, flow, processes
• More data, more platforms, more apps…
– Each needs solid metadata mgt

12. Why DW automation and modern metadata?
• DW automation & metadata support common uses
– Self-service, better modeling, logical DW…
• Consolidate metadata silos into an automation tool
– For single view, standards, governance
• DW automation & metadata are codependent
– You cannot automate without solid metadata
– Automation tools help modernize metadata mgt
– Metadata is the documentation that keeps a data
warehouse from becoming a data swamp

13. • Business Modernization should be ultimate goal
– Modernize to adapt to a changing market, economy,
customers, competition, etc.
• Ideally, upper management should set the goals
– Communicate biz goals to whole organization
– IT and Data Mgt teams must support biz goals
• DW Automation assists with many business goals
– Reduces time to biz use of data solutions
– The pace of modern business demands this
• DW Automation produces quick, aligned solutions
– Else, dep’ts build their own ungoverned silos
UPPER MANAGEMENT ROLE IN
DW Modernization and Automation

14. • Obviously, data management professionals are required
– Specialists in warehousing, integration, analytics, reporting.
Data modeling, architecture, metadata
• People & processes for data governance & stewardship
– Align the DW with biz goals, compliance, and data
standards, as you modernize and automate it
– Adjust governance policies as you go
• Affected parties must be part of the process
– Miscellaneous data consumers and user constituencies
• In some cases: partners, clients, customers
• Create a multi-phase plan, not “big bang.”
STAFFING and COORDINATING
DW Modernization and Automation

15. Many of the top priorities
for data management in
2019 that users selected
in a recent survey are
tool functions and
practices associated
with data warehouse
modernization and
automation.
Market Demand for
DW Mod/Automation

16. Users Surveyed
say that it is:
Very Important &
Important
to have
Tools & Practices
that enable
DW Automation
Source: TDWI 2019.
Based on 190 Respondents.
Market Demand for
DW Mod/Automation

17. Recommendations
• Know the many manifestations of DW modernization
– Select the ones that match your biz/tech priorities
– E.g., replatforming, augmentation, redesign, automation
• Create a plan for your DW modernization project
– New tooling usually comes first, data migration last
– Coordinate with related teams: analytics, reporting…
– Coordinate with affected parties: users, managers…
• Consider modernizing your dev/admin tool set
– E.g., tools for DW automation, metadata, quality, modeling
• Kick off DW modernization with an Automation First strategy
– New automation tools will boost the whole project’s productivity
– Their additional functionality will aid many modernization tasks

18. MICHAEL TANTRUM
Director of Alliances,
WhereScape

19. Modernizing with
Data Warehouse
Automation
Michael Tantrum
Director of Alliances, WhereScape

20. WhereScape helps IT teams fast-
track projects and deliver more
through data infrastructure
automation.
GLOBAL REACH
Portland, OR
Asi
a
Auckland,
NZ
Europ
e
PROVE
N
700+ customers
From small organizations to
large global enterprises.
20 years
Of automation innovations
for IT teams of all sizes.
EXPERIENCE
D
5x developer output
By automating the routine steps
that slow developers down.
ROI

21. The Traditional Approach to
Data Infrastructure Delivery
Hand-Coding and Manual Processes
• Lengthy, expensive and problematic
projects
• Lack of agility, standardization and best
practices
• Documentation and technical debt
• Tribal knowledge

22. Automation Modernizes
Data Warehousing
 By automating data warehousing
patterns, teams can deliver faster
 Reduce complexity of new
technologies and data platforms
 Lessen risk, adopt best practices
 Quickly evolve and agilely react to
change
 Deliver at less cost

23. Automation Leverages Data Warehousing Patterns

24. The Conventional Way
• Over-complicated
• Inefficient
• Difficult to address change

25. A Better Way
✓ Metadata-driven
✓ Built-in methodologies
✓ Best practices, standards
✓ Documentation and lineage
✓ Full lifecycle management
Simplified and Automated

27. An Automation First Approach
with WhereScape
 Modernization requires automation
 Boost team productivity to fast-track delivery
 Limit project risk and lower modernization
initiative costs
 Minimize complexity – migration to the cloud,
Data Vault, new data platforms and sources,
hybrid environments
 Use automation to support DevOps and agile
continuous integration and delivery

28. Thank You
More information on Automation
www.wherescape.com
product.info@wherescape.com
Follow us on LinkedIn or Twitter @wherescape

29. WhereScape.com
© Copyright 2019, WhereScape Software Ltd. All rights reserved. All trademarks, trade names, service marks and logos referenced herein belong to their respective companies.
About WhereScape
WhereScape helps IT organizations of all sizes leverage automation to design, develop, deploy and operate
data infrastructure faster. More than 700 customers worldwide rely on WhereScape automation to eliminate
hand-coding and other repetitive tasks to deliver data warehouses, vaults, lakes and marts in days or weeks
rather than months or years. WhereScape has global operations in the USA, UK, Singapore and New
Zealand.

30. CONTACT INFORMATION
If you have further questions or comments:
Philip Russom, TDWI
prussom@tdwi.org
Michael Tantrum, WhereScape
michael.tantrum@wherescape.com
tdwi.org