Are you struggling with poor user adoption and ROI on your BI investment. These 10 lessons learnt comes from over a decade of experience working for BI teams certain to give you clear tips on how to counter these roadblocks.

1. Raquel Seville
CEO - Caribbean, BI Brainz
How to improve ROI & User Adoption for your BI
Implementation

2. How to improve ROI &
User Adoption for your BI
Implementation
Raquel Seville
BI+Analytics 2018 | Huntington Beach, California

3. About Raquel
➔ Big Data Nerd: I have been working with massive volumes of
structured and unstructured data for over a decade
➔ Analytics Wrangler: I am keen on BI user adoption and having the
right tools, talent and processes to ensure maximum ROI
➔ Blogger: I blog all things data at www.exportBI.com
➔ Author: SAP OpenUI5 for Mobile BI and Analytics
➔ SAP Mentor
➔ Foodie, Travel Addict @QuelzSeville

4. BI user adoption strategies
Improving user engagement
Lessons Learnt
Questions
Key Points to Take Home
What I’ll Cover

5. Marketing promotions, sales leads and customer engagement campaigns outsourced
10+ days turnaround time for internal project or product business cases
Lack of confidence in BI and end users resort to building their own BI environment
Introduction & Overview
The Customer

6. Business case turnaround
reduced to 48 hours
Savings upwards of 500k by not
outsourcing
BI silos removed and DWH now
single source of truth
Introduction & Overview

7. How
Introduction & Overview

8. Pulling all data into the DWH assuming that users will use proved false
Focus on a data governance strategy that is agile and continuously improving
“Build it and they will not come”

9. Users did not have
confidence in data
Host workshops,
conferences, create
newsletters and get
feedback to improve
Focus on user buy-in

10. Subject Matter Experts
(SMEs) were not
involved initially
SMEs and domain users
helped to guide data
analysis and exploration,
their feedback and input
was critical to success
Rely heavily on Subject Matter Experts (SMEs)

11. IT decided on data and
reports for DWH
Made the switch to
business led
requirements and visual
storytelling
Ask the business for input

12. Focus was having all the latest
tools
Some tools do not fit all use
cases. Moved to using Tableau
for Data viz and exploration,
BOBJ for operational reporting.
Develop a use case for new tools

13. Self-service BI was not
utilized - too complex
Classified user groups
(Power Users, Analysts,
Operational) and aligned
self-service complexity to
user groups alongside
training
Self-service is valuable when users are educated

14. Some BI staff members were
talent and culture misfits
Trained and promoted
internal talent with existing
knowledge of the business
and its operations
Hire and promote the right people from Day 1

15. Some vendors did not
deliver on expectations and
fit company culture
Renegotiated some
contracts and insourced
some projects
Outsource with caution

16. Predictive and artificial intelligence projects
dragged on for months and years with no
deliverables
Changed focus to small tangible wins using AI.
Find a specific problem and build a hypothesis to
test, narrow and discard weak leads.
Do bite sized integration of AI and predictive analytics

17. Cloud DWH conflicted with
industry regulations
Structured regulated data
moved to on-premise
location, while unstructured
unregulated data stored in
cloud
Find the true alignment for cloud vs. on-premise

18. Data lakes are now swamps. Focus on data governance instead.
Business should drive data focus, not IT.
Champion and educate users - be your own CMO!
Approach AI iteratively by finding a specific problem, develop a hypothesis
and test. Repeat.
Key Points to Take Home

19. Connect with me
E: raquel.seville@bibrainz.com
M: +1 876 438 4392
Li: linkedin.com/in/raquelseville/
@quelzseville