1. Copyright © 2015 Splunk Inc.
Florian Huck
Marketing Analyst, Splunk
Data Practice 101: An
Intro to Business Data
Anne-Marie Chun
Splunk Data Practice

2. Disclaimer
2
During the course of this presentation, we may make forward looking statements regarding future
events or the expected performance of the company. We caution you that such statements reflect our
current expectations and estimates based on factors currently known to us and that actual events or
results could differ materially. For important factors that may cause actual results to differ from those
contained in our forward-looking statements, please review our filings with the SEC. The forward-
looking statements made in the this presentation are being made as of the time and date of its live
presentation. If reviewed after its live presentation, this presentation may not contain current or
accurate information. We do not assume any obligation to update any forward looking statements we
may make.
In addition, any information about our roadmap outlines our general product direction and is subject to
change at any time without notice. It is for informational purposes only and shall not, be incorporated
into any contract or other commitment. Splunk undertakes no obligation either to develop the features
or functionality described or to include any such feature or functionality in a future release.

3. About Us
3
Anne-Marie ‘Punky’ Chun
Management Consulting
MBA @ Wharton
Splunk!
Florian ‘Hucky’ Huck
1.0: Counter Strike Source
2.0: Business education
3.0: SF…and Splunk!

4. Agenda
4
Intro to Business Data
Data Practice 101 – From Requirements to Consumption
Powerful Commands for Business Data You Can Take Home Today
Main Takeaways
Additional Resources

5. Copyright © 2015 Splunk Inc.
Intro to
Business Data

6. Data is Everywhere
6

7. Business Data != IT Data
7
IT Data Business Data
Universally similar, rare singularities Varies for each team and company
Machine generated Human + Machine generated
Few data owners Many data owners
Mostly logs Leverages fixed CRM / historical data
Technical, understands
logs, data structures…
Business-minded,
not highly technical

8. Understand Your Audience
8
Is this data correct?
Short Attention Span
Not Technical
Might Not Know Work Needed
Needs Insights at Fingertips
How is this relevant?
What actions should I take?
Make it simple
What data can we use?
Make it pretty!
Project
scope…mmh
what?!
Doesn’t Need Technical Details

9. Be a Superhero to Your Business Folks
9

10. Copyright © 2015 Splunk Inc.
Data Practice 101

11. Data Practice 101: Process
11
Gather
Requirements
Collect &
Cleanse
Data
Build &
Develop
Deliver &
Consume

12. Data Practice 101: Gather Requirements
12
Identify the right stakeholders
Keep everyone honest
Ask the right questions
Understand the business problem to solve

13. Data Practice 101: Gather Requirements
13
AVOID: Rushing without properly understanding goal

14. Data Practice 101: Collect & Cleanse
14
Collect everything, identify relevant data
Investigate to ensure data quality
Work with data owners
Collect, understand & enhance/enrich the data you need
Identify extra sources to enrich the data

15. Data Practice 101: Collect & Cleanse
15
AVOID: Assuming the data is correct…and exists!

16. Data Practice 101: Build & Develop
16
Optimize your search structure
Don’t forget unique identifiers!
Use definitions
Manipulate the data to find answers to the questions

17. Data Practice 101: Build & Develop
17
AVOID: Going heads down without seeking guidance

18. Data Practice 101: Deliver & Consume
18
Tailor delivery to the audience
Anticipate questions that will come up
Keep it simple, straight to the point
Deliver answers (and identify next questions)

19. Data Practice 101: Deliver & Consume
19
AVOID: Information overload

20. Copyright © 2015 Splunk Inc.
Powerful Splunk
Commands

21. Easily Visualize Your Data
21
| table *
| fields

22. Enhance Your Data
22
| eval
if, case,
mv*
| stats
count, avg, values
| dedup
| rex

23. Enrich Your Data
23
| lookup
| join

24. Main Takeaways
24
Ask the right the business question
Investigate the data
– Splunk is FLEXIBLE – Collect everything & figure out later
– Splunk is AGILE – Ingest any data…no need of specific format
Use the power of SPL to do the magic
– SPL is POWERFUL – Refine/enhance the data as you wish!

25. .conf2015 – Next Steps
25
Search Party!
.conf2015 sessions – Go or watch
– “Unraveling Analytics and Data Science: An Expert Panel ” – Tom LaGatta,
Hal Rottenberg
– “Building Powerful Analytics with Ease” – Pierre Brunel
– “Enhancing Dashboards with Javascript!”– Satoshi Kawasaki

26. Copyright © 2015 Splunk Inc.
Resources

27. Additional Resources
27
Splunk Quick Reference Guide
Eval Functions
Statistical and Charting Functions
SplunkLive! Presentation: Data Models 101
Splunk Education: Searching and Reporting

28. Additional Resources (cont.)
28
Blog Post: Still Using 3rd-party Web Analytics Providers? Build Your
Own Using Splunk!
Blog Post: Capturing Omniture (or Google Analytics, or Webtrends)
Data into Splunk
External Whitepaper: Data Governance: A Business Value-Driven
Approach