This document discusses how Splunk can be used for business analytics. It provides examples of using Splunk for customer experience, business process analytics, product analytics, and digital marketing. Splunk allows organizations to gain insights from machine data from different parts of the business through various operational centers. While traditional BI focuses on structured data sources and data warehouses, Splunk can complement these investments by providing real-time access to a wider range of machine data sources. The document outlines skills needed for successful Splunk machine learning and identifies how to find business analytics use cases by following business processes and identifying inefficiencies.

1. Copyright © 2014 Splunk Inc.
Splunk for
Business Analytics
Brian Hoover
Market Specialist, Business Analytics & IoT

2. Different lenses into the same data
Smart Manufacturing Biz Ops Center
IT Ops Center
Smart Sales
Security Ops Center
Data Reuse = Greater Data Leverage
Fraud Ops Center, etc…

3. Customer Use Cases for Business Analytics
CUSTOMER
EXPERIENCE
DIGITAL
MARKETING
PRODUCT
ANALYTICS
BUSINESS
PROCESS
ANALYTICS

4. Business Analytics – Examples
Customer Experience
Business
Process Analytics
Product Analytics Digital Marketing

5. 5
Potential Value using Splunk
Documented through 1000+ engagements worldwide

6. ITSI,
UBA
Domain
Expertise
Data
Science
Expertise
Splunk
Expertise
Skill Areas for Successful Splunk Machine Learning
Identify use cases
Drive decisions
Understanding of
business impact
SPL
Data prep
Statistics / math background
Algorithm selection
Model building
Business Analytics

7. What About BI?
7
“Because of its real-time capabilities
and ability to integrate a unique class of data
with structured data sources, Splunk is often already
deployed in an organization for operational use cases
and used alongside to complement, rather than replace, existing BI
and analytics platform investments”
Gartner on Splunk

8. 8
Analysts Business Users
Structured Data Sources
CRM ERP HR Billing Product Finance
Data Warehouse
Traditional BI Approach

9. Splunk: Complementing BI Investments
9

10. How To ID Business Analytics Candidates
Follow the business flow, map the business activity and identify inefficiencies:
• data silos
• human intervention required
• machine data must be deciphered and loaded into structured storage
• spreadsheets! indicate analysis that might be automated
Human troubleshooting is required and you lack:
• access to event data (what actually happened)
• access to sufficient detail (fidelity) (the detail at every step)
Real-time decision making is desired:
• too much data too fast for a human to track
• requires automated alerts / decision making using machine learning
Business Qualification
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11. SMART RETAIL

12. SMART METER

13. SMART SOLDIERS

14. C A L L T O A C T I O N
Art Of The Possible Workshop
Data Source Assessment
Be The Splunk Expert
splunk.com/artofthepossible

15. Copyright © 2014 Splunk Inc.
Thank You