Splunk Forum Financial Services Chicago 9/13/17

© 2017 SPLUNK INC.© 2017 SPLUNK INC.
SEPTEMBER 13, 2017 | FINANCIAL SERVICES | CHICAGO

© 2017 SPLUNK INC.
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 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.
Splunk, Splunk>, Listen to Your Data, The Engine for Machine Data, Splunk Cloud, Splunk Light and SPL are trademarks and registered trademarks of Splunk
Inc. in the United States and other countries. All other brand names, product names, or trademarks belong to their respective owners. © 2017 Splunk Inc. All
rights reserved.
Forward-Looking Statements

© 2017 SPLUNK INC.
Welcome
Andrew Stein | Data Scientist & Business Analytics Practice, Splunk
SEPTEMBER 13, 2017 | FINANCIAL SERVICES

© 2017 SPLUNK INC.
Take the Survey on Pony Poll
ponypoll.com/finforumchi

© 2017 SPLUNK INC.
Program Agenda
Splunk Forum Financial Services Chicago | September 13, 2017
12:45 – 1:00
Welcome
Andrew Stein, Splunk
1:00 – 1:30
Financial Services Industry Keynote
Julie Conroy, Research Director, Aite Group
1:30 – 2:15
Panel Discussion Moderated by: Robert Wagner, Security Strategist, Splunk
Ed Giles, SVP, Security Technology/Enterprise Enablement, The Northern Trust
Subir Grewal, CFA, Head of Splunk Practice, Risk Focus
Mike Stankus, Director, Management and Systems Monitoring, CME Group
Bob Beard, Director of Systems Engineering, CME Group
2:15 – 2:30 Break in Cloud Gate Foyer
2:30 – 3:00 Session 1: Splunk for Innovation in Financial Services, Brian Hoover, Splunk
3:00 – 3:30 Session 2: Splunk for Electronic Trading Operations Monitoring, Subir Grewal, Risk Focus
3:30 – 4:00 Session 3: Machine Learning and Predictive Analytics for Financial Services, Andrew Stein, Splunk
4:00 – 4:30 Closing Remarks

Financial Services – Common Goals
6
Helping customers realize their ambitions
and succeed financially.
Grow the
Business
Strike revenue
growth by
capitalizing on
opportunities across
all lines of business
and geographies.
Streamline
Processes
Reduce complexity
and inefficiencies,
releasing capital to
be reinvested in
other areas of the
business.
Reward
Shareholders
Generate attractive
and sustainable
return for
shareholders.
Attract Top
Talent
Be a workplace that
attracts top talent
and which
collaboration
achieves results.
Customer
Driven
Make the financial
lives better for
customers, listening
to what they want
and delivering the
solutions they need.
Source: Annual reports for leading banks such as HSBC, Bank of America, Santander Bank…

© 2017 SPLUNK INC.
Overall FSI Digital Transformation
2017 Top Industry Drivers*
Fintech Disruption Digital Technologies
Cloud Services Cyber Security
Advanced Analytics/ML Business Process & IT Modernization
End-to-End Operational Visibility
(* Source: Deloitte Report, 2017)

© 2017 SPLUNK INC.
Big Data is
central to
these
priorities
and drivers*
(* Source: Cap Gemini Consulting, 2016)

GPS,
RFID,
Hypervisor,
Web Servers,
Email, Messaging
Clickstreams, Mobile,
Telephony, IVR, Databases,
Applications, Telematics, Storage,
Servers, Security Devices, Desktops,
Wire Data, Social Data, Middleware
Big Data Comes from Machines
Machine data is the fastest growing, most
complex, most valuable area of big data
Volume | Velocity | Variety | Variability

© 2017 SPLUNK INC.
Diversity & Silos of Machine Data Creates a Challenge

© 2017 SPLUNK INC.
FSI Processes & Services Use Data From Diverse
Systems
Trade
Capture
Trade
Execution
Validation
Trade
Booking
Trade
Clearing
Trade
Settlement
Sample Trade Processing Flow
Hardware NetworkingFront & Back
Applications
Message
Queues
(SWIFT/FIX)
Enterprise
Service
Bus
Devices
Diverse IT Systems Support Trade Processing

© 2017 SPLUNK INC.
Getting Visibility Across These Systems Is Challenging
Search & Identify
Monitor Systems/Process
Gain Operational Visibility
Get Business Insights
• What caused the delay in processing or settlement?
• Where are the transaction confirmation details?
• Does the user have access to the right data?
• How many transactions were processed?
• Can we prevent the problem from happening?
• How is the customer experience? Did we meet SLAs?
Hardware NetworkingFront & Back
Applications
Message
Queues
(SWIFT/FIX)
Enterprise
Service
Bus
Devices
Diverse IT Systems Support Trade Processing

© 2017 SPLUNK INC.
Gain Insights Across Financial Services Machine Data
COLLECT,
CORRELATE,
ANALYZESecurity
Messaging
(SWIFT/FIX)
Payment
Formats
(ACH/Wire)
Risk and
Compliance
Apps
Online
Banking
Core
Banking
Engines
Trading
Systems
Infrastructure
Payment
Services
Hubs
Cyber Security
System Modernization,
Monitoring, & Infrastructure
Cost Reduction
Regulation & Compliance
Real-Time Payments
Advanced Analytics
Digital Technologies &
Omnichannel
Emerging Technologies
(e.g. Blockchain)
Middleware

© 2017 SPLUNK INC.
Financial Services Industry Keynote
SEPTEMBER 13, 2017 | CHICAGO
Julie Conroy | Research Director, Aite Group

© 2017 SPLUNK INC.
Cybercrime on the rise: How to
beat the bad guys
September | 2017

© 2017 SPLUNK INC.
Source:
Informationisbeautiful.
net

© 2017 SPLUNK INC.
As counterfeit declines, CNP fraud, account
takeover and application fraud are rising around
the globe
$3.2 $3.3
$4.0 $4.4
$5.5 $5.9
$1.4 $1.6
$1.9
$2.2
$2.5
$2.8
$0.6
$0.7
$0.8
$0.8
$0.9
$1.0
2015 2016 e2017 e2018 e2019 e2020
U.S. ATO, CNP, and Application Fraud Growth, 2015 to e2020
(In US$ Billions)
ATO fraud
Application
fraud
CNP fraud

© 2017 SPLUNK INC.
The changing notion of identity: From face-to-face, to
personally identifiable information to digital identity

© 2017 SPLUNK INC.
Machine learning and AI: Buzz words realizing their
potential

© 2017 SPLUNK INC.
Machine learning turns the data lake into actionable intellige

© 2017 SPLUNK INC.
Artificial Intelligence
Cognitive computing
and automation
Natural language
processing
Cloud
computing
Big data
Distributed
networking
Low data costs
Concurrent/parallel/
distributed
computing
Marketing
Know
Your
Customer Trading
technology
Robo-advisors
Trade surveillance
Fraud
prevention
Technology roots
Financial services
use cases
Anti-money
laundering
Cyber-
security

© 2017 SPLUNK INC.
Fraudsters

© 2017 SPLUNK INC.
Legacy approaches don’t work in the face of
rapidly evolving fraud and cybercrime

© 2017 SPLUNK INC.
The evolution of risk analytics
1990s Late 2000s
2012 to
present
1980s
Static, inflexible rules-
based systems
Dawn of neural
network models
Emergence of big-
data-driven analytics
User-friendly ML
Combination of low-
tech rings and
opportunistic
fraudsters
Increasing
organization among
fraud rings, Europe
heavily targeted due
to batch authorization
processes
Fraud driven by
sophisticated rings,
fueled by skimming
and data breaches
International
organized cybercrime
rings rapidly evolve
tactics—FIs and
merchants hard-
pressed to keep up
Fraud
analytics
Criminal
tactics

© 2017 SPLUNK INC.
Advances in analytics coupled with ready
availability of data is driving significant leaps in
performance
$40,000
$295
$0.56
$0.02
1980 1996 2006 2016
Hard Drive Cost Per Gigabyte, 1980 to 2016

© 2017 SPLUNK INC.
Financial institution challenges
▶ Siloed data and
processes
▶ IT resource constraints
▶ Bureaucratic overhead
▶ Inconsistent UX

© 2017 SPLUNK INC.
Effective security is a competitive issue

© 2017 SPLUNK INC.
Solution: Increased data visibility
▶ Use Splunk to collapse the
data silos
▶ Differing business rules and
analytics for different use
cases
▶ Enables rapid identification
and response

© 2017 SPLUNK INC.
Thank you.
Julie Conroy| Research
Director
jconroy@aitegroup.com
Aite Group is a global research and advisory firm
delivering comprehensive, actionable advice on
business, technology, and regulatory issues and their
impact on the financial services industry. With expertise
in banking, payments, insurance, wealth management,
and the capital markets, we guide financial institutions,
technology providers, and consulting firms worldwide.
We partner with our clients, revealing their blind spots
and delivering insights to make their businesses smarter
and stronger.
Visit us on the Web and connect with us on Twitter
and LinkedIn.

© 2017 SPLUNK INC.
Panel Discussion – How Splunk Addresses Critical FSI Industry Drivers
Subir Grewal, CFA, Head of Splunk Practice, Risk Focus
Ed Giles, SVP, Security Technology/Enterprise Enablement, The Northern Trust
Mike Stankus, Director, Management and Systems Monitoring, CME Group
Bob Beard, Director of Systems Engineering, CME Group
Robert Wagner, Security Strategist, Splunk

THANK YOU
15 MINUTE BREAK

© 2017 SPLUNK INC.
Splunk for Innovation in Financial Services
Brian Hoover | Staff Analytics and IoT Practitioner

INNOVATION IS
HARD

© 2017 SPLUNK INC.
Innovation Requires Change
Change Is Hard
Shock
Denial
Anger
Time
Performance
Too much change too fast can result in a constant
state of lost productivity and dissatisfaction:
A single “change” typically goes through an adoption
lifecycle:
Kubler-Ross Change Curve
..

© 2017 SPLUNK INC.
Our Tools Have To Enable Change
Not Stand In The Way!
More Time Spent On Setup
AnalyzeExtract/Transform/LoadModel
Classic
More Time Spent On Iterative Analysis!
Now

Analytics Innovation: Time Series
44
Classic
Get all the data in one location (data-at-rest)
New
Get all the data at the same time (data-in-motion)

Analytics Innovation: Non Disruptive
45
Classic
Data only pulled periodically to avoid disrupting
operational systems
New
Data ingested as it is created, from “digital
exhaust” Machine Data
Operational Machine
Data Platform
Near real-time
operations

Analytics Innovation: Structure on Demand
46
Classic
Curated Structure - Model, Index, Aggregate in
advance for fast visualization
New
Index everything at ingestion, let the query/search
invoke structure

Technical Requirement: Correlation On Demand
47
Classic
Joins defined in advance using a unique identifier
or foreign key
New
Correlate on-demand using time and any text or
numeric string

Analytics Innovation: Anomaly “Self-Aware”
48
Classic
Discovery via Visualization
New
Exceptions automatically registered and alerted

Analytics Innovation: Operational Machine Learning
49
Classic
Machine learning is “glued” on
New
Machine learning and alerting are integrated

Analytics Innovation: Agile Changes
50
Classic
New data and new questions require a new data
model
New
New data and new questions can be incorporated
at any time

EXAMPLES

© 2017 SPLUNK INC.
Analytics Innovation
$12.7 BILLION
Across
836 DEALS
2016
Venture Capital
FSI Industry Examples

© 2017 SPLUNK INC.
Payments: Business Process Monitoring
This example recognizes
the value of monitoring
the entire stack from
technology to business
process with a single
platform.
Horizontal Integration
VerticalIntegration

© 2017 SPLUNK INC.
Schematic
Only Key Process Entry and Exit Points Are Being Monitored (For Now)

© 2017 SPLUNK INC.
The resulting
glass tables
capture key
payment
process points.
Any gateway
can be clicked
to drill down into
the underlying
process health
and measures.
Top Level View Of Payment KPI
Drill Down Into The Health Of Underlying Systems

© 2017 SPLUNK INC.
Regtech: Detect Anomalies For Non-Compliance
A Global Swiss
investment bank
prototyped a solution
to address MiFID II’s
Regulatory Technical
Standards (RTS) in
days instead of
weeks. It correlates
clock data from
application servers
with trade execution
data.
https://www.splunk.com/blog/2017/08/01/mifid-ii-the-clock-is-ticking-for-financial-services.html
Clock Drift
Trade Count
Fill Before Order
Global Swiss
Investment Bank

© 2017 SPLUNK INC.
Insuretech : Monitoring Business Activities
Generated By Partners With Disparate Systems
Real time monitoring of the
business impact and SLA
compliance of partner business
transactions generated by
disparate systems. Developed
in a matter of days.
European
Insurer
Aggregate Of All Partner
Purchase
Quote
Failed
Purchase

© 2017 SPLUNK INC.
Tradetech : FX Trade Monitoring
Transaction monitoring of
FIX data for anomalies and
unusual customer behavior
in real time.
A Large UK
Financial Institution
ML To Highlight Anomalies

© 2017 SPLUNK INC.
Marketing : Offer Monitoring
Offer and program
popularity in the market
through recent or even real-
time analysis. This helps
illustrate return on
investment (ROI) for specific
marketing campaigns.

© 2017 SPLUNK INC.
Operations : Call Center MonitoringLarge US
Credit Union
These dashboards are optimized
for “one click” access to three tiers
of information;
• The top level KPI color coded
for severity
• The recent trend of that KPI
• The specific metrics that
provide context around the
value of that KPI.
Clicking on any top tier KPI
refreshes the sub panels without
refreshing the page.
Color Coded KPI
Trend Of KPI
Relevant Metrics To KPI

© 2017 SPLUNK INC.
Summary
• The ability to analyze real time operations
utilizing machine data opens a whole new
set of innovation possibilities – Experiment
and Fail Early
Source: Karl G. Shoemer, MS

© 2017 SPLUNK INC.
Summary
and Fail Early
• Innovation requires change, and the
impact of change on the organization
can’t be ignored
20%
50%
30%
Source: Karl G. Shoemer, MS
Design
(promotes change)
Default
(accepts status quo)
Defiance
(resists change)

© 2017 SPLUNK INC.
Summary
and Fail Early
• The right platform for operational
innovation has to enable change, not
stand in the way
• Innovation requires change, and the
impact of change on the organization
can’t be ignored

THANK YOU
Q + A

© 2017 SPLUNK INC.
Splunk for Electronic Trading
Operations Monitoring
Subir Grewal, CFA | Head of Splunk Practice, Risk Focus

© 2017 SPLUNK INC.
Splunk partners of choice for Capital Markets

© 2017 SPLUNK INC.
Agenda
▶ About Us
▶ Splunk Adoption Path
▶ Challenges
▶ Use Case Examples
▶ Summary
67

© 2017 SPLUNK INC.
Risk Focus
▶ Expertise in Trading, Risk Management & Cloud Transformation
▶ Right-sized:
• technical organization big enough to deliver
• small enough to be efficient
▶ Capital Markets specialists
▶ Top Ten Most Promising Cloud Banking Solution Providers of 2015 by Banking CIO Outlook Magazine
▶ Splunk Practice
• Splunk Premier partner with Certified SEs, Architects and Consultants (US and EU).
• Integrated into every solution we deliver to provide operational visibility.
68

© 2017 SPLUNK INC.
Splunk Adoption Path
69
Log Aggregation
Operational
Intelligence
Automation and
Machine Learning

© 2017 SPLUNK INC.
Challenges
70
Deployment
Normalization and
Visualization
Scalability

© 2017 SPLUNK INC.
APIsMessagin
g layer
Standard Capital Market business process
71
Transactio
n
Data
Logs
Risk / Margin, Valuation
EngineMarket Data
Trade Capture
Client reporting
Post-trade
Reference
Data
Liquidity provider

© 2017 SPLUNK INC.
Validate.Trade Use Case
▶ Trade workflow/validation engine
▶ Objective is to improve Dodd-Frank/EMIR compliance.
▶ Product coverage (FX, FI, IR etc.)
▶ Supports all major regulatory regimes (US, EU, Canada, HK, etc.)
▶ Global trade repositories (such as DTCC, ESMA)
▶ Splunk dashboard delivered with Validate.trade
72

© 2017 SPLUNK INC.
Validate.Trade demo
73
Demo

© 2017 SPLUNK INC.
Cloud Infra monitoring case study
▶ Build a private cloud at Top 5 N.A. Bank:
• Hosting over 10,000 VMs
• Tools to manage provisioning, rollout process
▶ Splunk monitors cloud service layer, and all VMs.
▶ Delivers system and application logs across entire fleet in every environment.
▶ Met retention requirements for ephemeral infrastructure.
74

© 2017 SPLUNK INC.
Scaling Splunk within Capital Markets
▶ Segregated data to support information barriers
▶ Role-based access to allow some users to search across tenants
▶ Leverages administration benefits of common environment
▶ Manage varying retention requirements
▶ Scale to adapt to Splunk growth

© 2017 SPLUNK INC.
Manage the Splunk SDLC
▶ Splunk Infrastructure as CODE
• Provision VM
• Install Agents
• Bootstrap Splunk install
• Configure indexers, search-heads
▶ Follow the same SDLC for Splunk as for other software

© 2017 SPLUNK INC.
Multi-Tenant Splunk for a firm-wide data
lake
A rich topology with
many different
components
Deployment
sequencing can
be complex

© 2017 SPLUNK INC.
Multi-Tenant Splunk Management Console
78

© 2017 SPLUNK INC.
How We Do It
▶ Automation….
▶ Splunk infrastructure as code
▶ Fleet configurable at deployment
▶ Installs are automated with minimal manual intervention
▶ “Surge” capacity or additional nodes can be deployed in a similar manner

© 2017 SPLUNK INC.
Ideal Use Cases for Machine
Learning and Predictive
Analytics
Andrew Stein – Analytical Architect for Machine Learning

© 2017 SPLUNK INC.
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 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.
Splunk, Splunk>, Listen to Your Data, The Engine for Machine Data, Splunk Cloud, Splunk Light and SPL are trademarks and registered trademarks of Splunk Inc. in
the United States and other countries. All other brand names, product names, or trademarks belong to their respective owners. © 2017 Splunk Inc. All rights reserved.
Forward-Looking Statements

© 2017 SPLUNK INC.
▶ Improve decision making
▶ Uncover hidden trends or
relationships
▶ Alert on deviations
▶ Forecast or anticipate incidents
All of this requires diverse data
from across many silos. Lots
of unstructured, real-time data.
Why Do We Need Machine Learning?

© 2017 SPLUNK INC.
Overview of ML at Splunk
CORE PLATFORM
SEARCH
PACKAGED PREMIUM
SOLUTIONS
MACHINE LEARNING
TOOLKIT
Platform for Operational Intelligence

© 2017 SPLUNK INC.
▶ Assistants: Guided model building, testing
and deployment for common objectives
▶ Showcases: Interactive examples for typical
IT, security, business and IoT use cases
▶ Algorithms: 25+ standard algorithms included
with the toolkit
▶ ML Commands: New SPL commands to
fit, test and operationalize models
▶ Python for Scientific Computing Library:
Access to 300+ open source algorithms
Splunk Machine Learning Toolkit
Extends Splunk platform functions and
provides a guided modeling environment
Build custom analytics for any use case

© 2017 SPLUNK INC.
Custom Machine Learning – Success Formula
Domain
Expertise
(IT, Security, …)
Data
Science
Expertise
Splunk
Expertise
Identify use cases
Drive decisions
Set business/ops priorities
SPL
Data prep
Statistics/math background
Algorithm selection
Model building
Splunk ML Toolkit
facilitates and simplifies
via examples & guidance
Operational success

© 2017 SPLUNK INC.
AI
Machine
Learning
Deep
Learning
AI, Deep Learning, And Machine Learning
Intelligent Agents
No Human Involvement
Sentient Machines
Tensorflow
Data sets
are large and
unknowable
Guided Data Driven Decisions
Augmenting Human Reasoning
Operational
Intelligence
Splunk ML offerings today
Neural Networks

© 2017 SPLUNK INC.
Continuous Data Ingest at Scale
DevelopVisualize PredictAlertSearch
Engineers Data
Analysts
Security
Analysts
Business
Users
Native Inputs
TCP, UDP, Logs, Scripts, Wire, Mobile
Industrial Data
SCADA, AMI, Meter Reads
Modular Inputs
MQTT, AMQP, COAP, REST, JMS
HTTP Event Collector
Token Authenticated Events
Technology Partnerships
Kepware, AWS IoT, Cisco, Palo Alto
Maintenance
Info
Asset
Info
Data
Stores
External
Lookups/EnrichmentOT
Industrial Assets
IT
Consumer and
Mobile Devices Real Time

© 2017 SPLUNK INC.
Sense and Respond
OT
Industrial Assets
IT
Consumer and
Mobile Devices
Search
Third-Party
Applications
Smartphones
and Devices
Tickets
Email
Send an
email
File a
ticket
Send a text
Flash lights
Trigger
process flow
Every Search Can Use
Machine Learning
AlertReal Time

© 2017 SPLUNK INC.
Splunk: Data Fabric
OT
Industrial Assets
IT
Consumer and
Mobile Devices
Real Time
IT Users Analysts Biz Users
Ad hoc
Search
Custom
Dashboards
Monitor
and Alert
Reports/
Analyze
Clickstreams HadoopDevices Networks
GPS/
Cellular
Online
Shopping
Carts
Servers Applications
Data Warehouses
Structured Data Sources
CRM ERP HR Billing Product Finance
DB
Connect
Look-ups
ODBC
SDK
API
Analysts Biz Users

© 2017 SPLUNK INC.
Qualitative
Rule Engine
Identify
Transaction
Paths
Uncover
Relationships
and
Relevancy
Part of a
Solution Suite
Partner Solutions: Transaction Analysis
Investigate and Analyze Transactional Behavior.

© 2017 SPLUNK INC.
Coming Soon : Mini Solutions
Predicting when a system is going to be resource constrained
MACHINE LEARNING
TOOLKIT
ML Use Case
Technical
overview
•Preselected Algorithm
•Customer identifies the target field
•Focused on single use case
•A Customizeable Workflow
•Custom configuration of insights generated from workflow
•Requires the MLTK and Python for Scientific Computing

© 2017 SPLUNK INC.
▶ Get the Machine Learning Toolkit from Splunkbase
▶ Go watch Machine Learning Videos on Splunk YouTube Channel
http://tiny.cc/splunkmlvideos
▶ Go watch the Machine Learning talks from .conf2016:
• Advanced Machine Learning in SPL with the Machine Learning Toolkit
by Jacob Leverich
• Extending SPL with Custom Search Commands and the Splunk SDK
for Python by Jacob Leverich
▶ Early Adopter and Customer Advisory Program:
mlprogram@splunk.com
▶ Field ML Architects: Andrew Stein (astein@), Brian Nash (bnash@)
What Else?

Why Splunk?
FAST TIME-TO-VALUE
CLOUD, ON-PREMISE & HYBRID DEPLOYMENT
VISIBILITY ACROSS STACK, NOT JUST SILOS
ONE PLATFORM, MULTIPLE USE CASES
ANY DATA, ANY SOURCE, ASK ANY QUESTION
107

108
b e f o r e 2 0 1 52 0 1 2
Splunk for targeted
solutions only. Isolated
pockets of data and
dashboard expertise
2 0 1 4
Desktop/Server Events and
Performance for Plant Management.
Consolidated Splunk metrics & logs
for Proxy, App, Infra, Desktops
Federated Splunk
• All data accessible to all users
• Correlation across firm on a
global scale
Splunk Center of
Excellence providing self-
service and custom
visualization solutions
dashboard
expertise
indexed
data
Splunk install

Monitoring can be divided into three layers
It checks performance and availability of application
functionality e.g. by simulation end-user experience,
interfaces, queues etc. – legal constraints to be considered.
It checks performance and availability of end to-end
process leveraging also on functional monitoring
results/application.
It manages and monitors base infrastructure in terms of
resources utilization (memory, CPU, file systems, swap,
network, disk space, throughput…) and main subsystems
activity (processes, services…)
Business
Process
Function
Layer
Technology
< Components on Mainframe/Open
(JCL, DB, Server…)
< Application
< Process
Target
Group
Aggregation&Integration
< Business- / Process-
Owner
< IT Application
Manager
< IT Application
Manager
< Application Owner
< Application Owner
< Operating/
Provider
Server Data
Base
Sto-rage
etc..

Ally’s Splunk Journey
Multiple LoB
ADOPTION
2012 2013 2014 2015 2016
“Find & Fix”
Tool
Developers
LoB
Dashboards
Enterprise
Security
SINGLE LINE OF BUSINESS ENTERPRISE DEPLOYMENT,
MULTIPLE LINES OF BUSINESS
© 2017 Ally Financial. Ally is a registered trademark. All rights reserved.

© 2017 SPLUNK INC.
Splunk Enterprise at ING Bank Śląski
Collected data and its value
Data Operational Intelligence
Search and
Investigation
Proactive
Monitoring
Operational
Visibility,
DevOps
Real-time
Business
Insights
Online
Services
Web
Services
Servers Security
Networks
Domain
systems
Custom
Applications
Databases
Active
Directory
Logfiles
SocialMedia

© 2017 SPLUNK INC.
SEPT 25-28, 2017
Walter E. Washington Convention Center
Washington, D.C.
.conf2017
The 8th Annual Splunk Conference
conf.splunk.com
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© 2017 SPLUNK INC.
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Splunk Forum Financial Services Chicago 9/13/17