Extending BI with Big Data Analytics

Extending Analytics Beyond BI!
© 2014 Datameer, Inc. All rights reserved.

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Claudia Imhoff
President, Intelligent Solutions, Inc.
A thought leader, visionary, and practitioner,
Claudia Imhoff, Ph.D., is an internationally
recognized expert on analytics, business
intelligence, and the architectures to support
these initiatives. Dr. Imhoff has co-authored five
books on these subjects and writes articles
(totaling more than 150) for technical and
business magazines.
She is also the Founder of the Boulder BI Brain
Trust (BBBT), an international consortium of
independent analysts and experts. You can
follow them on Twitter at #BBBT or become a
subscriber at www.bbbt.us.
Email: claudia@bbbt.us
Phone: 303-444-6650
Twitter: Claudia_Imhoff
About Our Speaker!

Azita Martin @datameer
CMO
Azita Martin is Chief Marketing Officer at Datameer
with extensive marketing leadership experience at
high-growth start-ups and category-creating public
companies like Salesforce and Siebel.
Azita has global responsibility for scaling all
aspects of Datameer’s product and corporate
marketing, including defining go-to-market strategy,
driving thought leadership, and increasing brand
awareness and customer acquisition.
Prior to Datameer, Azita built and led marketing
teams for both fast-growing start-ups and major
public companies, including Get Satisfaction, Moxie
Software, LiveOps, Salesforce, Siebel and SGI.
#datameer @datameer
About Our Speaker!

Matt Schumpert @datameer
Senior Director, Solutions Engineering
Matt has been working in the enterprise
infrastructure software space for over 14 years in
various capacities, including sales engineering,
strategic alliances and consulting.
Matt currently runs the pre-sales engineering team
at Datameer, supporting all technical aspects of
customer engagement from initial contact through
roll-out of customers into production.
Matt holds a BS in Computer Science from the
University of Virginia.
#datameer @datameer
About Our Speaker!

Agenda
§ Extending the Data Warehouse Architecture
§ Use Cases
§ Major Trends and Examples
Copyright © Intelligent Solutions, Inc. 2014 All Rights Reserved
7

Disruptive Forces
§ Deployment Options
§ Mobile Work Force
§ Advanced Analytics
§ Big Data
§ Data Management
BUT disruption does not have to mean CHAOS!
8

Next Generation BI
generation
Based on a concept by Shree Dandekar of Dell
Next
BI
Slide compliments of Colin White – BI Research, Inc.
New business
insights
Reduced
costs
New
technologies
Enhanced
data
management
Advanced
analytics
New
deployment
options
DRIVERS
TECHNOLOGIES
9

A Complex Environment
Sophisticated analytics
+ complex Multiple data sources analytic workloads
Operational
data
DW historical
data
Text &
media files
Web & social
content
Sensor
data
Multiple output formats
Multiple user devices
Multiple deployment options
Increasing data volumes
& data rates
Decision
management
Data
management
Data
integration
Data
analysis
Decision
management
Slide compliments of Colin White – BI Research, Inc.
10

Next Generation – Extended Data
Warehouse Architecture (XDW)
Analytic tools & applications
Traditional EDW
environment
Investigative computing
platform
Data
refinery
Data integration
platform
RT analysis platform
Operational real-time environment
Other internal & external
structured & multi-structured data
Real-time streaming data
Operational systems
RT BI services Slide created by Colin White – BI Research, Inc.
11

Agenda
§ Use Cases
12

Systems of Record
§ Remember – It all starts here!
§ Transactional systems generate most of the data used for all other
activities – operational processes, BI & analytical capabilities, etc.
§ The point here is a reminder:
§ Extend OLTP systems of record as a “key” source of data
§ Many companies do not (or can not) leverage data they already
have in their operational systems
13
Operational systems
RT BI services

Use Case: Traditional EDW
Most BI environments today:
§ New technologies can be incorporated
Traditional EDW
environment
into the EDW environment to improve
performance, efficiency & reduce costs
14
Use cases:
§ Production reporting
§ Historical comparisons
§ Customer analysis (next best offer,
segmentation,
life-time value scores,
churn analysis, etc.)
§ KPI calculations
§ Profitability analysis
§ Forecasting
Data integration
platform
Operational systems
RT BI services
real-time
models
& rules

Use Case: Data Refinery
Ingests raw detailed data in batch
and/or real-time into a managed data
store
Distills the data into useful business
information and distributes the results
to downstream systems
May also directly analyze certain
types of data
Employs low-cost hardware and
software to enable large amounts of
detailed data to be managed cost
effectively
Requires (flexible) governance
policies to manage data security,
privacy, quality, archiving and
destruction
Traditional EDW
environment
platform
Data
refinery
Data integration
platform
15

Use Case: Investigative
Computing
New technologies used here include:
§ Hadoop, in-memory computing,
columnar storage, data compression,
appliances, etc.
Use cases:
§ Data mining and predictive modeling
for EDW and real-time environments
§ Cause and effect analysis
§ Data exploration and discovery (“Did
this ever happen?” “How often?”)
§ Pattern analysis
§ General, unplanned investigations
of data
Operational systems
RT BI services
16
Data
refinery
platform
Data integration
platform

Use Case: Real Time
Operational Environment
Embedded or callable BI
services:
§ Real-time fraud detection
§ Real-time loan risk assessment
§ Optimizing online promotions
§ Location-based offers
§ Contact center optimization
§ Supply chain optimization
Real-time analysis engine:
§ Traffic flow optimization
§ Web event analysis
§ Natural resource exploration
analysis
§ Stock trading analysis
§ Risk analysis
§ Correlation of unrelated data
streams (e.g., weather effects on
product sales)
17
Operational systems
RT BI services

All Components Must Work Together
Investigative
computing platform
Analytic tools & apps
18
analytic models
analyses
Data refinery
Traditional EDW
environment
Operational systems
existing
customer
data
next best
customer offer
3rd party data
location data
social data
feedback
call center dashboard
or web event stream
Slide created by Colin White – BI Research, Inc.

Agenda
§ Use Cases
19

1. What is the IoT?
20

Investigative Computing: Hadoop
Example
§ Predictive Analytics to Reduce Patient
Re-admittance
o Goal is to predict the likelihood of hospital
re-admittance within 30 days after
discharge
o Patients with congestive heart failure have
a tendency to build up fluid, which causes
them to gain weight
o Rapid weight gain over a 1-2 day period is
a sign that something is wrong
o Heart patients at home have a scale that
wirelessly transmits data (uses iSirona) to
Hadoop where an algorithm determines
risk of re-admittance and alerts a clinician
o All home monitoring data will be viewable
in the EMR via an API to Hadoop
“If Hadoop didn’t exist we would
still have to make decisions
about what can come into our
data warehouse or the electronic
medical record (and what
cannot). Now we can bring
everything into Hadoop,
regardless of data format or
speed of ingest. If I find a new
data source, I can start storing it
the day that I learn about it. We
21
leave no data behind.”
Source: Hortonworks

2. Evolution of Analytics
From Expanding to
22
§ Select few
§ IT managed
§ Reflecting the business
§ What & why?
§ Within the four walls
§ Command/control
§ Discrete activities
§ Configured
§ A conscious thought
§ Tactical necessity
§ Empowered many
§ Business led
§ Driving the business
§ What could & should?
§ The world around us
§ Sense/respond
§ Embedded everywhere
§ Composed
§ In everything we do
§ Strategic advantage
*From IBM

Four Forms of Analytics
Business Analytics
Descriptive
(Reactive)
What happened?
What is happening?
• Business reporting
• Dashboards
• Scorecards
• Data warehousing
Well-defined
business problems
and opportunities
What will happen?
• Data mining
• Text mining
• Web/media mining
• Forecasting
Accurate projections
of the future states
23
Prescriptive
(Proactive)
Predictive
(Proactive)
and conditions
What should I do?
Why should I do it?
• Optimization
• Simulation
• Decision modeling
• Expert systems
Best possible
business decisions
and transactions
Based on Delen, Dursun and Demirkan, Haluk, “Decision Support Systems, Data, information and analytics as services,”
from Elsevier, published online May 29, 2012
Outcomes Enablers Questions
Diagnostic
(Reactive)
Why did it happen?
• Behavioral analysis
• Cause and effect
analysis
• Correlations
Cause and effects of
changes in business
activities

Predicting the Future
§ Netflix uses predictive analytics to produce “House of
Cards” = most streamed piece of content in 40 countries
§ Netflix knew it was a hit BEFORE filming began by analyzing 30 M
“plays” a day, 4 M ratings, etc.
§ They also analyzed the director’s track record, Kevin Spacey’s
appeal, reaction to the British version, etc.
§ Benefit? To breakeven, Netflix
needed to gain 565,000 more
members. They brought in more
than 17 Million!
§ Downside – impact on quality,
diversity, even creativity?
24

3. Making Analytics More
Consumable
25
§ Use of BI for decision making continues to be a high
priority for organizations
§ Recent survey1 of 2,500 CIOs showed 83% of CIOs see BI &
analytics as the way to enhance an organizations’ competitiveness
§ But reach of BI is often restricted to those users with
experience to exploit analytics for business benefit
§ 59% of users say that they miss information that might be
of value to their jobs because they can not find it
§ 27% of managers time is spent searching for information
§ 50% say the information they obtain has no value to them
§ BI must be more easily understood and consumed!
§ You need an architecture
1 “IBM Global CIO Study: The New Voice of the CIO”

Making BI More Consumable –
Information Consumers
Make it easy
to access and
Blend data
Make DM solutions
fast to deploy &
easy to manage
Make BI tools
easy to use
26
Make BI results
easy to consume
& enhance
Access
Integrate Manage Report
Analyze Deliver
Office product integration
Portal integration + search
Business glossary & data lineage
BI automation
Mobile BI
Collaborative BI
Data visualization

Making BI More Consumable –
Information Producers
Make it easy
to access &
blend data
Make DM solutions
fast to deploy &
easy to manage
Make BI tools
easy to use
27
Make BI results
easy to consume
& enhance
Access
Integrate Manage Report
Analyze Deliver
Customizable BI
components
Ad hoc visual analysis tools
Investigative BI workbench
Cloud computing
BI sandboxes
Investigative BI
platform
Data virtualization
Big data
connectors
Data blending

Getting Started
§ Education is mandatory
§ This is not just training on BI tools
§ Education includes how to think analytically, how to interpret
results, who to ask for help
§ Advanced BI analysts (business analysts, data scientists, etc.)
must evangelize value of analytics
§ Many business people don’t know where to get training
§ May be embarrassed to ask for it or intimidated by it
§ May not even know what BI resources are available or what data
is available
From www. business-help.org 28

Getting Started
§ Governance still has an important role
§ Determine whether data used is “governed” (e.g., in a data
warehouse or MDM environment) or “ungoverned” (e.g., individual
spreadsheets, external source)
§ IT must have monitoring and oversight capability
§ BI/DW builder needs to administer and manage infrastructure
§ Must be able to monitor the environment
§ Must have oversight into the environment
§ Note: LOB IT or experienced information producers may
have to take on some previously traditional central IT roles
§ Security of data, adherence to privacy policies
29

Understand Your Customer Journey!
Social Media
Mobile
Ads
Web Logs
CRM
Product Logs
Transaction
Call Center
Are keywords related to
customer segments?
Which campaign
combinations accelerate
conversion?
Which product
features drive
adoption?
Which features do users
struggle with?
What content works be
best for each lead
segment?
What behavior
signals churn?

Reduce Customer Churn!
Public Data
CRM
Web
Call Center
Reduced customer churn
by 50%

Internet of Things!
Connected
Home
Energy
Consumption
Data
Time & cost savings for IT
Reduced false alarms
User Behavior
Improved customer experience

Smart Meter Analytics!
Smart Meter
Household Data
7 Billion lbs. reduction
In CO2 Output
$500M/Year in Energy Savings
Energy
Consumption
Data

For the webinar:
http://bit.ly/1zxY3Nl
!

Extending BI with Big Data Analytics

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