The Data Lake: Empowering Your Data Science Team

Data Science and BI in the Business
Landscape
THE DATA LAKE: EMPOWERING
YOUR DATA SCIENCE TEAM

• Introduction
• Empowering Your Data Science Team
– Data Science
– The Data Lake
– How to Get Started
• Special Offer
• Additional Resources
• Q&A
Agenda
2Copyright 2015 Senturus, Inc. All Rights Reserved

Bill Schmarzo
CTO
EMC Global Services, Big Data Practice
Author
Introduction: Presenters
Copyright 2015 Senturus, Inc. All Rights Reserved 3
John Peterson
CEO and Co-Founder
Senturus, Inc.

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Empowering Your Data Science Team
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science-team/
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Poll Question

7© Copyright 2015 EMC Corporation. All rights reserved.© Copyright 2015 EMC Corporation. All rights reserved.
Data Lake: Empowering Your Data
Science Team
Bill Schmarzo, CTO, EMC Global Services
Follow me on twitter: @schmarzo

• The Difference Between BI And Data Science And
The Complementary Nature Of Both
• How The Data Lake Can Empower Your Data Science
Team And Free Up Valuable Data Warehouse
Resources
• How Do I Get Started?
Data Lake: Empowering Your Data
Science Team Agenda

Introducing Data
Science

Data Science is about identifying
variables and metrics that are
better predictors of performance
“MoneyBall”: Identifying “Right” Metrics
0
0.5
1
1.5
2
2.5
3
A's vs. Yankees Cost Per Win ($M)
Athle cs Yankees
2000 2001 2002 2003 2004 2005 2013
“Moneyball: The Art of Winning an Unfair Game,” Michael Lewis

Data Science
• Predictive analytics
• Prescriptive analytics
• What is likely to happen?
• What should I do?
Business Intelligence
• Standard reporting
• What happened?
Business
Intelligence
Data
Science
Business
Value
Time
High
Low
Past Future
Evolution Of The Analytic Process
Business Intelligence versus Data Science

Step 2: Define question to be
answered
Step 3: Use Business Intelligence
(BI) tool’s graphical user interface
(GUI) to construct query
Step 4: BI tool creates SQL
Step 5: SQL is run against data
warehouse to create report
DW
Business Intelligence Engagement Process
Step 1: Pre-build data schema
(schema-on-load)

Transitioning The Business Questions
Prescriptive Analytics
(What should we do?)
Run Buy-One-Get-One-
Free Burrito promotion
Wednesday 7–9pm to
attract 55–75 more college
students
Add 2 more workers
11:00am–2:00pm and
5:00pm–9:00pm Tuesday,
May 5
Increase chicken inventory
next week by 15%
Increase hiring pipeline
next month by 20
candidates
Predictive Analytics
(What likely to happen?)
What will be revenues next
week?
How many customers will
visit the store during next
Sunday’s Farmer’s Market?
Which products will likely
sell the most next week?
How many new employees
will we need to hire next
month?
Descriptive Analytics
(What happened?)
What were revenues last
week?
How many customers
visited the store during
last Sunday’s Farmer’s
Market?
What products sold more
than the week before?
How many employees did
we hire last month?

Step 1: Define Hypothesis to test
or Prediction to be made
Step 3: Build schema
(schema-on-query)
Step 4: Visualize the data
(Tableau, Spotfire, ggplot2,…)
Step 6: Evaluate model results
(probabilities, confidence levels)
Data Science Engagement Process
Repeat
Step 5: Build analytic models
(SAS, R, MADlib, Mahout,…)
Kronos
Historical Google
Trends
Physician
Notes
Local
Events
Weather
Forecast
Epic
Lawson
CDC
Step 2: Gather data…and more
data (Data Lake: SQL + Hadoop)

Power Of Analytic Profiles (Healthcare)
Patient Profiles
• Age
• Gender
• Care History
• Current Vitals
• Wellness Score
• Exercise Score
• Stress Score
• Diet Score
• Cancer Risk
• Heart Attack Risk
• Pneumonia Risk
• Pulmonary Risk
• Oncology Risk
• Stroke Risk
• …
Schmarzo’s
Profile
NCE
Score Variance Trend
Wellness Score 92 1.85
Diet Score 67 3.25
Exercise Score 82 2.25
Stress Score 65 1.90
Current Health 92 1.89
Cancer Risk 14 1.74
Pulmonary Risk 02 1.15
Oncology Risk 08 1.20
Heart Attack Risk 09 1.25
Stroke Risk 06 1.10
….
Internal Patient Data
• Patient history (Epic)
• Patient current vitals
• Staff notes
• Patient comments
• …
External Patient Data
• WebMD
• DietPlanner
• MapMyRun
• MyFitness
• Nike Fuel Band
• Fitocracy
• Lumosity
• Pharmacy
• …
Basic
Wellness
Advanced
Wellness
Quality/Risk
Scores

Analytic Profiles x Use Cases (Healthcare)
Analytic Profiles
Admissions
Volume
Forecast
Service
Variance
Analysis
Procedures
Cost Analysis
HAI
Analysis
Unplanned
Readmissions
Employee
Retention
Patients √ √ √ √
Physicians √ √ √ √ √ √
Nurses √ √ √ √ √ √
Technicians √ √ √ √ √ √
Procedures √ √ √ √ √
Hospitals/Dept √ √ √ √ √ √
…
Care Quality Effectiveness Use Cases
Metrics, scores and analytic insights gleaned from one use case
strengthen analytic profiles models for other use cases

science-team/
The Senturus comprehensive library of recorded
webinars, demos, white papers, presentations, and case
studies is available on our website:
Hear the Recording

The Data Lake

Data
Lake
Data Preparation
and Enrichment
ALL data fed
into Hadoop
Data Lake
DW
BI Environment
• Production
• Predictable load
• SLA-constrained
• Heavily governed
• Standard tools
Analytics Environment
Analytic
Sandbox
• Exploratory, Ad Hoc
• Unpredictable load
• Experimentation
• Loosely governed
• Best tool for the job
Modern BI / Analytics Environment
ETL

Characteristics Of A Data Lake
INGEST
Capture data from
wide range of
traditional and new
sources as-is
(structured and
unstructured)
STORE
Store all your
data in one
environment for
cross-functional
business analysis
ANALYZE
Uncover new
customer,
product, and
operational
insights
SURFACE
Empower front-
line employees;
Drive more
profitable
customer
engagement
ACT
Integrate analytic
insights into
operational and
management
systems
Free up costly data warehouse and BI resources; enable your
advanced analytics / data science environment

Why A Data Lake? Enable Analytics!
Revenue
Protection
Predictive
Maintenance
Demand
Forecasting
Fraud
Reduction
Customer
Acquisition
Customer
Lifetime Value
Teacher
Retention
Network
Optimization
Pricing/Yield
Optimization
Patient Quality of
Care
Data
Lake
Analytics “Hub and Spoke” Service Architecture

#BigData Reference Architecture
DATA LAKE
RDBMS
MACHINE
IOT
STATISTICAL MODELING/NLP EXPLORATION
DISCOVER/MAP
BI
TRANSFORM ORGANIZE/TAG
DATA WAREHOUSESTREAM
CEP
MODELS MAY TAKE HOUR OR DAYS
QUERIES MAY RETURN IN SECONDS OR MINUTES
SECONDS
SEARCH/INDEX
ENTERPRISE LOG ANALYSIS
APPLICATIONS
3rd PARTY
EMAIL
SOCIAL
MEDIA
HADOOP SQL
CATALOG AND PROVISION

“Lean” Data Governance Lifecycle

How To Get
Started…

Repeat the process for
identified business cases
2–3 week process
12-16 week process
6-9 month process
Analytics
Operationalization
Integrate analytics into operational and
management processes; facilitate changes
across people, process, data and technology
Proof of
Value Lab
Deploy analytics sandbox
to quantify the business
case
Envisioning
Engagement
Identify big data
analytics business
use cases
• Start with business initiative / challenge
• Drive Business – IT organizational alignment
• Brainstorm and assess potential data sources
• Build illustrative analytics (data science)
• Identify, “flesh out” and prioritize use cases
For prioritized use case:
• Build business case (high-level ROI)
• Prove analytic feasibility (analytic lift)
• Create UEX mockup
• Install big data technology
• Build big data architecture
• Deliver “Go Live” recommendations
Prioritize Your Business Initiatives

Thank You!!
Email: William.Schmarzo@emc.com
LinkedIn: www.linkedin.com/in/schmarzo
Twitter: @schmarzo
Schmarzo Blog:
http://infocus.emc.com/author/william_schmarzo/
To Achieve Big Data’s Potential, Get It Into The Boardroom
http://www.entrepreneur.com/article/236125
Big Data Business Model Maturity Index
https://infocus.emc.com/william_schmarzo/big-ideas-big-data-business-
model-maturity-index/ (animation)
Big Data For Competitive Differentiation
https://infocus.emc.com/william_schmarzo/waiting-for-godot-developing-
competitive-differentiation/
A History lesson on economic-driven business transformation
https://infocus.emc.com/william_schmarzo/a-history-lesson-on-economic-
driven-business-transformation/
User Experience: the new king of the business
https://infocus.emc.com/william_schmarzo/store-manager-actionable-
dashboard/
How I’ve Learned To Stop Worrying And Love The Data Lake
https://infocus.emc.com/william_schmarzo/how-ive-learned-to-stop-worrying-
and-love-the-data-lake/
EMC Big Data Solutions & Services:
http://www.emc.com/big-data/solutions.htm
http://www.emc.com/infographics/succeed-big-data.htm

science-team/
The Senturus comprehensive library of recorded
webinars, demos, white papers, presentations, and case
studies is available on our website:
Hear the Recording

WHO WE ARE
SENTURUS INTRODUCTION

Laser Focused on Business Analytics
Copyright 2015 Senturus, Inc. All Rights Reserved. 29
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Copyright 2015 by Senturus, Inc.
This entire presentation is copyrighted and may not be
reused or distributed without the written consent of
Senturus, Inc.

The Data Lake: Empowering Your Data Science Team

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