The accelerating supply of big data is converging with accelerating data demand from everyday business users. What does it take to get from Hadoop as a data reservoir to Hadoop as a day-to-day data source for your business and end users? The answer to ‘what’ is ‘how’ and ‘who’. Reducing architectural reliance on ‘small data’ technologies and broadening access to Hadoop hold the key to big data payoff. Join Nik Rouda, Big Data Analyst and blogger at the Enterprise Strategy Group (ESG), as he hosts this webcast featuring guest presentations from real world practitioners Tanwir Danish, VP of Product Development at Marketshare (acquired by Neustar) and Rajiv Synghal, Chief Architect, Big Data Strategy at Kaiser Permanente. Latest research on Hadoop adoption patterns and anti-patterns Putting users at the center of big data utilization and avoiding the data scientist paradox Architectural misconceptions that can tank big data initiatives Security and multi-tenancy strategies to accelerate adoption Retooling skills and organizational thinking when big data is the rule, not the exception

1. When everybody wants Big Data
Who gets it?
Webcast
brought
to you by FEATURING
Featuring
Enter

2. Arcadia
is a
Hadoop-native
platform
that
connects
business users
to big data
Distributed
BI & Analytics
Engine runs
on each Hadoop
node
Users connect
via a web
browser
Brought to you by
Arcadia Data

3. Rajiv Synghal, Chief Architect,
Big Data Strategy at Kaiser
Permanente.
TODAY’S PRESENTERS
Nik Rouda
Senior
Analyst
-
ENTERPRISE STRATEGY GROUP
(ESG)
Rajiv Synghal
Chief Architect
Big Data Strategy
-
KAISER
PERMANENTE
Tanwir Danish
VP of Product
Development
-
MARKETSHARE PARTNERS
A NEUSTAR SOLUTION

4. Nik Rouda
Senior
Analyst
Enterprise Strategy Grou
BI and Analytics on Big Data
A Market View
Nik Rouda, Senior Analyst
Enterprise Strategy Group | Getting to the bigger truth.™
© 2016 by The Enterprise Strategy Group, Inc.
BI and Analytics on Big Data
A Market View
Nik Rouda, Senior Analyst

5. © 2016 by The Enterprise Strategy Group, Inc.
Big Data & Analytics: A Business Imperative
48%
32%
14%
3% 2% 1%
Our most
important
priority
One of our top 5
priorities
One of our top
10 priorities
One of our top
20 priorities
Not among our
top 20 priorities
Don’t know/no
opinion
will increase spending on big
data & analytics in 2016
63%
Relative to all of your organization’s business and IT priorities over the next
12-18 months, how would you rate the importance of its big data analytics
projects and initiatives? (Percent of respondents, N=475)

6. © 2016 by The Enterprise Strategy Group, Inc.
What Does Big Data Mean to You?
23%
32%
34%
40%
40%
40%
43%
Veracity (i.e., uncertainty of quality or results)
Support new business requirements
Scale of data (i.e., volume)
Reduced total cost of ownership (TCO)
Want more access to analytics across lines of
business
Diversity of data sources (i.e., variety)
Speed of analytics operations (i.e., velocity)
Which of the following requirements are most responsible for driving your organization to evaluate new
analytics solutions? (Percent of respondents, N=200, three responses accepted)

7. © 2016 by The Enterprise Strategy Group, Inc.
Common Challenges for BI, Analytics, & Big Data
Data sets too large for analytics
Difficulty spanning disparate systems
Lack of skills to manage and analyze data
Limited collaboration between IT, analysts, and LoB
Difficulty working with both structured and unstructured data

8. © 2016 by The Enterprise Strategy Group, Inc.
Business Outcomes from Big Data & Analytics
54% want faster tactical response to customers
54% want reduced risk on business decisions
50% want better sales & marketing performance
49% want improved operational efficiency

9. © 2016 by The Enterprise Strategy Group, Inc.
Hadoop is Steadily Gaining Market Adoption
Already using Hadoop, 20%
Very interested in Hadoop,
37%
Somewhat interested in
Hadoop, 27%
Not at all interested in
Hadoop, 5%
Not familiar with Hadoop
technology, 8%
Don’t know, 3%
How would you rate your organization’s interest in implementing Hadoop? (Percent of respondents,
N=475)
/

10. © 2016 by The Enterprise Strategy Group, Inc.
Hadoop Deployment Drivers
17%
26%
30%
30%
35%
37%
37%
37%
39%
To replace your BI tools
To replace your data warehouse(s)
To complement/offload your data warehouse(s)
To perform real-time and/or streaming analytics
To accommodate more diverse and/or unstructured data …
To distribute analytics workloads across commodity servers
To establish a centralized data lake or hub
To complement/offload your BI tools
To reduce costs of data storage and archive
What drove your organization’s decision to implement Hadoop? (Percent of respondents, N=46, multiple
responses accepted)

11. © 2016 by The Enterprise Strategy Group, Inc.
Most Important Features When Considering
Solutions to use SQL on Hadoop
13%
24%
24%
24%
26%
28%
28%
35%
45%
Manageable workloads with YARN
Schema-less or schema-on-read
Complete ANSI SQL support
Support for complex or nested data types
Breadth of file types supported (e.g., Parquet, JSON, text, AVRO, Hive, etc.)
Rich security controls
Reliability with full ACID compliance
High concurrency/parallelized
High performance/low latency
When considering solutions to use SQL on Hadoop, what are the most important features? (Percent of
respondents, N=94, three responses accepted)

12. © 2016 by The Enterprise Strategy Group, Inc.
How Hadoop Will Fit Against Traditional Data Warehouse Approach
Hadoop will largely replace
our existing data
warehouse, 26%
Hadoop will
offload/optimize our
existing data warehouse,
36%
Hadoop will be used only for
limited data warehouse -like
functions, 28%
No plans to use Hadoop for
any data warehouse -like
functions, 11%
How do you anticipate Hadoop will fit against your organization’s traditional data warehouse
approach? (Percent of respondents, N=94)
/

13. © 2016 by The Enterprise Strategy Group, Inc.
Core IT Teams Expected to Implement & Manage Projects
23%
23%
25%
27%
29%
32%
52%
53%
Service provider
Value-added reseller (VAR)
Business application vendor
Management consultancy
Systems integrator (SI)
Business analyst/data scientist team
IT applications team
IT infrastructure and operations team
Which of the following groups provides the skills and manpower to implement and manage the
technologies supporting initiatives in the area of big data and analytics? (Percent of respondents,
N=475, multiple responses accepted)

14. © 2016 by The Enterprise Strategy Group, Inc.
Skills Development is a Priority
Cybersecurity (i.e.,
information
security), 44%
Big data analytics,
26%
Infrastructure
management, 17%
Application
development and
deployment, 12%
36%
cite a problematic skills
shortage for business
intelligence & analytics
In which of the following functional areas do you believe skills development would be most
beneficial to your employees (i.e., IT staff) in terms of their career path and benefit to your
organization? (Percent of respondents, N=627)

15. Tanwir Danish
VP of Product
Development
A STORY OF GROWTH WITH ARCADIA
Tanwir Danish, Head of Product & UX, MarketShare a Neustar Solution
Copyright © 2016 Neustar, Inc. All Rights Reserved

16. MarketShare links
markeEng to revenue.
We do so by analyzing customer interacEons and
resultant business outcomes using data science
16

17. OUR TECHNOLOGY STACK EVOLVED TO SUPPORT KEY GOALS
Stage 2012 2013 2014 2015 2016
Raw Data S3 | self managed
HADOOP
PredicEve
AnalyEcs
Workflow
HIVE extract | Sampling |
R
ReporEng
workflow
HIVE extract | Oracle |
Tableau
Decision
AnalyEcs
Workflow
ORACLE | memCacheDB

18. A KEY NEED
UNDERSTAND CUSTOMER JOURNEY
AND IMPACT OF EACH INTERACTION
ON BUSINESS OUTCOMES
18

19. Consumer
visits store
location and
makes a
purchase
$
10s of data sources
10s of Terrabytes of data
10s of millions pathways
10s of touchpoints types
WHAT DOES CUSTOMER JOURNEY LOOK LIKE?

20. PERFORMANCE ZOOM INTO CUSTOMER JOURNEY
(POWER OF TENS)
TWO KEY CHALLENGES WE RAN INTO

21. ADOPTION OF
Stage Q1’15 Q2’15 Q3’15 Q4’15 Q1’16
Adop4on
Milestone
ACTION App MAP App
(Internal)
TV App STRATEGY App Release 5.0 of
ACTION App
Key
Func4onality
Improvement
Integrated Arcadia
with AlEscale
Parametrized
reports
VisualizaEon
enhancements
Progressive
disclosure
Config driven
reports
Benefit Visibility and
performance
Visibility for
internal team
Time to market Scale Efficiency

22. With Arcadia SoluEon
MarketShare clients understand their
customers beaer. In addiEon, our report
development process has become more agile
and client configuraEon more efficient.
22

23. Rajiv Synghal
Chief Architect
Big Data Strategy
-
KAISER
PERMANENTE
Formulating Big Data for Analytics in Health Care
Rajiv Synghal, Chief Architect, Big Data Strategy

24. 1
3600 MEMBER VIEW IN HEALTHCARE
Personal Behaviors
(Life Style Choices,
Preferences, Activities, QoL)
Social Factors
(Friends, Family, Affiliations,
Communication, Activities)
Demographic Factors
(Age, Address, Employer, Industry)
Family History and Genetics
Personal “-omics”
(Genomics, Proteomics, Transcriptomes,
Metabolomics)
Medical Care
(encounter, labs,
Rx, medical devices, etc.)
Environmental
Factors
Environment
(Temperature,
Humidity,
Pollen Count,..)
Geographic
(Closest Hospital,
Pharmacy,
Care Clinic,…)
Member

25. CHANGING ANALYTICAL NEEDS IN HEALTHCARE
1 2 3 4 5 6 7 8 9 10
Food
Recommendations
Environment
Monitoring
Resource
Recommendations
Drive
Recommendation
Biometric
Monitoring
Alerts/
Dashboards
Brand
Sentiment
Triage
Recommendations
Expert
Advice
Event
Prediction
The changing market conditions driven by healthcare reform will require Kaiser Permanente to
find new innovative ways to deliver on its mission of high quality affordable care.

26. FUTURE OF DATA AND ANALYTICS IN HEALTHCARE
OUR FUTURE
Analytics leveraged by users
OUR PRESENT
Massive data stores and interconnectivity
Network
Storage and Compute
Data
Analytics: SAS, “R”, Proprietary,
Open-source, Third party
Scientist Clinician Business
Ubiquitous Access
§ Provide access to a greater
breadth of data
§ Enable data accessibility to
more than data scientists and BI
experts
Rapid Prototyping
§ Enable more frequent
prototyping and development
§ Faster delivery of new
capabilities
Entities that are able to leverage
Data & Analytics are becoming
leaders in their respective
industries

28. FUTURE DATA LIFECYCLE MANAGEMENT IN HEALTHCARE
DataClassification DataAccess SLAs DataStore Characteristics
Hyperactive
High frequency access to data by multiple users and
applications with high temporal locality.
• Milliseconds –second • Compute centric cluster
• Data pinned in memory.
• Number of copies is dynamically adjusted based upon frequency and
recency of access
Active
Frequent access to data by multiple applications and
users with low temporal locality.
• Seconds -minutes • Compute centric cluster
• Multiple copies of data on nodes with SSDs or on nodes with smaller,
faster spinning disks
• On-demand in-memory cache
• Number of copies is dynamically adjusted based upon frequency of
access
Inactive
Sparse access to data over long periods of time.
• Minutes • Storage centric cluster
• Multiple copies of data on nodes with smaller, slower spinning disks
• Number of copies is determined by system fault-tolerance threshold
requirements
Dormant
No access to data for long periods of time.
• Hours • Storage centric cluster
• Multiple copies of data on nodes with bigger, slower spinning disks
• Number of copies is determined by system fault-tolerance threshold
requirements
Frequency, recency (temporal locality) and system fault-tolerance thresholds determine the tiered
storage requirements for ALL enterprise data.
In the Big Data Analytics World, Data is never deleted except when it is required for compliance reasons.
Create Store Obsolete DeleteFour Stages:
DataClassification DataAccess SLAs DataStore Characteristics
Hyperactive
High frequency access to data by multiple users and
applications with high temporal locality.
• Milliseconds –second • Compute centric cluster
• Data pinned in memory.
• Number of copies is dynamically adjusted based upon frequency and
recency of access
Active
Frequent access to data by multiple applications and
users with low temporal locality.
• Seconds -minutes • Compute centric cluster
• Multiple copies of data on nodes with SSDs or on nodes with smaller,
faster spinning disks
• On-demand in-memory cache
• Number of copies is dynamically adjusted based upon frequency of
access
Inactive
Sparse access to data over long periods of time.
• Minutes • Storage centric cluster
• Multiple copies of data on nodes with smaller, slower spinning disks
• Number of copies is determined by system fault-tolerance threshold
requirements
Dormant
No access to data for long periods of time.
• Hours • Storage centric cluster
• Multiple copies of data on nodes with bigger, slower spinning disks
• Number of copies is determined by system fault-tolerance threshold
requirements
Frequency, recency (temporal locality) and system fault-tolerance thresholds determine the tiered
storage requirements for ALL enterprise data.
In the Big Data Analytics World, Data is never deleted except when it is required for compliance reasons.
Create Store Obsolete DeleteFour Stages:
5
FUTURE DATA LIFECYCLE MANAGEMENT IN HEALTHCARE
DataClassification DataAccess SLAs DataStore Characteristics
Hyperactive
High frequency access to data by multiple users and
applications with high temporal locality.
• Milliseconds –second • Compute centric cluster
• Data pinned in memory.
• Number of copies is dynamically adjusted based upon frequency and
recency of access
Active
Frequent access to data by multiple applications and
users with low temporal locality.
• Seconds -minutes • Compute centric cluster
• Multiple copies of data on nodes with SSDs or on nodes with smaller,
faster spinning disks
• On-demand in-memory cache
• Number of copies is dynamically adjusted based upon frequency of
access
Inactive
Sparse access to data over long periods of time.
• Minutes • Storage centric cluster
• Multiple copies of data on nodes with smaller, slower spinning disks
• Number of copies is determined by system fault-tolerance threshold
requirements
Dormant
No access to data for long periods of time.
• Hours • Storage centric cluster
• Multiple copies of data on nodes with bigger, slower spinning disks
• Number of copies is determined by system fault-tolerance threshold
requirements
Frequency, recency (temporal locality) and system fault-tolerance thresholds determine the tiered
storage requirements for ALL enterprise data.
In the Big Data Analytics World, Data is never deleted except when it is required for compliance reasons.
Create Store Obsolete DeleteFour Stages:

29. EVOLUTION OF ANALYTICAL TOOLS IN HEALTHCARE
DataClassification DataAccess SLAs Data Preparation and Analytical Tools
Hyperactive
High frequency access to data by multiple users and
applications with high temporal locality.
• Milliseconds –second • Compute centric cluster
• Data pinned in memory.
• Data Tagging & Cataloguing –Waterline Data Science
• Data Preparation –Trifacta Tools
• Data Integration –Trifacta, Informatica
• Cubes and Visualization –Arcadia Data
• SAS – LASR (Visual Statistics, Visual Analytics, In-memory Statistics)
• H2O –In-memory deep machine learning, “R”
Active
Frequent access to data by multiple applications and
users with low temporal locality.
• Seconds -minutes • Compute centric cluster
• On-demand in-memory caching of data
• Data Tagging & Cataloguing –Waterline Data Science
• Data Preparation –Trifacta Tools
• Data Integration –Trifacta, Informatica
• Cubes and Visualization –Arcadia Data
• SAS – LASR (Visual Statistics, Visual Analytics, In-memory Statistics)
• H2O –In-memory deep machine learning, “R”
Inactive
Sparse access to data over long periods of time.
• Minutes • Storage centric cluster
• Data Tagging & Cataloguing –Waterline Data Science
• Data Preparation –Trifacta Tools
• Cubes and Visualization –Arcadia Data
• Data Visualization –Tableau Servers & Desktops
• SAS Tools running on separate SAS Grid Cluster
Dormant • Hours • Storage centric cluster
• Data Tagging & Cataloguing –Waterline Data Science
Analytical tools space is changing very fast. Data Preparation and Analytical tools that natively run
“IN” the cluster provide a higher degree of security and freedom than the ones that run “WITH” the
cluster.
EVOLUTION OF ANALYTICAL TOOLS IN HEALTHCARE
DataClassification DataAccess SLAs Data Preparation and Analytical Tools
Hyperactive
High frequency access to data by multiple users and
applications with high temporal locality.
• Milliseconds –second • Compute centric cluster
• Data pinned in memory.
• Data Tagging & Cataloguing –Waterline Data Science
• Data Preparation –Trifacta Tools
• Data Integration –Trifacta, Informatica
• Cubes and Visualization –Arcadia Data
• SAS – LASR (Visual Statistics, Visual Analytics, In-memory Statistics)
• H2O –In-memory deep machine learning, “R”
Active
Frequent access to data by multiple applications and
users with low temporal locality.
• Seconds -minutes • Compute centric cluster
• On-demand in-memory caching of data
• Data Tagging & Cataloguing –Waterline Data Science
• Data Preparation –Trifacta Tools
• Data Integration –Trifacta, Informatica
• Cubes and Visualization –Arcadia Data
• SAS – LASR (Visual Statistics, Visual Analytics, In-memory Statistics)
• H2O –In-memory deep machine learning, “R”
Inactive
Sparse access to data over long periods of time.
• Minutes • Storage centric cluster
• Data Tagging & Cataloguing –Waterline Data Science
• Data Preparation –Trifacta Tools
• Cubes and Visualization –Arcadia Data
• Data Visualization –Tableau Servers & Desktops
• SAS Tools running on separate SAS Grid Cluster
Dormant
No access to data for long periods of time.
• Hours • Storage centric cluster
• Data Tagging & Cataloguing –Waterline Data Science
• Data Preparation –Trifacta Tools
• Data Visualization –Tableau Servers & Desktops
• SAS Tools running on separate SAS Grid Cluster
Analytical tools space is changing very fast. Data Preparation and Analytical tools that natively run
“IN” the cluster provide a higher degree of security and freedom than the ones that run “WITH” the
cluster.

30. Rajiv Synghal, Chief Architect,
Big Data Strategy at Kaiser
Permanente.
PANEL DISCUSSION
Nik Rouda Rajiv Synghal
`
Tanwir Danish

31. Rajiv Synghal, Chief Architect,
Big Data Strategy at Kaiser
Permanente.
Q&A
Nik Rouda Rajiv Synghal
`
Tanwir Danish

32. When everybody wants Big Data
Who gets it?Thank you.