This webinar will provide an overview of the standard architecture components needed to perform analytics and derive data insights from within each of the three common database environments. This will include the sandbox environment for initial data assessment and data science modeling, the big data environment for batch analytics that includes critical governance components and the real-time analytics environment for real-time retrieval of data, and lastly, the integration of real-time data sources. We will also discuss: - Components for the data scientist sandbox / lab - Batch analytics with security and metadata - Data pipelines - Real-time access and streaming sources

1. The First Step in Information Management
www.firstsanfranciscopartners.com
Produced by:
MONTHLY SERIES
Brought to you in partnership with:
January 5, 2017
Data Insights and Analytics Frameworks

2. Welcome to the new series
§ The purpose of our new series is to:
− Grow understanding on Data Insights
and Analytics
− Cover the ins and outs of the Big
Data, Analytics, Business Intelligence
and reporting universe
− Focus on practical, realistic, value
− Want to bypass fluff, hype and false
promises
− Need your feedback − Use Q & A
pg 2© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
Data Lake
vs.
Data
Warehouse
Descriptive,
Prescriptive
and Predictive
Analytics
Governing
Quality
Analytics
The Role of a
Data Scientist
(Interview
with a CDS)
Analytics, BI
and Data
Science:
What’s the
Progression?

3. Topics for today’s webinar
Frameworks defined
Enterprise analytics architecture
Overview of standard data insights and analytics components
Big Data
Sandbox
Real-time Analytics
From Legacy architectures to data insight
Key takeaways
Q&A
pg 3
Frameworks defined
Enterprise analytics architecture
• Big Data
• Sandbox
• Real-time Analytics
• Legacy Architectures
Overview of standard data insights and analytics components
Key takeaways
Q&A
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

4. Frameworks defined
▪ The structure for delivering and getting value out of your data and an architecture
for decision-making, including organizational models
▪ Your enterprise analytics architecture needs to reflect holistic thinking
▪ Your starting point and business needs determine how you progress, not a pre-
defined curve
▪ Organizations that can barely deliver an accurate production report are doing
predictive analytics – Right? Wrong?
pg 4
Predictive
Managing
Proactive
Operating
Data Insight and Analytics Maturity
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

5. The Data Insight and Analytics Framework
Data Insight and Analytics Framework
Data Insight and Analytics Strategy
Technology Infrastructure
Data Insight and Analytics Operating Model
Components
“Best Fit” Data
Architecture
Data Quality Demand Management
Presentation Data Wrangling
Metadata
Management
GOVERNANCE
ORGANIZATIONAL ALIGNMENT
pg 5

6. Sample enterprise analytics architecture
ODS*
DM
Big Data*
Sandbox
Security and governance
Presentation (visualization, reports, algorithms, queries)
Data ingestion
Operations scheduling,
management, Data Quality, Controls
Sources
* = Includes Real
TimeETL and Movement
DW*
Metadata
Data Lake
pg 6© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

7. ODS*
DM
Big Data*
Sandbox
Security and governance
Presentation (visualization, reports, algorithms, queries)
Data ingestion
Operations scheduling, management, Data Quality,
Controls
Sources
ETL and Movement
DW*
Metadata
Data
Lake
Enterprise analytics architecture – Big Data
§ More than the Big Data “stack”
§ No longer linear – Production to
Access
§ Arranged by latency, access, intended
value, data velocity, data volume and
data movement capacity
1. Standard “Big Data”
2. Sandbox
3. Real time
4. Heritage
pg 7
1
2*3
4
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

8. Big Data
§ Components
− Data Sources
− Ingestion
− Structuring
− Analytics and Visualization
− Metadata
§ High Priority Concerns
− Metadata – Technical, Business, Lineage, Meaning, Interpretation
− Security and privacy - Access and usage must be managed
according to risk, permissions, policy, contractual agreements
− Data Governance
§ Oversight of semantics, lineage, quality
− Latency, access, usage
§ Persistent
§ Type of data structure – Hive, Hbase
pg 8
Data Sources Ingestion and
Transformation
Structuring Data
Analytics and
Visualization
Monitor Technical Metadata Business Metadata
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

9. Use case – Big Data
§ Telecom
− Analyze 500 discrete data elements including support call patterns, late or
delinquent payments and other ongoing vital signs via predictive analytics
− Identify “churn” prospects and take steps to prevent it
§ Results
− 47% reduction in customer churn, protecting $15 million in revenue
− Predictive analytics has spread organically to other parts of the company,
including collections
pg 9© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

10. Sandbox
§ Components – Similar to Big Data
− Stand alone sandbox is also a relevant component
− Ingestion – Batch
− Analytics and Visualization - Data Scientist/Analyst only
§ High Priority Concerns
− Data
§ Discovery
§ Understanding
§ Standardization
§ Usefulness
− Security and Privacy
§ Raw aspect implies no controls
§ Control the data, not the environment
− Data Governance focus, Usage, access
− Metadata, provenance and pedigree
§ Latency, access, usage
− Sandbox equals non-persistent, non-production
− Self-service
− Housekeeping
pg 10
Stand Alone
Sandbox
Data Sources Ingestion and Transformation
Analytics and
Visualization
Monitor Technical Metadata Business Metadata
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

11. Use case – Sandbox
§ Predictive Maintenance, Manufacturing
− Collected machine sensor data
− Created a sandbox environment to centralize the parts failure analysis
− Combined sensor data with operational data
− After finding insights, operationalize the processes in the line of business
§ Results
− Shorter time-to-insight; It took only three weeks (from 6 months) to develop a parts failure
prediction algorithm
pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

12. Real-time analytics
§ Components - similar to regular Big Data
− Streaming processing an added component
− Real time capability on non-Big Data as well
§ High Priority Concerns
− Speed of ingestion
§ Real-time Data Ingestion
− Data Streaming
− Data Messaging
− In-memory database for extreme low latency requirements
− Security and privacy
− Data Governance Focus
§ Metadata
§ Compliance
− Latency, access, usage
− Real-time Data Query
§ Enterprise Query and Reporting
§ Fast Query Database to store analytical results
§ Agents, messaging, new events
§ Flexible persistency and accessibility
§ Very low latency, high performance
pg 12
Data
Sources
Ingestion and
Transformation
Structuring Data
Analytics and
Visualization
Monitor Technical Metadata Business Metadata
Streaming
Data
Processing
Real time DW,
or ODS
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

13. Use case – Real-time analytics
§ Health
− Automatically sifts through millions of posts on dozens of social media sites, local news
reports, medical workers’ social networks and government websites to track instances
of disease
− Continually plots disease hotspots on a map
§ Results
− Identified a cluster of “mystery hemorrhagic fever” in Guinea over a week before the
Ministry of Health of Guinea notified the World Health Organization (WHO), that a day
later confirmed the Ebola outbreak
pg 13© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

14. Relevance of legacy or traditional data insight
§ Legacy structures still have relevance
− Reporting
− Standard BI
§ Components
− Familiar names – ETL, DW, ODS, DM
− Many aspects of Big Data technology are not relevant to
many data uses
§ Traditional Concerns
− ETL vs. web services pipelines via data layer
− Understanding need for traditional uses:
§ Departmental use
§ Historical reporting
§ Operational and ad-hoc reporting
− Support of multi-latency, historical, operational, etc., requirements
pg 14
ODS*
DM
Presentation (visualization, reports, algorithms,
queries)
Sources
ETL and Movement
DW*
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

15. Use case – Legacy or traditional data insight
§ Healthcare
− A data warehouse to facilitate information and best practices sharing between thousands of
providers and research professionals.
− Also deployed predictive analytics and artificial intelligence to derive better insights from
Electronic Health Records and improve patient outcome.
§ Results
− 400,000 patient records centralized in a single data warehouse which can scale up to 20
million records.
− 42% anticipated improvement in patient outcomes with Artificial Intelligence
− 58% anticipated reduction in cost per unit of outcome change
pg 15© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

16. Key takeaways
§ Reference architectures are just that −
You may not need a lake, data warehouse
or sandbox
§ Avoid cobbling together technical
components
§ Plan to match your architecture to needs
and usage, vs. existing components
§ Web Services are an important tool
− If using services, please consider a distinct data layer
pg 16© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

17. Q & A
pg 17© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

18. pg 18
Thank you and Happy New Year!
See you Thursday, February 2 for
Data Lake vs. Data Warehouse
John Ladley @jladley
john@firstsanfranciscopartners.com
Kelle O’Neal @kellezoneal
kelle@firstsanfranciscopartners.com
© 2017 First San Francisco Partners www.firstsanfranciscopartners.com