Topic: Driving Business Insights with Hadoop and Big Data Platforms Mr. Krishnan is a recognized expert worldwide in the strategy, architecture and implementation of high performance data warehousing solutions. He is a visionary data warehouse thought leader, ranked as one of the top 25 data warehouse consultants in the world, and an independent analyst, writing and speaking at industry leading conferences, user groups and trade publications. He has authored three eBooks, over 125 plus articles, viewpoints, whitepapers and case studies in Big Data, Business Intelligence, Data Warehousing and Data Ware-house Appliances and Architectures. He has co-authored a book with Bill Inmon titled “Building The Unstructured Data Warehouse” A recognized authority on Unstructured data integration, text mining and text analytics. Along with Bill Inmon, he is promoting the next generation of data warehousing, primarily on DW 2.0 platform with unstructured data integration and social intelligence as key areas in BIG data and analytics. Mr. Krishnan will also be joining us from Chicago, United States on video conference, his session will focus on the world we live in today, driven by an insane amount of data and information being flooded all the time at us whether on smartphones or social media networks or on our computing platforms. What is the need for this information? What is it influencing us towards? Who do we influence? Where is the profitability and margin in this mode of advertising and crowdsourcing? The answer to these questions and more of the type can be found by Business users and SME’s when they drive the information towards delivering successful business insights, the same mode that was used by companies like Google and Facebook to create their platforms and the position in the industry. In this session we will discuss the powerful platforms like Hadoop and NoSQL delivering business insights to the business teams and delivering vast amount of analytical insights that will bring more competitive strategy and positioning to the business. We will also quickly touchbase on ownership and governance models for the program.

1. Driving Business Value – Big Data Platforms
Hadoop Summit
Karachi, Pakistan
Nov 18th 2014

2. Content and services
via connected
products
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Hortonworks © 2014
New routes to market via
intelligent objects
Everything
has a URL
Remote sensing of
objects and environment
Augmented reality
Building and
infrastructure management
Cameras and
microphones widely
deployed
Over 50% of Internet connections are things:
2011: 15+ billion permanent, 50+ billion intermittent
2020: 30+ billion permanent, >200 billion intermittent
Situational
decision support
Source: Gartner Keynote at Hadoop Summit 2013

3. Disruption: It's Not Just About Technologies
Identity/Access
Management
Speech Recognition
Computer-
Brain
Interface
Truth
Verification
Object
Identification
Linux
Wikis
Grid Computing
Tablet PC
Information
Extraction
Location-Aware
Services
Semantic Web
Service-Oriented
Architecture
Unified
Communications
Ultra Wide Band
RFID
Social
Network
Analysis
Web-Services-
Enabled Business
Models
Electronic Ink/
Digital Paper
Web
Services
Instant
Messaging
VoIP
Sensor Networks
Smartphone
Really Simple
Syndication
Augmented Reality
4G Wireless
Blogging
Podcasting
IP Television
Location
Sensing
Design Innovation
Proactive
Transparency
Personalized
Pricing
Counterfeit Reality
Collective
Intelligence Perfect Recall
Real-Time Enterprise
Ubiquitous
Access
Smart Objects
and Ambient
Intelligence
Privacy Redefined
Global Sourcing
Business
Process
Management
Voice/Data
Convergence
Microcommerce
Self-Sufficiency
Greenfield
Business
Global Micro-Business
Seamless Service
to Self-service
Feedback Society
Semantic
Connectivity
Emerging
Technologies
Emerging
Capabilities
Emerging
Business Models
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4. From Transactional to Behavioral
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The challenge facing the business today is the ability to influence the buyer decisions in a window of opportunity that does not last long. The analytics available at a personalization level drives the buyer whether it is choosing a Doctor or buying a Donut.
To compete in this new era, businesses need to be driven by data and analytics, which are largely different from traditional transactions and campaigns
The “Gen Z” and “Millinieal Generation” of buyers will not be swayed by traditional engagement models of selling products and services
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5. A Growing Trend
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6. Executive Ask
•Why is our competition performing better in the same markets as us?
•What is the real revenue impact to the organization?
•Why do our campaigns not predict the accurate revenue lift?
•How can we improve our brand and align to customer sentiment?
•Who can help us get to the bottom of the data abyss?
•When do we get the real insights from Analytics?
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7. Forces Shaping Business
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8. Business Evolution – Product to Customer Focus Shifts
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Image source - internet
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9. Decision Support – Now & Then
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Customer
Promotion
Social Presence
Behavior Analytics
Competition
Market
Value
Transactions
Price
Product
Channel
Sales
Promotion
Market
Price
Transactions
Customers
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10. Innovation as Way of Life
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Old Brick
New Brick
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11. State of Data
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12. Big Data
•Data that cannot be processed using traditional data processing techniques due to size, scale and formats
•Its beyond just that
–Complexity
–Ambiguity
–Veracity
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13. Noise vs Value
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Is Big Data Noise or Value?
Is Big Data a passing hype?
Is there real value behind Big Data?
Is there any measurable and actionable insight from Big Data?
Is there a need to invest in this now?
Image source - internet
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15. Example
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Tweet: @jdoe – very disappointed with @united @checkin lousy svc, bad mgmt, long lines #fail.
20000 retweets. 4 hours ago
IT Perspective Text of 140 chars will be stored as string. The data model for this will be a table with source, content, datetime.
Business Perspective
User – JDoe
Brand – United
Sentiment – Negative
Process – Check-in
Time – Waiting in long lines
Impact – shared 20000 times in 4 hours
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16. Why Does Big Data Differ
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IT does not know data
Business knows the intelligence to be applied to the data to derive value
Business knows how to discover data patterns (manual and automated)
Business understands the semantics better
Business can perform data interrogation in an experiment and associate rules of engagement early on for data usefulness
Business can sift the data to curate the context
Big Data needs to be curated to be useful
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Source: McKinsey Presentation on Marketing

18. Big Data Challenges
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Complex
Ambiguous
Formats / Availability
Minimally Organized
Complex Data Quality Issues
Metadata / Semantics
Needs Intervention
Needs Discovery / Contextualization
Needs Analysis
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19. Perspective
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Data
Insights
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20. The Paradigm Shift
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IT
•Facilitate
•Maintain
•Support
•Manage
Business
•Driver
•Budget Sponsors
•Program Owner
•Define & Consume
IT
•Driver
•Program Owner
•Budget Sponsor
•Maintain
•Support
Business
•User
Data Warehouse
Big Data
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21. Analysis
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22. Processing – The Details
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Tag
Categorize
Classify
Cleanse (Data Quality Rules)
Semantic Integration
Measure
Visualize
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Big Data Platforms

23. Future Visualization
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24. Big Data Architecture
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Big Data Sources
Hadoop (and / or NoSQL)
Traditional Data Sources
ETL
ELT
CDC
Staging Or ODS
ETL ELT
EDW
BI Analytics
Discovery
Data Mining Algorithms
Acquire
Big Data Analytics
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26. Infrastructure
Infrastructure Challenges & Solutions
Challenges To Address
Semantic Data Integration
Compression & Storage
High Capacity Warehouse
Security and Governance
Scripting and Development Tools
Complex Event Processing
Solutions Available Today
Columnar Databases
Workload Optimization
Analytic Accelerators
Hadoop / Map Reduce
No SQL Engines
Stream Computing
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27. Big Data Analytics
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Existing EDW
Machine Learning
Text Mining
Analytics
Coding & Learning
Semantic Knowledge Base
Metadata & Semantic Layer
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Dealing with Big “Data Problems”
Data silos in disjointed systems
Multiple data sources - overlapping, conflicting
Timely processing of large volumes of data
Partial, inaccurate, inconsistent.. data
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Single Data Platform

29. Reports
Analytical
DBMS
Analytics Cluster
Data Asset Catalog
Analytical DBMS
Dashboards
Data Discovery
Interactive
Queries
Batch
Queries
Web Applications
Activity
Logs
NoSQL
Reference Data
Device Apps
Probes
3rd Party
Device
User Profile
POI, Map
Activity Sensor
Data Intake
ETL, data crunching, attribution, ML Algorithms
Aggregation
HDFS
Analytical
DBMS
Big Data Analytics Platform Data Flows
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30. Analytics
•Behavioral Analytics
•Cohort Analytics
•Collections Analytics
•Competitive Analytics
•Financial Services Analytics
•Fraud Analytics
•Marketing Analytics
•Pricing Analytics
•Sales Analytics
•Risk Analytics
•Supply Chain Analytics
•Talent Analytics
•Channel Analytics
•Logistics Analytics
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31. Trends 2015
•Business Users Drive Big Data Initiatives
•Analytics is the “new” center stage
•Data Discovery and Storyboard Patterns Emerge Stronger
•Hadoop-Based Data Lakes / Swamps Unite with Data Warehouses
•Predictive Analytics Lend Fresh Insight From Big Data Explorations
•Prescriptive Analytics Bring New Insight to Current Business Processes
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32. Measuring Value
Increasing Revenue / Opportunity / Market
Lowering Costs / Risks / Maintenance
Value
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33. Critical Success Factors
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Business needs to own and execute the Big Data program
Data collection and discovery is the most critical step
Metadata is needed to process the data prior and post Data Warehouse integration
Data quality can be processed by integrating Taxonomies
Data visualization is needed to discover data
Metrics and Metadata will be the bridge to integrate to the Data Warehouse
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34. Questions
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35. Thank You
Contact
rkrish1124@yahoo.com
Twitter: @datagenius
Images Source - Internet
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36. References
•Gartner
•McKinsey
•MIT Innovation Labs
•Google
•Facebook
•LinkedIn
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