This document discusses opportunities and challenges in big data analytics for professionals. It begins with an introduction by Naveen Agarwal about himself and his work at Johnson & Johnson Vision Care analyzing big data. The document then covers topics like what constitutes big data, why big data potential has been difficult to realize, assessing an organization's maturity with big data, and case studies of analytics projects at J&J Vision Care addressing questions in areas like product quality, sales forecasting, and cannibalization. It also discusses roles for data professionals like business analysts, data scientists, software engineers and the skills required for these roles.

1. Navigating the Business of Big
Data in Industry
Opportunities and Challenges for Professionals in
Big Data Analytics
Naveen Agarwal, Ph.D.
Email: creativeanalytics1@gmail.com
LinkedIn: https://www.linkedin.com/in/naveenagarwal/
for students at the
Jacksonville University
October 16th, 2017
16th October 2017 Navigating the Business of Big Data Ⓒ Creative Analytics Solutions, LLC

2. Used in accordance with the Classroom Usage Statement of Andrews McMeel Syndication
16th October 2017 Navigating the Business of Big Data Ⓒ Creative Analytics Solutions, LLC
Proceed with caution and beware of the jargon!

3. Big Data is Noisy….It Takes Work To Be Useful!
16th October 2017 Navigating the Business of Big Data
….That is how analytics professionals create value
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4. Topics for Today
16th October 2017 Navigating the Business of Big Data
 A little bit about myself and J&J Vision Care
 3 Things to know about big data
 Data is now called “big” – why?
 Big data is said to have big potential – why hasn’t it delivered?
 How do we find out where an organization is with big data?
 What kind of business questions are of interest to us at J&J Vision Care?
 Case studies of analytics at J&J Vision Care
 How do practitioners of analytics add value –
 Types of statistical analysis and tools
 Roles for statisticians and mathematicians
 Where is the big need?
 Looking ahead – where is big data headed?
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5. My Story……….. Ph.D. Engineering
Journey continues….
M.S.. Engineering
Product Development
New Ventures
Product Development
Business Analytics
Product Quality
Technology Development
16th October 2017 Navigating the Business of Big Data Ⓒ Creative Analytics Solutions, LLC

6. J&J Vision Care, Inc. – Manufacturer of Acuvue®
The World’s Best Selling Contact Lens Brand
16th October 2017 Navigating the Business of Big Data
 $2.8 billion global sales in 2016
 2 Manufacturing locations – Jacksonville, FL and
Limerick, Ireland
 Highly automated, high speed manufacturing
 Global distribution – products sold in over 100
countries
 Recently acquired Abbott Medical Optics (AMO)
and Tear Sciences
Sources: J&J Annual Report, Acuvue.com
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7. Big Data = Volume, Variety and Velocity
16th October 2017 Navigating the Business of Big Data
Structured Data
Employee Data
Sales Data
Survey Data
Lifestyle
Data
Geo Data
Vision Test
Data
Complaints
Data
Search
Data
Unstructured Data
Social Media
Chatter
Video Data
Voice Data
Image Data
Calls Data
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8. Big Data has Big Potential, but Mixed Record of Success
16th October 2017 Navigating the Business of Big Data
Weak
Economy
Talent
Org Culture
Technology
Org Culture
Slow to
Change
McKinsey Global Institute Report – The Age of Analytics (2016)
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9. Data Analytics Maturity Model
16th October 2017 Navigating the Business of Big Data
Operations
Efficiency
Reporting &
Data
Warehousing
Data based
Decision Making
Self-Service
Analytics
Democratization
of Data
New Business
Models
New Sources of
Revenue
Uses of Data
BusinessValueofData
Limited
Automation of
Data and
Processes
Structured
Data, Reporting
and
Visualization
Reporting and
Analytics
Throughout
Organization
Analytics
Driving New
Revenue
Growth
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10. Questions for Business Analysts
16th October 2017 Navigating the Business of Big Data
R&D/Clinical Testing
How do we get clinical superiority to launch market leading products?
Global Supply Chain
How do we deliver a perfect order every time, everywhere?
How do we test and improve our quality to delight customers?
Quality Control
How do accelerate our sales to achieve business results?
Sales and Marketing
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11. Case Study – Understanding Product Quality Issues
16th October 2017 Navigating the Business of Big Data
Key questions:
Monthly Complaint
Count
 Are we looking at the data correctly?
 What analytical tools should we use to better understand customer experience?
 Do we understand both quantitative and qualitative data?
 How can we detect and confirm quality signals?
 Trend vs. trigger points – when do we act?
 How do we monitor/measure the effect of our improvement actions?
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Complaints
rising? What
should we do?

12. Effect
of CAPA
Case Study – Detecting Quality Issues For Improvement
16th October 2017 Navigating the Business of Big Data
Applying Time Series Analysis for Forecasting Product Complaints
Should
we act?
Agarwal et.al.; 2015 ASQ World Conference
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13. Case Study – Detecting Quality Issues For Low Frequency Events
16th October 2017 Navigating the Business of Big Data
Applying Proportional Reporting Ratio (PRR) to assess frequency of
a product-specific event relative to other similar products
Event (R) All Other
Events
Total
Product (P) A B A+B
Other Similar
Products
C D C+D
Total A+C B+D N=A+B+C+D
Standard Deviation =>
95% Confidence Interval =>
As an example, we can trigger a signal if the lower bound on PRR
exceeds 1
Say, we are tracking the frequency of serious medical events
related to a device with respect to all other medical events
and we find the following data in a given month
MDR All Other
Medical
Total
Product (P) 2 46 48
Other Similar
Products
6 822 828
Total 8 868 876
According to our rule, we will trigger this signal as a “potential”
signal – Should we act?
PRR = 5.75
Lower Bound = 1.54
Agarwal et.al.; 2015 ASQ World Conference
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14. Case Study – Detecting Quality Issues From Unstructured Data
16th October 2017 Navigating the Business of Big Data
Data-mining of verbal feedback from customers about their actual experience, or their conversations on social media can provide insights into
patterns and possibly early warning of an issue
As an example, experience of general discomfort with soft contact lens wear is very hard to quantify and understand. By monitoring frequency
of “key words” associated with this experience, we can better understand shifts in customer experience over time
We can study:
1. Time series of indicators
2. Correlations between indicators
3. Correlation to demographic or geographic factors
4. Association with specific product lots or
manufacturing timeframe to indicate potential
impact of variation
Agarwal et.al.; 2015 ASQ World Conference
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15. Case Study – Understanding Product Cannibalization
16th October 2017 Navigating the Business of Big Data
Key questions:
Monthly Sales of
Product A
Monthly Sales of
Product BProduct B
Launch
 How do we detect cannibalization of Product A sales due to Product B?
 Is it really cannibalization or natural decline in sales due to other market factors?
 Where, and how much cannibalization is taking place?
 What is the effect on overall category?
 How well can we predict the sales trajectory of A and B in future? What actions should we take?
….Think of relevant questions first and develop a framework for analysis.
Then go after big data and appropriate tools.
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16. Common Statistical Analysis And Tools
16th October 2017 Navigating the Business of Big Data
Descriptive
What happened?
Inferential
Why?
Predictive
What could happen?
Prescriptive
What should we do?
Basic reporting
• Summarize past data
• Mean, median, mode,
min, max, variance
• Growth rates
• Compare against
benchmarks or goals
• Data distributions,
process capability
• Charts, graphs, tables to
visualize simple trends
Basic prediction
• Relating sample data to
general population
• Finding statistically
significant factors
• Regression Analysis
• Correlations
• Hypothesis testing
• DOE, ANOVA, GLM
• Multivariate analysis
Forecasting
• Delphi methods
• Trend analysis
extrapolation
• Moving averages, data
smoothing
• Time series, ARIMA
• Regression analysis
Future outcomes
• Data modeling and
simulations
• Sensitivity analysis
• What-ifs and
probabilities
• Decision tree analysis
• DOE/Robust Design and
optimization
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17. Traditional Roles in Big Data Industry
16th October 2017 Navigating the Business of Big Data
Unstructured DataBusiness Analysts
Typical Responsibilities
• Define project
requirements
• Develop relevant
business metrics
• Build simple data models
• Build data reports
• Apply basic statistics and
analytical skills to deliver
business insights
Key Skills
• Basic data analysis tools
– Excel, Minitab, JMP
• Data reporting tools –
SAP-BW, Excel,
PowerPoint
• Data visualization tools –
Tableau, Microstrategy,
QlikView
• Communication and
presentation skills
Senior Business Analysts
Typical Responsibilities
(Over Business Analyst)
• Define business
requirements
• Develop new data
capabilities
• Run data queries from
databases
• Build more complex
reports
• Internal consulting
services
Key Skills/Experience
(Over Business Analyst)
• Organizational know-
how and relationships
• Basic statistics
• Query and analyze both
structured and
unstructured data
• Advanced Excel and/or
programming skills
• Communication and
presentation
Typical Responsibilities
• Understand and design
business data
requirements
• Capture, store, analyze
and share data
• Modeling, machine
learning and forecasting
• Executive level business
presentations
• Internal consulting
Key Skills/Experience
• Advanced modeling –
SAS, R, Matlab
• Advanced statistics,
probability, Bayesian
statistics
• Machine learning
• Relational database
design
• Data management –
Python, Java, JavaScript
• Unstructured data –
Hadoop, Hive, Spark
• Cloud based – AWS,
Google, Microsoft
Data Scientists Software Engineers
Typical Responsibilities
• Design and build user
experience capabilities
• Real time data systems
• Data storage, processing
and retrieval systems
• Troubleshooting and
support
• Software development
and project management
• New reporting and data
modeling capabilities
Key Skills/Experience
• Advanced programming
– C and C++
• Advanced commercial
databases – Oracle,
Teradata
• Data management –
Python, Java, JavaScript
• Unstructured data –
Hadoop, Hive, Spark
• Cloud based – AWS,
Google, Microsoft
• Budgeting, project
management, agile IT
Increasing education, experience and responsibilities
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18. Emerging Role in Big Data Industry
16th October 2017 Navigating the Business of Big Data
Statisticians
Engineers
Analysts
Data Scientists
IT professionals
Chief Executive Officers
Presidents/VPs
Senior Directors
Both Technical
and Business
Management
Skills
* McKinsey Global Institute Report – The Age of Analytics, 2016
Functional Experts Senior Business Leadership
2M - 4M
Projected US
demand over the
next 10 years*
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19. Looking Ahead….The Coming Wave of Deep Learning
16th October 2017 Navigating the Business of Big Data
1Google’s AI Reads Retinas to Prevent Blindness in
Diabetics…
Early detection of diabetic retinopathy from OCT scans
2IBM Watson provides treatment options to
based on “digesting” large volume of research
and training by expert physicians….
 Analyzes clinical reports and patient-specific notes using
natural language processing
 Identifies potential evidence-based treatment options
 Finds and provides supporting evidence from a wide variety of
sources (290+ medical journals, 200+ textbooks, 12MM pages
of text)
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20. All models are wrong, some are useful
George E. P. Box
16th October 2017 Navigating the Business of Big Data Ⓒ Creative Analytics Solutions, LLC