This document provides an overview of data analytics trends for 2015-2016 presented by James Graham, a principal consultant. It discusses the differences between big data and small data, as well as tools used for analyzing each. For big data, descriptive, predictive, and prescriptive analytics tools are covered. Five trends for 2015-2016 involving smart systems, deeper customer insights, data democratization, sensor-driven data growth, and HR analytics are also summarized. For small data, Pareto analysis, sensitivity analysis, and linear programming tools are briefly described. The presentation concludes with time for questions.

1. Speaker: James Graham
MSc, Principal Consultant
Stratexology LLC
Data Analytics And Analysis Trends
In 2015

2. Housekeeping
• Slides will be available on our SlideShare page; the link will be emailed to you
• The recording of the webinar will be available to download; the link will be
emailed
• Please take time to complete a post-webinar survey that will pop up at the
end
• You can type your questions throughout the session
• James will allocate time at the end for the speaker to address your questions
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3. Speaker Introduction
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12 years management in
multinational companies
Consulting since 1993
Global Experience
Change Specialist
12 years senior management
in multinational companies
Consulting since 1993
Global Experience
Specialises in Strategy
Formulation and Execution

4. Agenda
The Development of Data and Analytics
Big Data and Small Data
Big Data tools
Five Big Data trends for 2015-16
Small Data tools
Q&A
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5. The Development of Data and Analytics
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6. Big Data and Small Data; what are they
and how are they used
Small Data
• Smaller, simpler systems than big data
• Often single system
• Thousands or millions of records
• e.g. company customer records, sales
invoices, building access records
Small Data
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• Large scale, complex, systems
• Many Terabyte (10004) to Petabyte scale
(10005)
• e.g. search engine behavioural data,
social networks data for mining
Big DataBig Data

7. Big Data Tools - Descriptive, Predictive
and Prescriptive Analytics
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Descriptive
• Filters ‘big data’ into
useful nuggets –
mitigates risk of
confirmation bias
• Provides a historical
summary of what
happened in the past
• Most statistics are
based on descriptive
analytics – e.g. year on
year sales
Predictive
• Based on descriptive
analytics
• Algorithmic approach
• Probability based
• ‘Fills in the blanks’
• Credit scoring
• Forecasting supply
chain requirements
• Offering new products
to existing customers
• And many others
Prescriptive
• Guides decisions
• Quantifies decision
making –multiple
options
• Multiple tools, e.g.
business rules,
algorithms, machine
learning
• Multiple data sources,
e.g. real time and
transactional, big data

8. Trends in ‘Big Data’ Analytics –
2015-16
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• Smart systems – dumb operators  (automation)
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• Deeper customer insights – ‘sweat the data’
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• Democratization of data/data as an organizational asset
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• Sensor driven data grows (device generated data, e.g. a jet engine
can create 20 TB per hour, smart electricity meters)
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• HR analytics – employee satisfaction, ergonomics, workflow etc.
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9. Small Data Tools - #1 Pareto
Analysis
• Frequency based
analysis
• Cumulative analysis
• Potential usage
– Quality control
– Product sales
analysis
– Delay analysis
– Cost analysis
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Failure by Error Code
Causes Freq Cum Freq %
E23 15 15 26.79%
E02 11 26 46.43%
E189 9 35 62.50%
E12 7 42 75.00%
E45 6 48 85.71%
E09 5 53 94.64%
E445 2 55 98.21%
E67 1 56 100.00%

10. Small Data Tools - #2 Sensitivity
Analysis
• Upside/downside
ranges
• Impact identification
• Potential usage
– Strategic analysis
– Business case
development
– Risk analysis
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Tickets sold 125 100 175
Mean price 75 45 115
Cost per flight 8,500 7,500 9,500
Profit if Base Low High Swing Swing2 VAR
Tickets sold 875 -1,000 4,625 5,625 31,640,625 28.2
Mean price 875 -2,875 5,875 8,750 76,562,500 68.2
Cost per flight 875 1,875 -125 -2,000 4,000,000 3.6

11. Small Data Tools - #3 Linear
Programming
• Addresses multiple constraints
• Provides optimum trade-offs for desired
outcome
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Production Capacity Planning
The objective is to maximise the Combined Profit
The decision variables are the number of rolls of each carpet style to produce
The constraints are the amounts of materiel and production time available
Carpet Style Loop Shear Saxony Berber
Rolls to Produce 0 0 0 0 Combined Profit
Profit per Roll 198 194 199 202 0
Resources Required Per Roll of Carpet Used Available
Polypropylene Yarn/Thread (Kgs) 50 47 52 55 0 9,233
Production time per roll (Hours) 1.5 1.5 1.5 1.75 0 240
Backing Lattice Kgs) 10 9 10 12 0 1,500
Latex Backing Glue (Kgs) 35 33 36 36 0 4,000

12. Questions and Answers
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