Speaker: James Graham MSc, Principal Consultant Stratexology LLC

Data Analytics And Analysis Trends In 2015

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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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

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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The Development of Data and Analytics

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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

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

Trends in ‘Big Data’ Analytics – 2015-16

1• Smart systems – dumb operators (automation)

2• Deeper customer insights – ‘sweat the data’

3• Democratization of data/data as an organizational asset

4• Sensor driven data grows (device generated data, e.g. a jet engine

can create 20 TB per hour, smart electricity meters)

5• HR analytics – employee satisfaction, ergonomics, workflow etc.

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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 CodeCauses 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%

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 175Mean price 75 45 115Cost per flight 8,500 7,500 9,500

Profit if Base Low High Swing Swing2 VARTickets sold 875 -1,000 4,625 5,625 31,640,625 28.2Mean price 875 -2,875 5,875 8,750 76,562,500 68.2Cost per flight 875 1,875 -125 -2,000 4,000,000 3.6

SA Example

Small Data Tools - #3 Linear Programming

• Addresses multiple constraints• Provides optimum trade-offs for desired

outcome11

Production Capacity PlanningThe objective is to maximise the Combined Profit The decision variables are the number of rolls of each carpet style to produceThe constraints are the amounts of materiel and production time available

Carpet Style Loop Shear Saxony BerberRolls to Produce 0 0 0 0 Combined ProfitProfit per Roll 198 194 199 202 0

Resources Required Per Roll of Carpet Used AvailablePolypropylene Yarn/Thread (Kgs) 50 47 52 55 0 9,233Production time per roll (Hours) 1.5 1.5 1.5 1.75 0 240Backing Lattice Kgs) 10 9 10 12 0 1,500Latex Backing Glue (Kgs) 35 33 36 36 0 4,000

LP Demo

Questions and Answers

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