Post on 22-Jul-2015
BIG DATA ANALYTICSThe New CEO Super Power
Copyright © 2015 Damian Mingle. All Rights Reserved.
3 KEY IDEAS
• What leaders need to understand about Big Data
• Harnessing analytic insights building new business models
• Sparking disruptive innovation across the organization
Copyright © 2015 Damian Mingle. All Rights Reserved.
WHAT LEADERS NEED TO UNDERSTAND ABOUT BIG DATA
BIG DATA
• Moving target
• Difficult to work with
• Varies on organization
Copyright © 2015 Damian Mingle. All Rights Reserved.
Rows Columns
1 million 10
100 million 200
SIX CHARACTERISTICS OF BIG DATA
•Quantity of dataVolume
•Type of dataVariety
•Speed at which data is generatedVelocity
• Inconsistency in dataVariability
•Quality of dataVeracity
•Number of sourcesComplexity
Copyright © 2015 Damian Mingle. All Rights Reserved.
Sources: http://en.wikipedia.org/wiki/Big_data
DATA NEVER SLEEPS – GENERATED EVERY MINUTE
Source Quantity
Email users 201,166,667 messages
Google 2,000,000 search queries
Facebook 684,478 shares
Consumers spend $272,070 on web shopping
Twitter users 100,000 tweets
Apple 47,000 app downloads
New websites 571 created
YouTube uploads 48 hours of new video
Brands & Organizations on Facebook 34,722 “likes”
Sources: http://news.investors.com, royal.pingdom.com, blog.grovo.com, blog.hubspot.com, simlyzesty.com, pcworld.com,
biztechmagazine.com, digby.com
Copyright © 2015 Damian Mingle. All Rights Reserved.
Sources: http://whatis.techtarget.com/definition/3Vs
Copyright © 2015 Damian Mingle. All Rights Reserved.
“
”
…SINCE THE BEGINNING OF TIME, 90% OF ALL DATA HAS BEEN CREATED IN THE PAST FEW YEARS.
- Laurie Miles, SAS
Sources: http://whatis.techtarget.com/definition/3Vs
Copyright © 2015 Damian Mingle. All Rights Reserved.
HOW MUCH DATA IS OUT THERE?
• No one knows
• We created 2.8 zettabytes
• Enterprise data will grow 650 percent
Copyright © 2015 Damian Mingle. All Rights Reserved.
WHY ARE COMPANIES ASKING WHERE THEY STAND WITH BIG DATA?
• Improving existing products and services
• Improving internal processes
• Building new product or service offerings
• Transforming business models
Copyright © 2015 Damian Mingle. All Rights Reserved.
Copyright © 2015 Damian Mingle. All Rights Reserved.
TOP QUARTILE - FINANCIAL PERFORMANCE
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Bottom Feeders Low Medium High Top Performers
Bottom Feeders Low Medium High Top Performers
Sources: Bain Big Data Diagnostic Survey; n=409
Copyright © 2015 Damian Mingle. All Rights Reserved.
MAKING DECISIONS – “MUCH FASTER”
0
1
2
3
4
5
6
Bottom Feeders Low Medium High Top Performers
Bottom Feeders Low Medium High Top Performers
Sources: Bain Big Data Diagnostic Survey; n=409
Copyright © 2015 Damian Mingle. All Rights Reserved.
EXECUTING DECISIONS – “HIGHLY EFFECTIVE”
0
0.5
1
1.5
2
2.5
3
3.5
Bottom Feeders Low Medium High Top Performers
Bottom Feeders Low Medium High Top Performers
Sources: Bain Big Data Diagnostic Survey; n=409
Copyright © 2015 Damian Mingle. All Rights Reserved.
USE DATA – “VERY FREQUENTLY”
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Bottom Feeders Low Medium High Top Performers
Bottom Feeders Low Medium High Top Performers
Sources: Bain Big Data Diagnostic Survey; n=409
Copyright © 2015 Damian Mingle. All Rights Reserved.
SO HOW ARE COMPANIES USING BIG DATA ANALYTICS?
Sources: http://newsroom.accenture.com/news/new-survey-from-ge-and-accenture-finds-growing-urgency-for-organizations-to-
embrace-big-data-analytics-to-advance-their-industrial-internet-strategy.htm
According to an Accenture and GE Survey in October of 2014:
66%
34%
MARKET POSITION
Lose Win
Copyright © 2015 Damian Mingle. All Rights Reserved.
SO HOW ARE COMPANIES USING BIG DATA ANALYTICS?
Sources: http://newsroom.accenture.com/news/new-survey-from-ge-and-accenture-finds-growing-urgency-for-organizations-to-
embrace-big-data-analytics-to-advance-their-industrial-internet-strategy.htm
According to an Accenture and GE Survey in October of 2014:
49%
51%
Plan
No Plan
48% 49% 49% 50% 50% 51% 51% 52%
NEW BUSINESS OPPORTUNTIES
Copyright © 2015 Damian Mingle. All Rights Reserved.
SO HOW ARE COMPANIES USING BIG DATA ANALYTICS?
Sources: http://newsroom.accenture.com/news/new-survey-from-ge-and-accenture-finds-growing-urgency-for-organizations-to-
embrace-big-data-analytics-to-advance-their-industrial-internet-strategy.htm
According to an Accenture and GE Survey in October of 2014:
88%
12%
Yes No
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
TOP PRIORITY
HARNESSING ANALYTIC INSIGHTSBUILDING NEW BUSINESS MODELS
Copyright © 2015 Damian Mingle. All Rights Reserved.
INDUSTRY USE CASES
Healthcare
• Detect pandemics faster
Telecommunications
• Improve customer churn
Financial Services
• Fast-track lending decisions
Copyright © 2015 Damian Mingle. All Rights Reserved.
IMPROVING HEALTHCARE
Historical Approach
Vaccine distributed based on:
- Regional population
- First-come, first-served
Vaccine distributed after reports from hospitals and agencies
Challenges with Historical Approach
Focus is not on the patients most in need of vaccine
Official reports take between 3-6 months
Pandemic may worsen waiting on official reports
Copyright © 2015 Damian Mingle. All Rights Reserved.
HEALTH AGENCIES USE SOCIAL NETWORKS
1. Search tweets by
keywords to find potential
patients
2. Look through these patients’
social networks to identify their
infection patterns
3. Make maps of people tweeting
to find pandemic trends
Copyright © 2015 Damian Mingle. All Rights Reserved.
TELECOMMUNICATIONS
• Summary: why am I losing customers?
• Business Challenge: protect revenue and retain customers by proactively detecting mobile phone users at the risk of canceling contracts (customer churn)
• Historical Approach to Churn Analysis:
• Look at spending patterns
• Review recurrent problems
Copyright © 2015 Damian Mingle. All Rights Reserved.
EXAMPLE: CELL PHONE CANCELLATION OUTBREAK – MONTH 1
Copyright © 2015 Damian Mingle. All Rights Reserved.
EXAMPLE: CELL PHONE CANCELLATION OUTBREAK – MONTH 2
Copyright © 2015 Damian Mingle. All Rights Reserved.
EXAMPLE: CELL PHONE CANCELLATION OUTBREAK – MONTH 3
Copyright © 2015 Damian Mingle. All Rights Reserved.
EXAMPLE: CELL PHONE CANCELLATION OUTBREAK – MONTH 4
Copyright © 2015 Damian Mingle. All Rights Reserved.
SOCIAL NETWORK ANALYSIS
High-risk mobile phone churners can now be identified in 1 hour, saving $40 MM in the first year.
If we had known two
customers’ calling
networks…
Could we have
prevented five more
from leaving?
Copyright © 2015 Damian Mingle. All Rights Reserved.
TRADITIONAL APPROACH TO LOAN PROCESSING
Copyright © 2015 Damian Mingle. All Rights Reserved.
BIG DATA ENABLED LOAN PROCESSING
Copyright © 2015 Damian Mingle. All Rights Reserved.
BIG DATA ENABLED LOAN PROCESSING
APPLICATION PRE-APPROVAL UNDERWRITING CLOSING
TODAY
BIG DATA ENABLED
3-4 WEEKS
2-3 WEEKS ~30%
IMPROVEMENT
Copyright © 2015 Damian Mingle. All Rights Reserved.
BUSINESS DRIVERS FOR BIG DATA ANALYTICS
Driver Examples
Optimize business operations Sales, pricing, profitability, efficiency
Identify business risk Customer churn, fraud, default
Predict new business opportunities Upsell, cross-sell, best new customer
prospects
Comply with laws or regulatory
requirements
Anti-Money Laundering, Fair Lending, Basel
II
Copyright © 2015 Damian Mingle. All Rights Reserved.
BIG DATA ANALYTICSREQUIRES NEW APPROACHES
Business Intelligence
Typical Techniques:
• Standard and ad-hoc reporting, dashboards, alerts, queries
• Structured data, traditional sources, manageable data sets
Common Questions:
• What happened last quarter?
• How much did we sell?
• Where is the problem?
Data Science
Typical Techniques:
• Optimization, predictive modeling, forecasting, statistical analysis
• Handling volume, variety, and velocity of Big Data
Common Questions:
• What if…?
• What’s the optimal scenario for our business?
• What will happen next? What if these trends continue? Why is this happening?
Copyright © 2015 Damian Mingle. All Rights Reserved.
LIFE IN THE AGE OF BIG DATA ANALYTICS
yesterday
Experiments are expensive, choose hypothesis wisely.
today
Experiments are cheap, do as many as you can!
Copyright © 2015 Damian Mingle. All Rights Reserved.
SPARKING DISRUPTIVE INNOVATIONACROSS THE ORGANIZATION
Copyright © 2015 Damian Mingle. All Rights Reserved.
SEEK GAME CHANGING INSIGHTS
RetailerHow can we identify previously undiscovered products for cross-selling opportunities?
Mobile Phone CompanyWhere can we find new revenue streams to offset the decline in revenues from calls and texts?
HealthcareHow can we translate the written notes on patients’ charts to improve patient care and outcomes?
Copyright © 2015 Damian Mingle. All Rights Reserved.
BIG DATA ANALYTICS –OBSERVED ORGANIZATIONAL PATTERNS
• Mismatch in languages
• Open-ended questions
• Leadership and direction
Copyright © 2015 Damian Mingle. All Rights Reserved.
6 EFFECTIVE STRATEGIES TO SQUEEZE OUT BUSINESS VALUE
1. Engage and authorize
2. Frame the outcomes
3. Encourage decision makers
4. Plug your data scientist in
5. Motivate data scientists
6. Construct analytic teams
Copyright © 2015 Damian Mingle. All Rights Reserved.
“
”
YOUR CAREER WILL ALWAYS BE A BYPRODUCT OF THE CHALLENGES YOU’VE TRIED TO SOLVE.
-Sean McClure, Data Science Central
CONNECT WITH ME
Email: DMingle@WPChealthcare.com
Web: DamianMingle.com
LinkedIn: linkedin.com/in/damianrmingle
Twitter: @DamianMingle
Copyright © 2015 Damian Mingle. All Rights Reserved.
Steiner, C. (2013). Automate this: How
algorithms took over our markets, our jobs, and
the world (Paperback ed.). New York: Penguin.
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