Big Data Analytics - The New CEO Super Power 20150124

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


WHAT LEADERS NEED TO UNDERSTAND ABOUT BIG DATA

BIG DATA

• Moving target

• Difficult to work with

• Varies on organization


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


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


Sources: http://whatis.techtarget.com/definition/3Vs


“

”

…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


HOW MUCH DATA IS OUT THERE?

• No one knows

• We created 2.8 zettabytes

• Enterprise data will grow 650 percent


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



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


Sources: Bain Big Data Diagnostic Survey; n=409


MAKING DECISIONS – “MUCH FASTER”

0

1

2

3

4

5

6





EXECUTING DECISIONS – “HIGHLY EFFECTIVE”

0

0.5

1

1.5

2

2.5

3

3.5





USE DATA – “VERY FREQUENTLY”

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2





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






49%

51%

Plan

No Plan

48% 49% 49% 50% 50% 51% 51% 52%

NEW BUSINESS OPPORTUNTIES






88%

12%

Yes No

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

TOP PRIORITY

HARNESSING ANALYTIC INSIGHTSBUILDING NEW BUSINESS MODELS


INDUSTRY USE CASES

Healthcare

• Detect pandemics faster

Telecommunications

• Improve customer churn

Financial Services

• Fast-track lending decisions


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


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


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


EXAMPLE: CELL PHONE CANCELLATION OUTBREAK – MONTH 1








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?


TRADITIONAL APPROACH TO LOAN PROCESSING


BIG DATA ENABLED LOAN PROCESSING


BIG DATA ENABLED LOAN PROCESSING

APPLICATION PRE-APPROVAL UNDERWRITING CLOSING

TODAY

BIG DATA ENABLED

3-4 WEEKS

2-3 WEEKS ~30%

IMPROVEMENT


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


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?


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!


SPARKING DISRUPTIVE INNOVATIONACROSS THE ORGANIZATION


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?


BIG DATA ANALYTICS –OBSERVED ORGANIZATIONAL PATTERNS

• Mismatch in languages

• Open-ended questions

• Leadership and direction


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


“

”

YOUR CAREER WILL ALWAYS BE A BYPRODUCT OF THE CHALLENGES YOU’VE TRIED TO SOLVE.

-Sean McClure, Data Science Central

CONNECT WITH ME

Email: [email protected]

Web: DamianMingle.com

LinkedIn: linkedin.com/in/damianrmingle

Twitter: @DamianMingle


Steiner, C. (2013). Automate this: How

algorithms took over our markets, our jobs, and

the world (Paperback ed.). New York: Penguin.

Recommended Reading:

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Big Data Analytics - The New CEO Super Power 20150124