Big data and Analytics

Big Data and Analytics

The emerging driver of competitive
advantage

Kevin Magee Contact details
Partner
kevin@openwindowanalytics.com

www.openwindowanalytics.com

© Open Window Analytics 2012

What do we do?
Our clients span organisations:

• looking to introduce Analytics into their business for the first
time,

• facing challenges leveraging the Analytics investment they
have already made,

• looking to bring innovative Analytics products and services to
market


Today’s Topics
• What’s Analytics All About?

• The Possibilities with Analytics

• A quick look at some emerging technologies

• Upcoming challenges & opportunities


The Challenge of Information
• Information is the principle driver of • Paradoxically, the greater the volume of
competitive advantage. *It’s the new oil+ information we collect, the greater the
prospect of uncertainty
– How it is collected, analysed and
communicated determines our success.

– No single resource is more critical to
organisational survival.

– But like oil, it must first be
found, extracted, refined and distributed
before its value can be truly appreciated

• “a wealth of information creates a poverty
of attention and a need to allocate that
attention efficiently among the
overabundance of information sources
that might consume it”
- Herbert Simon (1971)

So, what is Analytics anyway?
• It’s about uncovering • Depending on who you
patterns, outliers, relati talk to, Analytics
onships, and other projects are:
insights in data. – Technology projects
– Actuarial projects
• It’s part: – Machine learning
– Data – Applied statistics
– Information Technology – Operations research
– Human powered – Business Intelligence


Related History

Static & Interactive Query, Excel, OLAP, Dashboards, Statistics, data
Reports Visual discovery Scorecards mining, Optimisation
(mainstream)
1970’s 1980’s 1990’s 2000’s 2010’s

• But, Analytics have been used in business since the time of Frederick Winslow
Taylor in the late 19th century.
• Henry Ford also measured pacing of assembly lines.

What’s Big Data really all about?
McKinsey say: “Big Data: The next frontier for
innovation, competition, and productivity”

Data captured from:

Customers
Suppliers Transactional records
Unstructured text
Operations
Internet clicks
RFID
Geospatial GPS signals
Digital multimedia
Sensors And more…
Social networks
Public data Big Data
Multimedia


How much data?
Digital Universe
IDC Digital Universe Study
2011 1.8 Zettabytes
2020 35 Zettabytes

Sources: Cisco, comScore, Radicati Group, Twitter, YouTube
Number of emails every second 2.9 million
Data consumed by households each day 375 megabytes
Video uploaded to YouTube per minute 20 hours
Data processed by Google per day 24 Petabytes
Tweets per day 50 million
Minutes on Facebook per month 700 Billion
Data sent / received by mobile internet users 1.3 Exabytes
Products ordered on Amazon per second 72.9 items

1 Zettabyte = 1,000,000,000 Terabytes

The smartest organizations are already capitalizing on increased
information richness and analytics to gain competitive advantage.

Top performers use analytics 5 times
more than lower performers
– MIT Sloan (Autumn 2010)

Companies that invest heavily in advanced
analytical capabilities outperform the S&P 500
on average by 64%
- Accenture research 2011

Companies that invest heavily in developing analytical skills and
adopting an analytical mindset recover quicker from economic
downturns
- Accenture research 2011

The Value of Analytics


Analytics Landscape
Competitive Advantage

Optimisation
Predictive What’s the best that
can happen?
Visual Analytics
Intelligence
Show me Predictive
all of this... Modeling
What will happen next?

Forecasting

What if these trends continue?

Statistical
Analysis
Descriptive Why is this happening?
Analytics
Alerts

What actions are needed?
Query
Drilldown
Where exactly is the problem?
Ad Hoc
Reports
How many, how often, where?
Standard
Reports
What happened?

Degree of©Intelligence
Open Window Analytics 2012
© SAS Institute, with some OWA amendments

Common Analytic Applications
(in various industries)
Retail Promotions, replenishment, shelf management, demand
forecasting, inventory replenishment, price & merchandising
optimisation
Manufacturing Supply chain optimisation, demand forecasting, inventory
replenishment, warranty analysis, product customisation,
new product development
Financial services Credit scoring, fraud detection, pricing, underwriting, claims,
customer profitability
Transportation Scheduling, routing, yield management
Healthcare Drug interaction, preliminary diagnosis, disease management
Hospitality Pricing, customer loyalty, yield management
Energy Trading, supply, demand forecasting, compliance
Government Fraud / waste / error, case management, crime prevention
Online Web metrics, site design, online recommendations

Adapted from “Analytics at Work” (Davenport et al 2010) © Open Window Analytics 2012
Adapted from “Analytics at Work” – Tom Davenport, Jeanne Harris, Robert Morison (2010)

Analysis Framework
(it’s not just about what’s inside the walls…)

Feedback
Decisions
Requirements

Intelligence Operations
(Estimates) (Recommendations)

Primarily focused Primarily focused
externally (out of Goals internally (under
our control). our control).
Purposes

What is likely to People
happen that is What can we /
relevant to our should we do
success or failure? about it?

Framework courtesy of Kristan J. Wheaton (Mercyhurst College)

IT Needs to Help Businesses Run, Grow,
and Transform
IT Portfolio Spending

Introduce new products Improve existing Sustain existing products
and services products and services and services

The Business Challenge
Source: Gartner 2011 IT Metrics Spending and Staffing Survey © Open Window Analytics 2012

Big Data
Preparing Interpreting

Interpreting Big Data will lead to
new markets, products, and services© Open Window Analytics 2012

Data Supply Chain



THE POSSIBILITIES
(SOME EXAMPLES)


Reading list
• Competing on Analytics (Tom Davenport et al)
• Blink (Malcolm Gladwell)
• McKinsey Big Data report
• The New Know (Thornton May)
• Analytics at Work (Davenport et al)
• The Long Tail (Chris Anderson)
• Visualize This (Nathan Yau)
• Information is Beautiful (David McCandless)
• How to Lie with Statistics (Darrell Huff)


Chest Pain Diagnosis
• Cook County hospital in Chicago

• Problem
– No budgets; Cardiac care expensive to deliver;
Overwhelmed ER; 2-8% of patients across US get sent
home when having genuine heart attack; and lots of
other problems
– Wanted to figure out if there was a better and quicker
way of identifying who needs care and level of care

• Solution found with Analytics!

Less is More
Traditional medical model is to take
case history – gather as much info
as possible – more info = better
diagnosis

► Cook County implemented a radical system for
predicting chest pain cases that didn‘t bother with
history but on 4 pieces of specific information and a
decision tree.
► Turns out, less information is better in this case!

► Doctors guessed right between 75 and 89% of the
time.

► The algorithm guessed right >95% of the time!Window Analytics 2012
© Open

Penny Post
• Charles Babbage compared the cost of
transporting mail with the cost of sorting it

• He found the sorting to be inefficient and more
costly

• Standardising the cost of mail delivery (within a
delivery and weight range) to one penny greatly
reduced sorting costs.

• Sir Rowland Hill introduced Penny Post to Britain
based on Babbage recommendations


WWII Aircraft Armour Placement
• Common wisdom:
– Place heavier armour on parts of plane most shot
up after mission

• Physicist Patrick Blackett recognised data
statistically biased to surviving planes
– The shot down planes were the ones of interest

• He reasoned:
– If a part of a plane could be shot and not bring
down plane, that part needed no extra armour.

• Solution:
– Statistical analysis on the places in common
between returning planes not shot down were Make sure you are
likely where shot down planes HAD been shot. analysing the right data
– Therefore, where extra armour needed!

Another WWII Problem
• Are bigger or smaller warship
convoys better to protect
merchant ships from U-boats?

• Small convoys
– eluded U-boat detection better
than larger ones

• Large convoys
– better at counterattacking

• Analysis revealed
– Probability of detection did not vary significantly with convoy size
– Therefore making larger convoys the most efficient size


Amazon’s Long Tail
… a pioneer in its relentless use of
Web site design testing and
optimization, constantly evaluating
everything, including minutiae such
as the color and shape of tabs on the
site

….given the volume of traffic on
• Selling ‗less of more‘ Amazon.com's Web site, even a slight
optimization of its design can mean
millions of dollars in additional sales

• Amazon is the reverse …By basing its Web site design
of the 80/20 rule decisions on usage data and not
necessarily on aesthetics or internal
designers' gut instinct, Amazon.com
has managed to keep its user
interface closely aligned with its
ultimate goal, which is turning
visitors into buyers Open Window Analytics 2012
©

Sports, Medicine, and Cars
―The New England Patriots American Football
team have managed to win the Super Bowl three • The Veteran Administration's use of
times in four years — using an analytical evidence-based medicine and predictive
approach. analytics (along with automated
Patriots…renowned for their extensive study of decisions for treatment protocols)
game film and statistics…. reads articles by translates into this - only 25-30% of
academic economists on statistical probabilities of medical decisions are scientifically-
football outcomes. based!

The team uses data and analytical models • Honda makes good use of text analytics
extensively, both on and off the field. In-depth to flag early problems in cars by
analytics help the team select players and stay analysing warranty claims calls by
below the NFL salary cap. customers or dealers to HQ - a great
example where simple automated
analysis and flagging created value
Off the field, the team uses detailed analytics to
assess and improve the "total fan experience."
At every home game…people have specific
assignments to make quantitative
measurements of the stadium food, parking,
personnel, bathroom cleanliness and other
factors.‖ Be specific
Know your question
Read MoneyBall – about Oakland A‘s
Focus on the right
information

Supply and Demand
• Beer and Poptarts are not natural companions

• Walmart, using Analytics, have found that the 2
biggest selling items during a Hurricane warning are
Strawberry Poptarts and Beer

• They use this information for supply chain
management and goods distribution optimising
sales of both

• ….They sell the essential stuff too


Storm Planning

Home Depot Supply chain managers place orders in November, based on past storm
data, so products like gas cans, generators, and plywood are stocked in three hurricane-
specific distribution centers by June. Before Hurricane Gustav in 2008, 500 trucks full of
supplies went to distribution centers. © Open Window Analytics 2012

Hide n Seek
• O2 mobile phone company use
personalised menus to maximise value
of limited phone interface - and uses
predictive analytics to personalise

• The decision to display a certain set of
options to a mobile phone user is often
hidden as companies don't think of each
new list as a decision - they think of it as
"the list―

• Netflix is similar - giving each customer a
personalised website experience based
on recommendations, ratings,
segmentation

Test, Test, Test

• 5 keys to Obama Campaign Success
1. Define quantifiable success metrics.
2. Question assumptions.
3. Divide and conquer.
4. Take advantage of circumstances.
5. Turn your customers into evangelists

• Based on data from analytics, a passive
“Learn More” button with a static and
non-Obama centric picture of a family
trumped the rest of the variations

• No assumptions should be made and
that unique scenarios would need
unique approaches


THE POWER OF VISUALISATION


Traditional way of presenting
information
Defence Spending Corporate Revenues

Market values

2009 data


LESSONS LEARNED


Get your thinking right

• It's not all about "big data" and complex maths or even advanced software tools.
• More essential in transforming your business with analytics is to:
• ask the right questions of the data and then
• effectively communicate the results of the analysis,
• Therefore, critical thinking, and creative communication approaches are just as
important (if not more so) than the technology used to enable these Open Window Analytics 2012
© insights.

EMERGING TECHNOLOGY


Big-Data Processing Systems
(not a complete list – illustrative purposes only)

OLTP Analytic Hadoop NoSQL Real-time
Databases Platforms (MapReduce) Streaming
Oracle, DB2, SQL Netezza, Vertica, Cloudera, EMC, IBM, Cassandra, Storm, Hstreaming,
Server, etc Exadata, Teradata HortonWorks, etc MongoDB, S4, StreamBase
appliances, SAS, etc DynamoDB,
MarkLogic, Attivio,
etc

Transaction systems EDW to replace MySQL Online data archive for Distributed system for Distributed real-time
or SQL Server in fast- all data (but mostly querying unstructured + stream processing
Enterprise data growing companies unstructured) data
warehouse hub Continuous
Analytic data marts to Staging area to feed the Graph system for computation
offload the DW DW understanding
relationships
Free standing analytical Analytical system when
sandboxes (big data, you want to query all Key value pair storage
extreme performance, the raw data (Hbase, for rapid data capture
etc) Hive, Pig etc) and analysis

Analytical system when Key value cache for in-
you can’t wait until data memory lookups and
is modelled and put in operations
DW (Hbase, Hive, Pig)


The New Analytical Eco-system

Diagram courtesy of Wayne Eckerson
• These architectures are more analytical
• Give power users greater options (access & mix corporate with own data)
• Bring unstructured / semi-structured data – Hadoop / nonrelational DB’s

SOME UPCOMING CHALLENGES


Challenges & Opportunities
(not a complete list )
• Egocentric networks • Collaborative analytics
• SoLoMo intelligence • Beyond the Desktop
layering • Data Management for
– Social Local Mobile Analytics still a problem
• Mobile BI / Analytics • The real-time challenge
• Analytics in the Cloud • Relevancy & Recency
• The Big Data challenge challenges
• Ease of use / • Augmented analytics
Consumerisation of • Consumable analytics
Analytics
– The next Billion users

ANY QUESTIONS?


Big data and Analytics

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