BIG DATA ANALYTICS: CHANGING John Morton Data Science …mas01ds/dsmsc/dsrt/Goldsmiths College...
Transcript of BIG DATA ANALYTICS: CHANGING John Morton Data Science …mas01ds/dsmsc/dsrt/Goldsmiths College...
BIG DATA ANALYTICS: CHANGING THE WAY YOU DO BUSINESS
John Morton
Data Science MSc
Goldsmiths UoL
London March 201701/03/2017 1
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BIG DATA HYPE OR, SOMETHING REAL?
…….. and more….. 01/03/2017 7
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….OR THEORY?
Velocity
Variety
Volume
Gartner 2000
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TRIED AND TRUSTED
Reduce multiple reports to allow clear decision making
Complex inter-relationships visible
Dynamic visualization for interactive data exploration and visual queries
"If the rate of change on the outside exceeds the rate of change on the inside, then the end is near."
-Jack Welch
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WHY ALL THE INTEREST?
…. US healthcare reduction by $300 Bn a year 2/3rds from a 8% reduction in national healthcare
… retailers can increase operating margin by 60% by fully utilising data
… €100 Bn reduction in Government administration across Europe
. . . predict the buying behavior and decision criteria of your prospects weeks before your competition
. . . gain first-mover advantage by introducing new products and services to micro market segments that haven't been identified by anyone
. . . evaluate the impact of your marketing campaigns hourly and make adjustments in real-time
… Sustain a 4 to 6 % in efficiency and effectiveness over your competitors
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Creativity: £42 Billion
Innovation: £150 Billion
Productivity: £124 Billion
*UK CEBR - Center for Economics and Business Research
BIG DATA VALUE HYPE?
£216 Billion in 5 years,
58,000 jobs *
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Relative
contribution
to GDP
Is Big Data for everyone?
Source : US Bureau of Labor Statistics;
McKinsey Global Institute Analysis
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ABOVE: Types of industry sectors where companies are using open data in their businesses, sourced
from OpenData 500
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HAVE THEY MISSED THE BOAT?
34% of respondents
were drawn from
North America, 43%
from Europe
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SO WHAT ABOUT TECHNOLOGY?
My data
Open data
Your data
Integrated Data
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Technology shift
£30 Terabyte disks
£2000 fault tolerant, computers
Store everything file systems
In-Memory
In-Database processing
In-Memory Analytics
In-Chip Analytics
Visualisation of data - Gamefication
Massively-parallel processing (MPP) analytics
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BIG DATA DISRUPTION
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STAYING IN BUSINESS
productivityStay
My data
Integrated Data
Transparency
Security
Business Health
Provenance
Compliance
Governance
Business Improvement
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Missed
Opportunities
Wasted
Effort
productivity
competeDOING MORE WITH WHAT YOU HAVE
Don’t forget
Data Quality
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Observe Orient Decide Act
Lets talk about Decision process…..
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Analytics Changes the Process productivity
compete
Observe
Observe
COMPETITIVE
ADVANTAGE
Orient
Orient
Decide
Decide
Act
Act
BUSINESS
OPPORTUNITY
Streamlined processes
Decision driven steps
Data and process aligned
Moving to Exception Management
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Behavioral data
- Orders
- Transactions
- Payment history
- Usage history
Descriptive data
- Attributes
- Characteristics
- Self-declared info
- (Geo)demographics
Attitudinal data
- Opinions
- Preferences
- Needs
- Desires
Interaction data
- Offers
- Results
- Context
- Click streams
- Notes
CONNECTED VIEW
Web dataUp to 20%
better
predictions
Text data
Up to 40%
better
predictions
AttitudesUp to 30%
better
predictions
productivity
compete
Enterprise
Data
Sources
Marketing
Attitudinal
Interaction
Web
Call-center
Operational
Partner data
productivity
compete
Customer
Contact
Channels
Website
Phone
Branch
ATM
B2B
Agent
Mobile
…
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NEW BUSINESS
My data
Open data
Your data
Integrated Data
Transparency
Security
Health of business
Provenance
Exploitation Process
ValueAnalysis
Rules
Analytics
AlertAdministration
BusinessRules
Analytics
AnomalyDetection
PredictiveModeling
Case Management
Management &BI / ReportingLearn and
Improve
Cycle
New
Application Services
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OPERATING MODEL
CEO
Function Function FunctionFunctionCEO
Function Function FunctionFunctionCEO
Function Function FunctionFunction Function
Leading analytics CMO + CIO + CPO + COO £
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Communication and Collaboration
Analytics
Value
Data Support Services Methods
Tools
Predictive and
Scenario Analysis
Culture
Enabling Infrastructure
Interpretating
data
Data as a Service
Competence Centre
Learning and
development
Analytics as a
Service
Taking Action
Decision Making
Integration
Mindset and Business
transformation
Action Performance and Assessment
Governance and Analytic Realisation
Kno
wle
dge a
nd C
og
nition
BIG DATA FRAMEWORK
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Communication and Collaboration
Analytics
Value
Data Support Services Methods
Tools
Predictive and
Scenario Analysis
Culture
Enabling Infrastructure
Interpretating
data
Data as a Service
Competence Centre
Learning and
development
Analytics as a
Service
Taking Action
Decision Making
Integration
Mindset and Business
transformation
Action Performance and Assessment
Governance and Analytic Realisation
Kno
wle
dge a
nd C
og
nition
Discovering
DiamondsProwess
Realising
Value
Developing
Capability
BIG DATA FRAMEWORK
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ANALYTICS SUPPORTING PRICING
Business as Usual
Prices are set regionally or by products
Promotional pricing offered on new term deposits
When promotional pricing lapses Some customers leave
Other roll-over their deposits
Promotional and go-to prices vary significantly Across regions
Over-time
Relative to competition
Analytic pricing
Statistically predict customers sensitivity by product by price to pricing strategy
Target the right price for the customer
Consciously manage the fund for customer retention
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MARKET BASKET ANALYSIS
Classical Advanced next product to Buy
Basket = Collection of Customer Specific data that may include:
Socio-demographics
Product portfolio
Transactional Behaviour
Contact history
Debt and payments history
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Revenue 5-15% overall revenue increase
Engagement 12 – 18% of visitors engaged with product recommendations
Average Order Value 30-70% increase – visitors who engage with recommendations
Conversion Rate 2-4x increase – visitors who
Saving Staff Time Elimination of manual Content Management effort
Items per Order 20-40% increase- visitors who engage with recommendations
RECOMMENDATION ENGINES
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CROSS CHANNEL INTEGRATION
Multiple customer touch points
each with its own
Infrastructure,
islands of data,
isolated points of knowledge.
Brings Diverse Data sources together
Organises into meaningful customer journeys
Non standard business process
Service and dispute resolution
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The main challenge :
Which offer should be made to the customer through which channel at which time?
However you have to take care of:
Budget- and/or Resource restrictions
Limit of customer contacts (Customer Contact Strategy)
Strategic Changes (You have to push Product A!)
Unsatisfying response- or sales figures or unbalanced channel-usage
Customer buying practice
Customers sensitivity to price
CROSS CHANNEL INTEGRATION – VALUE
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TOOLS
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Forecasting Models
How many products will be sold this year, next year?
How does this break down into each product over the next 3 months, 6 months?
Some low hanging questions? productivity
compete
Data Mining Models
Which products are customers likely to buy?
Which workers are likely to quit/resign/be fired?
Text Models
What are people saying about my products and services?
Can I detect emerging issues from customer feedback or service claims?
Operations Research
What is the optimal inventory and stock to hold for each product to minimize out of stock and overall holding costs?
What is the least cost route for transporting goods from warehouses to final destinations?
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IBCV -Development Ideas
ReasoningServices
Intelligent Interface Services
Colla
bora
tion to
ols
and s
erv
ices
Information Services: Dashboard/scorecard
Resource Analytic Services(workforce, goods, Compute
power, capacity, growth)
Knowledge Services
Domain Analytic
Services (CRM, supply
chain, operations)
Aggregated Data Services (Data Warehouse)
Analysis Services and Rules
Data Integration Services
BI Environment
Reporting Services
Action Services
Control ServicesPrediction and forecast
Modelling
Analytic tools and Decision Services
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Data
Repositories
Production
Reports/
Analysed
Data
Flow of data into/out of Analytics world
IT Data
Management
77%
20%
Recipients
Analytics
Application
support
Self
Service
Reporting
Data Quality &
Transformations
Decisioning
Knowledge
Analytic Data
Model
RepositoryInfo
rma
tion D
elive
ryTxn DW
WIP
Source & reference
systems
Audit and Security
Management
DBA
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21 FEB 2007
Appliances
Structured
SAP
UNIX
Windows
Semi-
Structured
Other
Sources
Un-Structured
Real Time
High Volume Batch
Web Services
Message Queues
Source
systems
User
community
OLAP
Fact data
store
Data
mining
Mart
Text
mining
mart
Forecasting
mart
Solution
mart
Analytical and
Reporting
Data structures
Analytics and reporting
Exploitation
Framework
Reporting
Data mining
Text mining
Forecasting
Social network
analysis
Ad-hoc analysis and
investigation
Data Integration &
DQ
Visualisation
Model management
BI Development
Administration
Content categorisation
Data Acquisition
Re-use (ETL)
Data Q
uality & integrity
Transform
ations
Enterprise
Data
Warehouse
Enterprise
Data
Warehouse
EDW
Analytical
Structures
Operational Rules
Data
Services
(reusable)
Data Linkage
Data P
atterns
Provenance
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NEXT STEP FOR THE CMO
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TO DO LIST360° view of the Customer by moving from product-centric silos to a customer-centric view through integrated data
Integrate Internal Data with External, Unstructured Data (geo-spatial, text, social media, and voice data) to better visualize your customer and employ predictive analytics to prepare a marketing strategy that correlates to their anticipated future behaviour
Identify and Retain Your Most Valuable Customers through improved analytics and find out how to proactively engage with customers to reduce costs, optimise the customer wallet, boost retention and increase market share
Create an Omni-Channel Platform that Feeds in to an Internal Data Dashboard that feeds customer data live to all areas of the business and allows for an improved customer experience across all online and offline touch points
Maxmise Product Profitability, Real-Time Pricing and Customer Value through customer analytics to pinpoint, optimize, and execute real-time customer interactions
Dramatically Reduce Marketing Costs through Segmentation to create more targeted customer acquisition, a better understanding of channel preferences and company-wide response strategy based on transactional behaviour and social profile
Establish strong brand loyalty and move from product-centric to customer-centric thinking
Commercialise Your Customer Analytics Business Plan to ensure consistent buy in from the board room to feed analytics in to every part of your workplace and all customer touch points
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EXPLOITING YOUR ANALYTIC CAPABILITY
Is Big Data relevant ?
How to Exploit ?
What capabilities
?
How to Transform
?
ROAI
Roadmap
Investment plan
Talent
Risks
Third Party
Enduring?
Endearing?
Opex costs
Analytics
Technology
Innovate?
BAU?
Current issues?
Strategic?
Supplier?
Partners?
Governance?
R&D or Focus?
Mindset?
Value?
Impact
Capabilities:
• Business
• Functional
• Technology
• People
Gaps?
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DATA BUSINESS VALUE INVESTMENT FRAMEWORK
42
0Business Value
+-
+
0Data
Effectiveness
-Requires
incremental
budget
Necessary,
but low
value
Creates
resistance
from line of
business and
users
Improved Data
effectiveness
with no
business value
penalty
Improved
business value
with little or no
data
effectiveness
penalty
Improved
Data Driven
Value
Failure
Failure
Failure
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MATURING CAPABILITIES
Data –
Value
Drivers
Risk &
Governance
Competitive
Value
Effectiveness
Inhibiting
competition
Access new
Markets
Manage
Margin
Grow Sales
Simplification
Benefits Matrix
Lea
din
g
Emerg
ing
Sta
ndard
Nece
ssity
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(a) (b)
Figure 1: (a) the data scientist (Conway, 2011); (b) business analytics (Robinson, 2014)
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BUSINESS ANALYTICS ECO-SYSTEM
Data assets
Analyticsstrategy
Value propositions
ICT strategy
HR strategy
Business strategy
Vidgen, R., (2014). Creating business value from Big Data and business analytics: organisational, managerial and human resource implications. Hull University Business School Research Memorandum, no. 94, ISBN 978-1-906422-31-8.
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QUESTIONS
Questions?
….
Eur. Ing. John Morton BSc, CEng. FBCS, CITP, MIoD
+44 7771740203
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