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

01/03/2017

Copyright © 01/03/2017, Computers, Processes and Management Limited.. All rights reserved.

BIG DATA HYPE OR, SOMETHING REAL?

…….. and more….. 01/03/2017 7


….OR THEORY?

Velocity

Variety

Volume

Gartner 2000

01/03/2017 8

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

01/03/2017 9


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

01/03/2017 10


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 *

01/03/2017 11


Relative

contribution

to GDP

Is Big Data for everyone?

Source : US Bureau of Labor Statistics;

McKinsey Global Institute Analysis

01/03/2017 12


ABOVE: Types of industry sectors where companies are using open data in their businesses, sourced

from OpenData 500

01/03/2017 13

http://www.opendata500.com/stats/


HAVE THEY MISSED THE BOAT?

34% of respondents

were drawn from

North America, 43%

from Europe

01/03/2017 14


SO WHAT ABOUT TECHNOLOGY?

My data

Open data

Your data

Integrated Data

01/03/2017 15

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


BIG DATA DISRUPTION

01/03/2017 18


STAYING IN BUSINESS

productivityStay

My data

Integrated Data

Transparency

Security

Business Health

Provenance

Compliance

Governance

Business Improvement

01/03/2017 19


Missed

Opportunities

Wasted

Effort

productivity

competeDOING MORE WITH WHAT YOU HAVE

Don’t forget

Data Quality

01/03/2017 20


Observe Orient Decide Act

Lets talk about Decision process…..

01/03/2017 21


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

01/03/2017 22


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

Email

Phone

Mail

Branch

ATM

B2B

Agent

Mobile

…

01/03/2017 23


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

01/03/2017 24


OPERATING MODEL

CEO

Function Function FunctionFunctionCEO

Function Function FunctionFunctionCEO

Function Function FunctionFunction Function

Leading analytics CMO + CIO + CPO + COO £

01/03/2017 25

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

01/03/2017 27

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

01/03/2017 28


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

01/03/2017 29


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

01/03/2017 30


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

01/03/2017 31


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

01/03/2017 32


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


TOOLS

01/03/2017 34


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?


21 FEB 2007 36

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


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


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


NEXT STEP FOR THE CMO

01/03/2017 39


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

01/03/2017 40


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?

01/03/2017 41


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

01/03/2017


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

01/03/2017 43


(a) (b)

Figure 1: (a) the data scientist (Conway, 2011); (b) business analytics (Robinson, 2014)

01/03/2017 44


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.

01/03/2017 45


QUESTIONS

Questions?

….

Eur. Ing. John Morton BSc, CEng. FBCS, CITP, MIoD

[email protected]

+44 7771740203

01/03/2017 46

mailto:[email protected]

big data analytics: changing john morton data science …mas01ds/dsmsc/dsrt/goldsmiths college...

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