What do Analytics, Tom Cruise and Bob Dylan have in Common?

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What do Analytics, Tom Cruise and

Bob Dylan have in Common?

Presentation for Assimil8 September 2015

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Objectives

Analytics and Data in Context

Trends

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Economy

Competition

Customers

Regulation

Risk

Growth

Distribution

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A World of Increasing Complexity

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Mobile

Social

Cloud

Analytics

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Volume

Velocity

Variety

Veracity

Big Data: Fueling the Enterprise

The characteristics of big data: the 4V’s

Data at Rest Data in Motion Data in Many Forms

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Business

Value

Descriptive

Predictive

Prescriptive

Cognitive

Reporting

Last quarter’s results Analysis Product profitability

Discovery

# security breaches this month vs. last

Alerts

Unusual activity

Forecasting

Trending analysis

Simulation

Impact of rising rates

Modeling Predicting elasticity of insurance rates Optimization Highest returning portfolio based on risk appetite Stochastic Optimization Managed exposure to Cat Risks Understands natural language, hypothesizes, adapts & learns

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Cognitive Computing?

Cognitive computing and cognitive based systems accelerate, enhance and scale human expertise by:

–

Learning

and

building knowledge

,

–

Understanding natural language

and

–

Interacting

more naturally

with humans

than

traditional programmable systems

käg-nə-tiv (adjective): of, relating to, or involving conscious mental

activities (such as thinking, understanding, learning, and remembering)

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2014 - Four Transformative Shifts

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2

3

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A solid majority of organizations are now realizing a

return on

investments within a year

Customer centricity still dominates analytics, but organizations

are increasingly targeting

operational

challenges.

Integrating

digital

capabilities into business processes is

beginning to transforming organizations

The value driver for big data has shifted from

volume to

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Shift 1:

Most organizations get a return on their

analytics investments within the first year

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Return on investment period

of surveyed organizations realize a positive return on their analytic investments

within one year

63

%

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Shift 2:

Customer centricity still dominates analytics

but operational improvement is closing

are using

data and analytics to improve customer

acquisition

are using

data and analytics to improve customer

experience

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Organizational objectives

for use of data and analytics

22 %

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Shift 3:

Integrating ‘digital’ into business processes

is transforming organizations

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Shift 4:

A shift from volume and variety, to velocity

and veracity

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Data in many forms

Variety

Data at speed _Velocity Data at scale

Volume

Data as trustworthy _Veracity

4 Vs of big data

2012

2014

Scalable / extensible infrastructure Scalable storage infrastructures enable larger workloads High-capacity warehouses support the variety of data

Data integration topped the data priorities of most organizations

Agile and flexible infrastructure

 Big data landing

platform expands the structured and

unstructured data available for usage  Real-time analysis

processing enables ‘in the moment’ actions  Trustworthiness is

now the top data

priority across majority of organizations

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The are 4 Types of Organisations

Using analytics to drive business processes within most business functions Using advanced analytics technologies to manage the volume, velocity and variety of data available with agility and speed.

Using analytics to drive or inform business processes within multiple business functions Still building an integrated enterprise foundation for analytics, Using analytics to automate and optimize operations Using real-time analysis, and integrated and shared operational data, while piloting a wide variety of advanced

components;

Using only the

bare minimum of

analytics within business

processes, yet have aspirations

Few have the technical capabilities to support analytic capabilities beyond basic reporting and compliance levels 10% 14% 45% 31%

Front Runners

Joggers

The Pack

Spectators

Descriptions of the speed-driven clusters

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Front Runners Outpace the Rest of the Field

of Front Runners created a

significant positive impact on business

outcomes using data

and analytics in the past 3 years

significant positive impact on revenues

using data and analytics in the past three years

significant competitive

advantage using data

and analytics

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69 %

60 %

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Successful Organizations have 3 key capabilities

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

Acquire

a diverse dataset and manage it for speed



Analyze

a robust and unique dataset and rapidly create

meaningful insights



Act

on data insights

quickly to achieve targeted business outcomes

Note: Chart only shows percentage of respondents who indicated (4) Well or (5) Very Well

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Source and manage data in ways that create flexibility

and agility in how and when the data is used

Blend traditional data infrastructure components with newer big data sources

Use real-time data processing and analysis to act in the moment

Implement information governance to accelerate trust, integration and standardization within their data environments

Key capability characteristics

to acquire and manage data with speed

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2

3

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Analyze diverse datasets to create more meaningful insights

Use advanced analysis tools

Develop talent which combines business knowledge with analytics

Create meaningful insights and quickly analyze

robust datasets

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2

3

Key capability characteristics to accelerate the analysis of data

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Integrate digital and process transformations to quickly give insight that drives rapid business outcomes

Embed analytics within business processes to enable precise, quick actions

Use comprehensive visualisation to quickly understand and act on large or dynamic datasets