Becoming a data driven organisation

By Patrick Bryan

How do leaders describe data….

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• Being data-driven is the art and ability to leverage all business assetsto exercise judgment in the decision process – Stephane Hamel (Data & Analytics Leader)

• Data Driven Analytics is the mathematics which is the pattern of discovery & anomaly detection - Sammy Haroon (Director Enterprise Data Analytics, Baker Hughes, Inc.)

• Data is a raw material to be mined – Steven Adler (Chairman, Data Governance Council, IBM)

• Data is rapidly becoming a Business Issue, a Business Strategy Issue –Chris Mazzei (Chief Analytics Officer, EY)

• The goal is to move the company to be more insight driven – Hima Patel (VP

Enterprise Integration & Customer Collaboration, Merck)

Data Confidence

33

How bad can it be?

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Everyone's view of data is different

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• A man goes into a jewellers and buys an expensive watch …• Is it fraud – in which case the bank must stop it

• Is it money-laundering – in which case the bank must report it

• Does he have expensive tastes – in which case perhaps he would be interested in a loan?

• Has he just won the lottery – should the bank improve the services offered?

Threat

Obligation

Opportunity

Opportunity

How do we change?

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• Grow a data driven culture…can I change how data is used?

• Governance, Integration and Quality

• Performance

• Analytics

Focus points for a Data and Analytics Driven Organisation

Gartner’s key to establishing a data driven organization

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1

CDO/CIO Mobile/Web App DevelopersBusiness Users

Develop a curiosity

driven workforceMove from elite few to

empowered many

Imagine what’s possible!

Lead a data-driven

transformation

Fuel their curiosity

and creativity

Innovate faster

and scale securely

An example transformation

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1 THE ENTERPRISE DATA WAREHOUSE

An example transformation

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1• Time to deliver business value down from 6 months to 2 months

• Efficiency up 20%

• Daily cost of teams down 40%

• Feature delivery cost down 30%

• 100% of features delivered on time and on budget

• Happy Stakeholders (49% - 20% = NPS 29 )

• Happy Teams (65% - 22% = NPS 43)

The Results

How likely are you to recommend us to a colleague?

How do we change?

1010

• Grow a data driven culture

• Governance, Integration and Quality… can I trust it ?

• Performance

• Analytics

Focus points for a Data and Analytics Driven Organisation

Data governance in action

1111

Canadian National Railway Company – Establishes data governance to

improve decision making

Establishes governance providing an unambiguous way to process and share

information

"IBM empowers users with information so that they can work more autonomously."

— Philippe Chartier, BI Team Lead, Information Delivery,The Canadian National Railway Company

Standardizes terminology into market share, including specific areas of strength

and weakness as compared to competitors

Accelerates analyticsto deliver faster insight from trusted data

The transformation: Standardized its metadata and business

terminology across the enterprise, creating a company-wide

glossary and a universal data model to improve decision-making.

Governance, Integration and Quality

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Information Governance Catalog

Understand & Collaborate

•Catalog technical metadata &

align w/ business language

•Manage data lineage

DataQuality

Cleanse & Monitor

•Analyze & validate

•Cleanse & standardize

•Define, manage & monitor

data rules + exceptions

DataIntegration

Transform & Deliver

• Provide scalability

• Power for any complexity

• Deliver in batch and/or real-time

Information

Governance

Data

IntegrationData

Quality

How do we change?

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• Grow a data driven culture

• Governance, Integration and Quality

• Performance…. can I make it faster ?

• Analytics

Focus points for a Data and Analytics Driven Organisation

Increased performance grows revenue and reduces costs

1414

Carphone Warehouse increases profitability through new revenue streams

& reduced costs

Up to 1200X faster performance; reports that once took an hour to run now take seconds

“IBM provided huge technical advantages & big business advantages. We can now insure devices on behalf of a bank in the UK, which we couldn’t have done before.”

— Paul Scullion, Head of Business Intelligence

50% reductionin time to market for new business intelligence

services

Increased performance grows revenue and reduces costs

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Appliances for AnalyticsSpeed - 10-100x faster than traditional custom

systems1

Simplicity - minimal administration and tuning

Scalability - petabyte+ scale user data capacity

Smart - high performance, advanced analytics

1 Based on IBM customers' reported results. "Traditional custom systems" refers to systems that are not professionally pre-built, pre-tested and optimized. Individual results may vary.

Purpose-built analytics appliance

Integrated database, server and

storage

Standard interfaces

Low total cost of ownership

Try it with your dataBring your data to our POC server

Bring our POC Server to your data

How do we change?

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• Grow a data driven culture

• Governance, Integration and Quality

• Performance

• Analytics….. how do I do it better !

Focus points for a Data and Analytics Driven Organisation

Grow customers using data algorithms

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eHarmony Attracts New Members by Understanding Behavior and Fine-

tuning Matching Algorithm

"Through the entire subscription lifecycle, the company tracks everything members do on the website. This process generates an enormous amount of data, which would be completely wasted without the ability to extract hidden insights about how members behave.”

— eHarmony C-Level executive

96% decreasein query run times (from 1 hour to 2 minutes)

100% increaseinto market in subscriber base

Reduced spendingOn low-return promotional activities

Types of Analytics

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DescriptiveGet in touch with reality, a single source of

the truth, visibility

PredictiveUnderstand the most likely future scenario,

and its business implications

PrescriptiveCollaborate for maximum business value,

informed by advanced analytics

CognitiveDeeply analytical computing systems that learn &

interact naturally with people

What happened?

What will happen?

What should we do about it?

Volume (data at rest)

Velocity (data in motion)

Variety (many forms of data)

Veracity (data in doubt)

Watson Analytics

How do we optimize a dynamic, Data

environment?

How to get started…

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Imagine it. Realize it. Trust it.

Build a culture

that infuses

analytics

everywhere

Invest in a big

data & analytics

platform

Be proactive

about privacy,

security and

governance