BUILDING THE BUSINESS CASE

FOR WHY YOU NEED TO INVEST

IN YOUR DATA

HEALTHCARE DATA GOVERNMENT & EDUCATION DATA

99% 88% 94% 84%

Healthcare data comes from sources such as:

Government & education data comes from sources such as:

Patient

SensorsElectronic

Medical

Records

Test

Results

Vehicle Fleet

Sensors

Traffic

Sensors

Student

Evaluations

UTILITIES DATA MEDIA DATA

93% 84% 97% 82%

Utilities data comes from sources such as:

Media data comes from sources such as:

Utility

Sensors

Employee

Sensors

Location

Data

Video

and Film

Images Audio

growth by 2017 unstructured growth by 2017 unstructured

growth by 2017 unstructured growth by 2017 unstructured

Data flows from every device, replacing

guessing and approximations with precise

information. Yet 80% of this data is

unstructured; therefore, invisible to

computers and of limited use to business.

By 2020,

of new information will be created

every second for every human

being on the planet.

1.7 MB

IBM Analytics Campus

Consider:

The world is being rewritten in software

code, and cloud is the platform on which

the new digital builders—from developers

to business professionals—are

reimagining everything from banking to

retail to healthcare.

100,000,000lines of codein a new car

5,000,000lines of codein smart appliances

1,200,000lines of codein a smartphone

80,000lines of codein a pacemaker

IBM Analytics Campus

Consider:

of B2B

collaboration

will take place

through web

APIs next year.

50%

Smart TVs

represented 27% of

all TV sales in 2012;

by 2018, they will

represent 82%.

Smart LED lighting

will grow from 6M

units in 2015 to 570M

units in 2020, used for

safety communication,

health, pollution and

personalized services.

By 2017, there will be

1B connected things

in smart homes,

including appliances,

smoke detectors and

cameras.

Sensors for industrial asset

monitoring and management will

grow from just over 15M units in

2014 to over 40M units in 2018

Smart traffic sensors

and other devices

installed in smart

cities will grow from

237M units in 2015 to

371M in 2017.

Revenues for

smart grid sensors

will grow ten-fold from

2014 to 2021.

Code Tools

Analytics

Data

APIs

By 2020, there will be

925M smart meters

installed worldwide,

more than double the

400M in 2014.

So where do we want to go from here?

Predictive Prescriptive Cognitive

Cognitive systems can understand the world through sensing and

interaction, reason using hypotheses and arguments and learn from experts

and through data. Watson is the most advanced such system.

Today, businesses in

countries across.

There are

Watson ecosystem partner

companies, with

78%of business and IT executives

believe that successful business will

manage employees alongside

intelligent machines.

On average there are

36

17industries are applying cognitive

technologies.

350+

100of those have taken their product to market.

1.3BWatson API calls a month

and growing.

Among C-Suite executives familiar

with cognitive computing:

96%

84%

94%

89%

in insurance intend to invest in cognitive

capabilities.

in healthcare believe it will play a

disruptive role in the industry, and 60%

believe they lack the skilled professionals

and technical experience to achieve it.

in retail intend to invest in cognitive

capabilities.

in telecommunications believe

it will have a critical impact on the future of

their business.

IBM Analytics Campus

Consider:

IBM Analytics Campus

Understands

natural

language

Generates

and

evaluates

hypotheses

Discover

Adapts

and learns

Watson is

COGNITIVE

The Watson you know that won Jeopardy!

is now an IBM brand for a next generation

of cognitive computing solutions.

IBM Watson

To focus on bringing Watson to market, three

key areas were identified:

• Natural language processing

• Hypothesis generation

• Evidence-based learning

Trained using artificial intelligence (AI) and

machine learning algorithms to sense, predict,

infer, and in some ways, think.

Watson solutions aim to enhance, scale, and accelerate human expertise,

targeting a wide range of complex challenges

Natural Language

Discover

Learn

Understand

Confidence

Relationship

Extraction

Questions

&

Answers

Language

Detection

Personal

ity

Insights

Keyword

ExtractionImage Link

Extraction

Feed

Detection

Visual

Recognition

Concept

Expansion

Concept

Insights

DialogSentiment

Analysis

Text to

Speech

Tradeoff

Analytics

Natural

Language

Classifier

Author

Extraction

Speech to

Text

Retrieve

&

Rank

Watson

News

Language

Translation

Entity

Extraction

Tone

Analyzer

Concept

Tagging

Taxonomy

Text

Extraction

Message

Resonance

Image

Tagging

Face

Detection

Answer

Generation

Usage

Insights

Fusion

Q&A

Video

Augmentation

Decision

Optimization

Knowledge

Graph

Risk

Stratification

Policy

Identification

Emotion

Analysis

Decision

Support

Criteria

Classification

Knowledge

Canvas

Easy

Adaptation

Knowledge

Studio Service

Statistical

Dialog

Q&A

Qualification

Factoid

Pipeline

Case

Evaluation

The Watson that competed on

Jeopardy! in 2011 comprised what

is now a single API—Q&A—built

on five underlying technologies.

Since then, Watson has grown to

a family of 28 APIs.

By the end of 2016, there will

be nearly 50 Watson APIs—

with more added every year.

Natural Language

Processing

Machine Learning

Question Analysis

Feature Engineering

Ontology Analysis

IBM Analytics CampusIBM Watson

So where are we all right now?

Structured

Semi-structured

Unstructured

• Unfortunately, 40% to 50% of data warehouse initiatives end in costly failure.

International Association for Computer Information Systems

• Gartner estimates in 2012 that by 2014, fewer than 30% of business intelligence

(BI) initiatives will align analytics completely with enterprise business drivers,

despite alignment being the foremost BI challenge

• LinkedIn poll in 2013 asked BI experts how much of a BI project was spent in just

getting the data right. The answer came back as around 75–80%.

• Data warehouses are replaced on average every 3–5 years!

Statistical overview

What if we could increase and improve each of these?

Structured

Semi-structured

Unstructured

Draw out textual content and classify

» Pull transaction account and card credit and debit text

» Separate known payments for own account to account

» Draw out payees via Bpay, address book

» Run through pattern matching; sidefile suburb and country

» Run through textual analysis against brand and industry word lists

» Tokenise textual and geo to append

- Location

- Spend product area (restaurant, grocery, fitness, etc.)

- Spend frequency

And build real insights?Build Metadata from transactions

Identify and segment credits and

debits to identify:

» Budgeted spend

» Discretionary spend

» Habits and hobbies

So we need a new architecture

and a new approach

IBM Analytics CampusDifferent approaches require different systems

Systems Security

On premise, Cloud, As a service

Storage

IBM Watson Foundations

IBM Big Data & Analytics Infrastructure

New/Enhanced

ApplicationsAll Data

Real-time analytics zone

Enterprise warehouse data mart and analytic appliances zone

Information governance zone

Exploration, landingand archive zone

Information ingestion and operational informationzone

What could happen?

Predictive analyticsand modeling

What action should I take?

Decisionmanagement

What is happening?

Discovery andexploration

Why did it happen?

Reporting, analysis,content analytics

CognitiveFabric

• Capturing any type of information, fast/slow, big/small, structured, unstructured, text/images etc

• The ability to visualise, report. predict and action

BUT to make this a reality we need to apply:

• Retained auditability/integrity

• Flexible Access

• Appropriate security

• Single view of an entity

• Just Enough Governance,

• Just enough modelling,

• Automation

• Avoiding re-engineering,

New Tools & Methods are providing new capabilities to:

• Seamlessly allow SQL and noSQL data integration

• Only 3 ELT patterns to load ALL data into a warehouse

• Avoids all re-engineering

• Enables full automation of changes into data warehouse

• Enables true agile reporting and analytics

• Specific constructs to deal with business rules

• Retains auditability and security

• Linear scaling in process and development

Methods like DV that:

Operational Data Store

Bring data together in real time.

Create an alternative to querying operational

databases.

Make operational data easier to access and use.

Time Machine

Go back to any point in time and re-run a query.

Troubleshoot old decisions made on old report.

Analyse change over time.

Semantic Layer

Store data so that reporting and analytic tools can use it.

Model and name information so it can be understood

and used safely.

Querying a Vault

Product Customer Location

Hub Product

Hub Customer

Hub Location

Product to

Customer

Customer to

Location

Current Project + Section + Item

Time Machine Project + Section + Item

Filter on End Date

= High Date

Between

Date Filter

IBM Analytics CampusInformation integration and governance

New/Enhanced

ApplicationsAll Data

Information Integration and Governance

Information Governance and compliance reporting

Data Quality

Entity Management

Information Lifecycle

Management

Information Integration

On-Premises, As-A-Service (IBM DataWorks)and Cloud (SoftLayer)

Unlocks key insights, by providing trust and comprehensive view

IBM Analytics CampusMDM is essential

IBM Analytics CampusCertus Data Quality Framework

Implementing a Total Information Quality Management System across People,

Processes and Tools to create a continuous data quality improvement culture.

Data Quality Management is Essential

Pass Fail rate Potential Cost of Failures Cost to fix errors

Errors by

data domain

and quality

category

Errors by

criticality

A Stewardship Function is Essential

Business Process Management

for manual and automated

workflows required to implement

information governance and

manage data quality.

Governance can be justified by efficiency and avoiding rework but looking at the

impact of the cost of data quality is better.

Single entity view is boring plumbing that no one wants to do but if you look at what

you can achieve with a true single view of customer/patient/asset/product the effort

is worth it and the payoffs immense.

A Data Warehouse refresh is painful process and has a poor history however to

take advantage of new opportunities, markets or ways of doing business you need

to embrace more information. The justification for the effort is helped by the fact that

the new DW should be flexible to cater for a huge number of uses, automated so

maintenance is minimal, and designed to last and cost a fraction of what was spent

on the legacy DW.

So back to the business case:

Becoming an analytically driven or

cognitive business is a journey.

Businesses will be able to rapidly capitalize

on new opportunities if they have invested

in the foundations of their information

management systems.