if you can’t tell the story, you can't justify the investment

1 © Copyright 2015 EMC Corporation. All rights reserved.

IF YOU CAN’T TELL THE STORY, YOU CAN’T JUSTIFY THE INVESTMENT.

© Copyright 2015 EMC Corporation. All rights reserved.

SEBASTIAN DARRINGTON CTO - BIG DATA AND ANALYTICS, EMEA @THESTORAGECHAP

2 © Copyright 2015 EMC Corporation. All rights reserved. 2 © Copyright 2015 EMC Corporation. All rights reserved.

8 REASONS BIG DATA PROJECTS FAIL

1. SELECTING THE WRONG USECASES

2. MANAGEMENT RESISTENCE

3. ASKING THE WRONG QUESTIONS

4. LACKING THE RIGHT SKILLS

5. UNANTICIPATED PROBLEMS

6. DIASGREEMENT ON ENTERPRISE STRATEGY

7. BIG DATA SILOS

8. PROBLEM AVOIDANCE


1. Discovery

2. Data Preparation

3. Model Planning

4. Model Building

5. Communicate

Results

6. Operationalise

Do I have enough information to draft an

analytic plan and share for pier review?

Do I have enough quality data to start building the model?

Do I have a good idea about the type of model to try? Can I refine the

analytic plan?

Is the model robust enough? Have we failed

for sure?

ANALYTICS LIFECYCLE


2. Discovery

3. Data Preparation

4. Model Planning

5. Model Building

6. Communicate

Results

7. Operationalise

Do I have enough information to draft an

analytic plan and share for pier review?

Do I have enough quality data to start building the model?

Do I have a good idea about the type of model to try? Can I refine the

analytic plan?

Is the model robust enough? Have we failed

for sure?

1. Explore the Business Context

and frame the Business Outcome

Collaborate Throughout

ANALYTICS LIFECYCLE


IT ALL STARTS WITH THE BUSINESS OUTCOME

INCREASE SALES

KNOW YOUR

CUSTOMER

REDUCE COST

IMPROVE

EFFICIENCY

MINIMISE RISK

FINANCIAL, OPERATIONAL,

COMPLIANCE


FSI RETAIL MANUF TELCO UTILITIES TRANSPORT HEALTH

360 CUSTOMER VIEW

✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️

SENTIMENT ANALYSIS

✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️

RECOMMENDATION ENGINE

✔ ️

✔ ️

✔ ️ ✔ ️ ✔ ️

REALTIME INTELLIGENCE

✔ ️ ✔ ️ ✔ ️ ✔ ️

PREDICTIVE MAINTENANCE

✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️

STREAMING DATA ANALYSIS

✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️

SECURITY INFORMTION AND EVENT MGMT

✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️

SCORING/RISK ASSESSMENT

✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️

COMPLIANCE MANAGEMENT

✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️ ✔ ️

IN

CR

EA

SE

SA

LES

D

EC

REA

SE

CO

ST

M

IN

IM

IS

E

RIS

K


HEALTHCARE ANALYTICS

10 © Copyright 2015 EMC Corporation. All rights reserved. Confidential – internal use only

15% REDUCTION IN DEATH RATES



The RTI-T platform uses a flexible, customizable data architecture.

Streaming Data Grid - Collecting events to trigger action; collecting from mobile network, social network, mobile apps, and other third party sources

Dynamic Customer Profile – Composite view of customer profile to reflect customer behavior, likes, dislikes, and appetite for promotions and interaction; sources include customer DW from historical information, internal customer account data from CRM, Service Assurance, & Billing, and from external customer data sources such as Facebook, twitter, etc.

Business Rules & Policies – The criteria of events and customer information that should constitute the triggering of an action. Part of CEP functionality with inputs/updates from sources like a rules engine, product & service catalog, and policy management systems Business Rules Update – User interface to define and update business conditions

Event Correlation – Complex Event Processing function that correlates incoming events with customer profile to triggers action when the business rules & policies have been met; used to trigger real-time marketing promotions, dynamic network capacity provisioning, or other actions; Also feeds into Customer DW for updates

Historical Data DW – Collected historical information on customer behavior for big data analytics on customer patterns to be used to define and update customer profile and gain customer insight

Customer Profile DW – Data Warehouse for determining customer profile based on historical information on behavior, spending patterns and scoring pattern for appetite for customer interaction and promotions

A Reference Architecture - Real-time Intelligence


BUSINESS BENEFIT

TECHNOLOGY VS



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ADDITIONAL INFORMATION

EMC BIG DATA LANDING PAGE http://www.emc.com/bigdata

PIVOTAL POWERS DIGITAL TRANSFORMATION http://www.pivotal.io

SEBASTIAN DARRINGTON @TheStorageChap https://uk.linkedin.com/in/sebastiandarrington http://www.thestoragechap.co.uk

if you can’t tell the story, you can't justify the investment

Data & Analytics