disruptors and their applicability to next generation analytic ... · cassandra real time...

Disruptors and their applicability to Next

Generation Analytic Platforms

How to embed disruptors in your business strategy?

October 6th, 2015

Ashish Verma, Hybrid Services and Innovation Leader, Deloitte Consulting LLP

1. An Unprecedented Opportunity

2. The Data Management Life Cycle

3. How disruptors are impacting industries?

4. Organizing to Succeed

Agenda

The market is still

emerging and presents

an enormous

opportunity

While not necessarily new…an unprecedented

opportunity

Evolution not revolution Confluence of advances

lead to enormous

breakthrough potential

Technology disruptors continue to have the impact on

the business of tomorrow; today

Market momentum is rapidly growing :

200+TB of stored data in every sector

60 billion intelligent devices with a forecast of 26 billion

connected devices by 20201

Industry players with their own themes

- Cisco: “Internet of Everything - $14.6 trillion value at

stake by 2022”

- GE: “Industrial Internet + analytics”

- IBM: “Smarter Planet”

Rapidly forming ecosystem offerings and partnerships

due to early stage of maturity

- Cloudera, Intel Partnership, May 2014

- EMC Pivotal along with GE, Intel, Accenture, AT&T,

Cisco. September 2013

- IBM and Technicolor IoT and M2M cloud solution, Jan

2014

- AT&T & Qualcomm to enable and connect consumer

IoT devices, Jan 2014

Real Time Decisioning

Big Data

Cloud

Predictive Analytics

In Memory

Cyber Security and Privacy

Machine Learning

Cognitive Computing

Wearables

IoT

Sources:

1) Gartner, Nov. 2013

Disruptors

Within every organization understanding and applying

disruptors to key Business Triggers is critical to staying

relevant

Disruptor Key Business Triggers Key Technology

Big Data

• Handling data volumes that are more than 10 TB

• Data with a changing structure, or no structure at all

• Very high throughput systems, with millions of concurrent users and

thousands of queries per second

• Business requirements that differ from the relational database model,

for example swapping ACID (Atomicity, Consistency, Isolation,

Durability) for BASE (Basically Available, Soft State, Eventually

Consistent)

• Processing of machine learning queries that are inefficient or

impossible to express using SQL

Hadoop

Cloudera

HortonWorks

IBM Big Insights

Oracle Big Data Appliance

NoSQL Data Stores i.e. MongoDB,

Cassandra

Real Time

Decisioning

• Increase service velocity for the business by embedding analytics into

the operational processes to support frontline decision making based

on real-time events

• Provide a mechanism to route and correlate events in real time even

in scenarios of large volumes of data

Apache Kafka

Apache Storm

Apache Spark

SAP Real Time Offer Management

Oracle Real Time Decisions

Predictive

Analytics

• Predictive techniques enable strategic decision making by providing

future insights based on large volumes of structured and un-

structured data. Examples include forecasting sales effectiveness by

forecasting customer behavior, forecasting product demand, etc.

SAS Predictive Analytics

SalesForce (Analytics) Wave Cloud

IBM SPSS

RapidMiner

Oracle Advanced Analytics

Oracle Visual Analyzer

SAP Visual Insights

R



relevant


Cloud

• Rapid implementation: Less time is required to get up and running

on cloud-based systems

• Cost predictability: Cloud’s pay-as-you-go model makes it easier to

predict IT costs

• Balanced ROI: Cloud delivers a faster return on IT investments,

thanks to accelerated implementation and elimination of upfront

licensing and infrastructure costs

• Agility: Companies can quickly develop and deploy new IT

capabilities and business processes to stay ahead of the competition

and keep pace with changes in the marketplace

• Scalability: Cloud provides a flexible platform that can grow or shrink

as needed, enabling businesses to explore new markets, pursue new

innovations and serve new customer segments

Amazon Web Services

Microsoft Azure

Dimension Data

Google Cloud

IBM Big Insights on Cloud

HP Cloud Analytics

Bluelock

Salesforce.com

Cyber Security

& Privacy

• Threat Awareness: Automated network and malware forensic

analysis are needed, as well as intelligence collection from honeypots

or other ‘baiting’ operations

• Security Intelligence & Event Management Solutions: Detailed

logging and SIEM are also table stakes when it comes to building

advanced cyber-threat management capabilities. The stream of event

data, when combined with internal and external intelligence, can allow

correlation, analysis, and subsequent detection of threats that would

otherwise go unnoticed

• Unstructured and semi-structured inputs and intelligence: Invest

in data collection and analysis solutions — allowing automated

crawling and information parsing.

• Use cyber analytics — linked to threat rosters and known business

risks and fraud issues — to identify potential areas of escalating risk

Identity, Credential, and Access

Management(ICAM) solutions

Security Information & Event

Management (SIEM) solutions



relevant


In Memory

• Reduce total cost of ownership because the shift from physical to

logical reduces the hardware footprint, allowing more than 40 times

the data to be stored in the same finite space

• Thousand-fold improvement in query response times to transaction

processing speed increases of 20,000 times

• Crunch massive amounts of data in real time to improve

relationships with their customers

• In-memory responses are also more predictable, able to handle large

volumes and a mix of structured, semi-structured, and unstructured

raw data

• Operating costs can also be cut both by reducing maintenance

needs and by streamlining the performance of employees using the

technology

Oracle Exalytics In-Memory Machine

SAP HANA

Kognitio

Apache Spark

• Industries wrestling with massive amounts of unstructured data or

struggling to meet growing demand for real-time visibility should

consider taking a look. Cognitive analytics can be a powerful way to

bridge the gap between the intent of big data and the reality of

practical decision making

• As the demand for real-time support in business decision making

intensifies, cognitive analytics will likely move to the forefront in high-

stakes sectors and functions

• It can improve prediction accuracy, provide augmentation and scale

to human cognition, and allow tasks to be performed more efficiently

(and automatically) via context-based suggestions

IBM Watson

Cognitive Scale

Cognitive

Analytics

http://www.deloitte.com/view/en_AU/au/services/consulting/technology/tech-trends-2014/index.htm

Within every organization understanding and applying disruptors to

key Business Triggers is critical to staying relevant


Machine

Learning

• Applications of machine learning vary in complexity, from simplistic

spam filters in emails to more complex forms such as the virtual

employee that can function as a service i.e. desk employee in retail

and customer care operations.

• These applications are aided by technologies such as natural

language processing, voice recognition, handwriting recognition,

image processing, correlation analytics and quantum computing

• A whole range of products and services built on underlying

technology such as IBM’s Watson that can act as ‘Smart Advisors’

Mahout

SAS

R

IoT

• Support sensor driven decision analytics

• Provide product life extension (enabling product upgrades and

enhancements delivered via software commands) and

automated support that significantly reduces costs

• Provide process improvements through continuous precise

adjustments in manufacturing lines

• Optimize resource consumption across networks

Wireless technologies (WiFi,

Bluetooth, RFID)

Sensors

Cloud Storage and Processing

Platforms with Machine Learning and

Advanced Modeling Capabilities

Wearables

• Wearables value comes from introducing technology into previously

prohibitive environments — where safety, logistics, or even etiquette

have constrained traditional technology solutions

• Wearables generate data in real time and intelligently push it to a

devices according to the user’s current context — just-in-time digital

logistics. Such use cases suggest that wearables may be most

valuable in an organization’s operations, rather than in customer-

facing applications

• Wearables can be the first seamless way to enable workers with

digital information — especially where hands-free utility offers a clear

advantage. Using wearables, workers in harsh environmental

conditions can access data without removing gloves or create records

without having to commit data to memory and then moving to

sheltered workstation

Google Glass

mHealth

Fitness & Activity trackers

Smartwatches





Agenda

The Data Management Life Cycle from provisioning and

storage of data to delivery of insights

Common Data Acquisition Single source to acquire and cleanse structured and unstructured data

Common Data Services Services to manage master data, including quality, security, privacy, and lineage

Data Management Data stores, repositories, and provisioning points to supply clean data for processing

Business Semantic Layer Logical and physical representations of information in meaningful ways for end users

Business Intelligence User access to primarily structured data

for operational and management

reporting, and discovery

Performance Management Business performance, planning,

forecasting, consolidation, and strategic

scorecards

Analytics Descriptive, diagnostic, predictive, and

prescriptive analytical insights

Visualization User access to information in alternate

ways to ease understanding and action

Infr

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Reference Data Structured Data Unstructured Data

Knowing where disruptors apply impacts your choice

INFRASTRUCTURE ANALYTICS VISUALIZATION WORKFLOW

BUSINESS INTELLIGENCE PERFORMANCE MANAGEMENT

DATA PROVISIONING & EXCHANGES

DATA PLATFORMS

DATA SERVICES

COMMON DATA ACQUISITION

BIG DATA

PREDICTIVE ANALYTICS

MACHINE LEARNING

COGNITIVE

ANALYTICS

NLP & TEXT ANALYTICS

REAL TIME DECISIONING

CROWDSOURCING

CLOUD

VISUALIZAZTION

DIGITAL

BIG DATA

INTERNET OF THINGS

DIGITAL

CROWDSOURCING

WEARABLES

CROWDSOURCING CLOUD

CLOUD CYBER SECURITY & PRIVACY

BIG DATA CLOUD CORE RENEWAL IN-MEMORY

CLOUD

SOCIAL WEARABLES INTERNET OF THINGS

CLOUD

BIG DATA

CORE RENEWAL

IN-MEMORY

COGNITIVE ANALYTICS



AMPLIFIED INTELLIGENCE


CYBER SECURITY & PRIVACY

COGNITIVE ANALYTICS

MACHINE LEARNING


BIG DATA

BIG DATA

BIG DATA

Our hypothesis is understanding the problem type tied to

data constructs of variety, volume and velocity directs the

technology choice and not the other way round

Structured Low

Batch

Near Real Time

Real Time

Traditional Data Warehouse/Analytical Applications

MPP

Massively Parallel Processing

Technology Variety Volume Velocity

Le

ga

cy

Structured

Semi-Structured

Un-Structured

High

Low

High

Low

High

Batch

Batch

Batch

Batch

Near Real Time

Near Real Time

Real Time

Near Real Time

Distributed Clusters

MPP

Massively Parallel

Processing

In-Memory

In-Memory Appliances

MPP

Massively Parallel

Processing

Specialized MPP

Massively Parallel

Processing



Specialized System

Nex

t G

en

era

tio

n T

ec

hn

olo

gie

s

Traditional DW

Technology Choices as a result of disruptors

Structured Data Unstructured Data

INFRASTRUCTURE ANALYTICS VISUALIZATION WORKFLOW

BUSINESS INTELLIGENCE PERFORMANCE MFMT.

DATA PROVISIONING & EXCHANGES

DATA PLATFORMS

DATA SERVICES

COMMON DATA ACQUISITION

BIG DATA


MACHINE LEARNING

COGNITIVE

ANALYTICS



CROWDSOURCING

CLOUD

VISUALIZAZTION

DIGITAL

BIG DATA

INTERNET OF THINGS

DIGITAL

CROWDSOURCING

WEARABLES

CROWDSOURCING CLOUD

CLOUD CYBER SECURITY & PRIVACY

BIG DATA CLOUD CORE RENEWAL IN-MEMORY

CLOUD

SOCIAL WEARABLES INTERNET OF THINGS

CLOUD

BIG DATA

CORE RENEWAL

IN-MEMORY

COGNITIVE ANALYTICS



AMPLIFIED INTELLIGENCE


CYBER SECURITY & PRIVACY

COGNITIVE ANALYTICS

MACHINE LEARNING


BIG DATA

BIG DATA

BIG DATA

ETL + SQOOP + SPARK + Rabbit MQ

Cloud Provider + ML + Text Mining Kerberos + Sentry + Knox

HDFS + NoSQL + Relational Data Store + In Memory

API + Cloud Provider + Kerberos + Sentry + Knox

API + Digital Strategy

Cognitive Tools + ML + Tableau or Qlik + Digital

Tableau or Qlik + Digital

Clo

ud

+ N

oS

QL

+ In

Me

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AP

I’s +

ML

+ C

og

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Agenda

Unique Industry Solutions…Common Characteristics

Dozens of distinct industry use cases with proven value

FINANCIAL

SERVICES

ENERGY &

RESOURCES

AUTO /

TRANSPORTATION HEALTHCARE

MANUFACTURING MILITARY SMART

CITIES RETAIL

• Dealership of the

future

• Remote diagnostics

• Fleet management

• Autonomous vehicle

• Smart Grid (multiple)

• Wellhead

optimization

• Autonomous Mining

• Perf-based Insurance

• Personalized risk

profiles

• Retail banking

• Remote monitoring

• Patient experience

• Equipment

monitoring

• Hospital supply chain

• Wireless factory

• Preventative

maintenance

• Supply chain

• Connected

battlefield

• Supply chain

• Tailored offers

• Inventory

management

• Checkout optimization

• Supply chain

• Smart lighting

• Smart parking

• Smart waste

Real-time Analytics

Network connectivity

elements

Connected Devices

Mobile

Applications

Event

Orchestration

Edge

Gateways

Shared components

Sensors

Streaming

Data

Impact

• More responsive to

citizens’ needs

• Better control over

operations

• Improved supplier

relationships

Responsive City Initiative

Client: Municipality

Approach

• Implemented Technology functionality,

customized to fit the city’s unique needs

• Streamlined admin tasks and improved

coordination for 3,000 employees

• Mobile apps enable citizens to report issues

and inspectors to efficiently do their jobs

A citizen reports a damaged sidewalk using a smartphone.

The system receives the information and finds more notifications related to the same area: there is garbage pending collection and an uncovered storm drain. An inspector goes to the reported address to verify the received information and updates the information in the system.

Based on the given input, the system determines the right provider to perform the corresponding maintenance tasks

Once the maintenance tasks are finished, an inspector audits the work and submits their report into the system.

The reported incidents have been solved. The work has been done efficiently, optimizing actions and reducing times. The sidewalk is now restored and ready to be used.

Issue: Client wanted to be more responsive to service requests from citizens and increase control

over work performed by contractors

Chronic Care Disease Management - Solution

COMMITMENT

Isabel’s wearable

tracks her activities in

terms of #steps taken

and monitors her heart

rate real-time

Isabel’s PCP coaches

Isabel on avoiding

stressful situations and

explains some

breathing exercises for

the future

Wearable transmits data

to the IoT platform for

Isabel

Heart Rate Activity from

the wearable information

is compared against pre-

set thresholds for the

program in real-time

Isabel enrolls into a wellness

program sponsored by her

health plan targeted at

managing health for members

with heart diseases

As part of her enrollment she

provides approval for them to

track and monitor her heart

rate from her wearable

PROFILE

Isabel is 41 years old

Has Tachycardia heart condition

Enrolled into the Heart Monitoring Program sponsored by her health

plan

Wearable- User – Plan Interaction MONITOR

TRACK

COMPARE COMMUNICATE ENGAGE

ACTIVITY TRACKER KPI’s

Meet Isabel

When Isabel’s heart

registers palpitations

leading to a pulse higher

than threshold, the

platform sends her a

text message to

encourage her to seek

medical help

A health care provider focused, connected-devices solution that enables health

care organizations to deliver high quality patient experiences in an accelerated

fashion

Under the hood the platform has three key components

Wearable Devices

Aggregators

(MQTT Publisher)

Tableau Server

Mobile Alerts

HDFS

Stream

Sink

Queue

In-memory

DB

IoT

Platform

API

Stream

Ingest

Queue

Batch Processing

&Transformation

Custom Events

Processor

Mule soft

Restful Web

services

Health Plan

Member and

Enrollment Data

(SalesForce)

Real-time

Updates

Rea

l-ti

me

Fe

ed

Sink

Pe

rsis

ten

t L

oo

k-u

p

Google Cloud

Messaging

REST APIs REST APIs

ODBC

Connection

Daily Refresh

Daily Refresh

Real-time

Extensible to other IoT

protocols Extensible to using

predictive analytics

algorithms

Extensible to

integrating with other

IoT devices

Extensible to

integrating with any

other downstream

systems

Mule soft

Restful Web

services

Deloitte PaaS LEGEND

Patient CRM

(SalesForce)

Source

Real-time

Platform Dependent





Agenda

Key Competencies to enable Analytics

Functional Competencies

Analytical and Visualization Tools Expertise in Advanced analytics tools and techniques:

Regression / Time-series / Classification / Clustering /

Optimization/ Graph & Text Mining, Visualization

Techniques

Communications and Strategic Thinking Proficiency in simplifying analytical outputs and

influencing key business stakeholders through

effective communication of outputs

Knowledge of Function Possession of work experience, knowledge and skill

sets in specific functions

Enterprise Competencies

Data Expertise Expertise in small and Big Data Architectures,

Modeling, Extraction, Transformation, and Loading,

Data Management / Quality / Governance

Industry Expertise Understanding of industry trends and key business

drivers that impact measured metrics; ability to

evaluate business issues by applying data-driven

approaches

Technology / IT Expertise Knowledge of Infrastructure Management / Support,

Distributed Systems, Cloud Management, Big Data,

Advanced Data Management and Systems

Integration

The dimensions of a comprehensive Competency Center are much broader than just technology capabilities. A Competency Center needs various key skills to prioritize, manage, deliver and execute its projects.

It is challenging to find one person who has all of these competencies at the enterprise or

functional level; however, there are different means to acquiring necessary talent

Ashish Verma – [email protected]

Q&A

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