4e INDUSTRIELE REVOLUTIE - Digital Transformation Conference

Transcription

4e INDUSTRIELE REVOLUTIE
MOUWEN OPSTROPEN & KANSEN BENUTTEN
© Copyright 2016 EMC Corporation. All rights reserved.
1
4e INDUSTRIELE REVOLUTIE
Sander van Lingen, Pathfinder - EMC
ARCHITECTUUR
Kenny Pool, Advisory System Engineer - EMC
DIGITALE TRANSFORMATIE
Edwin Leinse, Global Director Business Data Lake as a Service - Capgemini
2
3
MOVEMENTS
SHIFT
DIY
GREEN
ACCESS
CONNECTED
4
5
6
OPPORTUNITIES
EFFICIENCY
GROW
NEW
SELL
7
CHALLENGES
CULTURE
INNOVATION
DATA
8
9
Let’s grap this
opportunity
Joe Biden
Vice President of the USA
10
EMC BUSINESS DATA LAKE SOLUTIONS
THE PLATFORM
11
CONSOLIDATED PLATFORM MANAGEMENT
INGEST
MANAGER
ANALYTICS
MANAGER
Provision data storage environment
Provision analytical processing environment
Provision ingest environment
Content indexing for all data types
DATA
MANAGER
PLATFORM MANAGER
AUTOMATED DATA MANAGEMENT WITHOUT THE IT ADMIN
“vagrant up” for data
12
AVOIDING A DATA SWAMP
HOW TO STAY OUT OF THE MUD AND KEEP INSIGHTS FLOWING
Policy-driven data storage and retention
Control and audit data usage
Demonstrate regulatory compliance
DATA GOVERNOR
Keep data relevant and reliable
Master data and metadata management
13
DELIVERING ANALYTICS FOR EVERYONE
DATA SCIENCE FOR EVERYONE WITHOUT THE DATA SCIENTIST
DATA AND ANALYTICS CATALOG
Provide data services to applications
But also analytics services
Build views and applications users really need
Rapidly move from tinkering to monetization
14
THE EMC BUSINESS DATA LAKE
DATA AND ANALYTICS CATALOG
INGEST
MANAGER
ANALYTICS
TOOLBOX
PIVOTAL BIG DATA SUITE
ANALYTICS
MANAGER
A P P L I C A T I O N S
A T S C A L E
D A T A
P R O C E S S I N G
GREENPLUM
DATABASE
SPRING XD
SPARK
HAWQ
A D V A N C E D
PIVOTAL HD
DATA
MANAGER
HADOOP
VMWARE VCLOUD SUITE
A N A L Y T I C S
REDIS
GEMFIRE
RABBITMQ
BDS ON PIVOTAL
CLOUD FOUNDRY
DATA GOVERNOR
PLATFORM MANAGER
OPEN
EMC II STORAGE
DATA LAKE FOUNDATION: ISILON
VCE VBLOCK | XTREMIO | DATA DOMAIN
15
FEDERATION BUSINESS DATA LAKE
LOGICAL VIEW
USERS
WORKSPACE
DATA
SCIENTIST
ENTERPRISE
ESI DISCOVERY
AND INGEST
BUSINESS
ANALYST
Unstructured
Data
APPLICATION
Structured Data
CATALOG
IT
ADMINISTRATOR
Public records
Social Networks, UGC
DEVELOPMENT
POLICIES
ANALYTICS
HADOOP
FABRIC
Dark Data
ENTERPRISE
GOVERNANCE
MANAGEMENT
Internet of things
Location Data
LINEAGE
DATA INFRASTRUCTURE
DATA LAKE
QUALITY
VIRTUALIZATION
SECURITY
NETWORKING
INGEST
COMPUTE
STORAGE
POLICY-DRIVEN
DATA LAKE
DATA
MANAGEMENT
LAKE MANAGEMENT
ENVIRONMENT
ANALYTICS
DATA SETS
16
A UNIFIED SOLUTION
APPS
SPEED
ANALYTICS
DATA
17
Capgemini
Business Data Lake As A Service
(BDLaaS)
Case Study: European Retail Major
Retail Business Operations cover 2 countries, 220 product categories, 929 sub-categories, 1.779 segments
and 522.987 products. Huge Challenges in timely reporting and analytics impacting agile business decisions –
for e.g., each category manager requires a custom report, which took more than an hour to run.
Problem Areas
Approach
Performance Bottlenecks
 Defined Use Case and Test Scenarios
 8bn records down to ticket line item level
Business Reports Delayed
Inability to Use
Predictive Analytics
Benefits Delivered
Ticket Header
Analytics
60x
SKU Item
Detail Analytics
100x
SKU Item
Availability
360x
YoY Comparison SKU
Item Detail
45x
YoY Comparison Ticket
Header
40x
faster
faster
faster
faster
faster
19
What does this mean?
Marketing
campaign
Operations
Business driven
SMAC
Distill
HDFS
Keep the history
Load everything
Transactional systems
Web
Sensor
Network
ERP
CRM
PLM
Supplier Social Media
Market
20
Business Data Lake as a Service
Platform as a Service
Access Layer 2
Access Layer 1
Service Orchestration Layer
Analytical Tools
Data Lake 1
Data Lake 2
Data Lake 3
Data Lake X
Infrastructure Broker Layer
Hadoop
Cloud
On
premise
Compliance and Process
Monitoring
Monitoring
Cloud Foundry Layer
Data
Lake
Pivotal/Hortonwor
ks/Cloudera/HAW
Q
Access Layer 3
21
DATA Node
Name Node
 If you need more Analytical capacity you need too add more Nodes
 If you need more Data spaces you need too add more Nodes
 If you need more Parallelization you need too add more Nodes
App
A
Distil
Compute power
Parallelization or IO/ups
Storage Capacity
Store
Traditional Hadoop cluster
Our Solution as a Service
Architecture of Traditional Hadoop and
Our solution
 If you need more Analytical capacity, You add more CPU & Memory
 If needed more Parallelization, You add More IO/up’s
 If you need more storage space, You add More Disk
22
Current offerings – BDLaaS (PAAS) options
Each offering scales up and out to do more in parallel and increase performance
Scale Out
Scale Out
Scale Out
Sandpit/POC
Business service
Enterprise Service
Primary Primary
Storage Storage
Primary Storage
Data Lake
Analytical Tools
Data Lake
Data Lake
Hortonworks
Hadoop
Availability Best Endeavor
Availability of 99,5%
Basic
Basic
Data
Lack
Data Lake
Data
Data
Processing
Processing
And
And
Analytics
Analytics
Scale Up
Data
Processing
And
Analytics
Scale Up
Pivotal/
Hortonworks/Cloudera
/ Cloud Foundry
Data
Processing
And
Analytics
Availability of 99,9%
Better Availability and Service Guarantees
23
References
Business objective of customers
Data
 Big Data & Analytics
 Case Management as a Service
•
High performance, assured application environment
•
High reliability with availability options for the
enterprise
•
Reduced dependencies between analytics tools
•
Enhanced availability and performance monitoring
Capgemini BDLaaS platform solution
Consumer Products
& Retail
Utilities
 All-Channel
Experience (CP&R)
 Digital Utilities
Transformation
 Demand Driven
Supply Chain
 Smart Energy
Services
Automotive
 Connected Customer
 Connected Vehicle
 Connected Insights
•
Platform is offered via Capgemini private EU cloud and delivers
a secure, flexible and scalable Environment
•
Capacity is delivered on a elastic usage principle to deploy BI
and Analytics programs then execute information strategy in
an agile and efficient manner to remain compliant with
legislation
•
This service is the foundation on which customers can perform
their Big Data Analytics and comes with all required Big Data
Suite installed.
•
The Data Lake service also includes a customer specific Data
Transport Layer, a technical helpdesk and technical application
support.
Results
•
BDLaaS (POC and Production) platform available for customers
since March 2015 and is located at the datacenter in
Amsterdam
•
Several POC and production customers are using the platform
24
Solution Advantages











Hadoop allows to store and analyze all Structured and unstructured data
Fully integrated solution
A fully governance and compliance SLA driven service
Provisioning in days or max week
Fully automated and monitored offering in a Hybrid provisioning model
A full Opex model possible
Release management streamlined with limited dependency
Specific tools for specific needs-Hadoop, MPP, in Memory
A fully managed Service
Technical Application Management included
Local Business fully empowered according global Business rules
Agility
Flexibility
Cost
Business
Satisfaction
25
Use cases increase competitive advantage
Fraud
Detection
Financial firms use big data to help
them
identify
sophisticated
fraud
schemes by combining multiple points
of data
Optimize Funnel
Conversion
Big Data
Use Cases
Big data analytics can track trends in
security breaches and allow companies
to proactively go after threats
Through Big Data (from multiple sources) retailers can
better optimize their product selection and pricing, as well
as decide where to target ads
Market Basket
Analysis and
Pricing
Optimization
Big data analytics allows companies to track
leads through the entire sales conversion
process, from a click on an ad-word ad to the
final transaction
Customer
Segmentati
on
Virtually every industry has invested in
big data to help solve specific challenges
those industries face. Healthcare, for
example, uses big data to improve
patient outcomes, and agriculture uses
data to boost crop yields.
Companies can learn what prompts a
customer loyalty through targeted
insights
on
their
customer’s
characteristics and purchasing habits
in order to maximize marketing &
profits
Assessing Big data about the consumer
from multiple sources, such as social
media data and transaction history,
companies can better segment and
target make personalized offers
Through sensors and other machinegenerated data, companies can identify
when a malfunction is likely to occur.
Companies can preemptively order parts
and make repairs in order to avoid
downtime and lost profits.
27
Data in Transit issues or Collecting data
AN Transactional systems
ERP
CRM
3rd Party
Web
Social Media
Supplier
Sensor
Data Transport Service
Data lake
Repor
t
Data
Processing
And Analytics
28

4e INDUSTRIELE REVOLUTIE - Digital Transformation Conference

Transcription

Similar documents

I`m helping to end MS by participating in the 2016 Jayman BUILT MS

TEXT ANALYTICS

The Board of Directors of Qatar Fuel (WOQOD) is pleased to invite

5 `S` Training Program at ABV-IIITM, Gwalior on 21.07.2016

SLC Flyer SEP2015.pub

Porky Expo Flyer

February 2016 - Electric Mobility Canada

Sept 30-Oct 2, 2016 Nov 4-6, 2016 @Camp Tadmor

4 star ALOE HOTEL, PAPHOS

the Guidelines for the registration on