Watch the on-demand recording here:
https://event.on24.com/wcc/r/1632072/803744C924E8BFD688BD117C6B4B949B
Evolution of Big Data and the Role of Analytics | Hybrid Data Management
IBM, Driving the future Hybrid Data Warehouse with IBM Integrated Analytics System.
The Future of Data Warehousing, Data Science and Machine Learning
1. IBM Cloud Š 2018 IBM Corporation
Modernize your Data Warehouse with
IBM Integrated Analytics System
Thomas Chu
Director, Offering Management
Hybrid Data Management, IBM Analytics
2. 2
Agenda
Evolution of Big Data and
the Role of Analytics
Hybrid Data Management
IBM, Driving the future
Hybrid Data Warehouse
with IBM Integrated
Analytics System
IBM Cloud Š 2018 IBM Corporation
3. 3IBM Cloud / DOC ID / Month XX, 2018 / Š 2018 IBM Corporation
Headline
Global data growth
By 2025, 163 Trillion Gigabytes of data
will be created
IBM Cloud Š 2018 IBM Corporation
4. 0.5%
of all data is
actually
analyzed
â MIT Technology
review
10%
increase in data
accessibility will
result in more
than $65 M
additional net
income
â Baseline Magazine
80%
of all data is
stored by
corporations
â Baseline Magazine
50%
of large
enterprises
will have
hybrid cloud
deployments
by the end of
2017
â IBM Institute for
Business Value
Data is proliferating, often stored in different locations and formats.
Itâs getting more difficult to provide data access and analytics to the business.
Why you need a hybrid data strategy
IBM Cloud Š 2018 IBM Corporation
5. Data Driven Insight Driven Digital Transformation
ď§ Culture Change
ď§ Breaking Silos
ď§ Discover âWhatâ
ď§ Understand âWhyâ
ď§ Self Service
ď§ Reports
ď§ Business Intelligence
ď§ Prediction
ď§ Optimization
ď§ Automation
ď§ Collaboration
ď§ Models
ď§ Visualization
ď§ Applications
Outcomes
Capabilities
Drivers
Most are here
Value from Data
ď§ New Business Models
ď§ Disruptive Technology
ď§ Real-Time Decisions
ď§ Instrumentation
ď§ Orchestration
ď§ Integration
Competitive
Cost Reduction
Modernization
Market Leader
None of this is possible without the right hybrid data management strategy!
As data becomes more accessible, it provides more value
IBM Cloud Š 2018 IBM Corporation
6. 6
What is your organization trying to solve?
Innovation
Create and own the data management strategy,
leverage data virtualization and cloud.
New Data Types
The ability to integrate unstructured, semi-and
structured data into a single analytic
architecture. Leverage both SQL and NnSQL
data sources
Flexibility
Ability to choose between a flexible set of
deployment and licensing models, workload
types, technologies, data sources and storage
tiers.
Efficiency
Optimize data architecture and life cycle
management to reduce cost, increase
performance and protect existing investments
in skills, applications and ecosystem
Enterprise-strong
Address data sprawl, workloads and open
source technologies that can scale with
the business in a highly and continuously
available manner.
Portability
The ability to move data and insights where
needed, without the requirement to
re-architect applications. Write-once-run-
anywhere application architecture.
IBM Cloud Š 2018 IBM Corporation
7. 7
Agenda
Evolution of Big Data and
Analytics
Hybrid Data Management
IBM, Driving the future
Hybrid Data Warehouse
with IBM Integrated
Analytics System
IBM Cloud Š 2018 IBM Corporation
8. Digital transformation journey with hybrid data management
More intelligent analytics
and insights
Go at the speed
of your business
Write once, run anywhere,
from any source
Deploy your data
where you need it
Write once, access
anywhere with a common
access layer to promote
application independence
Hybrid Data Management Unified Governance & IntegrationData Science & Business Analytics
Prepare, publish and
protect your data to drive
insights while mitigating
compliance risks
Descriptive, predictive,
prescriptive to understand
the current, predict the
future and change outcomes
Organize Your Data
Analyze Your Data
Machine Learning
On-Premises and Cloud
Common SQL Engine
Infused with
Seamless between
Powered by
IBM Cloud Š 2018 IBM Corporation
9. âŚHybrid
IBMâs strategy isâŚ
NOT about Cloud OR On-premisesâŚ
NOT about Traditional Relational OR Open SourceâŚ
NOT about SQL OR NoSQLâŚ
NOT about Structured OR Unstructured DataâŚ
Itâs about Cloud AND On-premises
Itâs about Traditional Relational AND Open Source
Itâs about SQL AND NoSQL
Itâs about Structured AND Unstructured Data
IBM Cloud Š 2018 IBM Corporation
10. Built on a Common SQL Engine
⢠Application Agility
Write once, run anywhere
One ISV product certification
for all platforms
⢠Operational compatibility
Reuse operational and
housekeeping procedures
⢠Standardized analytics
Common programming model
for in-DB analytics
⢠Common Skills
One skill set for all deployments
Drive higher efficiencies and
portfolio rationalization
⢠Licensing
Flexible entitlements for business
agility and cost-optimization
⢠Integration
Common Data Virtualization
capabilities for query federation and
data movement
Managed
public
Cloud DBaaS
Db2
Warehouse
on Cloud
Software
defined
warehouse
on-premises
or in cloud
Db2
warehouse
Custom
deployable
database
Db2
Open source
Hadoop with
Hortonworks
Big SQL
Dedicated
analytics
appliance
IBM
Integrated
Analytics
System
IBM Hybrid Data Management solutions
Anchored by a Common SQL Engine enabling true, highly scalable
hybrid data warehousing solutions with portable analytics
IBM Cloud Š 2018 IBM Corporation
11. 11
Agenda
Evolution of Big Data and
Analytics
Hybrid Data Management
IBM, Driving the future
Hybrid Data Warehouse
with IBM Integrated
Analytics System
IBM Cloud Š 2018 IBM Corporation
12. Introducing the IBM Integrated Analytics System
A Next Generation Hybrid Data Warehouse That Does Data Science Faster
Cloud-ready to support multiple workload
deployment options
Built-in IBM Data Science Experience to
collaboratively analyze data
Optimized for high performance to support
the broadest array of workload options for
structured and unstructured data in your
hybrid data management infrastructures
Reliable, elastic and flexible system
that reduces and simplifies
management resources
Real time analytics with machine learning
that accelerates decision making, bringing
new opportunities to the business â ready
for business analyst and data scientist
Leverages a Common SQL Engine for
workload portability and skill sharing across
public and private cloud
12http://www.ibmbigdatahub.com/blog/dispelling-myths-about-ibm-integrated-analytics-systemIBM Cloud Š 2018 IBM Corporation
13. Evolution of Netezza and PureData System for Analytics
Worldâs first Data
Warehouse appliance
Worldâs first 100 TB Data
Warehouse appliance
Worldâs first petabyte Data
Warehouse appliance
Worldâs first Analytic Data
Warehouse appliance
NPSÂŽ
8000 Series
TwinFin⢠with i-Class
Advanced Analytics
NPSÂŽ
10000 Series
TwinFinâ˘
2012 2014 Sept 20172003 2006 2009 2010
Worldâs fastest and âgreenestâ
analytical platform
PureData System
for Analytics
N2000
PureData System for
Analytics N3000
IBM Integrated
Analytics System
Future
Worldâs First Hybrid Data Warehouse
and Data Science Platform
13
NEW
IBM Cloud Š 2018 IBM Corporation
14. Hardware Architecture Overview
2x Mellanox 10G Ethernet switches
⢠48x10G ports
⢠12x40/50G ports
⢠Dual switches form resilient network
IBM SAN64B 32G Fibre Channel SAN
⢠16Gb FC Switch
⢠48x 32Gb/s SFP+ ports
Up to 3 Flash Arrays in 1 rack containing
⢠IBM FlashSystem 900
⢠Dual Flash controllers
⢠Micro Latency Flash modules
⢠2-Dimensional RAID5 and hot swappable spares for high
availability
7 Compute Nodes in 1 rack containing
⢠IBM Power 8 S822L 24 core server 3.02GHz
⢠512 GB of RAM (each node)
⢠2x 1.2TB SAS HDD
⢠Red HatŽ Linux OS
User data capacity:
324 TB
(Assumes 4x compression)
Power requirements:
9.4 kW
Cooling requirements:
32,000 BTU/hr
Scales from:
1/3rd Rack to 8 Racks
(initial GA is 1/3rd to 4 Racks and
supports Tier Storage expansion)
14IBM Cloud Š 2018 IBM Corporation
15. IBM Integrated Analytics System Configurations
IBM Power 8 S822L 24 core server 3.02GHz
IBM Flash System 900
In-place Expansion, Tiered storage
Mellanox 10G Ethernet switches
Brocade SAN switches
-003
1/3 Rack
-006
2/3 Rack
-010
Full Rack
-020
2 Racks
-040
4 Racks
Servers 3 5 7 14 28
Cores 72 120 168 336 672
Memory 1.5 TB 2.5 TB 3.5 TB 7 TB 14 TB
Available User Space1 27 TB 54 TB 81 TB 162 TB 324 TB
Optional Tier Storage (Flash
+ HDD)
Available User Space1,2
32 TB
+ 166 TB
32 TB
+ 299 TB
32 TB
+ 432 TB
64 TB
+ 831 TB
128 TB
+ 1,629 TB
1Assume up to 4x compression to calculate user data (pre-load uncompressed user data).
Example a full rack user data capacity = 4 x 81TB = 324 TB
1Example Total user data capacity for full rack Tier Storage models = 4 x 81TB + 4 x 32TB (Flash) + 4 x 432 TB (HDD) = 2,180 TB
15IBM Cloud Š 2018 IBM Corporation
16. IBM Cloud Š 2018 IBM Corporation
IBM Integrated Analytics System Console
17. Always Available Analytics
Redundancy to ensure no
single point of failure
â Fault tolerant design to ensure continued
operation in the event of hardware failure
99.999% reliability hardware components
â Built with IBM Power and IBM FlashSystem
reliability, combined with automated failovers for
application continuity
Single monitoring solution for all of your data
â IBM Data Server Manager can easily monitor and
manage all components on the systems and can
be used across all of your data
17IBM Cloud Š 2018 IBM Corporation
18. Expansions and Upgrades
In-place incremental expansion
⢠Reduce disruptions to your analytics systems as you scale
out computer power
In-place tiered storage expansion
Independently scale storage for cost
effective capacity growth
Cloud-ready
⢠Tools to shift workloads within a hybrid public/private cloud
and on-premises environments based on your application
requirements
Cost efficient multi-temperature storage
⢠Most frequently accessed data (âhotâ) on faster
flash storage
⢠Less frequently accessed data (âcolderâ)
on cost efficient storage systems
18IBM Cloud Š 2018 IBM Corporation
19. Data Science and Hybrid Data Management with IBM Integrated Analytics
System
Machine Learning Demo on Youtube
High Performance IBM
Integrated Analytics System
External
Data Sources
Stock
Portfolio Analytics
Applications leverage
In-database Machine
Learning (ML) models
and R analytics
Db2 Warehouse on
Cloud
(Structured Data Store)
IBM BigSQL on
HortonWorks Data
Platform (Hadoop)
(Unstructured Data Store)
Macro economic
data feeds
(Source: FRED)
News data feeds
(Source: NASDAQ)
Stock and customer
portfolio data
(On-Premises)
Move data and federate
queries with Common SQL Engine
DSX LOCAL
19
The new use case âŚ
IBM Cloud Š 2018 IBM Corporation
20. Analysis of viewership data generated from
fragmented audiences in this multi-platform,
multi-channel business takes a lot of time,
money, and resources.
AMC Networksâ Business Intelligence team
spent 80% of their time evaluating audience
data and only 20% doing actual research â
making it challenging to uncover the
insights they needed, when they needed
them.
Time lost, unexpected costs, and
limited access to data adds up to
missed opportunities
NEED
To combine, store,
and quickly analyze
third-party ratings &
viewer data within a
logical data
warehouse
CHALLENGES
Requires a simple
method to pull together
disparate data
sources.
Solution must support
an integrated data
science and analytics
platform
IBM Cloud Š 2018 IBM Corporation
https://ecc.ibm.com/case-study/us-en/ECCF-MEC03010USEN
21. Do Data
Science Faster
IBM Integrated Analytics
System uses cognitive
machine learning to assist
your data scientists, all
collaborating inside one
unified platform.
Support Hybrid
Workloads
IBM Fluid Query federates
queries across all your data
repositories â with a single,
shared API.
Support Hybrid
Deployments
IBM Common SQL Engine
enables logical data
warehousing on open
standards, across
on-premises and
cloud deployments.
âThe combination of high performance and advanced analytics â from the Data Science Experience to the open
Spark platform â gives our business analysts the ability to conduct intense data investigations with ease and
speed...
The Integrated Analytics System is positioned as an integral component of an enterprise data architecture
solution, connecting IBM Netezza Data Warehouse and IBM PureData System for Analytics, cloud-based Db2
Warehouse on Cloud clusters, and other data sources.â
Vitaly Tsivin â Executive Vice President, AMC Networks
IBM Cloud Š 2018 IBM Corporation
22. Get Started Today
Start Your
Journey
Try
It Out
Learn
More
Learn more:
Visit: marketplace on IBM.com
Read: Now is perfect time to move from
Netezza to the Integrated Analytics
System Solution Brief
Read: Integrated Analytics System-Do
Data Science Faster Solution Brief
Visit: Integrated Analytics System content
hub
Trials and downloads:
Trial: Contact us to get stared
IBM Marketplace
Try it: Proof of Technology
DataFirst Method:
Engage the IBM DataFirst Method to build the
strategy, expertise, and roadmap needed to gain the
most value from data and achieve your goals
22
IBM Knowledge Center
IBM Integrated Analytics System YouTube Channel
Data Warehouse User Community
IBM Cloud Š 2018 IBM Corporation
The hybrid Enterprise Data Warehouse of the Future â Do More with your Data
Â
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The Enterprise Data Warehouse (EDW) has traditionally been the foundation for data storage. So how do you leverage current investments while remaining relevant and competitive? It is important for your organization to continue to evolve, accelerate development/deployment times, provide high performance and to provide a cloud-ready platform. Â
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Thank you for joining us today. In this webinar we will begin talking about current data challenges, and how Data Science and Machine Learning is driving the need for advanced analytic decisioning. We will also discuss the need for a Hybrid data strategy, and how the Enterprise Data Warehouse of the future remains an integral part of that strategy. Finally we will introduce you to the IBM Integrated Analytics System
Thank you for joining us today. In this webinar we will begin talking about current data challenges, and how Data Science and Machine Learning is driving the need for advanced analytic decisioning. We will also discuss the need for a Hybrid data strategy, and how the Enterprise Data Warehouse of the future remains an integral part of that strategy. Finally we will introduce you to the IBM Integrated Analytics System, our next generation data warehouse appliance for advancing analytics, Machine Learning and Data Science.
The volume, velocity and variety of data is growing at a rapid pace, challenging many of todayâs organizations. In the article âData is Eating the Worldâ it is predicted that by the year 2025, 163 Trillion Gigaytes of data will be created.
For most organizations this rapid growth is seen as a challenge. And while this is definitely true, it can also be seen as an opportunity. By harnessing and analyzing this data, companies are able to gain drive analytics, data science, machine learning that improve analytic that improve customer interactions, streamline processes and improve operations.
data to pre Artificial Intelligence (AI), Internet of Things (IOT), cloud, mobile and other new technologies are driving the need for real and near-time analytic decisioning. Todayâs data environments are not just a vital piece of IT infrastructure, but a key component of corporate strategy. Organizations are realizing that better insights can improve customer interactions, streamline processes and improve operations.
Data is Eating the World: 163 Trillion Gigaytes Will be Created in 2025
With the volume, velocity and variety of data growing at a rapid pace, many businesses are uncovering lucrative opportunities. It is well known that businesses thrive when they uncover trends and patterns, and make richer data driven decisions, no matter where the data resides or how it is structured.
Consider the facts: âŚâŚ
Having a Hybid Data Management strategy enables your enterprise architect leaders to build the right foundation for their data. They are gaining actionable insights around customer behavior and market opportunities to grow market share, reduce costs, and deliver superior customer service
Every organization wants to become a digital business. But before you they become a digital business, they have to be insight driven, before they are insight driven they have to be data driven.
Data Driven: How do these projects start? It begins with one project in an organization where a team comes together and they are relentless about solving a perceived notion. They are motivated by factual data. They succeed and put everyone else to shame. It requires a cultural change to be intentional with data driven decisions. Itâs about breaking down silos in the business to get the data (from IT, from finance, from HR, etc). Most questions in this phase are just about understanding âwhatâ happened. Some go into âwhyâ did it happen.
Insight Driven: This is where organizations know whatâs happening and why itâs happening, but they want to get predictive and answer what will happen next. They want to optimize their outcomes. They want to automate decisions. The foundation for this stage is AI, Machine Learning, and Deep Learning. Itâs the basis of a data science business. They are looking for answers to become more competitive and begin to disrupt.
Digital Transformation: This is business model transformation. Itâs where organizations move from a one time, perpetual charge to as a service and selling outcomes. Things like availability, uptimes, and revenue share.
Most organizations think they are in the digital transformation stage, but in reality most of them area really still in the data driven stage.
So whatâs needed in each of these stages for success?
So letâs begin with talking about what your company is trying to solve.
Innovation â For most organizations becoming more innovative is key to remaining competitive. A first step to doing this is to enable your data scientists/analysts, line of business owners and developers to deliver more intelligent insights from their data with embedded machine learning and analytics.
New Data Types â historically, companies have used data that is highly structured. New forms of semi and unstructured data such as streaming audio, video, click stream, and social media are changing the status quo.
Flexibility â Run analytics on data across multiple locations for quick insights, letting you put data where it's needed Provide portability. You have the flexibility to switch cloud platforms or database there are more and more choices when storing, accessing and analyzing data.
Efficiency â Save on storage investments with in memory analytics and deliver data and analytics quickly with high performance workload processing. Save DBAs time by moving data between on -premises and cloud seamlessly at
200 - 300 GB/s. Democratize access to data. Deliver data and insights where itâs needed so that developers and data workers are empowered to find, access, trust and gain insights from their data
Enterprise Strong â Data is everywhere in your organization, siloed across regions, lines of business, etc. Adressing data sprawl and scalability is key to your organizations growth.
Portability â Finally, accessing your data where it resides is key in
In this next section we will discuss hybrid data management and the important considerations in creating a solid corporate strategy,
1.   Collect data. Example: The head of claims at an insurance company needs to reduce labor costs of claims while improving the customer experience. The first step is gathering and ingesting all that data: pictures taken from smartphones at the crash, incident details, claim history, etc.Â
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2.   Organize and Protect the data. Processes must be in place to ensure all data is protected and in compliance with current regulations so that only authorized people see just the necessary information to perform their task. The data must be clean, trusted and easily accessible: a prerequisite for processing and extracting value.Â
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3.   Deliver Value. In the insurance company example, machine learning image recognition algorithms used on pictures of car accidents combined with analysis of all related claim data can help automate claims processing without bias for optimal outcomes.
At IBM we have organized our portfolio to address these three stages.Â
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¡     Hybrid Data Management is designed to help gather and ingest ALL relevant data with no limit of volume, variety or velocity. Clients can choose any style of database or data warehouse, best-of-breed and open source software and leverage their existing skill set. It enables data to be viewed as an unified and easily accessible asset.Â
¡     Unified Governance and Integration helps satisfy all aspects of integrating and governing data, from compliance, e-discovery, data retention and archiving, data masking / obfuscation, to securely organizing that information so it can be used in tools like our own data science business and analytics tools, or any third-party tools.Â
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¡     Data Science and Business Analytics is the only complete stack, across the entire analytics lifecycle, that enables clients to apply collaborative data science no matter the skill level, support all data, no matter what it is or where it is, and deploy advanced data science to where the data lives.Â
At IBM we believe in setting expectations upfront. In doing so we feel it is important to show what our strategy is and what it is not.
IBMÂŽ is committed to delivering SQL commonality, on database platforms implementing the Common SQL Engine, in a way that is common and portable and supports the ANSI/ISO SQL standards. Since products are configured and optimized for select workloads, some products with Common SQL Engine provide greater focus on OLTP applications, while others are fine tuned for delivering operational analytics, or supporting big data open analytics environments.
IBM Db2, Db2 Warehouse, Db2 Hosted, Db2 on Cloud, IBM Integrated Analytics System (NEW), and IBM Db2 Big SQL are all designed with the Common SQL Engine. Since the Common SQL Engine supports data federation, other databasesânon-IBM and open source databasesâ also can plug into the engine for SQL processing. To make things even easier, IBM Data Server Manager provides administration, alerting, monitoring, federation, and SQL execution support across the Common SQL Engine platforms
The Enterprise Data Warehouse (EDW) has traditionally been the foundation for enterprise data storage. As the volume, velocity and variety of data continues to evolve, so should the data warehouse. It is important that it continue to evolve, providing high performance, accelerating the time to development/deployment and providing a cloud ready platform. So I hope to answer questions how you leverage your current investments while staying relevant and competitive.
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The IBM Integrated Analytics System is all that IBM Puredata Systems and Netezza are and much more, it is a revolution in how we provide you analytics. Itâs a unified data science platform. Everything you need to connect your data scientists with data and provide them with the right tools is in this solution. We can talk about a few different facets to the solution:
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Common SQL Engine â for you, this is about workload portability and skill sharing across public and private cloud
data science tooling, built in â IBM Data Science Experience is included, or data scientists can to collaboratively analyze data or they can use their own tools like Jupyter Notebooks
ease of use â one of the core elements of the solution, reliability (to ensure the system is available to run the analytics), elastic and flexible to grow with your requirements and all of this reduces and simplifies management resources
hybrid data management â supporting the broadest array of data types and workload deployment options so that the data scientists are not limited to what data is available to them
in-place analytics â runs analytics where the data resides, reduces process and increases performance. This is done on the Apache Spark processing engine
Machine Learning â new types of workloads that your data scientists need to accelerate decision making bringing new opportunities to the business
Performance â as an optimized single solution (links with âease of useâ above) itâs easy to deploy and manage while still providing the highest levels of performance you need.
IBM has a history of innovating and evolving our data warehouse appliance. As the volume, velocity and variety of data changes, IBM has responded.
So letâs look at the actual hardware configuration in each rack and the details; Power System server components, the FlashSystem storage and the networking switch. IAS is a fully integrated hardware and software system offering you convenience rather that the time and cost of building it out. The system is delivered configured and performance optimized for the purpose of letting you run your analytics faster.
This chart shows the specific system configurations that are available to the client. Start with filling 1/3 of a rack and then expand to 2/3 or a full rack. Multiple racks can be configured to be a system as well. These systems are a single part number., one serial number.
7 Compute Nodes in 1 rack containing
IBM Power 8 S822LÂ 24 core server 3.02GHz
512 GB of RAM (each node)
2x 600GB SAS HDD
Red HatÂŽ Linux OS
Up to 3 Flash Arrays in 1 rack containing
IBM FlashSystem 900
Dual Flash controllers
Micro Latency Flash modules
2-Dimensional RAID5 and hot swappable spares for high availability
2x Mellanox 10G Ethernet switches
48x10G ports
12x40/50G ports
Dual switches form resilient network
IBM SAN64B 32G Fibre Channel SAN
16Gb FC Switch
48x 32Gb/s SFP+ ports
1Assume up to 4x compression to calculate user data aka pre-load uncompressed user data.
Integrated Analytics System Console
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But performance is only part of it of what sets this offering apart. You need to ensure that the analytics you run are always available to your users and the organization. These are workloads that must be available and must hit your service level agreements. This is why we designed IAS to have no single point of failure with redundancies and fault tolerance. Weâve selected the most reliable hardware components in the form of the Power System and FlashSystem for server and storage respectively. And of course, we provide the monitoring with the IBM Data Server Manager, used across the family of IBM hybrid data management offerings.
On question we always get is scalability and expansion options. When you think about expansion on the IBM Integrated Analytics System, itâs important to think about it in two ways. The first is the actual hardware. The IBM integrated Analytics System offers in-place expansion that is non-disruptive. So when you order your system if you need to add more compute and storage, itâs done without disruption to your system as you scale out.
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The other aspect of expansion is the cloud-readiness of the IBM Common SQL Engine. Workloads you have on the system can be seamlessly moved to the cloud based on your requirements. You have the option to put workloads where you need them for a greater level of flexibility to run your infrastructure.
A video this demonstration is available at https://www.youtube.com/watch?v=XTzEc00jx_E
NEED: âTV has evolved into a multi-channel, multi-stream business, and cable networks need to get smarter about how they market to and connect with audiences across all of those streams. Relying on traditional ratings data and third-party analytics providers is going to be a losing strategy: you need to take ownership of your data, and use it to get a richer picture of who your viewers are, what they want, and how you can keep their attention in an increasingly crowded entertainment marketplace.
CHALLENGE: âThe challenge is that there is just so much information availableâhundreds of billions of rows of data from industry data providers such as Nielsen and comScore, from channels such as AMCâs TV Everywhere live web streaming and video on demand service, from retail partners such as iTunes and Amazon, and from third-party online video services such as Netflix and Hulu.
RESULTS: Many of the results delivered by this new analytics capability demonstrate a real transformation in the way AMC operates. For example, the companyâs business intelligence department has been able to create sophisticated statistical models that help the company refine its marketing strategies and make smarter decisions about how intensively it should promote each show.
With deeper insight into viewership, AMCâs direct marketing campaigns are also much more successful. In one recent example, intelligent segmentation and lookalike modeling helped the company target new and existing viewers so effectively that AMC video on demand transactions were higher than would be expected otherwise.
This newfound ability to reach out to new viewers based on their individual needs and preferences is not just valuable for AMCâit also has huge potential value for the companyâs advertising partners. AMC is currently working on providing access to its rich data-sets and analytics tools as a service for advertisers, helping them fine-tune their campaigns to appeal to ever-larger audiences across both linear and digital channels.