tdwi chicago presentation: is the logical data warehouse, logical?
TRANSCRIPT
© 2015 IBM Corporation
Is the Logical Data Warehouse, logical? Nancy Hensley
Director, IBM Analytics Marketing
March 2015
© 2015 IBM Corporation 2
Agenda
Embrace all data to drive competitive advantage
IT systems evolution
The traditional data warehouse yielding to the logical data warehouse
IBM’s point of view
© 2015 IBM Corporation 3
Data is the new basis of
competitive advantage Front runners will:
Drive business outcomes by applying more sophisticated analytics across
more disparate data sources in more parts of
their organization.
Capture the time value of data by developing “speed of insight” and “speed of
action” as core differentiators.
Change the game in their industry and profession by
infusing analytics into everything.
Our point of view
© 2015 IBM Corporation 4
Enable all analytics
Embrace all data
Run at the speed
of business
1
2
3 Results quickly
& easily digested by anyone
Analytics deployed anywhere
Data analyzed
anywhere
and infused
everywhere
Successful Organizations… The Analytics Culture
Analytics is only as good as the data that fuels it
© 2015 IBM Corporation 5
Disruptions in today’s data centers
Today’s Requirements Disruption to the
Data Center Market Data Points
Users want the ability to build and consume
things on their own – Self-service & Agility Cloud
Spending on Cloud based BD&A solutions will
grow 3x faster than on-premise solutions1
• Increase in social, machine and other new
data types
• Lower cost of data for analytics
• Take advantage of the pace of open
source innovation
Hadoop, Spark
Hadoop, Map-Reduce market expected to hit
$2.2B and CAGR of 58% over next 5 years2
Need for insight and faster time to value Data Appliances
Appliances can provide an impressive 15-
30% TCO reduction for data warehouses3
Faster ability to get to relevant data and
insight Cognitive
50% of consumers will interact with services based on cognitive
computing on a regular basis by 20181
Need for operational analytics at point of
engagement
Low latency analytics on
transactional data
There is increasing pressure to exploit data
for decisions ”in the moment”4
The ability to access all forms of data for
analytics regardless of the data container in
which they reside
Logical data warehouse
By 2017, most business intelligence and
analytics platforms
will natively support multi-structured
data and analysis5
1”IDC FutureScape: Worldwide Big Data and Analytics 2015 Predictions”, IDC, Dec. 2014. 2”10 Hadoop Predictions for 2015”, Computer Business Review, Dec. 2014. 3”The Future of Data Warehouse in an Era of Appliances and Big Data”, Wikibon, Feb. 2013. 4”The Analytic-Transactional Data Platform: Enabling the Real-Time Enterprise”, IDC, Carl W. Olofson, Dec. 2014. 5”Information and Analytics Predictions Through 2020”, Gartner: Douglas Laney, Ehtisham Zaidi, Date: Jan. 2015.
© 2015 IBM Corporation 6
Is it time to invest or pull back?
Front runners reap great rewards
69% of front runners created
a significant positive
impact on business
outcomes using data and
analytics in the past three
years2
60% of front runners created a
significant positive impact
on revenues using data
and analytics in the past
three years2
53% of front runners created a
significant competitive
advantage using data
and analytics2
Analytics pays back $13.01
for every dollar spent1
250% is the ROI of solutions
that incorporate predictive analytics3
1 Analytics Pays Back $13.01 for Every Dollar Spent” Nucleus Research, September 2014 2 Analytics: The speed advantage” IBM Institute of Business Value, 2011 3 The Business Value of Predictive Analytics, IDC, June , 2011
© 2015 IBM Corporation 7
Employees
• Data Warehouses
• Business intelligence
Systems of Insight
Systems of Engagement
• Mobile apps
• Customer management
• Social technologies
• Data analytics
Systems of Record
• Enterprise Resource Planning
• Financial Systems
• Transactional & Operational
Systems of Insight
• Analytic applications (descriptive,
evaluative, predictive, cognitive)
• Data Warehouses
• Business intelligence
Consumers
Line of business Data Scientists Business Analysts
Partners
Executives
IT Systems have evolved and affect how we think
Digital Devices
© 2015 IBM Corporation 8
Smart products Employees
• Data Warehouses
• Business intelligence
Systems of Insight
Systems of Engagement
• Mobile apps
• Customer management
• Social technologies
• Data analytics
Systems of Record
• Enterprise Resource Planning
• Financial Systems
• Transactional & Operational
Systems of Insight
• Analytic applications (descriptive,
evaluative, predictive, cognitive)
• Data Warehouses
• Business intelligence
Consumers
Line of business Data Scientists Business Analysts
Partners
Executives
Let’s look closer at the evolution
© 2015 IBM Corporation 9
Smart products Employees
• Data Warehouses
• Business intelligence
Systems of Insight
Systems of Engagement
• Mobile apps
• Customer management
• Social technologies
• Data analytics
Systems of Record
• Enterprise Resource Planning
• Financial Systems
• Transactional & Operational
Systems of Insight
• Analytic applications (descriptive,
evaluative, predictive, cognitive)
• Data Warehouses
• Business intelligence
Consumers
Line of business Data Scientists Business Analysts
Partners
Executives
Let’s look closer at the evolution
Systems of Record
• Transactional &
Operational
• Enterprise Resource
Planning
• Financial Systems
Systems of Insight
• Data Warehouses
• Business intelligence
• Analytic applications
(descriptive, evaluative,
predictive, cognitive)
Systems of
Engagement
• Mobile apps
• Customer management
• Social technologies
• Data analytics
© 2015 IBM Corporation 10
Systems of Record
The Logical Data Warehouse Emerges
Internal Insight
Reporting
Enterprise
Content
Discovery
Exploration
Decision
Management
Predictive
Analytics
Visualization
Systems of
Engagement
Web or Mobile
Systems of
Engagement
Information Governance
Real-time Analytics
NoSQL Doc
Store
Data Warehouse Deep Analytics,
Modeling
Transactional
Systems
Landing,
Exploration,
Archive
Reporting,
Analytics
Logical Data Warehouse
Transactional
Social
Application
ERP
Financial
Video & Audio
Machine & Sensor
Documents
Third Party
Systems of Insight
© 2015 IBM Corporation 11
IBM’s Point of View
Become an analytics drive organization –
embracing all analytics and all data 1
Recognize the logical data warehouse –
utilize the best data store & platform for the type of analytics required 2
Accelerate and simplify analytics with new technologies 3
fuel decisions with better data & analytics
transform service levels for analytics
redeploy resources to strategic initiatives
© 2015 IBM Corporation 12
But where do I start? Common entry points
Add new data sources,
increase usage or
analytic capability
Accelerate analytic
queries or boost
performance
Exploit technology
innovations to
leverage big data
Add flexibility through deployment model choices
Appliances, Software Solutions, Cloud
IBM has solutions to help with each entry point
© 2015 IBM Corporation 13
Data warehousing
The engine for making data smarter, faster
• Data warehouse appliances to
accelerate analytic performance and
increase data & analytic capacity
• Latest in-memory, columnar
technology to boost performance and
lower cost
• Support for new data types to drive
insight and enable exploration
• Cloud services to speed deployment,
and increase agility for your business
Benefits
• Analytics delivered with speed and
simplicity
• Integrated logical data warehouse
to exploit all data assets
• Flexible architecture deployment
options
Key Features
© 2015 IBM Corporation 14
IBM can help you navigate a path beyond the barriers—to better,
faster outcomes
• Diagnose the right architecture for
your needs
• Maximize the value of your existing
investment
• Reduce the cost of implementation
• Get you to your business objectives
faster
1. Leverage experience with
multiple technologies
2. Compare performance
profiles
3. Optimize the deployment mix
4. Apply the right technologies to
accelerate innovation
Our Objectives Our Approach
Leverage technology innovation
for business value