How can you make sense of messy data? How do you wrap structure around non-relational, flexibly structured data? With the growth in cloud technologies, how do you balance the need for flexibility and scale with the need for structure and analytics? Join us for an overview of the marketplace today and a review of the tools needed to get the job done. During this hour, we'll cover: - How big data is challenging the limits of traditional data management tools - How to recognize when tools like MongoDB, Hadoop, IBM Cloudant, R Studio, IBM dashDB, CouchDB, and others are the right tools for the job.

1. © 2014 IBM Corporation1
Making sense of data
Integrating Structure and Analytics with Unstructured Data
John Park – Lead Product Manager for dashDB

2. Disclaimer
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3. © 2014 IBM Corporation3
Gordon Moore stated,
“The future of integrated electronics is the future of electronics itself. The advantages of
integration will bring about a proliferation of electronics, pushing this science into many new
areas. Integrated circuits will lead to such wonders as home computers–or at least terminals
connected to a central computer–automatic controls for automobiles, and personal portable
communications equipment.”
John Park postulates (who incidentally is NOT as smart as Gordon Moore, nor as successful)
“Just as efficiencies in electronics have made electronics more powerful, accessible and
consumable. Technology in itself benefits from this “Moore’s law” where efficiencies are made in
software, and innovation leading to more powerful, accessible and consumable Technologies”.

4. Lets Level Set on DBs
Larry Ellison is still rich (Forbes March 2015)
Big Data is still a big opportunity and reality.
(insert some ridiculous statistic here)
Innovation & technology makes data easier to
obtain, analyze and consume

5. Innovation driving down TCO changing the DBMS landscape
Commoditization of technology is driving a new DBMS landscape
Emerging DBMS technologies are replacing today's infrastructure

6. What database technologies are driving the most rapid change ?
o Database as a Service
o In Memory
o Document Store
o Hadoop
o Graph
o Table / Time Series

7. Database as a Service
What is it ?
Cloud hosted and provider provisioned
Scalable
Multi tenant and dedicated
Included services (administration, monitoring, security)
Abstraction layer of operational functions – focus on user end points
Who’s playing in this space ?
1010data.com; Microsoft Azure SQL Database, Dynamo DB, Amazon Redshift
Factoids ..
Great for application development, sandbox and POC
NoSQL has gained traction for production purposes specifically in the mobile / Web 2.0
arena
SQL / ACID used for analytics processing, consolidation and data mart use cases.

8. In Memory
Moves the database into memory either partially or in totality
Supports both transactional and analytical processing
In memory is lending itself to the creation of Hybrid Transactional Analytical Processing
(HTAP)
SAP, Oracle, IBM, VoltDB, Kognitio, ParStream
If HTAP becomes a reality – real time analysis could be supported
Workload optimized In-Memory DBMS are replacing traditional RDBMS systems

9. Document Store
Supports JSON or XML
NoSQL
Basic availability, Soft State, and Eventual Consistency (BASE) NOT ACID
JSON datatype support in traditional RDBMS (IBM, Teradata)
Open Source options as well. MongoDB and CouchDB
Perfect for Web 2.0 and Gaming applications
Technology startups adopting the technology due to ease of scalability and speed to
deployment

10. What are these technologies causing ?
2 “..zation”s … Modernization Consolidation
As organizations ask how to leverage data from their current infrastructure
How to control the sprawl of desktop DBMS
How to collate and aggregate large data sets
How to control license and capital costs
Technology forces Modernization
Replacing old technology with new. XML to JSON. Row based databases to columnar
for analytics.
Moving to Open Source Databases and Cloud based infrastructure
Technology forces Consolidation
Appliances to move data to "one source of truth"
HTAP systems (hybrid transactional analytical systems)

11. Generation D driving Modernization & Consolidation
There are four distinct approaches to data and analytics… and one group of enterprises uses
insights, cloud, and data very differently
Generation D enterprises are:
3x more likely to excel at developing
insights regarding their customers
and marketplace
Dataandanalytics
2x more likely to automate processes
and decisions based on insights from
analytics
2x more likely to believe cloud is
transforming their business model
Cloud
2x more likely to engage customers
via digital channels (mobile and social)
Engagement
Analyticmaturity
Data breadth and sophistication
Traditional
Analytically
ambitious
Data-rich,
analytically enabled
Generation D:
Data-rich,
analytically driven
19%
31%
21%
29%

12. Generation D All others
Generation D
versus all
others
34%
37%
43%
42%
33%
Developing new revenue streams
Penetrating new markets
Improving interactions with customers
Operating efficiently
Managing risk
Responding to security threats
Faster time to market
3.7x
2.5x
2.9x
2.4x
3.0x
9%
13%
18%
14%
14%
46% 18%
2.6x36% 14%
2.5x
Generation D enterprises are extremely effective at addressing
business challenges

13. Generation D enterprises use data and analytics throughout the
business
Generation D All others
Generation D
versus all
others
Educate employees on the use of data and analytics
Provide data and analytics in real time
Extensively share data and analytics internally
2.7x
1.6x
1.5x
31%
40%
43%
85%
60%
70%

14. Generation D enterprises view cloud as key to enabling
transformation
Generation D All others
Generation D
versus all
others
60%
56%
54%
53%
Believe cloud is transforming their business model
Use cloud for analytics
Use cloud for data management
Use API-based services
1.9x
1.8x
2.5x
1.8x
31%
23%
30%
29%

15. Competing faster on
the cloud
Using APIs to
speed performance
• A real estate company desired to
collect and distribute property
information between employees
faster.
• Through a cloud database, the
company gives employees the
ability to sort through existing data
and upload their own, such as
photographs of properties and
location information, while in the
field.
• It now has a competitive advantage
during time-sensitive biddings and
can predict imminent vacancies.
• A bank wanted to give their
institutional clients access to some
of its efficient, in-house capabilities.
• The bank offers API services over
the cloud which provide access to
proprietary platforms and stream
real-time data.
• Institutional clients can now view
real-time information (e.g.,
exchange rates) and perform
actions such as foreign exchange
transactions more easily.
How would you answer Generation D’s call ?
Improving the customer
experience with real-time data
• A trucking company wanted to
revamp their communication with
drivers on the road.
• It equips all of the company’s
trucks with a telematics system that
logs GPS, engine use, and speed/
braking data.
• The real-time information allows the
company to provide updated quotes
and delivery status while optimizing
fuel costs and reducing its mobile
units by 60%.

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40. Traditional
Analytically
ambitious
Data-rich,
analytically enabled
Generation D:
Data-rich,
analytically driven
Infusing the majority of processes and decisions with
analytics
Tackling complex data sources and applying more
predictive and prescriptive analytics
Managing more of their data and analytics on
the cloud
Moving toward mobile and social as their primary
methods of engaging customers
Changing their culture, not just their technology
Generation D is:

41. One more week to Enterprise Data World 2015 !
Drop by the Booth #306 – lets talk.
John Park – jjpark@ca.ibm.com
Merci J