big data & information management channel manager

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Information Management & Business Analytics April 3 rd 2014 Big Data/Information Management Mark Chandler Big Data & Information Management Channel Manager Email: [email protected] Mobile: 07738 310900

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Information Management and Business Analytics, DB2, Netezza, InfoSphere portfolio and Cognos Data Manager.

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Page 1: Big Data & Information Management Channel Manager

Information Management & Business Analytics April 3rd 2014

Big Data/Information Management

Mark Chandler

Big Data & Information Management Channel ManagerEmail: [email protected]: 07738 310900

Page 2: Big Data & Information Management Channel Manager

Information Management & Business Analytics April 3rd 2014

Agenda

Where do you go to for information?

Business Analytics & DB2

Netezza & Business Analytics

InfoSphere portfolio & Business Analytics

Cognos Data Manager

Page 3: Big Data & Information Management Channel Manager

Information Management & Business Analytics April 3rd 2014

The Right Tools

IM Exclusive BP Portal

Business Partner Locator Tool

PartnerPlan & SVP Readiness Dashboard

Web ContentSyndication

Ready to Execute Campaigns

Software Briefing Center

IBM Market Insights (Comp)

All labels are hyperlinked on this page in Slide Show mode

Financing a Smarter Planet

Getting Started with Social Media

IBM Global Financing

Page 4: Big Data & Information Management Channel Manager

Information Management & Business Analytics April 3rd 2014

Cognos & DB2

Page 5: Big Data & Information Management Channel Manager

Information Management & Business Analytics April 3rd 2014

Page 6: Big Data & Information Management Channel Manager

© 2013 IBM Corporation© 2013 IBM CorporationApril 12, 2023

Make Better Business Decisions...Faster Accelerate Business Intelligence Performance with Cognos BI 10 & DB2 10.5

<<Speaker Name Here>><<Speaker Title Here>><<For questions about this presentation contact Speaker Name [email protected]>

Page 7: Big Data & Information Management Channel Manager

Information Management & Business Analytics April 3rd 2014

Instructions Data

Results

C1 C2 C3 C4 C5 C6 C7 C8C1 C2 C3 C4 C5 C6 C7 C8

Dynamic In-Memory In-memory columnar processing with dynamic movement of data from storage

Actionable CompressionPatented compression technique that preserves order so data can be used without decompressing

Parallel Vector ProcessingMulti-core and SIMD parallelism

(Single Instruction Multiple Data)

Data SkippingSkips unnecessary processing of irrelevant data

Encoded

“A query that takes hours on a 120 node Teradata system runs in seconds on DB2 with BLU Acceleration on a 24 core system.”

Beta Test Client

Why is DB2 with BLU Acceleration Different

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Information Management & Business Analytics April 3rd 2014

© 2013 IBM Corporation8

Benefits of Cognos BI and DB2 with BLU Acceleration 18X Faster Cube Loading1 provides more timely information

– Can refresh cubes more often – In one test, 1TB dynamic cube load took around 9.5 hours without BLU Acceleration and

30 min with BLU.

14X Faster Click Through Performance2 – More data in memory to improve performance of detailed query drill-downs – When data not found in Cognos dynamic cube, system looks for the data in BLU

Acceleration table – also in memory – Result is significant performance improvements

Storage savings – Use of actionable compression – use data in compressed format – BLU Acceleration requires less database objects like indexes and MQTs that can often

take up considerable amounts of space even when compressed

Simplicity – BLU Acceleration tables don’t need indexes or MQTs that have to be created and/or

tuned– Just create the table, load data and run reports – Tools recommend which tables should be converted to BLU for maximum performance

1. To fill the Cognos Dynamic Cube aggregate cache, we saw an 18X improvement between DB2 10.5 and 10.1. We went from 9.5 hours to load the cache using DB2 10.1 compared with less than 30 minutes to load the cache using DB2 10.5 with BLU acceleration.2. To further understand the benefits of using BLU acceleration with Cognos Dynamic Cubes, we isolated Cognos report queries against DB2 10.5 and DB2 10.1. These queries are examples of the SQL that would be run when a report has to query the database directly, rather than leveraging the in-memory aggregate cache. On average, the report query workload showed a 10x improvement. From 100 seconds to 10 seconds.*Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

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Information Management & Business Analytics April 3rd 2014

Cognos and Netezzaa blazing combination

© 2013 IBM Corporation9

1. Interactive analysis – engaging self-service interfaces2. Enterprise scalability – supports thousands of users3. Compelling visualizations – on the web, mobile, or emailed4. Optimized queries – intelligently balances local and remote data processing5. No wait time – instantaneous responses when in-memory cache is

leveraged

C O G N O S + Blazing Results=

PureData System for Analytics

85+ Joint Customers5 reasons to use Cognos BI with the Netezza

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Information Management & Business Analytics April 3rd 2014

UK Examples of Netezza and Cognos customers

Greene King Ace Insurance Coventry Building Society

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Information Management & Business Analytics April 3rd 2014

11

Lots of data: 250 GB– 1.000 TB New data mart project in

development Lots of complex and ad hoc queries Encountering performance

challenges Price sensitive Old technology installations: e.g.

Sybase, HP NeoView and Red Brick customers (end-of-life concerns)

Mid-range Oracle customers: Exadata and all Oracle DW BI projects

Limited IT-resources – need for simplicity

Industry focus: Digital Media Born-on-the-Web Data Aggregators Retailers Financial Services Life Sciences

Management unable to answer important questions from existing data warehouse

Exploiting information for competitive advantage

Users want answers in seconds and minutes (SLA’s)

Business needs to analyze up-to-date data all the time

Netezza buying indicators

Technology side Business side

Page 12: Big Data & Information Management Channel Manager

Information Management & Business Analytics April 3rd 2014

InfoSphere portfolio and Cognos

Value proposition - InfoSphere & Cognos:

– Ensure highest quality data for trusted Cognos reports (Data Quality solution)

– Know what information is on your reports and where it came from (Business Information Exchange solution)

– Make decisions based on up-to-date information (Data Replication solution)

– Expand support for broader enterprise data access (Data Integration)

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Information Management & Business Analytics April 3rd 2014

Cognos Data Manager

The Cognos Data Manager (DM) product – low cost, simple to deploy and use ETL tool– developed to fulfil the needs of feeding data to the Cognos cubes and schemas

It has worked well but is not as comprehensive as the InfoSphere Portfolio

There will be clients who need a more powerful solution

Chance for Cognos and IM partners to work together to identify opportunities

Page 14: Big Data & Information Management Channel Manager

Information Management & Business Analytics April 3rd 2014

Big Data/Information Management

Mark Chandler

Big Data & Information Management Channel ManagerEmail: [email protected]: 07738 310900