Cognitive Data Warehouse and Analytics

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Branden Peters
5 years ago
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1 Cognitive Data Warehouse and Analytics Hemant R. Suri, Sr. Offering Manager, Hybrid Data Warehouses, IBM or feel free to reach out to me via LinkedIN!)

Over 90% of the world s data cannot be googled 2 Source: http://www.tennessean.

2 Over 90% of the world s data cannot be googled 2 Source:

3 The winners in this new digital era are data and analytics driven companies Yet only 15% of organizations have the capability to leverage data and advanced analytics across their organization. HBR Insight Economy Study 3 Source:

4 The bottom line today.. It s no longer about enabling analytics but about how analytics can change your business model It s no longer about enabling analytics, but about how analytics can change your business model 4

5 Value The push today is not simply to analytics but to use analytics to drive new business models Operations Reporting & data warehousing Self-service analytics New business models Most are here Efficiency Modernization Democratization Monetization Uses of data 5

6 So How to get out of the starting gates...? Lead with a self service mindset Extend the warehouse with a data lake for additional data discovery and self service Continue to build out the logical or extended data warehouse in a modernization strategy Invest in predictive and learning capabilities (cognitive) 6

Meeting new analytic demands is hard, but a hybrid data architecture can help Use more data sources for

of respondents leverage external data (such as government or third party data) as part of a hybrid cloud

59% of best in class organizations have cloud / hybrid data discovery and analytics solutions.

7 Meeting new analytic demands is hard, but a hybrid data architecture can help Use more data sources for deeper analytics Leverage hybrid data infrastructure and tools For Experience better customer retention 45% of respondents leverage external data (such as government or third party data) as part of a hybrid cloud data warehouse. 59% of best in class organizations have cloud / hybrid data discovery and analytics solutions. Customer retention rate of 74% for those who leverage a hybrid data warehouse vs 65% for those who use on-premises only solutions. 7 Source: Aberdeen, Hybrid Data Warehouse: Fluid, Flexible and Formidable, June 2016

8 How to implement the Hybrid Data Warehouse? Leverage existing investments while taking advantage of new technology, data sources and types Pick the right tool for the job Enable self-service analytics Be more flexible and more agile Analyze more data, faster than ever before And as always do more with less from a cost, staff and skills perspective 8

data Easy access Self-service BI / Analytical Tools Analyst Process: 1) Shop for data 2)

9 Architecting a Better User Experience Data This is the goal! Data And more data! DI DI EDW New analytical platforms to handle un-modelled, multistructured and high velocity data Easy access Self-service BI / Analytical Tools Analyst Process: 1) Shop for data 2) Prepare data 3) Analyze data The Key is to design the analytic architecture in this direction Not in this direction! 9

Top Down Analytic Architecture Design to Enable Self-Service Self Service Analytics Business Analysts Data Engineers Data Scientists Developers Consumers of Data (search, shop, access, explore,

10 Top Down Analytic Architecture Design to Enable Self-Service Self Service Analytics Business Analysts Data Engineers Data Scientists Developers Consumers of Data (search, shop, access, explore, prep, exploit with tools) Composable Data Services on-premises, on cloud, software-defined Data Lakes Data Warehouse Data Marts Information Integration & Governance Enterprise & External Data Sources 10

Thinking Forward to the Next Generation The data warehouse will continue to be the system of trust for most reporting and analytics Self service data discovery

services for the user type and move towards self service The data warehouse continues down it s logical path and becomes hybrid in every way, location, data types

11 Thinking Forward to the Next Generation The data warehouse will continue to be the system of trust for most reporting and analytics Self service data discovery will be driven from the data lake Governance will remain a key design point for both the lake and the warehouse Top down design allows us to customize data services for the user type and move towards self service The data warehouse continues down it s logical path and becomes hybrid in every way, location, data types etc. Spark will help simplify and accelerate the processing of multi-structured data Cognitive Moving from Predictive to Prescriptive Analytics powered by Deep Learning 11

How is Cognitive Different than Advanced Analytics? Programmable computing responds to requests and makes determinations, analyzing data according to predefined parameters.

12 How is Cognitive Different than Advanced Analytics? Programmable computing responds to requests and makes determinations, analyzing data according to predefined parameters. Cognitive systems interact with humans naturally to interpret data, learning from virtually every interaction and proposing new possibilities through probabilistic reasoning. 12

13 Continuous Intelligence: Machine Learning made Simple Infuse Continous Intelligence throughout the Enterprise Data Discovery Data Prep ML Algorithm Model Deploy Predict Continuous Monitoring, Training and Feedback 13

Data Fabric Summary: An Integrated platform for

Data Scientists Developers Consumers of Data (search,

Composable Data Services Data Virtualization / SQL on

DW & marts Advanced Analytics (structured data) MDM

graph DB EDW Mart DW Appliance C Prod Cust Asset D U

data clickstream web logs XML, JSON Enterprise

14 Data Fabric Summary: An Integrated platform for Advanced Analytics Business Analysts Data Engineers Data Scientists Developers Consumers of Data (search, shop, access, explore, prep, exploit with tools) Composable Data Services Data Virtualization / SQL on Everything Advanced Analytics (multi-structured data) DW & marts Advanced Analytics (structured data) MDM System R NoSQL DB e.g. graph DB EDW Mart DW Appliance C Prod Cust Asset D U Streaming data 14 Stream processing sensors Filtered data clickstream web logs XML, JSON Enterprise Information Management Tool Suite web services RDBMS feeds social cloud files office 2017 IBM docs Corporation

IBM Cognitive Infused Hybrid Data Warehouse offers clients choice in selecting the best (combination of) data stores to satisfy their hybrid data warehouse and Cognitive solution needs dashdb Managed

15 IBM Cognitive Infused Hybrid Data Warehouse offers clients choice in selecting the best (combination of) data stores to satisfy their hybrid data warehouse and Cognitive solution needs dashdb Managed dashdb Local Appliance Custom Deployable Software BigSQL on BigInsights (managed & on-prem) 15 Elastic capacity & pricing SQL & NOSQL capabilities Fast time to deployment, load & go Full MPP scalability Built-in analytics Application and analytic portability Hybrid by design IBM managed On-prem or hosted Appliance Agile, Simple Cost equivalent to Hadoop FPGA-powered Prebuild integration for structured data performance with other cloud data Utilizing existing Built-in disaster services Infrastructure recovery Parallel data load Seamless scale-up & out In-place expansion powered by Aspera HW / OS-agnostic Elastic resource Elastic growth Combine compute / storage consumption resources as needed In-memory performance Native data encryption Advanced workload management & monitoring IBM or Client-managed IBM or Client-managed Analytics on operational Data Lake or Day-0 archive data or ODS Unstructured or hybrid data Custom deployment on types broad range of operating Land data fast and apply systems instant analytics Data Discovery and Data Discovery, exploration Exploration, Transformation Highly available and disaster recovery needs

16 Thank you! Questions?

Hybrid Data Management

Hybrid Data Management Gain value from your data without limits Chris Reuter North America Data Warehouse Sales IBM Analytics March 2018 Agenda 1 Themes of data 2 Data Management Strategy 3 IBM s Vision