How to Leverage Big Data in the Cloud to Gain Competitive Advantage

Transcription

1 How to Leverage Big Data in the Cloud to Gain Competitive Advantage James Kobielus, IBM Big Data Evangelist Editor-in-Chief, IBM Data Magazine Senior Program Director, Product Marketing, Big Data Analytics Chair, CSCC Big Data in the Cloud Working Group 1

2 Cloud supporting Big Data is the foundation of the Smarter Planet Cloud Mobile Social Internet of Things 2

3 Cloud Ensures Scalability for Big Data Application Workloads Big Data = realizing differentiated value from advanced analytics and trustworthy data at cloud scales. Volume Velocity Variety 12terabytes of Tweets create daily s million trade events per second video feeds from surveillance cameras Analyze sentiment Identify potential fraud Monitor events of interest 350 billion 500 million 80% data meter readings per annum growth call detail records per day are images, video, documents Predict power consumption Prevent churn Improve constituent satisfaction 3

4 Why is Big Data in the Cloud Important Now? The power of all data coming together to deliver improved outcomes Top 5 Use Cases We ve Observed 1. Enrich your information base with Big Data Exploration 2. Improve customer interaction with Enhanced 360º View of the Customer with the power of cloud services 3. Optimize operations with Operations Analysis 4. Gain IT efficiency and scale with Data Warehouse Augmentation 5. Prevent crime with Security and Intelligence Extension 4

5 Cloud is the Latest Major Wave of Technology Confluence of Social Mobile Cloud Big Data / Analytics Back Office Computing Client Server PC World Wide Web and ebusiness 60 s 80 s 90 s We are here 5

6 Industry Movement from Traditional Environments to Clouds Many users are already on the way to cloud with consolidation and virtualization efforts CLOUD Dynamic provisioning for workloads STANDARDIZE Operational Efficiency SHARED RESOURCES Common workload profiles AUTOMATE Flexible delivery & Self Service VIRTUALIZE Increase Utilization CONSOLIDATE Physical Infrastructure 6 Traditional IT Leon Katsnelson (leon@ca.ibm.com) 6

7 Technology continues to be critical to business: Software is emerging as a growth engine IBV CEO Studies Technology factors 1 Strategic reinvention Market factors Macro-economic factors People skills Regulatory concerns Socio-economic factors Globalization Environmental issues Geopolitical factors 136% are more likely to use cloud to reinvent customer relationships Better decisions 170% more likely to use analytics extensively via cloud to derive insights Deeper collaboration 79% more likely to rely on cloud to locate & leverage expertise in the ecosystem Source: IBM Institute for Business Value: The Customer-activated Enterprise - bit.ly/1od8p0f When will it become critical to implement Digital Transformation across your organization? Don t know (4%) Never (5%) 3+ years Within 2 years This year That time has passed - it is already a matter of survival 13% 33% 18% 27% Source: MIT Sloan Management Review, Research Report 2013: Embracing Digital Technology - bit.ly/1stger9 7

8 What are Industries doing with Big Data in the Cloud Financial Services Fraud detection 360 View of the Customer Utilities Weather analysis Smart grid management 8 Transportation Logistics optimization Traffic congestion Health & Life Sciences Epidemic early warning ICU monitoring Telecommunications Geomapping / marketing Network monitoring Multiple Industries Customer Retention Customer Acquisition Manufacturing Manufacturing Efficiency IT System Log Analysis Cybersecurity Outage prevention Resource Prediction Warehouse Integration Retail Marketing Campaign Efficiency Targeted Marketing MicroSegmentation Law Enforcement Multimodal surveillance Cyber security detection

9 Cloud-based Big Data spans Systems of Record & Engagement Established Approach Structured, analytical, logical Systems of Record Transaction Data Internal App Data Mainframe Data OLTP System Data ERP Data Data Warehouse Structured Repeatable Linear Hadoop and Streams Unstructured Exploratory Dynamic Emerging Approach Creative, holistic thought, intuition Systems of Engagement Multimedia Web Logs Social Data Text Data: s Sensor data: images RFID Traditional Sources New Sources 9

10 How to get there? Step 1: Build your business case for Big Data in the cloud Do thorough risk & cost analysis of cloud deployment Highlight strategic business drivers for Big Data analytics in cloud 10 Articulate the advantages of cloud-based Big Data deployment Identify candidate Big Data cloud applications Enlist business champions for Big Data in cloud

11 How to get there? Step 2: Assess which Big Data functions are best deployed in cloud Considerations Enterprise apps already hosted in cloud? High-volume data requiring extensive preprocessing? Tactical apps beyond capabilities of legacy platforms? Elastic provisioning of very dataintensive but short-lived analytic and data management apps? Provision Big Data development resources for cloud initiatives Identify Big Data sets most suitable for cloud storage 11 Specify Big Data analytic processing to execute in the cloud

12 How to get there? Step 3: Develop your cloud Big Data technical approach Considerations Public vs. private vs. hybrid vs. MPP RDBMS vs. Hadoop vs. NoSQL vs Consolidated vs. multi-tier vs. federated vs Homogeneous vs. hybrid Identify the optimal Big Data platform for each role, application, or workload 12 Define the optimal Big Data cloud topology

13 How to get there? Step 4: Maintain tight controls over your big data in the cloud Considerations Governance Security Privacy Risk Accountability Compliance Integrate your business controls with the platforms on which you implement Big Data in the cloud Align controls with the sensitivity levels of disparate Big Data subject domains in cloud Ensure that controls scale with your Big Data cloud deployments 13

14 How to get there? Step 5: Deploy, integrate, & operationalize your Big Data cloud Converge Big Data cloud operational siloes Administer Big Data cloud through consolidated system management tools Provide Big Data cloud users with a single throat to choke on Big Data cloud support Automate Big Data cloud support functions to maximum extent feasible Deliver consulting support to users considering implementing new Big Data cloud initiatives Don t do Big Data in the cloud unless you have a concrete plan for making it production-ready! 14

15 Key Takeaways Big Data is integral to many cloud services applications Big Data platforms ensure scalability, flexibility, and costeffectiveness for cloud analytics: Massively parallel processing, in-database execution, storage optimization, data virtualization, and mixed-workload management Cloud services enable Big Data objectives: Ubiquitous, convenient, on-demand network access to data analytics Shared pool of configurable data analytic computing resources Dynamic provisioning and release of data analytic resources Download the CSCC whitepaper: Deploying Big Data Analytics Applications to The Cloud: Roadmap for Success" ( Provides guidance for organizations to realize value from cloud-based Big Data initiatives 15

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