Data. Does it Matter?

Transcription

1 Data. Does it Matter? Jarut N. Cisco Systems

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3 Data & Analytics are Top of Mind in Every Industry Automotive Auto sensors reporting location, problems Communications Location-based advertising Consumer Packaged Goods Sentiment analysis of what s hot, problems Financial Services Risk and portfolio analysis New products Education and Research Experiment sensor analysis High Technology/ Industrial Mfg. Mfg. quality warranty analysis Life Sciences Clinical trials Genomics Media/ Entertainment Viewers/advertising effectiveness On-Line Services/ Social Media People and career matching Web-site optimization Health Care Patient sensors, monitoring, EHRs Quality of care Competitive success depends on Data and Analytics Oil and Gas Drilling exploration sensor analysis Retail Consumer sentiment Optimized marketing Consumer Travel and Transportation Sensor analysis for optimal traffic flows Customer sentiment Utilities Smart-meter analysis for network capacity, Law Enforcement and Defense Threat analysis - social media monitoring, photo analysis

4 Data Management and Agility CHALLENGES 3.8 PETABYES Under 1 management domain! INSIGHTS AND IMPACT Unlock the business value of large data sets Provide SLAs for internal customers using Big Data analytics services Support multiple internal users on same platform SOLUTION Implemented enterprise Hadoop platform on Cisco Integrated Infrastructure for Big Data Automated job scheduling and process orchestration using Cisco Tidal Enterprise Scheduler Analyzed service sales opportunities in one-tenth the time, at one-tenth the cost $40 million in incremental service bookings in the current fiscal year as a result of this initiative Implemented a multitenant enterprise platform while delivering immediate business value

5 Big Data/Analytics Data Management Data Virtualization / Integration Analytics / Business Intelligence

6 A Holistic View of a Big Data System Real-Time Streams Real-Time Processing Analytics ETL Real-Time Structured Database (hbase, Gemfire, Cassandra) Big SQL (Greenplum, AsterData, Etc ) Batch Processing Unstructured Data (HDFS)

7 How Do We Define Big Data? Big Data is not just a technology. It is a business strategy for capitalizing on information resources.

8 More than just Data Warehousing: Big Data s Value Is in the Analytics 12+ terabytes of Tweets created daily. Volume

9 More than just Data Warehousing: Big Data s Value Is in the Analytics 12+ terabytes of Tweets created daily. Volume 100s of different types of data. Variety

10 More than just Data Warehousing: Big Data s Value Is in the Analytics 12+ terabytes of Tweets created daily. Volume Velocity 5+ million trade events per second. 100s of different types of data. Variety

11 More than just Data Warehousing: Big Data s Value Is in the Analytics 12+ terabytes of Tweets created daily. Volume Velocity 5+ million trade events per second. 100s of different types of data. Variety Veracity Only 1 in 3 decision makers trust their information.

12 More than just Data Warehousing: Big Data s Value Is in the Analytics 12+ terabytes of Tweets created daily. Volume Velocity 5+ million trade events per second. 100s of different types of data. Variety Veracity Only 1 in 3 decision makers trust their information. It is all about better analytics on a broader spectrum of data, and therefore represents an opportunity to create even more differentiation among industry peers.

13 Data Warehousing Data warehousing is the process of centralizing or aggregating data from multiple sources into one common repository. Example Use Cases: Facebook and Retail

14 Data Virtualization is an approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted or where it is physically located. Example: Google

15 Data Warehouse Optimization with Data Virtualization Data Sources Relational, Mainframe Data Warehouse (DW) Deliver Enriched BI/Analytics Virtualize data from DW and Hadoop Deliver richer & deeper data for Analytics BI/Analytics Documents and s Social Media, Web Logs Machine Device, Cloud HDFS HDFS HDFS Optimize Storage / Costs Migrate infrequently used data to Hadoop Process Unstructured, New Sources in Hadoop Server Offload ELT & Virtually Expand the DW

16 Data Analytics and Business Intelligence is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. Example Use Case: Retail Inventory Accuracy

17 Big Data vs. Data Analytics Big Data: Save money: Cost effective scale Consolidate many types of data Operational efficiencies Data Analytics: New ways to make money: Predict, understand, and monetize customer behavior Fact-based decision making Enable real-time tactical decisions Also save money: Increase security, reduce fraud, predict failures

18 Business Intelligence or Business Analytics? What s the Difference? Business Intelligence: Rear-view mirror Answers what happened? Tactical/departmental/siloed Predefined and canned What did we sell, to who, and how? Business Analytics: Windshield and beyond Answers what is next? Strategic/enterprise/holistic Discovery and ad hoc What can we sell and to who?

19 Source of the Data? 12+ TBs of tweet data every day 30 billion RFID tags today 4.6 billion camera phones worldwide 2.5 exabytes of data every day 500 / Second Data sent per 4G LTE Enabled Car 25+ TBs of log data every day 100s of millions of GPS enabled devices sold annually 200 million smart meters

20 How the Data Is Sourced

21 Where the Data Is Sourced Co-Location XaaS Provider Remote Branch Retail Outlet Head Office Manufacturing What We Need: Localized Processing Intercloud Connectivity Data Optimization Data Virtualization Branch Office

22 The Classic Enterprise Challenge Growing Data Volumes Tight IT Budgets Shortened Processing Windows Latency in Data The Challenge Escalating Costs ETL Complexity Demanding Business Requirements Hitting Scalability Ceilings Do More with Less

23 Data Warehouses Cannot Cost-Effectively Support Data Growth Today, growth is accommodated by additional investment in your data warehouse 100% DATA GROWTH 100TB 100TB 100TB Data Warehouse $20,000 $100,000/TB To Add Capacity to the Data Warehouse Incremental spend of $2M $10M Big Data complements your data warehouse, offloading data to defer/avoid more costly spend 100TB Keeps the Right Data in the Data Warehouse: Operational Analytics Reporting Business Analytics LOWER VALUE DATA HIGH VALUE DATA 50TB 100TB Hadoop Cluster Cost $1000 $2000/TB Incremental Cost $240K $300K 50TB Offloading Everything Else to Big Data: Saves $1.85M $9.8M Historical Data Data Processing Data Hub/Ad Hoc Exploratory Transformation/Batch

24 Opportunity: Turning Big Data into Wisdom MORE IMPORTANT WISDOM (Scenario Planning) KNOWLEDGE INFORMATION Increase productivity (MIT, 2003) Speed delivery of new innovations to market Create highly specific customer segmentation Tailor products and services Anticipate requirements and outcomes Collect more accurate performance data Analyze variability, understand root causes DATA LESS IMPORTANT Data-driven enterprises outperform their industry peers by up to 6%, are up to 26% more profitable* Gartner s 2013 CIO survey: Analytics/business intelligence was the number one technology priority *MIT, 2013

25 Summary Big Data is a large ecosystem comprised of over ISVs. Cisco views the market as centered around three major pillars, which are data management, data warehouse optimization and expansion, and analytics and BI. Data can be virtualized, warehoused, optimized, mined, analyzed, or used to produce new data. CEOs, the heads of business lines, board members, marketing and financial leaders, all may be the drivers of Big Data initiatives.

26 Q & A