The Challenge of Big Data Benchmarking Large-Scale Data Management Insights from Benchmark Research

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1 Benchmarking Large-Scale Data Management Insights from Presentation

2 Confidentiality Statement The materials in this presentation are protected under the confidential agreement and/or are copyrighted materials of Ventana Research. Any use of the materials beyond the intended use for education must be approved in writing by Ventana Research. Ventana Research On-Demand team level and above clients may quote and reference research. This presentation and any related content cannot be redistributed to anyone except for the intended audience and management reviewing Ventana Research. 2

3 Ventana Research Focused on Education Ventana Research is the leading benchmark research and strategic advisory services firm. Our unparalleled analytic insights and best practices guidance are based on our rigorous research-based benchmarking, business, technology and best practices services. Unique Combination of Capabilities Members (245,000+) and Reach to Professionals (3 million) Research and Reach across Finance, Ops and IT Expertise Across Business and Technology Understand Business Domain and Processes Conduct and Deliver Develop Analytic and Best Practice Assessments Formalized Research Coverage of Technology Vendors Deliver Research on Technology Impact to Business 3

4 for Business and IT Our benchmarks reflect the current state and direction of business, technology and best practices across business and IT They encompass business and technology metrics across all industries, organization sizes, titles and functional roles Benchmarks assess and set objective standards along the people, process, information and technology dimensions Benchmarks provide guidance on effectiveness of technology 4

5 Our Ingredients and Science to Research Our Research Agenda is driven by our analysts deep understanding of the technology buyer s business requirements and our knowledge of vendor solutions and technology. All research builds on structured analysis of the people, process, information and technology dimensions that describe business issues. Value Index Research Experience and Knowledge People Benchmark Research Supplier Research Technology Research Process Business and IT Areas Technology Best and Worst Practices Information Buyer Research Clients and Feedback Ventana Research

6 Business Technology Innovations: Changing Information Management Big Data Business Collaboration Mobile Technology Social Media Cloud Computing Business Analytics After a decade of limited technology innovation, the business computing landscape has changed significantly, driven by a set of technologies that will redefine how, where and when work is performed. These innovations will be used to drive effectiveness, not just efficiency Ventana Research

7 Thanks To Our Sponsors For Supporting Our Research MEDIA SPONSORS 7

8 Research Methodology and Demographics Designed survey to collect data from organizations dealing with big data In-depth survey of people, process, information and technology issues (approx minutes to complete) Promoted via website, social media, , media partners Collected 163 qualified responses between data from February and April 2011 Data collected as part of our Hadoop and Information Management benchmark research Majority of respondents (69%) from North America Variety of industries and company sizes By function, approximately two-thirds IT and one-third business Source: Ventana Research The Challenge of Big Data 8

9 Agenda Analytics and Metrics Methodology and Approach Process and Timelines Big Data Sponsorship Program Q&A and Follow-ups Technology and Data Landscape 9, Inc.

10 Maturity of Big Data Although there are relatively few innovative leaders, a majority is advancing beyond the tactical level of maturity. Overall Large-Scale Data Maturity 60% 50% 48% 40% 30% 24% 20% 18% 10% 10% 0% Tactical Advanced Strategic Innovative Source: Ventana Research The Challenge of Big Data 10

11 Information Needs Are Outpacing Processes However, organizations are maturing unevenly. Information needs are outpacing other elements. 60% 50% 48% 40% 38% 30% 29% 28% 31% 30% 29% 20% 19% 24% 18% 22% 18% 20% 22% 14% 10% 10% 0% Tactical Advanced Strategic Innovative People Process Information Technology Source: Ventana Research The Challenge of Big Data 11

12 Volume and Velocity of Data Are Most Important In Evaluating Big Data Technology less than 1 TB 10% 1-10 TB 29% TB 31% 101 TB-1 PB 13% more than 1 PB 11% Don't know 7% 0% 10% 20% 30% 40% less than 10 GB per day 26% GB per day 33% 101 GB-1 TB per day 20% 1-10 TB per day 4% More than 10 TB per day 6% Don't know 12% 0% 10% 20% 30% 40% Source: Ventana Research The Challenge of Big Data 12

13 Most Often Analyzing Customers and Transactions Source: Ventana Research The Challenge of Big Data 13

14 Most Common Types of Big Data Customer/member data Transactional data from applications 60% 65% Application logs 47% Other types of event data Network monitoring/network traffic Online retail transactions Other log files Call Detail Records Web logs Text data from social media and Search logs Trade/quote data Intelligence/defense data Multimedia (audio/video/images) 36% 36% 34% 34% 31% 29% 21% 19% 16% 13% 13% Weather Smartmeter data Other (please specify) 5% 5% 4% Source: Ventana Research The Challenge of Big Data 14

15 Bringing Internal and External Data Together In a world of growing data sources including social media, external data can be a valuable supplement to internal data. More than 50% are using some form of external big data source. Source: Ventana Research The Challenge of Big Data 15

16 RDBMS is Still King for Large-Scale Data RDBMS RDBMS currently used by nearly 9 out of 10 organizations (89%) and it is the primary big data tool for three-quarters (73%) Wide use of flat files (7 out of 10) represents a market opportunity Source: Ventana Research The Challenge of Big Data 16

17 but the Vast Majority Use a Variety of Big Data Technologies An RDBMS (for example, IBM DB2, Microsoft SQLServer, MySQL, Oracle) on standard hardware 89% 2% 2% 3% 3% Flat files 70% 7% 1% 4% 18% A DW appliance (for example, Netezza, Exadata, EMC Greenplum, Teradata) 34% 11% 3% 21% 31% In-memory databases 33% 13% 4% 17% 33% Hadoop 22% 12% 3% 17% 45% Other 26% 4% 4% 10% 57% A specialized DBMS (for example, Aster Data, Infobright, Kognitio, Paraccel, SybaseIQ, Vertica) 15% 9% 5% 19% 51% Currently in production Plan to use in months No plans to use Plan to use within 12 months Still evaluating Source: Ventana Research The Challenge of Big Data 17

18 Hadoop Adoption Has Been Dramatic Over half the organizations are using or evaluating Hadoop Most often used for advanced analytics Different types of data See our Hadoop and Information Management Benchmark (www.ventanaresearch.com/him) Source: Ventana Research The Challenge of Big Data 18

19 Analyzing All Data In Memory In-memory systems rival data warehouse appliances in popularity Advantage is performance fast retrieval and calculation Dilemma is trying to fit everything in memory Over half (55%) of organizations have more than 10TB of raw data Practical limit for a single machine today is about 1TB of memory Assuming 10:1 compression that s about 10TB raw data Source: Ventana Research The Challenge of Big Data 19

20 Organizations Lack Advanced Capabilities to Maximize the Value of Their Big Data Most organizations do query and reporting against their big data As data volumes grow these become insufficient Predictive analytics, planning and forecasting and visualization techniques can help, but they are the least often available and least often integrated Source: Ventana Research The Challenge of Big Data 20

21 Availability Of Capabilities Data storage 95% 3% Query and reporting 94% 4% Access data from spreadsheets 83% 10% Analytics 83% 14% Data transformation/etl 79% 14% Search for needed data Share/review information in dashboard or portal 66% 75% 29% 20% Visualization Data mining/predictive analytics What-if analysis for forecasting/planning 49% 58% 55% 44% 37% 41% Available Not available Not used / needed Source: Ventana Research The Challenge of Big Data 21

22 Top Four Benefits of Big Data Technologies Allow us to retain and analyze more data 74% Increase the speed of analysis 70% Produce more accurate results 61% Reduce or eliminate manual processes 59% Source: Ventana Research The Challenge of Big Data 22

23 Obstacles in Analyzing Big Data It s not just technology; the top issues are people issues. Staffing 79% Training 77% Real-time Licensing cost Integration 67% 64% 64% Source: Ventana Research The Challenge of Big Data 23

24 Training & Staffing Will Be Critical Half (52%) of big-data teams consist of 10 or fewer people Two-thirds will train staff to meet the needs for current projects and more than half (56%) for future projects Skills shortfall will grow as more than two thirds (69%) said they will have to hire for future projects. More than half (58%) expect to bring in external consultants. This rise in demand for trained resources will create challenges Source: Ventana Research The Challenge of Big Data 24

25 Technical challenges Despite the benefits many technical challenges remain Lack of real-time capabilities (67%) Lack of integration with existing business intelligence and data warehousing tools (64%) Systems management and monitoring Historical data retention and archiving More than three-fourths (79%) are concerned about a data privacy or security breach As a results big-data projects will require more manual effort and resources to overcome these limitations. Source: Ventana Research The Challenge of Big Data 25

26

27 What To Do Next Organizations are still looking for the best way to handle big data, and we expect the evolution to continue as newer solutions mature. At least for the near future, it is reasonable for organizations to deploy multiple solutions that address specific application requirements. If you are working with one or more of these technologies, you may be asked how you can improve your usage. This study also provides valuable lessons and comparisons of different implementations. Source: Ventana Research The Challenge of Big Data 27

28 Purchase and Evaluation process

29 Stakeholders in Recommending Big Data Technologies Big data technologies purchases are driven by IT management and staff. Application developers also play a significant role. IT Management IT staff Application developers Consultants Analysts (Business, Financial or other) CIO or Head of Information Technology Data center personnel Other line of business leaders Head of Marketing 66% 53% 39% 34% 34% 33% 25% 18% 16% Head of Sales CFO or Head of Finance COO or Head of Operations Other staff CEO, President 14% 10% 10% 7% 7% Source: Ventana Research The Challenge of Big Data 29

30 Stakeholders in Evaluating Big Data Technologies IT Management IT staff 66% 59% Big data technologies purchases are driven by IT management and staff. Application developers Analysts (Business, Financial or other) CIO or Head of Information Technology Data center personnel 40% 35% 33% 28% Application developers also play a significant role. Consultants Other line of business leaders Head of Sales CFO or Head of Finance Head of Marketing Other staff COO or Head of Operations CEO, President 23% 19% 14% 13% 13% 10% 8% 4% Source: Ventana Research The Challenge of Big Data 30

31 Stakeholders in Approving Big Data Technologies C-Level executives are involved in approving big data technology purchases suggesting a high level of importance and cost to the organization. CIO or Head of Information Technology CEO, President CFO or Head of Finance COO or Head of Operations IT Management Other line of business leaders Head of Marketing Head of Sales 59% 46% 38% 32% 26% 11% 6% 5% Analysts (Business, Financial or other) IT staff Other staff Application developers Data center personnel 4% 4% 3% 2% 1% Source: Ventana Research The Challenge of Big Data 31

32 Technology And Vendor Considerations Ranked by Very Important Usability Functionality 61% 69% Reliability Adaptability Manageability TCO/ROI 59% 50% 48% 47% Validation 30% Source: Ventana Research The Challenge of Big Data 32

33 Clusters are getting bigger Small clusters are giving way to larger clusters. Half of all clusters today are ten nodes or less. In the future only one fifth will be ten nodes or less. Source: Ventana Research The Challenge of Big Data 33

34 Sourcing Large-Scale Capabilities Software installed locally and managed by the company ("onpremises") 83% 12% 5% Software purchased by the company but managed offsite ("hosted") 41% 33% 26% Capabilities provided as a service paid for monthly or annually ("software as a service" or "SaaS") 35% 31% 34% Currently Planned within 12 months Planned in months Big data implementations are mostly on-premises currently, but are moving to cloud and hosted solutions Source: Ventana Research The Challenge of Big Data 34

35 Next Steps Assess your organization s maturity in managing big data. Determine the types of big data that you need to analyze. Investigate the range of capabilities big-data technologies can provide for your organization. Consider investing in advanced capabilities to maximize the value of your big data. Realize that storing all data in memory may not be a practical option as volumes grow. Take training and staffing issues into account for big-data projects. Source: Ventana Research The Challenge of Big Data 35

36 Next Steps Acknowledge the technical challenges of managing big data. Include Hadoop in your evaluation, especially for managing and analyzing unstructured data and performing advanced analytics. Consider volume and velocity of data as key factors in evaluating big-data technologies. Include both IT and Finance in the adoption and use of big-data systems. Think about the growing size of bigdata clusters and whether to move them off-premises. Source: Ventana Research The Challenge of Big Data 36

37 We Can Help You Improve Big Data We Can Help Today: Benchmark Comparison Advice and Research Educational Workshops Best Practice Guidance Technology Assessment Vendor Selection Benchmark Report Download Executive Report at: 37,

38 Collaborate and Download Executive Report Collaborate with Us: Ventana Research Blog: blog.ventanaresearch.com * Social Media Links Can Be Found On Ventana Research

39 The Ventana Research Value & Difference Ventana Research Gartner & IT Analysts Consulting Firms Tech. Vendors Research & Education Conduct Research as Foundation of Business Research Across Memberships and Market Reach across Finance, Ops and IT Conduct Benchmarks & Generate Market Indexes Assess People, Process, Information & Technology Develop Maturity Model Assessments Technology Knowledge Formalized Research Coverage of Technology Research and Analyze Technology Offerings Write Research Notes About Technology Business & Technology Expertise Across Business & Technology Understand Business Domain and Processes Balance of Effectiveness and Efficiency Focus Ventana Research

40 Benchmarking Large-Scale Data Management Insights from Presentation

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