• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Demystify Big Data Breakfast Briefing: Martha Bennett, Forrester
 

Demystify Big Data Breakfast Briefing: Martha Bennett, Forrester

on

  • 1,102 views

Martha Bennett's presentation from the Demystify Big Data Breakfast Briefing 9th July London

Martha Bennett's presentation from the Demystify Big Data Breakfast Briefing 9th July London

Statistics

Views

Total Views
1,102
Views on SlideShare
1,102
Embed Views
0

Actions

Likes
3
Downloads
49
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Demystify Big Data Breakfast Briefing: Martha Bennett, Forrester Demystify Big Data Breakfast Briefing: Martha Bennett, Forrester Presentation Transcript

    • Big Data – What’s Hype, What’s Reality? Martha Bennett Principal Analyst Hortonworks Breakfast Seminar London, July 9th, 2013 A review of trends and developments
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 2 We’re in a data-driven world
    • Firms recognize the importance of data . . . 13% 17% 18% 18% 19% 19% 20% 26% 27% 27% 28% 32% 37% 4% 6% 6% 7% 7% 8% 7% 7% 8% 8% 9% 11% 18% Implement a bring-your-own PC, smartphone, and/or tablet strategy Create a comprehensive mobile and tablet strategy for employees Shift spending from core systems to applications driving engagement with customers Create a comprehensive cloud strategy Develop smart product APIs that improve product & service capabilities Create a comprehensive mobile and tablet strategy for customers or business partners Cut overall IT costs due to economic conditions Reorganize or retrain IT to better align with business outcomes and drive innovation Help the organization better manage and integrate its partners and suppliers Improve IT budget performance Develop new skills to better support emerging technologies and business innovation Improve IT project delivery performance Improve the use of data and analytics to improve business decisions and outcomes High priority Critical priority Source: Forrsights Business Decision-Makers Survey, Q4 2012 Base: 3,616 business decision-makers from firms with 100 or more employees
    • 1 2 3 4 5 6 7 . . . and BI is a top investment priority . . . The top seven software applications in firms’ adoption plans by year Source: Enterprise and SMB Software Survey, North American And Europe, Q3 2007; Enterprise And SMB Software Survey, North America And Europe, Q4, 2008; Enterprise And SMB Software Survey, North American And Europe, Q4 2009; Forrsights Software Survey, Q4 2010; Forrsights Software Survey, Q4 2011; and Forrsights Software Survey, Q4 2012 Note: We first included industry-specific software in the Q4 2008 survey; we first included finance and accounting in the Q4 2009 survey. 2008 (N = 1,158) 2009 (N = 1,021) 2010 (N = 455) 2011 (N = 913) 2012 (N = 1,092) 2013 (N = 1,631) Source: May 27, 2011 , “Forrsights: The Software Market In Transformation, 2011 And Beyond” Forrester report Business intelligence Customer relationship management Collaboration software Finance & accounting Industry-specific software Enterprise resource planning Human capital management
    • . . . but they don’t use most of their data Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012 Unstructured 50TB Semi- structured 2 TB Structured 12 TB Utilized 12% Average data volume per company 9 TB 75 TB 0.6 TB 5 TB 4 TB 50 TB SMBs: LEs: Base: 634 business intelligence users and planners
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 6 Data sources continue to multiply
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 7 Most BI remains backward-looking Source: September 20, 2011, “Understanding The Business Intelligence Growth Opportunity” Forrester report Information about
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 8 Fundamental shifts in BI and analytics › The “Google Effect” › The “good enough” principle › Self-service tools › Data visualization › Predictive analytics › Big data › Mobile › Cloud-based delivery models
    • “Big data” is: Techniques and technologies that make handling data at extreme scale affordable.
    • “Big data” is: Techniques and technologies that make handling data at extreme scale affordable. Several different technologies! Many different use cases! Extreme in different dimensions!
    • What is “extreme scale” in big data?
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 12 Assessing the need for big data tech DRAFT – WORK IN PROGRESS Machine-generated, e.g.: Transaction data Call records Smart meter data Location information System or web log files Human-generated, e.g.: Tweets Facebook activity Email Blog posts Pictures, videos High Low High Degreeofstructure Percentage of data elements (potentially) of value Traditional BI and data warehousing systems Content and document management systems Various content types (doc, xls, ppt, bmp) on PCs and servers; email
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 13 Assessing the need for big data tech DRAFT – WORK IN PROGRESS Machine-generated, e.g.: Transaction data Call records Smart meter data Location information System or web log files Human-generated, e.g.: Tweets Facebook activity Email Blog posts Pictures, videos High Low High Degreeofstructure Percentage of data elements (potentially) of value Traditional BI and data warehousing systems Content and document management systems Various content types (doc, xls, ppt, bmp) on PCs and servers; email
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 14 There is no single “big data” technology › Core Hadoop › The wider Hadoop ecosystem › Appliances › Preconfigured/preintegrated systems › Streaming technologies › In-memory processing/analytics › Advances in semantic technologies/NPL › Cloud-based services
    • 7% 13% 7% 17% 31% Implemented, not expanding Expanding/upgrading implementation Planning to implement in the next 12 months Planning to implement in more than 1 year Interested but no plans Base: 634 business intelligence users and planners “What best describes your firm's current usage/plans to adopt big data technologies and solutions?” Source: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012 Big data analytics is growing quickly 20% have implemented some big data technology 37% are planning a big data technology project in 2013 or beyond
    • Production Logistics Sales ServiceSourcing (Singapore bank) high-performance risk analysis [SAS]. 45,000 instruments with 100,000 parameters: 8.8 billion risks analyzed in less than 1minute (down from 18 hours), aggregated risk portfolio. Upfront strategy evaluation. (Retailer) price optimization [SAS]. Based on sales and competition -> 270 million price calculations in less than 2 hours (down from 30 hours); now several price changes per day. (Telecom) churn/ loyalty management [HP]. Call analysis (more than 500 million/day) combined with social media analysis to assign risk scores to business lines and individual customers. (Bus service) carrier service optimization [Fujitsu]. 200,000 input/output operations/second. Response <1 ms: status, position, ETA, consumption, co mpliance -> all real-time (Semiconductor) manufacturing optimization [Exasol]. 5 billion data points for production processes, material, movements, produ ct per production cycle -> monitoring, archiving, comparison, opti mization. (Retailer) workforce scheduling and optimization [Blue- Yonder]. Predictive analysis (450,000 /week) based on sales, weather, traffic -> improved employee/customer satisfaction (Retailer) inventory optimization [BlueYonder] Based on weekly sales forecast (135 GB), 300 million data sets (sales, campaigns, products), improved forecast 40% (1 billion/year), real-time Royal Tech Institute Stockholm [IBM] optimized traffic management. Real-time 250,000 GPS/s (signals) -> 20% less traffic/emissions, 50% shorter trips High-performance computing for drilling site evaluation [IBM, summer 2010]. 50 TB per survey. Increased success rate from 1 in 5 to 1 in 3.
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 17 Key lessons learned to date › Skills requirements are often underestimated • BI or research project? › Many of the emerging tools and technologies aren’t yet enterprise-grade • Lack of management features and security › The security and privacy implications are far- reaching • We’re in uncharted territory, from an ethical as well as a legal perspective
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 18 Never lose sight of the fundamentals › Always question the source of the data • You may find it’s biased › Skepticism is as important as statistical skills • The numbers may be telling the wrong story › Data sets may require specialist expertise • Errors can be very costly › Data scientists aren’t miracle workers • All findings need business context › Legal constraints apply, particularly in Europe • A risk-based approach will be key
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 19 Make sure the foundations are in place › A close working partnership between business and IT is not an optional extra • Focus on alignment of goals and objectives › Work towards business ownership of data • Don’t allow data quality to be made a pure IT issue › Consider projects within IT as well • A good way to gain familiarity and expertise › Make room for big data technologies and techniques in your BI Center of Excellence • No lengthy evaluations or pilots – try it, move on
    • © 2013 Forrester Research, Inc. Reproduction Prohibited 20 Questions
    • Thank you Martha Bennett +44 (0) 20 7323 7674 mbennett@forrester.com Twitter: @martha_bennett Blog: http://blogs.forrester.com/martha_bennett