Embedded Analytics: The Next Mega-Wave of Innovation


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Could embedded analytics change the way consumers do business? A whole range of Web-based and traditional software providers are now embedding analytical power into their applications such that users can do more complex analysis of their data. The use cases span such industries as eCommerce, telecom, security and other such data-intensive verticals. As a result of this trend, the providers and their customers can gain greater insights about their businesses and thus improve decisions.

Check out this episode of The Briefing Room to hear Analyst John Myers of EMA explain how delivering embedded analytics can expand the value of analysis to customers and partners all over the world, while raising the bar for how business is done. Myers will be briefed by Susan Davis of Infobright, who will tout her company’s success in enabling solution providers to deliver real-time analytical capabilities to their customers.

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Embedded Analytics: The Next Mega-Wave of Innovation

  1. 1. Eric.kavanagh@bloorgroup.comTwitter Tag: #briefr
  2. 2. !   Reveal the essential characteristics of enterprise software, good and bad !   Provide a forum for detailed analysis of today s innovative technologies !   Give vendors a chance to explain their product to savvy analysts !   Allow audience members to pose serious questions... and get answers!Twitter Tag: #briefr
  3. 3. !   June: Intelligence !   July: Disruption !   August: Analytics !   September: Integration !   October: Database !   November: CloudTwitter Tag: #briefr
  4. 4. !   The last ten or so years have seen a massive influx of business intelligence tools: reporting, analytics, data mining, online analytical processing, querying, etc. !   BI technologies are designed to let organizations take all their capabilities and convert them into knowledge, ultimately getting the the right information to the right people at the right time. !   Vendors face the challenge of providing organizations with tools robust enough to get at their data and provide the right actionable insight.Twitter Tag: #briefr
  5. 5. Analyst: John Myers John Myers joined Enterprise Management Associates in 2011 as senior analyst of the BI practice area, where he delivers comprehensive coverage of the BI and data warehouse industry. During his career, John spent over ten years working with BI implementations associated with the telecommunications industry. In 2005, John founded the Blue Buffalo Group, a consulting and analysis firm, providing BI expertise to outlets such as BeyeNetworks Telecom Channel, The Data Warehousing Institute (TDWI) and BillingOSS magazine and go-to-market industry analysis, enabling organizations to penetrate the telecommunications industry vertical.Twitter Tag: #briefr
  6. 6. ! InfoBright’s columnar database is used for applications and data marts that analyze large volumes of machine-generated data. ! InfoBright leverages patented compression techniques and a “knowledge grid” to achieve real- time analytics. ! Infobright offers both an open source and a commercial edition of its software. Both products are designed to handle data volumes up to about 50TB of data.Twitter Tag: #briefr
  7. 7. Susan Davis, Vice President of Marketing at InfoBright, is responsible for the companys marketing strategy and execution. Davis brings more than 25 years of experience in marketing, product management and software development to her role at Infobright. Prior to joining the company, she was vice president of marketing at Egenera and director of product management at Lucent Technologies/ Ascend Communications where she was responsible for the release and launch of the telecommunications industrys first commercially available softswitch. She holds a B.S. in economics from Cornell University.Twitter Tag: #briefr
  8. 8. Enabling Real-time Data Analysis Susan Davis, VP Marketing, Infobright
  9. 9. The Need for Analysis Ent. Apps SaaS Huge data Demand for market market growth embedded data • 18% growth • Machine- analysis •  Grew to $115B in 2011 2012, projected generated $22B by 2015 • Unstructured
  10. 10. Requirements Customers/Users Technology Provider §  Fast access to the data, even §  Provide superior analytics for near-real time competitive advantage §  Total flexibility for ad hoc §  Meet their customers analysis requirements §  High performance §  Reduce database costs §  Ability to keep longer data §  Eliminate need for DBA tuning histories §  Minimize hardware and §  Less hardware software footprint §  No DBA work needed §  Ease of implementation and integration with their application
  11. 11. Case Study: JDSU §  Annual revenues exceeded $1.8B in 2011 §  4700 employees are based in over 80 locations worldwide §  Communications sector offers instruments, systems, software, services, and integrated solutions that help communications service providers, equipment manufacturers, and major communications users maintain their competitive advantage §  JDSU Service Assurance Solutions §  Ensure high quality of experience (QoE) for wireless voice, data, messaging, and billing. §  Used by many of the world’s largest network operators
  12. 12. Telecom Example: JDSU Project Goals§  New version of Session Trace solution that would: §  Support very fast load speeds to keep up with increasing call volume and the need for near real-time data access §  Reduce the amount of storage by 5x, while also keeping much longer data history §  Reduce overall database licensing costs §  Eliminate customers’ “DBA tax,” meaning there should require zero maintenance or tuning while enabling flexible analysis §  Continue delivering the fast query response needed by Network Operations Center (NOC) personnel when troubleshooting issues and supporting up to 200 simultaneous users
  13. 13. TDR-Store Used by Session Trace Solution
  14. 14. TDR-Store Used by Session Trace Solution For deployment at Tier 1 network operators, each site will store between 6 and 45 TB of data, and the total data volume will range from 700 TB to 1PB of data.
  15. 15. Session Trace Solution
  16. 16. Infobright at JDSU Data Compression & Reducing Capex & Getting Data in Quickly History Opex•  5X space reduction •  Rates of 20,000 TDRs •  No indexing or tuning per second (or up to required•  5X more history 40,000 database rows •  Fewer servers or online per second storage disk required •  Appending the new •  Lower licensing costs data in less than 10 than alternatives milliseconds
  17. 17. Bango: Mobile Payments and Analytics §  Delivers technology solutions that enable and enhance the monetization of internet-distributed video §  Enables publishers, advertisers, ad networks and media groups to manage, target, display and track advertising in online
  18. 18. Example in Mobile Analytics: Bango Bango’s  Need   Infobright’s  Solu6on  A  leader  in  mobile  billing  and  analy/cs   §  Reduced  queries  from  minutes  to  seconds  services  u/lizing  a  SaaS  model    Received  a  contract  with  a  large  media   Query   SQL Server   Infobright  provider   1 Month Report (5MM events)   11 min   10 secs  §  150  million  rows  per  month  §  450GB  per  month  on  SQL  Server   1 Month Report (15MM events)   43 min   23 secs    SQL  Server  could  not  support  required     Complex Filter 29 min   8 secs   (10MM events)  query  performance  Needed  a  database  that  could   §  Reduced  size  of  one  customer’s  database  §  scale  for  much  larger  data  sets     from  450  GB  to  10  GB  for  one  month  of  §  with  fast  query  response   data  §  with  fast  implementa/on  §  and  low  maintenance  §  in  a  cost-­‐effec/ve  solu/on  
  19. 19. Infobright Analytic Database Technology Columnar   Intelligence,   Administra/ve   Database   not  Hardware   Simplicity   Designed  for   Knowledge   No  manual   fast  analy/cs   Grid   tuning   Minimal   Deep  data   Itera/ve   ongoing   compression   Engine   administra/on  
  20. 20. Infobright Architecture Overview Data  Packs  and  Compression   Knowledge  Grid     Based  on  MySQL  
  21. 21. Getting the Data In: Multiple Options §  Infobright loader §  High-speed, multi-threaded loader. Load speeds of 80 – 150GB / hour §  MySQL loader §  More flexible data formatting options, enhanced error checking. §  Load speed up to about 50GB/hour Distributed Load Processor §  Distributed Load Processor (DLP) §  Multi-machine data processing engine Database §  Load speed can exceed 2TB/hour server §  Hadoop connector §  Data Integration tools §  Pentaho, Talend, Informatica, etc
  22. 22. Intelligence Not Hardware Creates  informa/on   •  Stores  it  in  the  Knowledge  Grid  (KG)   (metadata)  about  the   •  KG  is  loaded  into  memory   data  upon  load,   •  Less  than  1%  of  compressed  data  size       automa/cally   Uses  the  metadata  when   •  The  less  data  that  needs  to  be  accessed,  the   processing  a  query  to   faster  the  response   eliminate  /  reduce  need   •  Sub-­‐second  responses  when  answered  by  the  KG   to  access  data   •  No  need  to  par//on  data,  create/maintain   indexes,  projec/ons  or  tune  for  performance   Architecture  Benefits   •  Ad-­‐hoc  queries  are  as  fast  as  sta/c  queries,  so   users  have  total  flexibility  
  23. 23. Big Data Analytics: Unique Infobright Features DLP and DomainExpert Rough Query Hadoop •  Web data •  Distributed •  Instantaneous intelligence data drill-down into •  Add your processing very large domain •  Simple extract datasets knowledge from Hadoop/ •  Find the HDFS needle in the haystack
  24. 24. Growing Customer Base across Use Cases andVerticals Ø 300  direct  and  OEM  customers  across  North  America,  EMEA  and  Asia   Ø 8  of  Top  10  Global  Telecom  Carriers  using  Infobright  via  OEM/ISVs   Logis6cs,   Online  &  Mobile  Adver6sing/Web   Government   Financial   Telecom  &   Gaming,  Manufacturing,   Analy6cs   U6li6es   Services   Security   Social   Business   Research       Networks   Intelligence      
  25. 25. Get Started At infobright.org: §  Download ICE (Infobright Community Edition) §  Download an integrated virtual machine from infobright.org §  Join the forums and learn from the experts! At Infobright.com §  Download a free trial of Infobright Enterprise Edition, IEE §  Download a white paper from the Resource library §  See the videos at www.youtube.com/infobrightdb §  Follow us on twitter at twitter.com/infobright
  26. 26. Twitter Tag: #briefr
  27. 27. Pushing Analytics to the “Edge”John L MyersEnterprise Management AssociatesSenior AnalystJMyers@EnterpriseManagement.com © 2012 Enterprise Management Associates, Inc.
  28. 28. Speaker John L Myers Enterprise Management Associates Senior Analyst John Myers joined Enterprise Management Associates in 2011 as senior analyst of the business intelligence (BI) practice area. John has 10+ years of experience working in areas related to business analytics in professional services consulting and product development roles, as well as helping organizations solve their business analytics problems, whether they relate to operational platforms, such as customer care or billing, or applied analytical applications, such as revenue assurance or fraud management. JohnLMyers44Slide 29 © 2012 Enterprise Management Associates, Inc.
  29. 29. What is Machine to Machine Big DataSlide 30 © 2012 Enterprise Management Associates, Inc.
  30. 30. New Definition of Many to ManySlide 31 © 2012 Enterprise Management Associates, Inc.
  31. 31. There is Big Data and There is LOTS of DataSlide 32 © 2012 Enterprise Management Associates, Inc.
  32. 32. How to Handle Response Time?Slide 33 © 2012 Enterprise Management Associates, Inc.
  33. 33. Rather than Center, Push to the “Edge”Slide 34 © 2012 Enterprise Management Associates, Inc.
  34. 34. Question and Answer Thank you! John Myers Senior Analyst JMyers@emausa.com www.EnterpriseManagement.com JohnLMyer44 twitter JohnLMyers44 SkypeSlide 35 © 2012 Enterprise Management Associates, Inc.
  35. 35. •  What are the types of use cases that InfoBright is getting the most traction from? We have telecom and mobile payment in the case study, but I would be looking for top-5 that may or may not include those two. •  Are there differences in the geography adoption of InfoBright products? Just wondering about the distribution of particular use cases geographically by region: North America, CALA, EMEA, AsiaPAC. •  Talk about the attributes of the telecom and mobile payment markets that are “sweet spots” for InfoBright. I would guess it is the “limited” amount of data values (ie., dates, towers, amounts) and the “exploratory” nature (ie.,not set columns of data set).Twitter Tag: #briefr
  36. 36. •  Talk about the choice of MySQL vs. another SQL “interface” for InfoBright. I like the choice, but I would just like to hear the qualitative and quantitative reasons from InfoBright’s perspective. •  Many people talk about Big-Data requirements (3Vs).  What is InfoBright’s specific competitive advantage over other Big Data vendors/players (structured and unstructured)? I am guessing implementation cost, time to implementation and load speed. •  Why purpose built Columnar over Columnar indexing which has become “popular” from row-based RDBMS vendors?Twitter Tag: #briefr
  37. 37. !   June: Intelligence !   July: Disruption !   August: Analytics !   September: Integration !   October: Database !   November: CloudTwitter Tag: #briefr