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TDWI NYC Chapter - Tony Baer Ovum on Big data, Data quality, and BI Convergence
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TDWI NYC Chapter - Tony Baer Ovum on Big data, Data quality, and BI Convergence

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Intersecting with Neil Raden's keynote, Ovum Principal Analyst Tony Baer asks, “what does it take to turn the promise of Big Data into tangible results?” Big opportunities to benefit from new …

Intersecting with Neil Raden's keynote, Ovum Principal Analyst Tony Baer asks, “what does it take to turn the promise of Big Data into tangible results?” Big opportunities to benefit from new technology have come and gone, yet the consistent challenge has been translating new potential into concrete benefits. Mr. Baer shared a practical perspective on making big data manageable by understanding key challenges you must overcome to leverage big data, especially the unique data quality issues the Big Data sources introduce.

Mr. Baer also shared his insight that while Business Intelligence and Big Data are viewed and managed separately, in reality "Big Data and Business Intelligence must converge." Big Data needs to be approached with "less of a silo mentality," and so does Business Intelligence.

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  • 1. Big Data and Business Intelligence Must Converge Tony Baer tony.baer@ovum.com March 6, 20131 © Copyright Ovum. All rights reserved. Ovum is a subsidiary of Informa plc.
  • 2. Agenda  Challenges traditional data stewardship practice  Privacy – is all the world a stage?  Limits to data lifecycle?  Data quality: the big, the bad, the ugly – and it all might be good!2 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 3. Data stewardship challenges – What’s old is new Remember?  Back to undifferentiated ‘gobblobs’ of data  Programmatic access reigns  File systems, not (always) tables 10.102.8.152 - - [05/Nov/2003:00:19:54 -0500] "GET /inventory/index.jsp HTTP/1.1" 200 4028 "http://www.mycompany.com/index.jsp" "Mozilla/4.08 [en] (Win98; I ;Nav)"  Batch is back 192.168.114.201, -, 03/20/01, 7:55:20, W3SVC2, SALES1, 172.21.13.45, 4502, 163, 3223, 200, 0, GET,/DeptLogo.gif, -, 172.16.255.255, anonymous, 03/20/01, 23:58:11, MSFTPSVC, SALES1, 172.16.255.255, 60, 275, 0, 0, But… if index(tempvalue,?) then tempvalue=scan(tempvalue,1,?); else if index(tempvalue,&)>1 then tempvalue=scan(tempvalue,1,&);  Volume, variety, velocity, and where’s the value??  Just because you can, should you?3 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 4. Data stewardship questions for Big Data  Can we, should we “control” this data?  Are there limits to how much we should know?  Can we just keep piling up data forever?  Can we cleanse terabytes of data?  Do we still need “good” data?4 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 5. Agenda  Challenges traditional data stewardship practice  Privacy – is all the world a stage?  Limits to data lifecycle?  Data quality: the big, the bad, the ugly – and it all might be good!5 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 6. Privacy – the more things change… “You have zero privacy anyway…. Get over it” -- Scott McNealy, 1999 Facebook does not actually delete images… but instead merely removes the links – a fix “is in sight” -- ZDNet, 2/6/12 Facebook agrees to 20 years of federal privacy audits -- NY Times, 11/29/116 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 7. What privacy? Florida made $63m last year by selling DMV information (name, date of birth, type of vehicle driven) to companies like LexusNexus & Shadow Soft. -- Terence Craig & Mary Ludloff Privacy and Big Data (O’Reilly Media, 2011)7 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 8. Big Data privacy 101 – Don’t be creepy  Governance problem first, How Companies Learn Your technology second Secrets  Understand the relationship with your customers & business partners  Keep communications in context  Don’t catch your customers by “My daughter got this in the mail!” he surprise said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to  The law still trying to catch up encourage her to get pregnant?” -- NY Times 2/16/128 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 9. Agenda  Challenges traditional data stewardship practice  Privacy – is all the world a stage?  Limits to data lifecycle?  Data quality: the big, the bad, the ugly – and it all might be good!9 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 10. Data lifecycle – How long can this go on?  Google, Yahoo, Facebook, etc. don’t deprecate web data  Hadoop designed for economical scale-out  Moore’s Law, declining cost of storage  Is Hadoop Archive the answer?  Is Hadoop the new tape?Management & skills will be the limit Aerial view of Quincy, WA data ctrs10 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 11. Agenda  Challenges traditional data stewardship practice  Privacy – is all the world a stage?  Limits to data lifecycle?  Data quality: the big, the bad, the ugly – and it all might be good!11 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 12. Data Quality & Hadoop – Big Quality Questions  Can we cleanse terabytes of data?  Do we still need “good” data?  Are there new approaches to cleansing Big Data?12 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 13. Framing the issue  “Garbage in, garbage out,’ but DW forced the issue  Traditional approaches  Profiling, cleansing, MDM  DW vs. Hadoop data quality challenges  Known data sets & known criteria vs. vaguely known  Bounded vs. less bounded tasks  Limitations of MapReduce*  Cleansing & transformation within a single Map operation;  Profiling & matching of unstructured data  Matching of data in operations without inter-process communications *Source: David Loshin, "Hadoop and Data Quality, Data Integration, Data Analysis" at http://www.dataroundtable.com/?p=884113 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 14. Is data quality necessary for Hadoop?  The App  How mission-critical?  Regulatory compliance impacts?  What degree of business impact?  The Data  The 4V’s (volume, variety, velocity, value) determine what approaches to quality are feasible14 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 15. Examples  Web ad placement optimization  Counter-party risk management for capital markets  Customer sentiment analysis  Managing smart utility grids or urban infrastructure15 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 16. Bad data may be good  Sensory data  Outlier or drift?  Time to recalibrate devices?  Time to perform preventive maintenance?  Are new/unaccounted environmental factors skewing readings?  Human-readable data  Flawed concept of reality?  Flawed assumptions on data meaning?  Changes producing ‘new norm’16 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 17. Big Data quality in Hadoop – Emergent approaches  Crowdsourcing data –  Collect data far & wide from as many diverse sources as possible. Torrents of data overcome the noise.  Comparative trend analysis of incoming streams to dynamically ID the norm or sweet spot of “good” data  Apply data science to “correct the dots”  Don’t go record by record. Statistically analyze the data set in aggregate.  Iteratively analyze & re-analyze nature of data, keep analyzing outliers  Apply off-the-wall approaches  Enterprise Architectural approach  Semantic (domain) model-driven  Apply cleansing logic at run time  Critical for sensitive, regulatory-driven apps17 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 18. Summary  Challenges traditional data stewardship practice  Combination of old & new  Privacy – is all the world a stage?  Best practices, legal requirements still in flux  Don’t be creepy!  Limits to data lifecycle?  Few enterprises are Google or Facebook  Ability to manage large infrastructure will be major limit  Data quality  Strategy depends on type of app & data set(s)  A spectrum of approaches -- from none to classic ETL to aggregate statistical  No single silver bullet18 © Copyright Ovum. All rights reserved. Ovum is an Informa business.
  • 19. Disclaimer All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the publisher, Ovum (an Informa business). The facts of this report are believed to be correct at the time of publication but cannot be guaranteed. Please note that the findings, conclusions and recommendations that Ovum delivers will be based on information gathered in good faith from both primary and secondary sources, whose accuracy we are not always in a position to guarantee. As such Ovum can accept no liability whatever for actions taken based on any information that may subsequently prove to be incorrect.19 © Copyright Ovum. All rights reserved. Ovum is an Informa business.