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.

1. Big Data and Business Intelligence
Must Converge
Tony Baer
tony.baer@ovum.com
March 6, 2013
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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!
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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?
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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?
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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!
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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, 11/29/11
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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)
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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 2/16/12
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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!
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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 ctrs
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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!
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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?
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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=8841
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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 feasible
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15. Examples
 Web ad placement optimization
 Counter-party risk management
for capital markets
 Customer sentiment analysis
 Managing smart utility grids or
urban infrastructure
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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’
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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 apps
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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 bullet
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