Nick Patience, Director Product Marketing & Strategy at Recommind spoke at the CIO Event, March 2013

1. BIG DATA: THE ROLE OF THE CIO IN DEALING WITH
LARGE AMOUNTS OF UNSTRUCTURED INFORMATION
Nick Patience, Director, product marketing & strategy
March 19 2013
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2. ABOUT RECOMMIND
 Founded in 2001
 450+ employees
 Recognized leader by top analyst
firms
˗ Gartner Magic Quadrant Leader
˗ IDC MarketScape Leader
 Offices in San Francisco, Boston,
NYC, London, Bonn & Sydney
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3. WHAT WE DO…
Software solutions &
infrastructure for large-
volume unstructured
information management
and analysis
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4. PRODUCTS AND SOLUTIONS
VERTICAL
MARKETS
ENTERPRISE
APPLICATIONS 3rd
Solutions
Party
NoSQL ENRICHED
ANALYTICS
CORE DATABASE INDEX
PLATFORM
ENTERPRISE
DATA
Databases Machine Office System Social ESI
Email Web XML
Data Documents Logs Media
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5. SAMPLE CUSTOMERS
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6. AGENDA
 Big Data and the importance of analysing both
structured and unstructured information
 Role of the CIO in helping to alleviate risk &
compliance issues within the enterprise
 How to categorise, find, manage and analyse
information from disparate repositories into one
overarching platform
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7. BIG DATA RISKS &
OPPORTUNITIES
AND WHAT YOU CAN DO TO AVOID ONE AND EMBRACE THE OTHER
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8. BIG DATA DEFINITION
“ Data that exceeds the processing
capacity of conventional database
systems. The data is too big, moves too
fast, or doesn’t fit the structures of your
database architectures.
”
Source: Edd Dumbill, Forbes
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9. VOLUME, VARIETY AND VELOCITY
FURTHER DEFINE BIG DATA
VOLUME (Petabytes)
>3500 >2000
North America Europe >250
China >400
Japan
>50
>200 >50
Middle East India
Latin America
VARIETY VELOCITY
PEOPLE PEOPLE MACHINE TO 2.9 MILLION 20 HOURS 50 MILLION
TO PEOPLE TO MACHINE MACHINE Emails sent every Of video uploaded Tweets per
Email, social Medical devices, Sensors, GPS, bar second every minute day
networks, blogs ecommerce, bank code scanners
transactions Source: McKinsey, comScore, Radicati
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10. TRADITIONAL VS. BIG DATA
TRADITIONAL DATA BIG DATA
Gigabytes to Terabytes Petabytes to Exabytes
Centralised Distributed
Structured Unstructured
Known Relationships Complex, Undefined Interrelationships
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11. MASSIVE GROWTH IN UNSTRUCTURED CONTENT
Worldwide Corporate Data Growth
80% of Data Growth is Unstructured
45,000
40,000
35,000
Exabytes
30,000
25,000
20,000
15,000
10,000
5,000
0
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Source: IDC
The Digital Universe, Dec 2012
Structured Data Unstructured Data
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12. WHAT BIG DATA IS NOT…
NOW
THEN
Doing what you did before
- just scaled up
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13. BUT…
You can do things now you could not do
a few years ago because of:
• New analytics techniques
• Faster, more powerful computers
• New architectures, such as the cloud
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14. AGENDA
 Big Data and the importance of analysing both
structured and unstructured information
 Role of the CIO in helping to alleviate risk &
compliance issues within the enterprise
 How to categorise, find, manage and analyse
information from disparate repositories into one
overarching platform
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15. [1]Source: Horison Information Strategies
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16. INFORMATION VALUE DECLINES OVER
TIME, COST AND RISK DO NOT
Risk-value
delta
Cost-value
delta
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17. SPECIFIC BIG DATA RISKS
• Unmanaged file servers can pose legal and
compliance risks
• Unmanaged information represents a risk because it
makes it hard to find
• When litigation occurs, if information cannot be
found, organisations may ultimately face court sanctions
• Compliance risks - sensitive client information may reside
on servers that are not managed may be misused, lost or
even destroyed
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18. BIG DATA OPPORTUNITIES
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19. BIG DATA MARKET OPPORTUNITIES BY
INDUSTRY
Source: Gartner
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20. BIG DATA INVESTMENTS BY INDUSTRY
This Year Next Year Within 2 Years
17%
21% 15%
29% 11%
15%
17% 21%
15% 9%
11% 18%
17% 18%
12% 20%
18% 18%
12% 8%
39% 36% 36%
29% 31%
25% 21% 22% 23% 23%
Source: Gartner
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21. BIG DATA ANALYTICS - OPPORTUNITIES
Recommendation Sentiment Marketing Campaign Fraud
Engines Analysis Analysis Analytics
Match and recommend Determine the how Improve accuracy of Identify fraudulent activity
users to one another or to consumers feel about forecasting, prediction of and stolen credit cards
products and services by particular buyer behavior by through active monitoring
understanding profiles and companies, brands or reviewing increasingly of customer
buyer behavior. products from Tweets and granular data, click behavior, historical and
Facebook posts. streams, call details. transaction data.
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22. FRAUD, WASTE AND ABUSE (FWA) IN
HEALTHCARE
According to a 2010 white paper by the US National Health Care
Anti-Fraud Association (NHCAA)
 The US Federal Bureau of Investigation (FBI) estimates that 3-
10% of $2.34 trillion spent on healthcare in 2008 was lost to fraud
 Represents $70-$234 billion annually
 $234 billion is roughly equivalent to the gross domestic product
(GDP) of Finland
Source: http://www.nhcaa.org/media/5994/whitepaper_oct10.pdf
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23. BIG DATA ANALYTICS - OPPORTUNITIES
Customer Network Contract Patent
Churn Management Analysis Analysis
Evaluate customer Ingest data from Mine large volumes of Comb through enormous
behavior to identify servers, storage devices transactional data and volumes of text-based
patterns that indicate and other hardware to documentation to information and prior art to
which customers are most monitor network determine risk and assist in the development
likely to leave for a activity, diagnose exposure of financial of new products, guide
competing vendor. bottlenecks. assets. portfolio strategies.
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24. MITIGATING BIG DATA RISKS
THROUGH DEFENSIBLE DELETION
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25. THE LIFECYCLE OF DATA & DEFENSIBLE DELETION
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26. DEFENSIBLE DELETION: PIPE DREAM OR
REALITY?
• Survey by Enterprise Strategy Group in Q4 2012
• 253 business and IT professionals familiar with their organisation’s data
disposition policies (all organisations currently dispose of data)
- 36% IT professionals
- 64% business professionals
• Midmarket (100 to 999 employees) and enterprise-class (1,000+
employees) organisations
- 32% midmarket
- 68% enterprise-class
• Multiple verticals
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27. RESPONDENTS BY NUMBER OF EMPLOYEES
How many total employees does your organisation have worldwide?
(N=253)
100 to 249, 13%
20,000 or more, 21%
250 to 499, 10%
10,000 to 500 to 999, 9%
19,999, 13%
5,000 to 9,999, 8% 1,000 to 2,499, 13%
2,500 to 4,999, 12%
© 2012 Enterprise Strategy Group
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28. RESPONDENTS BY INDUSTRY
What is your organisation’s primary industry? (Percent of respondents, N=253)
Government
(Federal/National, Sta
te/Province/Local), 22
Other, 28% %
Retail/Wholesale, 1% Manufacturing, 15%
Health Care, 5%
Communications & Financial
Media, 6% Business Services (banking, securities, in
(accounting, consultin surance), 12%
© 2012 Enterprise Strategy Group g, legal, etc.), 10%
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29. HOW ORGANISATIONS DISPOSE OF DATA
Which of the following best describes the manner in which
your organisation disposes of data? (N=253)
Data is disposed
of on an ad hoc
basis, 20%
We have a formal
data disposition
policy in
place, 80%
© 2012 Enterprise Strategy Group
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30. DRIVERS BEHIND DATA DISPOSITION
What are the biggest drivers behind your organisation’s data disposition?
(N=253, multiple responses accepted)
Improving overall data management for better future retrieval 66%
Mitigating the risk of exposing sensitive/confidential data past its
58%
retention mandate to potential future security breaches
Reducing exposure to risk from future e-discovery/regulatory
56%
productions
Reducing costs of storing legacy data or records with third parties 56%
Improving systems performance 50%
Reducing costs of future e-discovery/regulatory productions 46%
Reducing maintenance costs (i.e., OPEX) associated with storing
46%
data volumes
Reducing systems costs (i.e., CAPEX) associated with storing data
46%
volumes
0% 10% 20% 30% 40% 50% 60% 70%
© 2012 Enterprise Strategy Group
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31. APPLICATION OF DATA RETENTION POLICIES
Which of the following best describes your organisation’s application – or expected
application – of data retention policies? (N=245)
We have – or will have –
retention policies or records 85%
management in place for paper…
We retain – or will retain –
regulated data according to its 80%
mandated retention schedule
We preserve – or will preserve –
71%
data under legal hold
We have – or will have –
retention policies or records 71%
management in place for…
© 2012 Enterprise Strategy Group 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
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32. GROUPS RESPONSIBLE FOR CREATION AND
SETTING DATA DISPOSITION POLICIES
Which of the following groups assist/are expected to assist in the creation of data
disposition policies? Which group is/will be responsible for setting data disposition
policies? (N=235, multiple responses accepted)
Records management group 38%
78%
Legal department/general counsel 21% Group that
77%
11% sets – or will
IT 77% set – data
Executive team 9% disposition
54%
policies
Business users 3%
51% All groups
Compliance group 6% with input
49%
into data
Accounting or auditing firm 2%
31% disposition
2% policies
Outside counsel 23%
Service provider 2%
12%
© 2012 Enterprise Strategy Group
0% 20% 40% 60% 80% 100%
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33. GROUP RESPONSIBLE FOR EXECUTION OF DATA
DISPOSITION POLICIES
Which group is – or will likely be – responsible for the execution of
data disposition policies (i.e., actually removing data from systems)?
(N=235)
Service provider, 1%
Other, 2%
Accounting or
auditing firm, 1% Don’t know, 1%
Legal
department/general
counsel, 2%
Compliance
group, 3%
Business users, 4% IT, 49%
Records
management
group, 38%
© 2012 Enterprise Strategy Group
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34. AMOUNT OF DATA DISPOSED ANNUALLY
On average, approximately how much data would you estimate your organisation
disposed on an annual basis? (N=137)
45%
41%
40%
35%
30% 27%
25%
20%
15%
15% 12%
10%
5%
4%
0%
Less than 1 TB 1 TB to 5 TB 6 TB to 10 TB 11 TB to 25 TB More than 25 TB
© 2012 Enterprise Strategy Group
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35. PERCENT OF TOTAL AMOUNT OF DATA
DISPOSED OF ANNUALLY
On average, what percentage of your organisation’s total amount of data do you
estimate is disposed on an annual basis? (N=171)
40%
37%
35%
30%
25%
25%
20%
16%
15%
11%
10%
9%
5%
2%
0%
Less than 5% 5% to 10% 11% to 15% 16% to 20% 21% to 25% More than 25%
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36. IMPACT OF FORMAL DATA DISPOSITION
POLICIES
How significant has the impact of formal data disposition policies been on cost
savings and/or risk avoidance for your organisation?
(N=203)
Don’t know, 13% Very significant, 9%
Too soon to
tell, 13%
Insignificant, 2%
Significant, 39%
Neither significant
nor
© 2012 Enterprise Strategy Group
insignificant, 23%
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37. AGENDA
 Big Data and the importance of analysing both
structured and unstructured information
 Role of the CIO in helping to alleviate risk &
compliance issues within the enterprise
 How to categorise, find, manage and analyse
information from disparate repositories into one
overarching platform
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38. CUSTOMER CASE STUDY –
US DEPT. OF ENERGY
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39. CASE STUDY – US DEPARTMENT OF ENERGY
The Challenge
We have thousands of users
generating many records a day. How
do we manage this information like an
asset so that it can be useful and we
comply with the government’s records
management mandate?
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40. EMAIL & RECORDS MANAGEMENT AT US
DEPARTMENT OF ENERGY
 Need to preserve history
 Importance of vital records for continuity of operations if
emergencies arise
 Need to provide copies of records for legal actions or FOIA
legal requests
 Lack of motivation to categorize content
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41. AUTOMATIC CATEGORISATION APPROACH
Auto
Categorization
Uncategorized
Journaling Content
Drop-Off Library
Categorized
Content
Organizer Categorized
Mov
e
Site 1 Site 2
Site 3 Site 4
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42. IMPACT
 Requires one system administrator/engineer &
two people to manage the electronic records
center – for 1,000 users
 End users more productive due to no longer
having to categorize content
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43. EMAIL CATEGORISATION ACCURACY
100%
80%
60%
Accuracy Average
B a s e d
R u l e -
40%
86%
20%
0%
Administrative Notices
Budget Records Customer Service
IT Management Improvement
Procurement Records
Travel Records
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44. SIMILAR USE CASES
 Email compliance in financial services
˗ Email archiving capture emails from target employees
˗ Random sampling & manual review of emails
˗ Automatic sampling, initial review & assignment to senior reviewers is more cost and time
efficient, accurate & defensible
 Predictive coding in e-Discovery
˗ Predictive Sampling to estimate the percentage of responsive documents
˗ Predictive Analytics (Concepts, Phrases, Smart Filters) to find potentially relevant documents
˗ Complete iterative cycle until zero documents are computer-suggested or responsive
˗ Use Predictive Sampling to QC the non-reviewed documents
 Predictive modelling in healthcare:
˗ Find at risk patients using guided data mining against a pre-built, validated predictive model for a
specific issue such as hospital acquired conditions
˗ Predict the patients who should be isolated upon arrival, and the most reliable approach to
screening
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45. DIFFERENT USE CASES, DIFFERENT ROIs
• Predictive Analytics
Optimize • Operational Efficiencies
Value • Business Intelligence
Lower
Costs
• Storage Management
• Personnel optimization
• Operational efficiencies Minimize
Risk
• Security Breaches
• eDiscovery Costs
• Data Leakage
• Regulatory Inquiries
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46. CUSTOMER CASE STUDY –
SWISS RE INSURANCE
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47. SWISS RE - ACCESS, COMPLIANCE & EDISCOVERY
 100s of TB data
 Index once, use many
 True 360 degree view of enterprise data
 Based on CORE platform
Custom-built apps
NoSQL ENRICHED
ANALYTICS INDEX
DATABASE
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48. PRODUCTS AND SOLUTIONS
VERTICAL
MARKETS
ENTERPRISE
APPLICATIONS 3rd
Solutions
Party
NoSQL ENRICHED
ANALYTICS
CORE DATABASE INDEX
PLATFORM
ENTERPRISE
DATA
Databases Machine Office System Social ESI
Email Web XML
Data Documents Logs Media
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49. WHAT MAKES CORE UNIQUE?
Powerful and scalable indexing and retrieval
FIND Keyword and language-agnostic machine learning
Unstructured information “joins”
CONNECT
Unstructured data extraction & analytics
ANALYSE
Delivers ability to confidently act on data
ACT
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50. SUMMARY
 Big Data and the importance of analysing both structured and
unstructured information
˗ What it is
˗ What it is not
˗ Risks & opportunities
 Role of the CIO in helping to alleviate risk & compliance issues
within the enterprise
˗ Defensible deletion
˗ Categorization – US Dept of Energy
 How to categorise, find, manage and analyse information from
disparate repositories into one overarching platform
˗ CORE platform
˗ Swiss Re
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51. THANK YOU
– QUESTIONS?
@nickpatience
nick.patience@recommind.com
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