http://denreymer.com Gartner - Big Data Industry Insights 2015

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Big Data Industry Insights
Lisa Kart
@Kart_Lisa
http://denreymer.com

2. 20%
10%
0%
30%
40%
50%
60%
Has your organization already invested in technology specifically designed to address the big data challenge?
Investments in Big Data Technology
100%
90%
80%
70%
2012
n=473
2013
n=720
2014
n=302
2015
n=437
Don't know
No plans
Plan within 2yrs
Plan within 1yr
Yes
Big Data Investments Continue to Rise but Slowing Down
64% 73%
Percentage
investing or
planning 58% 76%
3 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

3. Key Issues
4 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
1. What are the vertical industry trends around big data?
2. What business problems are top priority in different industries?
3. Where should I focus?

4. 5 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
55% 55% 53%
48% 47% 44% 42% 41% 39% 38%
26%
17% 15%
16%
14%
12%
14%
8%
23% 28%
23%
14%
7% 9% 13%
14%
6%
17%
21%
18%
33%
8%
21%
21% 19% 19%
24%
29%
17%
29%
18%
15% 33%
1% 1%
6% 8%
15%
5%
Don’t know
No plans at this time
Plan to within 2 yrs
Plan to within 1 yr
Have invested
Has your organization already invested in technology specifically designed to address the big data challenge?
Total sample
Big Data investment – industry
n=
5
Percentage
investing or
planning
Percentage
investing
Retail/ Svcs Insur- Trans- Health- Banking Edu Manu & Utilities Comm./ Gov
Trade ance portation care N.Res. Media
29 78 32 21 17 59 24 80 18 13 57

5. 6 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Which of the following best describes your organization's stage of big data adoption?
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
n=437
n=720
n=302
Despite the Opportunities, Organizations Struggle with
Getting to Deployment
2013
2014
2015
Deployed
Piloting and experimenting
Developing strategy
Knowledge gathering
No plans to invest at this time
Don’t know
5% 31% 19% 18% 20% 8%
4
%
24% 13% 19% 27% 13%
4
%
21% 13% 18% 30% 14%

6. 7 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Which of the following best describes your organization’s stage of big data adoption?
State of Big Data adoption- by industry
6%
Services n=78
1%
Manufacturing n=80
1%
29%
21%
19% 1%
12% 18% 6%
Percentage piloting or deployed-
Total sample 44%
14%
15%
31%
32%
36%
33%
19%
29%
21%
18%
10%
10%
7%
Healthcare n=17
Retail/Trade n=29
Education n=24
Utilities n=18
Insurance n=32
Comm./Media n=13
Banking n=59
Government n=57
Don't know if currently investing
Currently investing/planning- don't know adoption stage
Developing strategy
Deployed
5% 14%
33% 12%
18% 18% 21%
17% 2% 15% 12%
52% 5%
Transportation n=21
No plans to invest
Knowledge gathering
Piloting and experimenting
7
Percentage full deployment-
Total sample 14%
15% 15% 0% 31% 23% 15%
19% 6% 9% 47% 16%
22% 39% 22% 17%
29% 17% 17% 21% 17%

7. 8 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Rank 1 Rank 2 Rank 3
What are your organization’s top 3 hurdles or challenges with big data?
The Top Big Data Challenge Remains the Same
Sum
Determining how to get value from big data 33% 13% 9% 55%
Obtaining skills and capabilities needed 6% 16% 13% 36%
Risk and governance issues (security, privacy, data quality) 11% 11% 11% 33%
Funding for big data-related initiatives 9% 12% 10% 31%
Defining our strategy 7% 11% 13% 31%
Integrating multiple data sources 6% 11% 8% 26%
Integrating big data technology with existing infrastructure 3% 8% 13% 25%
Infrastructure and/or architecture 5%
7%
10% 22%
Leadership or organizational issues 6% 4% 8% 18%
Understanding what is "Big Data" 10% 3%2% 15%
Other 3% 3%3% 9%

8. 9 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Big Data Challenges Are More Practical As You Adopt
31%
20%
20%
10%
17%
36%
37%
42%
16%
21%
62%
14%
22%
20%
25%
20%
33%
30%
43%
54%
9%
14%
23%
30%
34%
23%
23%
44%
39%
53%
Understanding what is "Big Data"
Leadership or organizational issues
Infrastructure and/or architecture
Integrating big data technology with existing infrastructure
Integrating multiple data sources
Funding for big data-related initiatives
Defining our strategy
Risk and governance issues (security, privacy, data quality)
Obtaining skills and capabilities needed
Determining how to get value from big data
Have invested (n=192)
Planning (n=138)
No plans (n=86)
9

9. 67%
47%
26%
24%
23%
22%
18%
17%
12%
20%
33%
42%
39%
36%
40%
45%
41%
34%
87%
80%
68%
63%
59%
62%
64%
58%
46%
Transactions
Log data
Geospatial/location data
Social media profile data
Emails/documents
Social media chat/interaction data
Sensor/machine-generated data (Internet of Things)
Free-form text
Images
Currently Analyze
(n=195)
Not analyzing today
but plan to analyze
in the future
(n=138)
10 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Which types of big data does your organization currently analyze and which do you plan to add to your analytics in the future?
SUM
Types of Big Data Analyzed- now vs. planned
Multiple responses allowed
Audio 9% 32% 41%
Video 8% 33% 41%
Other 10% 6% 16%

10. Key Issues
11 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
1. What are the vertical industry trends around big data?
2. What business problems are top priority in different industries?
3. Where should I focus?

11. Heatmap of Big Data Business Problems by Industry
12 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Multiple responses allowed
Manu &
N. Res.
Media/
Comm
Svcs Gov. Edu Retail Banking Insur-
ance
Health-
care
Trans-
portation
Utilities
Enhanced customer
experience
52% 78% 66% 43% 76% 83% 77% 77% 73% 69% 44%
Process efficiency 45% 33% 35% 49% 65% 43% 41% 50% 73% 69% 78%
More targeted marketing 43% 89% 53% 17% 41% 78% 66% 58% - 38% 17%
Cost reduction 42% 33% 35% 37% 35% 30% 41% 31% 45% 56% 61%
Improved risk management 14% 22% 29% 29% 35% 22% 52% 58% 55% 31% 61%
New products 23% 67% 37% 14% 24% 35% 27% 50% - 19% 33%
Developing information
products
26% 33% 44% 31% 12% 22% 23% 19% 9% 19% 11%
Enhanced security capabilities 17% 22% 21% 34% 29% 13% 27% 27% 9% 19% 28%
Regulatory compliance 11% 22% 18% 23% 18% 9% 25% 23% 27% 31% 44%
n= 65 9 62 35 17 23 44 26 11 16 18

12. Tackling the problem of game scheduling
§ Opportunity
– Schedule NFL games to maximize profit
§ Data and Analytics
– 20,000 variables and 50,000 constraints were analyzed using
‘FICO Xpress Optimization’ suite to come up with optimized
schedule while evaluating 7000 game options
– Best games schedules were selected which can fetch higher
TV ratings and revenue opportunities
§ Results
– NFL’s revenue and sponsorship grew substantially after using
the solution in the last five years
– Saves on time as new schedule can be produced in 24 hours,
a task which could take months earlier
13 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

13. § Opportunity
– A rapidly growing Turkish credit card business targeting lower value
segments caused an increase in fraud
§ Data and Analytics
– Replaced manual process of credit card application review with
automated real-time scoring and flagging
– Increase from 13% to 100% of applications reviewed
– Implemented fraud modeling in 15 days using KXEN
§ Results
– Increased number of identified actual fraudulent applications by 3x;
92% of fraud cases identified
– Reduced number of fraud alerts from 300,000 to 30,000 per quarter
by tuning and discovering new patterns
– Saving $25,000 per day; ROI achieved in one week
Finding Fraud Faster
14 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

14. § Opportunity
– Improve citizen safety and save city resources
§ Data and Analytics
– The New York City Fire Department algorithm analyzes 2400
factors from 330,000 commercial and public buildings
– Determines a risk score that guides inspectors to prioritize
certain buildings and their likely fire safety issues
§ Results (TBD)
– 70% success in identifying fire hazards in buildings
– Reduce fires and other safety related events; Save on
personnel and firefighting resources; Reduce insurance
claims
Heat Mapping Potential Fire Risk Hotspots
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15. § Opportunity
– Offer credit to the underbanked – those without a credit history
§ Data and Analytics
– A sophisticated self-learning scoring model
– Up to 15,000 dynamic data points for each individual, including
social networks, mobile usage, location, e-commerce data etc.
§ Results
– Ability to lend to the 73% of those people with no traditional
credit scores
– <7% loss rate in established markets (lower than using
traditional credit scores alone)
– Loan payments in 15min vs 1-3 days
Giving Credit When Credit is Due
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16. § Opportunity
– Improve health and well-being of dogs
§ Data and Analytics
– Whistle: Wearable device and mobile app that tracks behavior of
your dog and compares it to baselines of similar breeds & ages.
– FitBark: Simply, FitBit for dogs. Its "BarkScore" tells you how
much activity Rover has had each day.
§ Results
– Alerts owners to possible health issues before they become
evident
– How much is your dog walking, playing, resting?
– Is your dog walker doing her job?
Big Data and Analytics Have Gone to the Dogs
17 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

17. § Opportunity
– Increase the value, breadth, quality and consistency of
company valuation information available to subscribers of the
Credit Suisse Holt Platform
§ Data and Analytics
– Generates and updates natural language narratives in real-time
to ensure they reflect company’s latest performance using
Narrative Science’s natural language generation platform
– Applies proprietary investment-research methodology to
describe the operational quality, valuation, and riskiness of
companies using data, risk, valuation and operations data
§ Results
– Increased company research available to subscribers by 250%;
eliminated issues with quality and consistency of language
Improving Quality of Financial Research, Naturally
18 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

18. § Opportunity
– Understand activities within a building, without having to go
inside
§ Data and Analytics
– Indoor Atlas maps the interior of a building using the unique
magnetic “fingerprint” of the structure caused by distortions of the
Earth’s magnetic field.
– Determines location of internal structures and people to within six
feet, including which floor
§ Results
– Emergency personnel can use a Google Map overlay to navigate
a building
– Competitors can see changes to layouts and foot traffic
– Pay $99/mo to keep your building’s magnetic map private
Buildings Have Magnetic Personalities Too
19 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

19. § Opportunity
– Lack of consistent pricing, long negotiations and slow quote
turnaround for deals
§ Data and Analytics
– PROS pricing solution integrated millions of historical transactions
along with attributes to create pricing segments and derive pricing
recommendations
– Enhanced pricing envelope for over 500K specific price points based on
the product, customer and deal type available on demand for new quotes
§ Results
– Customer-specific pricing based model led to 2% increase in profit margin
– Reduced quote turnaround time by 50% enabling auto-approvals,
leaving pricing analysts more time to spend on newer customers
and strategic deals
An Offer They Cannot Refuse
20 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

20. § Opportunity
– Improve reliability of the electric grid and the utilization of energy to
meet state renewables goals
§ Data and Analytics
– Real-time visualization and analysis of 25,000 miles of power lines
– Space-Time Insight, OSIsoft, Oracle
– Hourly reforecasting of generation needs based on wind and solar
estimates; real-time alerts for crisis conditions
§ Results
– No system-wide outages since implementation
– Enabled implementation of 4,000 pricing nodes (up from 5) to
facilitate cost-effective local market pricing
– 50% improvement in renewable forecast accuracy
Keeping the Lights on in California
21 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

21. § Opportunity
– Improve anti-money laundering investigation speed, cost
and transparency
§ Data and Analytics
– Automated data access, investigation and analytics via Pneuron in
single integrated “fabric”. Data from internal and external sources
are presented as one complete package without replacing or
rewriting existing hardware and software investments
– Avoid need for data centralization by pushing the analytics to the data &
employing non-invasive late-binding dynamic scoring model
§ Results
– Increased speed and reduced cost of investigation by 60%
– All AML alerts are consistent, auditable and documented, and
thresholds now can be set to zero
A Fresh, Clean Approach to Optimized Anti-Money
Laundering Performance
Multinational
Financial
Ins titution
22 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

22. § Opportunity
– Create defensible revenue forecast models to pass Comprehensive Capital
Analysis and Review (CCAR) “stress test”
§ Data and Analytics
– Correlated and analyzed 2600 macro-economic variables with revenue
streams for dozens of business units using Ayasdi’s machine intelligence
software
– Uncovered variable permutations that were hard to identify using
incumbent analytics approaches and shortened the variable selection
process – from three months to two weeks
§ Results
– Achieved the cleanest Federal Reserve test pass of top US banks
– Citi stock added $9B in market capitalization the following day; and
announced a dividend increase of 500%
Financial Stress Tests: Stressed No More
23 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

23. 13%
4%
17%
35%
34%
38%
18%
Don’t know
Other
ROI is/will be measured by the non-financial
impact to the company.
ROI is/will be measured by improvements to
decision-making or process efficiency.
ROI is/will be measured by improvements to
organizational effectiveness.
ROI is/will be measured by financial returns
ROI is not being measured/We do not plan to
measure ROI
41% of Organizations Don't Know if Big Data ROI
Will Be Positive or Negative
Multiple responses allowed
Positive
ROI
57%
Negative
ROI, 3%
Don't know,
41%
ROI Measurement
24 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Positive/negative ROI
How is ROI being measured/How will ROI be measured for your
organization’s big data investment?
Please indicate whether your organization's big data
investment has /is expected to have - a positive or negative
ROI?

24. 25 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
How is ROI being measured/How will ROI be measured for your
organization’s big data investment?
ROI measurement- by industry
Retail/Trade n=23
Comm./Media n=9
Insurance n=26
Services n=62
Manufacturing n=65
Utilities n=18
Government n=35
Banking n=44
Transportation n=16
Education n=17
Healthcare n=11
ROI is not being measured/We do not plan to measure ROI
ROI is/will be measured
25
Please indicate whether your organization's big data investment has /is
expected to have - a positive or negative ROI?
36%
40%
47%
51%
54%
61%
61%
63%
66%
70%
89%
3%
2%
4%
6%
2%
6%
2%
9%
64%
53%
48%
42%
33%
36%
31%
32%
11%Comm./Media n=9
Retail/Trade n=23
Services n=62
Transportation n=16
Banking n=44
Utilities n=18
Insurance n=26
Manufacturing n=65
Education n=17
Government n=35
Healthcare n=11
Positive ROI Negative ROI Don't know
9% 87%
22% 78%
12% 77%
15% 73%
18% 72%
22% 67%
14% 66%
20% 64%
38% 63%
18% 59%
36% 36%

25. Key Issues
26 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
1. What are the vertical industry trends around big data?
2. What business problems are top priority in different industries?
3. Where should I focus?

26. It's All About Analytics Maturity
Hybrid,
integrated
Unstructured,
external
Structured,
internal, siloed
Ad hoc, batch,
offline analytics
Pervasive, real-time,
embedded analytics
Data
Increase analytics maturity by:
• Analyzing new data sources
• Broadening your portfolio of
analytic capabilities
• Applying analytics to
more decisions, faster
Descriptive
Diagnostic
Predictive
Prescriptive
27 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

27. Build Your Portfolio of Analytics Capabilities
Human Input
Data Decision
Predictive
What will happen?
Diagnostic
Why did it happen?
Descriptive
What happened?
Prescriptive
What should I do?
Analysis
Action
DecisionAutomation
28 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Decision Support

28. Future
Past
Apply Relevant Data and Analytics to Decision Making
Create
Awareness;
a Decision
Must Be
Made
Understand
the Scope
and Context
of the
Decision
Identify
Likely
Outcomes
Identify the
Best Course
of Action
Report on
the Results
of the
Action
Descriptive
PredictivePrescriptive
Diagnostic
29 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

29. Three Core Skill Areas Are Needed
Analytics
Skills
IT
Skills
Business
Skills
Ask Good Questions
Decision Making
Transparent Versus
"Black Box"
Which Analytics to Choose?
Data Exploration
High- Performance
Computing
Build, Buy, Outsource
Feature Engineering
Know the Constraints
(E.g., Legal, Ethics, Market)
Data Governance
Deployment
30 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

30. 8%
5%5%6%
8%
10%10%10%10%11%11%12%
15%16%
31%32%
3%
5%
5%
5%
6%
7%
11%
8%
5%
8%
13%
15%
17%
16%
25%
37%
2015 survey (n=333) 2014 survey (n=218)
31 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Please indicate who initiated your organization’s big data initiatives
Both IT and Business Should Drive Big Data Initiatives
Multiple responses allowed

31. Art of the Possible
Seek Big Ideas Beyond Your Borders
I waited to see
what leaders
in our industry
were doing
with data
I came up with
some great ideas
for using data on
my own
I worked
with business
partners to
develop new
ways to
use data
We adopted and
adapted winning
ideas from other
industries for
using data
32 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.

32. Key Takeaways
33 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
üBig data is maturing, moving away from the challenges of data volume and
variety, toward getting value.
üIncrease analytics maturity by:
- Integrating and analyzing new relevant data sources.
- Moving beyond basic BI to diagnostic, predictive and prescriptive analytics.
- More closely tying insights to business decisions.
üBuild your strategy around use cases, business goals and outcomes. The
decisions will guide the required data and analytics.

33. Recommended Gartner Research
34 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
è
Survey Analysis: Practical Challenges Mount as Big Data Moves to
Mainstream
Nick Heudecker, Lisa Kart (G00289494)
è
Survey Analysis: Big Data Investment Grows but Deployments Remain
Scarce in 2014
Nick Heudecker, Lisa Kart (G00263798)
è
Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind
the Hype
Lisa Kart, Nick Heudecker, Frank Buytendijk (G00255160)
è Toolkit: Big Data Business Opportunities From Over 100 Use Cases
Frank Buytendijk and others (G00252112)
è Extend Your Portfolio of Analytics Capabilities
Lisa Kart and others (G00254653)
For more information, stop by Gartner Research Zone.

34. 35 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Questions?

35. 36 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
Appendix:
Methodology & Respondent Profile

36. 37 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
What Is "Big Data"?
"Big data" is:
• high-volume, high-
velocity and high-
variety information
assets
• that demand cost-
effective, innovative
forms of information
processing
• for enhanced insight
and decision making.
VOLUME
VELOCITY
VARIETY

37. Methodology
38 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
38
§ This research was conducted via online survey in June 1-16, 2015 among Gartner Research Circle
Members – a Gartner-managed panel comprised of IT and business leaders.
§ In total, 437 members participated by indicating their organization’s investment plans around technology
to support big data:
DEFINITION PRESENTENTED: Gartner defines “big data” as high volume, velocity and variety information assets that
demand cost-effective, innovative forms of information processing for enhanced insight and decision-making.
Ø 333 have invested, or plan to invest within the next two years
Ø 91 do not have plans to invest at this time
Ø 13 did not know
§ The survey was developed collaboratively by a team of Gartner analysts covering ITL Enterprise
Software, and was reviewed, tested and administered by Gartner’s Research Data Analytics team.
NOTE: The results of this study are representative of the respondent base and not necessarily the market as a whole.

38. 18%
18%
14%
13%
7%
5%
5%
5%
4%
4%
2%
1%
1%
2%
Manufacturing & Natural Resources
Services
Banking
Government
Insurance
Education
Retail
Transportation
Utilities
Healthcare Providers
Communications
Media
Wholesale Trade
Other
Respondent profile – company characteristics
n=437
Primary industry: Annual Revenue:
DK, 12% Non profit,
11%
# employees worldwide:
DK,
0.00686498
9 <1000, 23%
<$500M,
25%
$500M -
$1B - $3B $1B, 6%
,
13%
$3B - $10B,
13%
$10B +,
19%
n=437
1000-9999,
38%
39 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
39
10000 +,
39%
n=437 MEAN=9016
MEAN=$4101M

39. Respondent profile – region
14%
APAC
42%
EMEA
40%
N. America
5%
LaHn America
40 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
40
n=437
Unknown 1%

40. Technology adoption profile
Aggressive
16%
41 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
41
Mainstream
63%
Conservative
21%
n=437

41. 6%
43%
51%
Primarily business-
focused
Blend of business
and IT
Primarily IT-focused
42 © 2015 Gartner, Inc. and/or its affiliates. All rights reserved.
42
Respondent profile – job function
IT roles:
Business roles:
n=437
Enterprise Architecture 42%
IT Infrastructure and Operations 36%
Applications: Development, Integration, CRM, ERP,
SCM and Portals, Content and Collaboration
30%
Business Intelligence and Information Management 30%
C-level IT Executive Leadership 28%
Security, Risk & Governance 27%
Sourcing and Vendor Relationships 25%
Business Process Improvement 24%
Program and Portfolio Management 24%
Other 5%
Business Strategy 53%
Business Unit/Executive Leadership 36%
Product Development and Management 21%
Marketing 11%
Sales 8%
Other 17%