Survey Results Age Of Unbounded Data June 03 10

Leading in
The Age of
Unbounded Data
Survey Results, 2010

Introduction
Welcome to the age of unbounded data
2 | © 2010 nGenera Corp. All Rights Reserved.

Leading in the Age of Unbounded Data
Enter the highly-instrumented enterprise
Enterprises Have Become
Highly-Instrumented;
Using new sources of data
combined with sophisticated analytics to
distinguish signal from noise, create
better situational awareness, drive
new insights, and uncover the ROI of
collaborative initiatives.

It’s not just that we have more data…
More data
Over 50% of respondents ‘agree’ or ‘strongly agree’ that more data leads to better
decision making, but 46% spend more time looking for information today than before.
More data from new and expanding sources
Almost 60% of respondents report an increase in the number of data sources used for decision-making in
the past 12 months; two-thirds of companies believe managing data from new sources is an important issue.
More interactions among data types and between people and data
70% of respondents ‘agree’ or ‘strongly agree’ that executives who have
more varied types of data will improve the quality of their decisions.
Growing availability of open data
Over 70% of companies say that deciding how much data to open or share
is either an ‘important’ or ‘very important’ issue.
Making sense of this data ecosystem is the fundamental challenge
facing enterprise decision-makers, analysts, and IT departments
Despite the glut of available data, only 33% of respondents indicated that
they have the data they need to do their jobs.

…enterprises must make sense of the data ecosystem
Sense-making trumps all other data priorities
Improving the ability to interpret data (and get it to senior executives) is the
number one data priority; far more important than getting access to more data.
Data quality is a bigger problem than data availability
‘Data integrity and quality’ is the number one data problem vexing survey respondents;
deemed to be a far greater issue than ‘data availability.’ The use of unstructured data in
measurement is a significant contributor to quality issues.
Focusing on customer data will be a competitive priority
Data from customers will drive competitive advantage, but currently data quality is low and
sharing of information back to customers (i.e. creating a two-way value proposition) is a low priority.
Both new data and legacy data are causing problems
Managing legacy data is proving almost as hard as managing data from new channels
(rated as ‘very important’ by 32% and 34% of respondents respectively).
Companies still lag when it comes to measurement
Only 23% of respondents are measuring the ROI of collaborative initiatives; just over half are measuring
employee productivity. In both cases, fewer than 40% report ‘good’ or ‘excellent’ quality data.

Leading in the Age of Unbounded
Survey methodology
• Survey results gathered from
January 15th to March 1st 2010.
• Responses from over 70 major
organizations, including several
state/provincial governments and
many global corporations.
• Majority of respondents are
director-level or higher executives
from within various functions.
23%
16%
13%
9%
5%
5%
5%
4%
4%
4%
3%
3%
6%
0% 10% 20% 30%
Consulting
Software/Technology
Government
Finance/Banking
Medical/Healthcare
Retail/Consumer Products
Advertising/PR
Transportation
Education/Training
Telecomunications
Publishing
Utilities
Other
16%
19%
30%
35%
Staff
Manager
Director
Senior/Executive
Management

nGenera Data Survey
Data-driven competitive advantage

Survey Overview: Data-Driven Competitive Advantage
External data is a key driver of competitive advantage
88% of respondents say that data drives competitive advantage.
Collaboration around data is an important part of seizing the opportunity. While internally created data and
data gleaned from user and customer interactions are still seen as most important, increasingly data from
outside the enterprise is also driving competitive advantage. Data created with customers and partners, data
acquired from third parties, and open data are all considered integral contributors to competitive strategies.
77%
68%
49%
44%
43%
22%
12%
0% 20% 40% 60% 80% 100%
From customer and user interactions
Internally created
Co-created with customers
Co-created with business partners
Acquired from external parties
Open data
Other
Competitive advantage is not data-driven
What sources of data drive competitive advantage in your organization?

Most pronounced worry among decision makers is data integrity and quality
How important are the following problems related to enterprise data?
73%
56%
53%
53%
39%
39%
36%
34%
9%
18%
31%
30%
26%
32%
32%
32%
27%
6%
0% 20% 40% 60% 80% 100%
Data integrity & quality
Timeliness of data
Data availability
Data security
Managing data rights
Deciding how much data to open or share
Managing data from new channels
Managing legacy data
Other
Very Important Important
More data improves decision-making, metrics, and agility, but also creates complexity and more noise in the
system. Some critical issues include availability and timeliness of data for decision-making, data security and
access rights, and deciding how to share data and with whom.

Across data types, fewer than half rate the quality of data as ‘good’ or ‘excellent’
The low quality of customer
data is particularly worrying
(only 27% say it is ‘good’ or
excellent’), as this data was
seen as a key driver of
competitive advantage.
Fortunately, many new tools
and technologies are emerging
to help address this, including
‘voice-of-the-customer’
listening platforms and
sentiment analysis tools, as
well as prosumer platforms
that harness customer insight
and ideas, and next-generation
social CRM solutions that
promise to integrate data from
social media interactions into
customer databases.
Rate the quality of data for day-to-day decision making
47%
42%
29%
27%
17%
27%
25%
26%
27%
32%
23%
23%
40%
34%
35%
0% 20% 40% 60% 80% 100%
Function-specific data
Employee data
Enterprise data (cross-function)
Customer data
Partner and supplier data
Good/Excellent Average Below Average/Poor

Sense-making is paramount in a world of abundant information
What are the data priorities for your organization?
(percentages shown are based on respondents rating priorities as ‘high’ or ‘very high’) Sharing data is still not huge
priority, but we believe that
it’s going to have to be given
the growing importance of
data ecosystems. In order to
fully leverage opportunities
related to customer data and
data from external partners,
companies will need to
share their own information
and create two-way value
propositions. The lack of
priority being placed on
sharing and measuring
return points towards a real
opportunity for leading
organizations to redefine
competitive advantage.
79%
75%
74%
70%
69%
62%
61%
61%
58%
52%
43%
40%
34%
0% 20% 40% 60% 80% 100%
Getting data to senior executives more quickly
Improving our ability to interpret data
Improving data quality
Getting more timely data
Measuring customer experience
Getting data to front line employees more quickly
Sharing data with employees
Managing unstructured data
Getting access to more data
Measuring return on collaborative initiatives
Managing data from social media
Sharing data with customers
Sharing data with external partners

Data and the Ability to Measure
What was previously unknown can now
be known

What are companies measuring?
The Age of Unbounded Data is a result of a dramatic increase in the amount of sensor technology, web analytics,
document tracking, and other instrumentation that is now commonplace in our homes, organizations, and
public places. The influx of more and different types of data provides organizations with an unprecedented
opportunity to improve what and how they measure and report.
23%
65%
53%
ROI of collaborative initiatives
Customer experience
Employee productivity
Which of the following do you measure?

The ROI of collaborative initiatives
Today, only 23% of respondents are measuring the impact of collaborative initiatives.
• Among those that are having success, most are using a combination of analytics
and proprietary techniques.
• As workflows are increasingly digitized, process mining will uncover new types of
ROI metrics for tasks and initiatives that were previously qualitatively measured (if
at all) due to their unstructured nature.
• Over half of companies say that measuring ROI of collaborative initiatives is a high
priority. Given how important it is, we expect the number of organizations
measuring ROI to increase significantly over the next 12-24 months.
• nGenera’s research has shown that measuring ROI depends on identifying an intent
for the collaborative initiative that is tied to a specific business outcome—why are
you collaborating and what type of collaboration are you going to use?

Customer experience
65% of survey respondents actively measure customer experience.
• There isn’t a huge difference in the type of methods used by those having success in
this area and those struggling—the vast majority use customer surveys and
feedback forms—indicating that the major issue for companies with customer
experience measures may be the questions being asked and the processes
surrounding customer feedback rather than the data-gathering methods.
• By systematically gathering and analyzing customer anecdotes (e.g., using social
media monitoring and text mining), companies can augment survey measures and
satisfaction scores with more story-driven measures of experience.
• Just about any organization can listen to and leverage the stories of average people
that write online in blogs, forums, Twitter, and social networks every day.
• There are effective new methods for collecting and analyzing customer data that are
not yet widely used including social media monitoring tools, listening platforms, text
analysis, and customer sentiment analysis.

A little over half (53%) of respondents are actively measuring employee productivity.
• The leading types of measurement used are a combination of time tracking,
performance management software, and 360-degree peer reviews.
• New sources of data can create visibility into poorly-understood informal networks
and allow organizations to redirect their attention towards what’s going on ‘below
the surface’ of established structures.
• Software is now available that can track e-mail messages, shared documents,
calendar information, call logs, and contact information to model collaborative
behaviour and map informal lines of communication.
• By mining employee processes, companies can target key influencers, find new
efficiencies, strengthen existing forms of collaboration, and encourage nascent
creativity. We can know which employees are producing high-value information,
which employees are good curators of information, and which employees may be
engaging in harmful activities.

The role of unstructured data
Unstructured data is playing a significant role in what is being measured.
Over 50% of those that measure collaboration, employee productivity, or customer engagement incorporate
some form of unstructured data.
What type of data do you use to measure?
44%
44%
44%
16%
32%
56%
40%
24%
0% 20% 40% 60% 80% 100%
ROI of collaborative initiatives
Customer experience
Structured Unstructured Both

Consistency of data quality decreases as amount of unstructured data increases
How would you rate the quality of the data used for measurement?
Those using structured data reported higher
quality rating than those using unstructured
data. Unstructured data like text, images,
audio, and video is hard to organize and
analyze; however, the technologies that allow
companies to do so are starting to become
enterprise-grade. Companies that harness tools
like text mining, picture and video tagging, and
voice analysis will definitely have an edge in
measurement.
17%
38%
23%
42%
38%
48%
42%
24%
30%
0% 20% 40% 60% 80% 100%
Structured
Unstructured
Both
Below Average/Poor Average Good/Excellent
0%
22%
39%
39%
0%
4%
14%
44%
34%
4%
ROI of collaborative initiatives Customer experience Employee productivity
10%
20%
44%
24%
2%
Very Poor
Below Average
Average
Good
Excellent

Data Improves Decisions
More information, more decision-makers,
and greater agility

Over 50% ‘agree’ or ‘strongly agree’ that more data leads to better decisions
26%
26%
27%
16%
5%
Strongly Agree
Agree
Neither Agree nor Disagree
Disagree
Strongly Disagree
The majority of survey
respondents agree that more is
better when it comes to data.
Additionally, 70% ‘agree’ or
‘strongly agree’ that executives
who have more varied types of
data (e.g., audio, video, text,
statistics) will improve the
quality of their decisions.
Yet more data can also lead to
more noise and distraction.
There was also a contingent—
21% of respondents—that
‘disagreed’ or ‘strongly
disagreed’ that more data lead
to better decisions. Clearly,
simply having more data is not
a panacea.
To what degree do you agree with the statement
“having more data lead to better decisions”?

“If HP knew what HP knows, we would be three times as profitable.”– Former HP CEO Lew Platt
Improving the ability to interpret data is a top priority for companies. A major obstacle is that, in many
companies, data still tends to be siloed. Close to 80% of respondents indicate that data sharing is sub-optimal:
44% state that data is siloed by department and 27% state that even when data is shared across departments, it
is often inconsistent. Sharing and making sense of data in real-time accomplishes two goals: greater agility
through immediate response and better predictions about the future behavior of people and markets.
What statement most accurately reflects the situation in your organization?
8%
44%
27%
5%
16%
0% 10% 20% 30% 40% 50%
Nobody knows anything
Data tends to be siloed by department
Data is shared but is often inconsistent
There is a single version of the truth accessible to all departments
Data is available for simulation and modeling across the enterprise

Emerging data opportunities tied to predictive analytics
36%
38%
35%
29%
19%
18%
12%
9%
9%
6%
4%
0% 10% 20% 30% 40% 50%
We do not use predictive analytics
Customer relationship management
Financial modeling
Up-selling or cross-selling
Risk management
Direct marketing
Supply chain or inventory management
Fraud detection
Security threats
Manufacturing or equipment failures
Other
How does your organization use predictive analytic tools? Predictive models can
help decision makers
refine business plans in
response to unexpected
challenges or
opportunities by giving
them insight into the
likely outcomes of
decisions. Everyday
workers can optimize
some of the most
important decisions and
signal which initiatives
to launch, accelerate, or
stop using ‘what-if’
scenarios that leverage
both historical and
current data.

Beyond local optimization: Leveraging and sharing data enterprise-wide is the goal
While the majority said that certain individuals use data to support decisions, the clear opportunity is in the
collaborative and automated spaces. While there is little activity in those areas today—a little over a third using
collaborative data and only 16% using automated decisions—we believe there is a big upside for companies
willing to take a leadership position in these areas. Incorporating collaboration and automation into the decision-
making process could bring more effective and faster means of making successful decisions.
69%
60%
33%
27%
16%
9%
0% 20% 40% 60% 80%
Data is used to drive decisions by certain individuals
Data is used to conduct analytics that support decisions
Data is used to drive collaborative decision-making
Data is used to support professional expertise or "gut-feel"
Data is used to automate decision-making
Data is rarely used for decision-making
How is data used for decision-making?

Enabling ‘everyman analytics’
• With the proliferation of data, we’re also seeing the
democratization of analytics. This will have vast
implications for the role of the analyst, which will
become much more specialized.
• Our survey shows that while analytics is pervasive, it’s
not always strategic: 66% of respondents conduct
analytics themselves but only 10% have a dedicated
analytics group.
• Since we didn’t define “analytics” in the survey, we can
assume that the 66% includes everything from ‘Excel
warriors’ and power users, to users of free tools such as
Google analytics, to more sophisticated business
intelligence software.
• 17% are not conducting analytics at all.
10%
4%
66%
17%
3%
We have an analytics group
We outsource most of it
We do it ourselves
We do not currently use analytics
Other
How does your department
handle its analytic needs?

Data Enables Customer Engagement
A clearer view of customers’ behaviours,
preferences, and actions

Customer data is highly valued
Already, data created by customers and users—either indirectly by mining their interactions or directly via co-
creation—was ranked very high when respondents were asked to identify which sources of data drive
competitive advantage in their organizations (1st and 3rd respectively). Not surprisingly, almost two-thirds of
companies are measuring customer experience (see Slide 15 for details).
77%
68%
49%
44%
43%
22%
12%
0% 20% 40% 60% 80% 100%
From customer and user interactions
Internally created
Co-created with customers
Co-created with business partners
Acquired from external parties
Open data
Other
Competitive advantage is not data-driven
What sources of data drive competitive advantage in your organization?

Customer priorities are often out-of-synch
While measuring customer
experience was rated a ‘high’
or ‘very high’ data priority by
69% of respondents, sharing
data with customers was
deemed a priority by only
40% of respondents.
is one way of creating a more
valuable customer
experience. Organizations
that share data and are
transparent will build trust
with customers, open the
door for co-innovation, and
ultimately gain competitive
advantage from customer-
and user-created data.
79%
75%
74%
70%
69%
62%
61%
61%
58%
52%
43%
40%
34%
0% 20% 40% 60% 80% 100%
Getting data to senior executives more quickly
Improving our ability to interpret data
Improving data quality
Getting more timely data
Measuring customer experience
Getting data to front line employees more quickly
Sharing data with employees
Managing unstructured data
Getting access to more data
Measuring return on collaborative initiatives
Managing data from social media
Sharing data with external partners
What are the data priorities for your organization?
(percentages shown are based on respondents rating priorities as ‘high’ or ‘very high’)

Many organizations are stuck in a CRM-centric view of customer data
64% of respondents report monitoring social media.
Social media data can reveal an individual’s or group’s attitudes towards a brand, a person’s influence within a
target demographic, or an emerging issue in the marketplace. Unfortunately, only 43% of respondents view social
media as an ‘important’ or ‘very important’ data priority. We expect to see this channel become more of a
priority as organizations get better at mining and finding value in that data.
36%
39%
30%
29%
27%
22%
18%
0% 10% 20% 30% 40% 50%
We do not collect data from social media
Market research
Brand management
Relationship management
Customer experience management
Hiring and recruiting
Product development
Other
How do you use data collected from social media tools such as social networks,
Twitter, blogs, and forums?

Data As a Product
Aggregated, anonymized data is a valuable
commodity

Data As a Product
Future opportunities extend beyond enterprise data to data ecosystems
• There is a potential market for data: Over 40% of respondents said data from
external sources led to competitive advantage.
• Companies that have social platforms are increasingly seeing a business model
around providing free services and aggregating anonymized customer and user data
for sale. This user data is being leveraged in many ways, with 77% indicating that
data from customer and user interactions are a source of competitive advantage.
• 71% of respondents said deciding how much data to open and share is ‘important’
or ‘very important,’ but sharing of data with external partners and customers was
rated as a relatively low priority (last and second-last respectively on a list of 13
data priorities).

‘Open IP,’ where companies and institutions add to the data commons, is an emerging, if somewhat immature
trend. Currently only 30% of respondents have open data identified as an important part of their strategy; 31%
have not yet considered a strategy for open data. Discouragingly, 17% of respondents say they have considered
but rejected an open data strategy.
Data As a Product
Open data initiatives are still immature
31%
30%
17%
22%
0% 10% 20% 30% 40% 50%
An open data strategy has not yet been
considered
Open data is an important part of our future
growth strategy
Open data has been considered and is not on
our current strategy agenda
Our open data strategy is still being debated
What is the organization’s open data strategy?

Key Takeaways
Uncover new opportunities and unleash
hidden potential

Key Takeaways
Leading in an age of unbounded data requires new thinking
Leading in an Age of Unbounded Data is Not Just
About Having More Data, but also about
how we manage interactions among data types and
interactions between people and data, our ability to
interpret data and find meaning, and the extent to
which we embrace data sharing and open data
strategies. Decision-Makers Must
Understand the Data Ecosystem.

Key Takeaways
Leading in an age of unbounded data requires new thinking
Key learnings from the project include:
• Data is a critical enabler of the next generation enterprise.
• The data revolution is not just about more data.
• Future opportunities extend beyond enterprise data to data ecosystems.
• Digitizing processes will lead to new types of measurement and optimization.
• Customer data is a leading contributor to competitive advantage.
• More types of data lead to better decision making.
• Sense-making is paramount; the most successful companies compete on analytics.
• Aggregated, anonymized data is a good way to monetize interactions.

Key Takeaways
Low hanging fruit: Opportunities for leading enterprises
We believe they are several elements of data strategy that are critical to driving the next
generation enterprise, but that are still nascent. The lack of activity in the following areas reveals
an opportunity for leading organizations:
• Leverage tools to get high-quality customer data – Although customer data is identified as a key
driver of competitive advantage, few companies are currently getting data that is of high quality.
New tools such as ‘voice-of-the-customer’ software, listening platforms, prosumer platforms, and
sentiment analysis tools, as well as emerging social CRM offerings, will help close this gap.
• Share data – Companies that open and share their data will reap the benefits of an ecosystem of
customers, partners, and employees. Sharing data with customer creates a two-way value
proposition and generates new opportunities for co-innovation. Sharing data internally improves
analytic capabilities , customer responsiveness, executive visibility, and overall agility.
• Measure ROI – Over 50% of companies say that measuring the ROI of collaboration is a high
priority, yet only 23% actually do so. Part of the problem is the difficulty related identifying
metrics. Still, companies that have success in this area will be able to optimize collaboration and
improve productivity.

Key Takeaways
Low hanging fruit: Opportunities for leading enterprises
• Focus on social media – Customers are focused on social media, and companies should be too.
Communicating via social media can lower costs and data gathered from social media channels
can not only lead to new insights, it can even generate new revenue when anonymized and
packaged for interested third parties.
• Prepare the enterprise for analytics – The most successful organizations compete on analytics.
Data analytics leads to better data interpretation and sense-making. Companies that are really
good at analytics are also good at gathering data, sharing data, and consolidating it to get a
‘single version of the truth’ across the enterprise.
• Support decision-making with automation and collaboration – Few companies are currently
looking to decision-automation or collaborative decision-making as high-priority data
opportunities. Leveraging machine intelligence will improve the speed and accuracy of decisions
and also help push decisions to front-line employees making for a more responsive organization.
Collaboration via simulations, visualizations, and data sharing platforms allows companies to
harness the knowledge of a much broader base of individuals.

Survey Results Age Of Unbounded Data June 03 10

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