Survey results: The age of unbounded data

Leading in
The Age of Unbounded Data
Survey Results, 2010

Nauman Haque

Introduction
Welcome to the age of unbounded data

© 2011 Moxie Insight. All Rights Reserved. 2

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
0% 10% 20% 30%

• Survey results gathered from Consulting 23%
Software/Technology 16%
January 15th to March 1st 2010.
Government 13%
Finance/Banking 9%
• Responses from over 70 major Medical/Healthcare 5%
Retail/Consumer Products 5%
organizations, including several Advertising/PR 5%
state/provincial governments Transportation 4%
and many global corporations. Education/Training 4%
Telecomunications 4%
Publishing 3%
• Majority of respondents are Utilities 3%
director-level or higher Other 6%

executives from within various
functions.
Staff 16%
35%
Manager
19%
Director

Senior/Executive
Management 30%


Moxie Insight Data Survey
Data-driven competitive advantage


Survey Overview: Data-Driven Competitive Advantage
External data is a key driver of competitive advantage
What sources of data drive competitive advantage in your organization?
0% 20% 40% 60% 80% 100%

From customer and user interactions 77%
Internally created 68%
Co-created with customers 49%
Co-created with business partners 44%
Acquired from external parties 43%
Open data 22%
Other
Competitive advantage is not data-driven 12%

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.


Most pronounced worry among decision makers is data integrity and quality

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.

How important are the following problems related to enterprise data?
0% 20% 40% 60% 80% 100%

Data integrity & quality 73% 18%
Timeliness of data 56% 31%
Data availability 53% 30%
Data security 53% 26%
Managing data rights 39% 32%
Deciding how much data to open or share 39% 32%
Managing data from new channels 36% 32%
Managing legacy data 34% 27%
Other 9% 6%

Very Important Important


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


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
0% 20% 40% 60% 80% 100% priority, but we believe that
it’s going to have to be given
Getting data to senior executives more quickly 79%
the growing importance of
Improving our ability to interpret data 75% data ecosystems. In order to
Improving data quality 74% fully leverage opportunities
Getting more timely data 70%
related to customer data and
data from external partners,
Measuring customer experience 69%
companies will need to
Getting data to front line employees more… 62% share their own information
Sharing data with employees 61% and create two-way value
Managing unstructured data 61%
propositions. The lack of
priority being placed on
Getting access to more data 58%
sharing and measuring
Measuring return on collaborative initiatives 52% return points towards a real
Managing data from social media 43% opportunity for leading
Sharing data with customers 40%
organizations to redefine
competitive advantage.
Sharing data with external partners 34%


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.

Which of the following do you measure?

ROI of collaborative initiatives 23%

Customer experience 65%

Employee productivity 53%


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.
• Moxie Insight’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.

Employee productivity

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?

0% 20% 40% 60% 80% 100%

ROI of collaborative initiatives 44% 56%

Customer experience 44% 16% 40%

Employee productivity 44% 32% 24%

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

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


“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?
0% 10% 20% 30% 40% 50%

8%
Nobody knows anything

Data tends to be siloed by department 44%

Data is shared but is often inconsistent 27%

There is a single version of the truth accessible to all departments 5%

Data is available for simulation and modeling across the enterprise 16%


Emerging data opportunities tied to predictive analytics

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

How is data used for decision-making?
0% 20% 40% 60% 80%

Data is used to drive decisions by certain individuals 69%

Data is used to conduct analytics that support decisions 60%

Data is used to drive collaborative decision-making 33%

Data is used to support professional expertise or "gut-feel" 27%

Data is used to automate decision-making 16%

Data is rarely used for decision-making 9%


Enabling „everyman analytics‟
How does your department
• With the proliferation of data, we’re also seeing the
handle its analytic needs?
democratization of analytics. This will have vast
implications for the role of the analyst, which will 3%
10%
17% 4%
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. 66%

• Since we didn’t define “analytics” in the survey, we can We have an analytics group
assume that the 66% includes everything from ‘Excel
We outsource most of it
warriors’ and power users, to users of free tools such as
We do it ourselves
Google analytics, to more sophisticated business
We do not currently use analytics
intelligence software.
Other
• 17% are not conducting analytics at all.

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).

What sources of data drive competitive advantage in your organization?
0% 20% 40% 60% 80% 100%

From customer and user interactions 77%
Internally created 68%
Co-created with customers 49%
Co-created with business partners 44%
Acquired from external parties 43%
Open data 22%
Other
Competitive advantage is not data-driven 12%


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


Many organizations are stuck in a CRM-centric view of customer data
How do you use data collected from social media tools such as social networks,
Twitter, blogs, and forums? 0% 10% 20% 30% 40% 50%

We do not collect data from social media 36%

Market research 39%

Brand management 30%

Relationship management 29%

Customer experience management 27%

Hiring and recruiting 22%

Product development 18%

Other

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.

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).


Data As a Product
Open data initiatives are still immature

What is the organization’s open data strategy?

0% 10% 20% 30% 40% 50%

An open data strategy has not yet been 31%
considered

Open data is an important part of our future 30%
growth strategy

Open data has been considered and is not on 17%
our current strategy agenda

Our open data strategy is still being debated 22%

‘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.


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
Decision-Makers Must
strategies.
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.


Nauman Haque
nhaque@moxieinsight.com
(416) 863-8825
www.MoxieInsight.com

Survey results: The age of unbounded data

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