Demystifying Big Data for Associations

DEMYSTIFYING
‘BIG DATA’ FOR
ASSOCIATIONS
Actionable Approaches to Capturing Data and
Gaining Insights to Strengthen Engagement
DEMYSTIFYING
‘BIG DATA’ FOR
ASSOCIATIONS

2
Introduction (Re)Defining ‘Big Data’
‘The Next Frontier’ of Big
Data: Real-World Examples
Getting Started
Pitfalls, Practices and
Progress
Conclusion Resources
Introduction............................................................................................. 3
(Re)Defining ‘Big Data’.............................................................................. 4
‘The Next Frontier’ of Big Data: Real-World Examples.................................... 6
Getting Started........................................................................................ 8
Pitfalls, Practices and Progress ................................................................ 11
Conclusion............................................................................................ 14
Resources............................................................................................ 14
Table Of Contents

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3
93 percent of
Big Data
represents
the latest
technology
trend in a
procession of new
breakthroughs
that promise
big benefits
and, sometimes,
big changes.
Introduction
“The numbers have no way of speaking
for themselves. We speak for them.”
—Nate Silver
In The Signal and the Noise: Why So Many Predictions
Fail — but Some Don’t, author Nate Silver makes an
important point about data.
The conclusion is surprising given the author’s reputation as one
of the country’s leading statisticians. The value of data analysis,
Silver argues, has as much, if not more, to do with people than it
does with the actual numbers and computations themselves.
This also represents a crucially important argument for
associations to keep in mind as an ever-increasing surge of data
begs to be transformed into lucid insights that drive decision-
making around strengthening member recruiting, engagement,
and retention efforts.
Repeatedly, this transformational process has been described
as the harnessing of “Big Data” (usually with the aid of
“advanced analytics”). If you’ve picked up a newspaper
or clicked through a website in the past year, you’ve
quickly learned that Big Data has no shortage of pundits
speaking for it. The term is overused or misunderstood by
many, yet it remains undervalued by some, including, for the
most part, associations.
This is unfortunate because associations stand to
gain tremendous value from Big Data; however, it is
understandable. Big Data represents the latest technology
trend in a procession of new breakthroughs that promise
big benefits and, sometimes, big changes. Additionally,
those who speak for Big Data tend to focus too much on
terabytes and technology and too little on strategy and
better decision-making. Although the value that Big Data can
deliver often sounds alluring, the complex descriptions of how
it works can be understandably off-putting. These portrayals
can also give rise to false impressions, including notions
that Big Data solutions are expensive (not true), demand
significant IT attention (they should be put in the hands of
business decision-makers), or require lengthy implementations
(quite the opposite).
This eBook seeks to remedy the confusion by demystifying Big
Data and relaying user-friendly terms to speak to the ways
associations can understand and benefit from it.

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Each day, humans
create 2.5
quintillion bytes
of data, which
explains why 90
percent of all the
data in the world
today has been
created in the last
two years.
4
(Re)Defining ‘Big Data’
The sheer amount of data coursing inside and, more
importantly, outside of organizational systems is
increasing at a staggering rate.
Each day, humans create 2.5 quintillion bytes of data, which
explains why 90 percent of all the data in the world today has
been created in the last two years.1
The number of Big Data
definitions and interpretations that arise weekly appear to be
multiplying in a similar vein.
IBM has defined Big Data as “a term that describes large
volumes of high velocity, complex, and variable data that
require advanced techniques and technologies to enable the
capture, storage, distribution, management, and analysis of
the information.”2
Although that definition is helpful, other technical experts have
more recently put forth briefer interpretations. Viktor Mayer-
Schonberger and Ken Cukier, co-authors of Big Data:
A Revolution That Will Transform How We Live, Work, and
Think, define Big Data as “things that one can do at a large
scale that can’t be done at a small one.”3
Narrative Science
Chief Technology Officer and Professor of Computer Science
and Journalism at Northwestern University Kristian Hammond
offers an even pithier definition: “evidence-based
decision-making.” 3
For those new to Big Data, including many associations, an even
more practical definition would suffice: consider “Big Data” to
be “Better Decision-Making.”

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55
This improved decision-making is enabled by technology that
can process much more information — and deliver much
greater benefits. “The rise of easy access to cheap analytic
tools will require you to rethink your business model,” asserts Paul
Magnone, co-author of Drinking from the Fire Hose: Making
Smarter Decisions without Drowning in Information.5
How does this value look when
associations harness it? For starters,
it looks like a much less instinctual
and much more fact-based
understanding of who your most
valuable members are, what
makes them tick, and what it takes
to attract, develop, and retain
these highly valuable members.
These facts can be harvested from
internal information systems, as well
as from the 2.5 quintillion bytes of
data added to the world from rich
sources of data like social media
platforms, each day.
5 Ways Big Data Delivers Value:
1. Making information
transparent and more readily
available for use.
2. Providing more accurate
performance information.
3. Greatly improving customer
segmentation capabilities.
4. Strengthening decision-
making throughout the
organization.
5. Improving development of
next-generation product or
service offerings.
Source: McKinsey Global Institute6

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Forward-thinking
organizations
are identifying
ways they can
gain comparable
value from
similar analytical
approaches.
‘The Next Frontier’ of
Big Data: Real-World
Examples
Fresh news about the “next frontier for Big Data”
seems to break every few weeks.
Professional sports, healthcare, sales and marketing, financial
forecasting, election forecasting, human resources, and
many other realms all have had their moments in the Big
Data spotlight. As stories illustrating the value that data
analysis delivers to specific disciplines mount, forward-thinking
organizations are identifying ways they can gain comparable
value from similar analytical approaches.
One of the most recent “next frontier” narratives from The
Wall Street Journal depicts Big Data’s value in the realm
of the virtual administrative assistant. Jon Porter, the chief
executive of a private wealth-management company, relies
on analytical software to continually scour and compare all
of the digital content and crumbs that he and his team
produce in the course of their workday — phone calls,
calendar entries, emails, social network posts, and more.
The resulting analysis points to which client activities are
important to perform at specific times. Porter told The
Wall Street Journal that the information the analytical
software produced recently alerted him of the need to
follow up on a time-sensitive investment opportunity for a
client in the nick of time.7
In addition to increasing client
satisfaction, this type of Big Data tool also saves Porter
personal time (an estimated two hours a week), requires
minimal training to use, and comes at a cost that pales
in comparison to the traditional IT system investments
companies routinely made in the 1990s and 2000s.
Those benefits are what associations should bear in mind
when considering Big Data case examples. A fast-growing
segment of these case examples concern Big Data’s application
to addressing crucial questions about human behavior:
• Why do people join our organization?
• Which people are likely to be most helpful in helping our
organization achieve its objectives?

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7
• Why are some people more engaged in their work
than others?
• Why do people choose to leave our organization?
• What behaviors and activities produce the greatest value
to our organization?
The specific results that a wide range of organizations, the vast
majority of which are in private industry, have achieved in
applying Big Data solutions to address these questions is both
inspiring and instructive.
Best Buy has learned that any one of its individual retail store’s
annual operating income increases by $100,000 when the
engagement scores of the employees who work in that store
increase by 0.1 percent. Sprint has managed to figure out
which of a handful of factors (e.g., failing to sign up for an
optional retirement program) indicate that a newly hired
employee is likely to leave the organization. Professional
services firm Cognizant employed Big Data to figure out what
behaviors and activities top-performing employees in one
location tend to do. The results showed that employees who
blogged for the company posted higher levels of satisfaction
and engagement; and these employees perform roughly 10
percent better than their less-engaged, less-satisfied, and less
social-media-inclined colleagues.8
Each of these case examples should spark a related question
within associations: How can we use Big Data to learn much
more about why members join, become engaged with, stay,
and leave our organization?
As associations further their own analytics-building efforts for
their own recruitment, engagement, and retention objectives,
they may soon step into the spotlight as Big Data’s next frontier.

Getting Started
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“One must be
willing to take
creative stabs
at action, and
then measure
with data.”
—Steve Boland, Nonprofit Quarterly
Getting Started
If associations are to achieve the benefits that a
rapidly growing number of private industry
organizations already have realized, they should
begin their endeavors by thinking both big and small.
Starting big means understanding Big Data as an ongoing
strategic initiative — one designed to improve an association’s
most important performance levers: recruiting, member
engagement, and member retention. Starting small means
starting quickly and with only a few, carefully selected
metrics. One of the most attractive features of Big Data
analysis is that its inputs and analytical processes can be
easily adjusted and repeated — greatly increasing the
speed with which organizations can gain actionable, fact-
based insights. “One must be willing to take creative stabs
at action, and then measure with data,” writes Nonprofit
Quarterly contributor Steve Boland.9
The first steps require minimal time along with a dose of
scrutiny: Looking at the data an association currently
collects, and determining if it is the correct data and if it is
organized (i.e., “tagged”) properly. Unlike traditional large-scale
IT investments, Big Data tools can be deployed with relative ease
and at a low cost (see “Big Data in Less than 30 Days” sidebar).
Before associations identify and execute specific Big Data
processes and steps, it makes sense to consider how this
capability differs — markedly so — from previous enterprise-
software and analytical-application investments and
implementations. The table below highlights several
key differences:
8

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In addition to understanding these differences, it is
beneficial for associations to consider taking several
initial steps prior to implementing, or upgrading to,
more powerful Big Data tools. These include:
• Framing Big Data as a business endeavor. Big Data delivers
a straightforward output (better decision-making)
by conducting complex, automated analyses based
on data pulled from numerous sources inside and
outside of the organization. The quality of output,
however, depends on the quality of input. The right
data needs to be selected, so it is important that the
data-selection process be guided by an association’s
strategic objectives. Big Data initiatives that succeed
over the long term tend to be those that are framed
as a strategic business effort — one that is sponsored
by the top-ranking association executive and
supported by the technology function.
• Measure the initiative. Measuring the return on
investment (ROI) of the Big Data initiative requires
a plan. In cases where ROI is an afterthought, the
value of the initiative remains unclear at best. Before
capturing data, identify the business problem the
Analytics Capabilities: Then and Now
PRIOR TO BIG DATA ERA OF BIG DATA
Key sponsor CIO Executive Director/
President
Primary user of analysis Information Technology
(IT) function
Entire organization
Nature of initiative Discrete IT project Ongoing strategic
program
Technology purchasing
model
Own Rent
Cost of analytical
technology
High Low
Ownership of data/
application
IT Entire organization
Sources of data Internal only Internal and external
Nature of implementation Lengthy with
complicated design
Immediate and easy to
adjust

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Big Data
initiatives that
succeed over
the long term
tend to be those
that are framed
as a strategic
business effort
initiative is designed to address. Where possible, quantify
the business problem in its current state (e.g., the response
rate to XYZ offer is 1.5 percent) and then evaluate the same
metric once the Big Data initiative has been completed
(e.g., the response rate improved to 6 percent).
• Assess how and why you collect data. If some information is
collected “because it’s always been done that way,”
rethink that approach. A member’s two previous home
addresses do not reveal much about that member’s
engagement level, for example. The number of times a
member comments on an association chat group or social
media platform, on the other hand, may help produce
highly relevant insights about that member’s likelihood to
recommend association membership to peers.
• Evaluate how you score, or segment, members. In the past,
many associations typically grouped members into broad
categories (e.g., whether the member attended the
annual conference or not). The information that Big Data
tools produce helps associations see members in a
new, much more precise light — and new groups or
segments based on behaviors that are more relevant
to crucial business measures (i.e., those most likely
to leave, those most likely to enlist new members,
those most likely to take a volunteer leadership role,
those most likely to generate the greatest value over
their membership lifecycle, etc.). Equipped with such
behavioral insights and tendencies, associations can
begin to tailor different, more relevant communications
to different groups of members.

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Pitfalls, Practices
and Progress
Once association executives adjust their mindset
about Big Data, they should recognize and avoid
some common pitfalls that can delay and/or weaken
their initiatives.
These pitfalls include:
Managing Big Data as a project vs. an ongoing program: To be effective
in helping associations achieve their business objectives, Big
Data efforts should be ongoing and strategic in nature. They
should not be managed as discrete IT projects. This can be a
difficult problem to sidestep given that many associations use
the project model to perform the vast majority of their work. That
said, it is important to get beyond “start date/end date” thinking
when it comes to Big Data.
Applying traditional IT-implementation thinking: Prior to the
emergence of Cloud technology and software-as-a-service
(SaaS) models, the typical IT implementation process was, at
best, complex. Software was purchased. The software was
customized, tested and adjusted. The business processes the
software supported were redesigned. The implementation was
planned. And, finally, the software was rolled out — typically, in
a lengthy and highly disruptive manner that required significant
change-management. That buy-customize-test-adjust-process-
redesign-implement process does not apply to Big Data tools,
which favor much more of a rent-apply-get-results-learn-apply-
again approach. These tools encourage an iterative process
where insights can be gained quickly and then the parameters
for collecting and analyzing data can be easily adjusted to
improve the relevancy of information that is produced.
Skills shortages: Although “data scientist,” “director of analytics,”
and even “chief analytics officer” represent job titles that
are increasingly in high demand, the expertise that people
with these titles possess may be in short supply within some
associations. Big Data initiatives require unique skill sets.
Associations conducting these initiatives should ensure that
they have this expertise on staff or access to this expertise via
relationships with external parties.
Delaying due to cost considerations: Associations stung by lengthy
and expensive IT projects in the past are understandably gun
To be effective
in helping
associations
achieve their
business
objectives,
Big Data
efforts should
be ongoing
and strategic
in nature.

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shy when new technology-related opportunities arise. Big Data
tools, though, represent an affordable technology investment.
“How would you redesign your customer interaction if data
collection could be put exactly where and when you want
it?” asks Magnone. “What could your business do with cheap,
powerful processing . . . nearly anywhere you choose?”10
Once associations get started (with an eye toward avoiding the
preceding pitfalls), the tactical work is fairly straightforward. At a
high level, these steps include:
1. Looking at the internal member data you are collecting.
2. Making sure the right amount of internal data — and
accurate data — is being collected.
3. Making sure the data is tagged appropriately.
4. Running the analysis using a Big Data tool or application.
5. Evaluating the results and adjusting your data sources.
6. Beginning the process again after adjustments.
7. Gradually adding external data sources (e.g., search- and
index-based data) to your inputs.

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• After these general steps have been executed, results and
benefits should appear fairly quickly, if not immediately.
The following signs indicate that an association’s Big Data
initiative is on the right track:
• The “dirty-data” realization: One of the first things most
organizations, including associations, realize when
launching a Big Data initiative is that their data is a mess.
Many associations have amassed too much member data
— too much irrelevant data and too little relevant data.
This situation should be addressed by cleansing the data
and putting in controls, where necessary, to prevent future
data messes.
• Better measurements: When associations begin using Big
Data tools, the precision of their measurements — of
specific marketing programs, for example — typically
increases to a substantial degree. This clarity is evident
when an association realizes that a specific campaign had
a much higher success rate with one group of members
compared to another group of members — or that a
marketing offer distributed via, say, a social media platform
achieved a much higher response rate than the same
message delivered via email.
• “Ah-ha” moments: More precise measurements also lead
to some surprising, fact-based realizations. These
realizations often veer toward the surprising: “Wow, we
never hear a peep from Susan but she — and members
like her — are critical to our retention success!”
Something as straightforward as breaking down
retention rates according to member age can qualify
as a valuable insight.
• Integrating Big Data into business decisions: The wow factor
of these initial Big Data insights can be encouraging,
but the long-term value of these insights depends on
the degree to which they are integrated into business
decision-making. For example, if Susan and her ilk are
valuable from a retention perspective, the association
now needs to implement strategies to attract (and retain)
more people like her and also convert other members to be
as valuable as she is.
Something as
straightforward
as breaking
down retention
rates according
to member age
can qualify
as a valuable
insight.

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Case Study: Big Data in Less than 30 Days
“Become a data-driven organization.” That was the directive Tim
Ringlespaugh, director of information technology (IT) at Sigma Theta
Tau International (STTI), the Honor Society of Nursing, received from his
CEO. The imposing-sounding mandate drove Ringlespaugh to a Big
Data solution that was up and running in an unimposing 30 days.
STTI, a global organization based in Indianapolis, serves 130,000 active
members in 487 chapters in more than 85 counties. As a user of Avectra
Social CRM, STTI had consolidated all of its databases into netFORUM,
including its fundraising data into the fundraising module. When
Avectra learned of STTI’s data-driven goal, it connected Ringlespaugh
with Data RPM, a company that offers a Big Data analytics solution.
Ringlespaugh responded with interest, in part, he reported, because
the existing data warehouse he and his IT team had built was
expensive and time-consuming to operate, and it was failing to meet
all of their needs.
After learning that Data RPM could get its solution up and running for
STTI within 45 days, Ringlespaugh closed the legacy data warehouse
and moved ahead with Data RPM’s “Starter Kit,” a solution that provides
more than 60 reports and 50 dashboards.
The solution was up and running in less than 30 days, and Ringlespaugh
reports that it allows STTI to “quickly and easily pull in and search data
from multiple sources.” He also reports that the Big Data solution is
more cost-effective and labor-efficient than other options, including
the previous data warehouse.
14
Conclusion
Integrating Big Data insights into business decision-
making is not only a sign of progress, it also
represents an ongoing strategic imperative if
associations are to maximize the benefit they derive
from their Big Data efforts.
When positioned strategically, managed actively and constantly
evaluated and improved upon, Big Data tools can produce
potentially valuable insights. The degree to which that potential
value is realized depends on the specific decisions and actions
associations take once they are equipped with the information.
As Silver also notes in his book, “Before we demand more of Big
Data, we need to demand more of ourselves.”

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Resources
1
IBM, “What is Big Data?” http://www-01.ibm.com/software/
data/bigdata/.
2
IBM, “Demystifying Big Data: A Practical Guide to Transforming
the Business of Government,” http://public.dhe.ibm.com/
common/ssi/ecm/en/iml14336usen/IML14336USEN.PDF.
3
Forbes,” Before You Can Manage Big Data, You Must
First Understand It,” http://www.forbes.com/sites/
gregsatell/2013/06/22/before-you-can-manage-Big-Data-you-
must-first-understand-it/.
4
Harvard Business Review Blog Network, “The Value of Big Data
Isn’t the Data,” http://blogs.hbr.org/cs/2013/05/the_value_of_
big_data_isnt_the.html.
5
Forbes, “Four Big Data Trends That Change Everything,” www.
forbes.com/sites/paulmagnone/2013/06/22/4-Big-Data-trends-
that-change-everything/.
6
McKinsey Global Institute, “Big Data: The Next Frontier for
Innovation, Competition and Productivity:” http://www.
mckinsey.com/insights/business_technology/big_data_the_
next_frontier_for_innovation.
7
The Wall Street Journal, June 13, 2013, “Your New Secretary: An
Algorithm:” http://online.wsj.com/article/SB1000142412788732394
9904578539983425941490.html.
8
Harvard Business Review, October 2010, “Competing on
Talent Analytics:” http://hbr.org/2010/10/competing-on-talent-
analytics.
9
Nonprofit Quarterly, “Avoid Getting ‘Stunned’ by Big Data,”
http://nonprofitquarterly.org/management/22245-avoid-getting-
stunned-by-big-data.html.
10
Forbes, “Four Big Data Trends That Change Everything,” www.
forbes.com/sites/paulmagnone/2013/06/22/4-Big-Data-trends-
that-change-everything/.

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Headquarters
7901 Jones Branch Drive
Suite 500
McLean, VA 22102
Phone (703) 506-7000
Fax (703) 506-7001
About Avectra
For two decades, Avectra has translated its customers’ needs into market leading software and services. Using
Avectra’s donor management, fundraising, Crowd-contributing and membership management solutions, not-
for-profits and member-based organizations can connect with constituents, partners and prospects in entirely
new ways, create more meaningful engagements and drive bottom-line results.
For more information, visit www.avectra.com.
About the Authors
Don Prodehl has over 20 years of experience leading technology teams and providing technical solutions to the
association industry. He’s experienced in: eBusiness, eCommerce strategies for mobile and UI, as well as best
practices for managing software projects and product or application development. He previously worked at
CVENT and served in executive positions at association management system vendors and for SMITHBUCKLIN, the
largest U.S. association management firm for trade associations, professional societies and corporations. Don is
currently the VP of Research and Development at Avectra.
Patrick Dorsey has a strong sales and marketing background, and expertise and experience in: demand
generation, corporate communications sales best practices, Crowd-contributing and customer engagement
strategies. He has authored several articles in leading industry publications on on-demand association
management solutions and social CRM. Patrick is currently the VP of Marketing at Avectra, and previously was
Avectra’s Director of Sales for netFORUM PRO.

Demystifying Big Data for Associations

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