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

May 12, 2015

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Patrick Dorsey

DEMYSTIFYING ‘BIG DATA’ FOR ASSOCIATIONS.
Actionable Approaches to Capturing Data and
Gaining Insights to Strengthen Engagement
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Page 1: 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

Page 2: Demystifying Big Data for Associations

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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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Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and

Progress Conclusion Resources

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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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Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and

Progress Conclusion Resources

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.

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(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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Progress Conclusion Resources

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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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Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and

Progress Conclusion Resources

Forward-thinking organizations are identifying ways they can gain comparable value from similar analytical approaches.

‘The Next Frontier’ of Big Data: Real-World ExamplesFresh 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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Progress Conclusion Resources

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• 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.

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Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and

Progress Conclusion Resources

“One must be willing to take creative stabs at action, and then measure with data.”

—Steve Boland, Nonprofit Quarterly

Getting StartedIf 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:

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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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Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and

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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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Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and

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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.

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ConclusionIntegrating 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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Resources1 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/SB10001424127887323949904578539983425941490.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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About AvectraFor 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-

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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.