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DEMYSTIFYING ‘BIG DATA’ FOR ASSOCIATIONS.

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

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    Demystifying Big Data for Associations Demystifying Big Data for Associations Presentation Transcript

    • 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
    • 3 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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.
    • 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. 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.”
    • 5 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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
    • 6 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 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?
    • 7 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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.
    • 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 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
    • 9 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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
    • 10 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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.
    • 11 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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.
    • 12 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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.
    • 13 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources • 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.
    • 14 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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.”
    • 15 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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/.
    • 16 Introduction (Re)Defining ‘Big Data’ ‘The Next Frontier’ of Big Data: Real-World Examples Getting Started Pitfalls, Practices and Progress Conclusion Resources 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.