Almost every conversation I’ve had recently with peers, industry experts, and clients comes around to the idea of big data; specifically, what it is and how to collect it.
Some are asking, “Is big data more trouble than it’s worth?” Absolutely not. Consultants with McKinsey & Company suggest big data can unlock significant value by:
- Creating transparency
- Segmenting audiences
- Enabling experimentation
- Supporting operational decision making
- Supporting innovation
However, for associations and nonprofits whose value has historically been built on relationships, the appeal of big data is even more obvious: Leveraging key personal details to improve membership value or donor engagement creates a deeper, more relevant experience that fosters new constituent acquisition and mitigates attrition risks.
Big Data = Big Effort?
Despite the opportunities big data presents and the questions it can help answer, only 30 percent of organizations report collecting it. Why not more?
For associations and nonprofits, even collecting data can present a significant challenge. Data often comes from multiple sources, including departments that maintain their own records and affiliates who create duplicates out of the need for a separate dataset to support local, state, and/or regional chapter initiatives.
The resulting fragmentation not only creates the operational headaches of duplicate records, but can also create incomplete, or even misleading, pictures of each member and the membership community as a whole. These issues are compounded by the speed at which new data is coming in, how much of it is coming, and the mind-boggling variety of formats data can take.
Getting started can, and should, employ a decidedly low-tech approach. Savvy organizations take the time to audit their existing resources and to understand:
- Where is our data currently? Who “owns” it?
- Is our data clean, complete, and accessible? If not, how will we fix it?
- What key data are we missing? Can we acquire it?
- How can we improve integration between various data sources?
- How can we discover and track what’s important?
- What tools and resources do we need to more effectively analyze our data? Are these tools and resources accessible to all the decision makers within our association?
At a minimum, the typical association dataset, for example, includes the membership database, accounting software, survey tools, and website analytics. In addition to proprietary data from members, external datasets can provide additional detail and help enrich the demographics of a membership.
The Crystal Ball
But, as technology analysts at Gartner astutely point out, big data, “for all its value is inherently dumb. It doesn’t actually do anything unless you know how to use it.”
Data are facts with no meaning or purpose in and of themselves – it’s only when they’re put into context that relationships and/or patterns emerge. Discovering the meaning of those patterns transforms data into information, which, through careful selection, filtering, and interpretation, then becomes useful knowledge. Combining knowledge with experience empowers good, evidence-based decisions.
But, what decisions need to be made? Many organizations utilize a scientific approach, looking at their data with a specific hypothesis to prove or disprove. For others, the introduction of big data-driven insights may provide an opportunity to reflect on many deeper, mission-driven questions including:
- How does our organization currently make decisions? Are we using data and intuition/experience appropriately in making decisions? Do we follow the hypothesis-experiment-learn-iterate process?
- What is our organization trying to achieve? What actually drives success for our association?
- Who are our customers and what do they need from our organization? How are current and prospective members or donors alike and different?
- How do we engage members and donors? What does engagement mean to us?
Even starting small with big data, and making incremental shifts into evidence based decision making, can drive big results and help create or maintain competitive advantage. All you need to do is understand where it fits in within your organizational framework, and use it in the right context. Answering these questions upfront will provide a roadmap for the journey ahead, and help you take a structured approach for turning data into action.