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The Age of (Too Much) Data

The Age of (Too Much) Data

NOTE: This blog was originally posted on Forbes. Read it here.

I recently went to a restaurant with a few friends for lunch. They offered a very large buffet and had large signs that said to “eat as much as you can.” I was amazed to see people pile massive amounts of food on their plates, but I was even more amazed by the abundance of leftovers and the waste.

Too many nonprofit organizations are bringing that “eat as much as you can” approach to their data collection policies. They grab as much information as possible about their constituents and even come back for more if their board members have the appetite. Just like eating a poor-quality diet, collecting too much data could have an impact on your organization’s health. Rather than focusing on achieving your mission, you will spend time developing meaningless dashboards with a focus on non-critical items.

Collecting Meaningful Data

This isn’t to say that data is not important — it’s critical for nonprofit organizations to thrive in the digital age. The question is what kind of matrices are important for your work. The end goal should not be more data; it should be engaging with your constituents in a more meaningful way.

The idea of collecting data on your constituents might be a paradigm shift for many organizations that depend mainly on direct mail marketing practices. Until recent years, organizations segmented their donor lists based mainly on their previous contributions and their ability and inclination to give. Donors were treated as passive audiences that may have attended events but were mainly sending checks in response to massive appeals.

While technology has opened the floodgates of data, things don’t always work out the way organizations hope they will. According to Big Data Executive Survey, more than 85% of executives say that their organizations are trying to be data-driven, while only 37% report that they have been successful.

How can your organization make better use of data?

1. Start with the ‘why.’

Before you start collecting data, ask yourself these questions to help you focus on mission-critical data collection.

• Will this data help me build a stronger relationship with the individual or company?

• How will this information help me refine my “ask” for a gift?

• Is this information necessary to evaluate the success of my programs?

• Will the data help me improve my services?

I recommend taking a scientific approach to data collection. Form a hypothesis. Conduct experiments, and constantly evaluate and analyze the results. If they are not what you expected, redefine your hypothesis and evaluate whether you are collecting the right information.

2. Involve the end users.

We already know a lot of information about our constituents, including every gift they give, every event they attend, how often they visit our website and what mobile device they use. Having this information is useful, but we still don’t know with certainty why someone pulls out their credit card and donates to a cause. As scientists say, correlation does not imply causation. That’s where your end users (fundraisers, social workers and program managers) come into play.

For example, it is important for a marketer (who uses data to help drive the pipeline) to constantly collaborate with the advancement team (end users), have a good understanding of the organization’s mission and know about the various giving personas. This makes them better equipped to get into the heads of the donors and understand the factors that influence their behaviors.

The more we can understand the motivation behind the giving, the better we can build those relationships. This simply cannot be done by a data scientist without the collaboration of end users.

While big data is popular these days, we shouldn’t collect data for the sake of collecting data. The information we collect should directly impact the outcomes and changes we are looking to drive. It is also important to acknowledge the people at the heart of the facts, figures and statistics. Hypothesizing like a scientist and striving to better understand the intentions like a psychologist are two important stepping stones in getting into the minds of your constituents and building lasting relationships.