Can an organization that has historically managed its data poorly start "doing" Big Data well? It's an interesting question, and I'm not the only one asking it.

In an HBR piece Nonprofits: Master "Medium Data" Before Tackling Big Data, Jacob Harold writes:

...nonprofits face legitimate challenges in gathering, organizing, and using even basic data. First, most nonprofits are simply too cash-strapped to invest in cutting-edge information systems to track their activities, engage with their stakeholders and understand their context. Second, the diversity of organizations makes comparison difficult: how could we possibly compare the work of the University of Chicago to a homeless shelter in Albuquerque or to Greenpeace? Third, it isn't easy to know which are the most effective programs for battling climate change or child slavery or homelessness. Finally, the unique economics of the nonprofit sector—the buyer (donor) is frequently a different person from the user (beneficiary)—interrupt the direct feedback loop that often drives innovation in business.

It's a good read and worth checking out.

For my part, I agree with many of Harold's points. Few organizations these days are flush with cash - and nonprofits don't seem to represent an exception to that rule. It's not hard to imagine a nonprofit lacking a multimillion dollar budget to implement new Big Data solutions. And who's going to make this stuff work? Hiring data scientists isn't easy or cheap.

On a broader level, I find it interesting how many people think that their organizations can leapfrog to Big Data. I'm talking about the companies that routinely make decisions based upon policy and gut feeling - not data. Think about the many enterprises that can't generate accurate and complete lists of their customers, products and employees. Master record? What's that? 

Simon Says

I doubt that all nonprofits might qualify as being data-challenged. (I've not worked with enough to say one way or the other.) Forget for a minute small, medium and big data. Regardless of the size or description of the data in question, data is important.

Let's say that your organization ignores or fails to effectively manage small or medium data. Yet, it wants to start jumping into the big stuff. Bad idea. While not impossible, Big Data is unlikely to yield its expected results.

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What say you?