Big Data in Small Pieces

Big Data is confusing (both the concept and the data itself). As more grantmakers begin to explore how their data can work in a larger context, and how that context relates to the mission and vision that are at the center of all their activities, we need more and better explanations, examples, and ideas about what big data is and how to use it (and whether to capitalize it). GMN compiled these resources to help raise awareness, expand understanding, and engage conversations.

While passive data gathering can be useful, measurement is far more valuable when coupled with conscious, active experimentation and sharing of insights. Likewise, the value of undertaking the experiments themselves is proportionately greater if the organization can capitalize on those experiments in more locations and at greater scale. In combination, these practices constitute a new kind of “R&D” that draws on the strengths of digitization to speed innovation.

Big Data Is on the Rise, Bringing Big Questions (Wall Street Journal, Nov. 29, 2012)
The next Next Big Thing is Big Data.

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by McKinsey Global Institute and McKinsey’s Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.

Big Data: Experts say new forms of information analysis will help people be more nimble and adaptive, but worry over humans’ capacity to understand and use these new tools well.

Tech experts believe the vast quantities of data that humans and machines will be creating by the year 2020 could enhance productivity, improve organizational transparency, and expand the frontier of the “knowable future.” But they worry about “humanity’s dashboard” being in government and corporate hands and they are anxious about people’s ability to analyze it wisely.

The Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center asked digital stakeholders to weigh two scenarios for 2020, select the one most likely to evolve, and elaborate on the choice. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence. The other expressed the view that Big Data could cause more problems than it solves between now and 2020.

Philanthropy’s Data Boom (Barron’s, Jan. 2, 2013)
Huge quantities of data are a blessing and a curse for many industries these days. Philanthropy is no exception. In a recent industry forecast, “Philanthropy and the Social Economy: Blueprint 2013,” written by self-described “philanthropy wonk” Lucy Bernholz, all the big shifts she identifies in philanthropy are data-related. The way she sees it, “foundations, donors, and nonprofits are soon to be drinking from the ‘data firehose.’”

Big data is not just a buzzword; it’s real. But it disrupts many who don’t see anything new in it or don’t see the tremendous opportunity firms have to harness it for competitive advantage. My prediction: Time magazine will name big data its 2013 person of the year.

How can organizations benefit from the promise of big data?

One of the key differences between analytics in the traditional mode, and what we are dealing with in terms of the Big Data era is that we are gathering data that we may or may not need—and from the perspective of analysis, this means “we don’t know what we don’t know”—hence, the variables and models are likely to be entirely new, requiring a different infrastructure strategy and, perhaps most importantly, new skill sets. »

Why We Need To Kill “Big Data” (Tech Crunch, Jan. 5, 2013)
Why have I grown to hate the words “big data”? Because I think the term itself is outdated, and consists of an overly general set of words that don’t reflect what is actually happening now with data. It’s no longer about big data, it’s about what you can do with the data. It’s about the apps that layer on top of data stored, and insights these apps can provide. »