The Ethnographer’s Complete Guide to Big Data: Conclusions (part 3 of 3)

Statistics House, Kampala, Uganda

As promised here is the final installment of my short series about ‘big data.’ I started out by declaring myself a ‘small data’ person. My intention was to be a bit provocative by suggesting that forgoing or limiting data collection might sometimes be a legitimate or even laudable choice. That contrast was perhaps overdrawn. It seemed to suggest that ‘big data’ and ethnographic approaches were at the opposing ends of some continuum. ‘How much’ is not necessarily a very interesting or relevant question for an ethnographer, but who among us hasn’t done some counting and declared some quantity (1000s of pages of notes, hundreds of days in the field, hours of audio or video recordings) that is meant to impress, to indicate thoroughness, depth, effort, and seriousness?

So the game of numbers is one we all probably play from time to time.

Now to answer my few remaining questions:

1) How might big data be part of projects that are primarily ethnographic in approach?

My first exposure to ‘big data’ came from a student who managed to gain access to a truly massive collection of CDR (call detail record) data from a phone network in Rwanda. Josh Blumenstock was able to combine CDR data with results from a survey he designed and carried out with a research team in Rwanda to gain insights into the demographics of phone owners, within country migration patterns, and reciprocity and risk management. I was terribly excited by the possibilities of what could be found in that kind of data since I had been examining mobile phone ownership and gifting in nearby Uganda. I wondered how larger patterns in the data might reflect (or raise questions) about what I was coming to see at the micro-level about phone ownership and sharing, especially its gendered dimensions. Indeed Josh’s work showed a strong gender skew in ownership with far more men than women owning phones and women phone owners more affluent and well-educated. My work explained the marital and other family dynamics that put far fewer phones into the hands of women than men.

However, combining these two approaches is more a standard mixed methods approach than anything new. Is something more innovative than that possible?

The Ethnographer’s Complete Guide to Big Data: Answers (part 2 of 3)

Statistics House, Kampala, Uganda

I’ve come away from the DataEdge conferencewith some answers…and some more questions. While I don’t intend to recap the conference itself, I do want to take advantage of time spent with this diverse group of participants and their varied perspectives to try to offer the bigger picture sense I’m starting to develop of the big data/data analytics trend.

The idea that big data might usher in a new era of automatic research and along with it researcher de-skilling or that it would render the scientific method obsolete did not prove to be a popular sentiment (*phew* sigh of relief). The point that data isn’t self-explanatory, that it needs to be interpreted was reasserted many times during the conference coming from people who occupy very different roles in this data science world. No need to panic, let’s move along to some answers to those questions I raised in part I.

What is big data? Ok, this was not a question I raised going into the conference, but I should have. Perhaps unsurprisingly there wasn’t a clear consensus or a consistent definition that carried through the talks. I found myself at certain points wondering, “are we still talking about ‘big data’ or are we just talking about your standard, garden-variety statistics now?” At any rate, this confusion was productive and led me to identify three things that appear to be new in this discussion of data, statistics, and analysis.

The Ethnographer’s Complete Guide to Big Data: Small Data People in a Big Data World (part 1 of 3)

Statistics House, Kampala, Uganda

Part I: Questions

Research is hard to do. Much of it is left to the specialists who carry on in school 4-10 more years after completing a first degree to acquire the proper training. It’s not only hard to do, it’s also hard to read and understand and extrapolate from. Mass media coverage of science and social research is rife with misinterpretations – overgeneralizations, glossing over research limitations, failing to adequately consider the characteristics of subject populations. Does more data or “big data” in any way, shape, or form alter this state of affairs? Is it the case, as Wired magazine (provocatively…arrogantly…and ignorantly) suggests that “the data deluge makes the scientific method obsolete” and “with enough data, the numbers speak for themselves?”