Schedule > 'Raw Data' is an Oxymoron Discussion

"Raw Data" is an Oxymoron Discussion

Gitelman, L. & Jackson, V. (2013). Introduction: Raw data is an oxymoron. In L. Gitelman (Ed.), Raw data is an oxymoron (pp. 1–14). MIT Press. Link

  1. Please take a moment to read this quote from the introduction (pp. 2-3):

    At first glance data are apparently before the fact: they are the starting point for what we know, who we are, and how we communicate. This shared sense of starting with data often leads to an unnoticed assumption that data are transparent, that information is self-evident, the fundamental stuff of truth itself. If we're not careful, in other words, our zeal for more and more data can become a faith in their neutrality and autonomy, their objectivity. Think of the ways people talk and write about data. Data are familiarly “collected,” “entered,” “compiled,” “stored,” “processed,” “mined,” and “interpreted.”

    Less obvious are the ways in which the final term in this sequence — interpretation — haunts its predecessors. At a certain level the collection and management of data may be said to presuppose interpretation.“ Data [do] not just exist, ” Lev Manovich explains, they have to be “generated.” Data need to be imagined as data to exist and function as such, and the imagination of data entails an interpretive base.

    What is the central message of this quote and why is it important?

  2. Gitelman and Jackson assert that photography is subjective — and data are too! What do they mean by this? Do you agree?

  3. If interpretation is always present in data work, how should that affect the way we design data systems and data visualizations? What steps can researchers or organizations take to make the interpretive choices in their data practices more transparent?

  4. Read this quote (pp. 8-9):

    It follows that the imagination of data is in some measure always an act of classification, of lumping and splitting, nesting and ranking, though the underlying principles at work can be hard to recover. Once in place, classification schemes are notoriously difficult to discern and analyze, since “Good, usable systems disappear almost by definition. The easier they are to use, the harder they are to see.” (Bowker & Star, 2000)...When phenomena are variously reduced to data, they are divided and classified, processes that work to obscure — or as if to obscure — ambiguity, conflict, and contradiction.

    Then, try two exercises:

    • See if you can name a few different common classification systems.
    • Then, pick one or two and consider: does this classification seem "natural"? ...And if you dig a little deeper, there are some important underlying values and principles in play?