Socializing Big Data through BRPs











Big Data Needs Thick  Data

Consider a case described in another post here, Business Exceptions Are Not Always What They Seem. As I noted in that post:

In  Quality vs Quantity: Why Faster Is Not Always Better Barry Lynch of GE explains a case in which GE’s Proficy Workflow solution prevented the use of informal work practices on the factory floor.

One company implemented the Workflow solution with two main goals: reduce the number of “off quality” batches per month and increase the speed of batch production. On one of my visits, the automation manager and I were talking about the system and how they had not had a single batch rejected due to production issues since it went live.  Based on the costs of a lost batch, this was huge for them, but they had not seen the increased speed they were expecting in the primary batching area.

To investigate, we both went down to the line and met with one of the operators using the system. We asked her honest opinion of the system, and she was very complimentary on how it helped her manage daily tasks. Then we asked, “But why have you not been able to speed up the process?” Her response was straight to the point:  “With this system in place no one takes the shortcuts we used to. The system makes sure we all make the same batches the same way consistently.”

So, we concluded that the quality issues seemed to be caused by people trying to speed up the process as opposed to following their own documented processes. They made a few more batches, but those gains were offset by expensive rejects that occurred.

In other words, the practical knowledge gained from informal learning on the factory floor allowed employees to take shortcuts to speed up batch production, even though the unintended result was to increase off-quality batches. A classic speed vs. quality tradeoff. It would, of course, be useful to know why operators in this situation felt the need to speed up production prior to the GE system implementation. A variety of scenarios could explain it.

As in many instances of Big Data implementation, without insights from associated Thick Data the decision-makers’ assumptions define what to expect, i.e. that uptime leads to increases in production, power, flight and transportation. Thick Data, as Tricia Wang characterizes the use of ethnography to supplement analytics, provides the context for Big Data.


Without the stories and tacit knowledge in Thick Data, the guiding assumptions about Big Data implementations proceed without regard to the context of their use. What Barry Lynch of GE uncovered, that taking discretion out of the process of batch production slowed the process while increasing quality, describes an organizational practice in which a barely repeatable process (BRP) involving local, informal knowledge occurred and informed efforts to speed up batch processing.

It seems plausible that speeding up batches meant taking shortcuts which, one must assume, also implies avoiding doing certain actions associated with running a batch. In other words, the context of the coordination decisions constituted the Thick Data used to manage the coordination process.

Thomas Erickson of IBM puts it somewhat differently but his point is in line when he notes:

People are everywhere. They are experts on local information– their homes, their places of work, the routes they travel every day. That’s what they care about, and that’s what will get them involved. And when we design systems that make a place for people to be active, first class participants, where millions can share what they know, then we’ll really see smarter.

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