An Impossible Task

Today, I had some difficulties with system testing at work, especially not knowing how to analyse such a large set of data in little time. The experience reminded me that we process so many bits of information daily, without ever realizing how much is being done behind the scenes to organize one's current world. System "testing" is like challenging the habitual world: it asks that a person not just take things  as given, but that one carefully scrutinize a system to see what can be improved and made more efficient. Such a task is not as easy as it seems, because it challenges the tester to try to engage the same data in new ways.
  I have found in the past weeks that two approaches have been most helpful in system testing. The first is assume that something is wrong. If everyone just expects the system analysts and IT people to be always right all the time, then there is no purpose in testing, and the business users had might as well sweep the problems under the proverbial rug. The second principle is to try to chunk the data in ways that maximize the effort, given the small time in which a person has to test. This latter principle is actually difficult to put into practice, because it requires a strategy which isn't going to be uniformly applied. One strategy might be to pass over the "perfect" or "near perfect" system matches and focus on more questionable or "deficient" data discrepancies.  Another might be the opposite: to look for the chinks in the armor, or focus on data that seems deceptively perfect or good. I am beginning to focus more on the data that is less clean or might need remedial help to become fully cleansed. But this strategy doesn't always necessarily work, and one has to budget according to the needs and profits as well.
   When I say "impossible", I mean the metaphor of data testing is something like trying to bite into a cookie the size of a building. It's possible to take a little bit, but that doesn't mean there will be a complete analysis of everything. Sometimes it helps to know that, like a cake, all the ingredients in a data "mixture" are reflected in each tiny piece. It only takes a sharp willingness to look at a small piece to know how the larger piece works. I believe that this is where the Huayen sutra can be helpful in understanding the way parts of data reflect the whole. It can also remind me that an intuitive acquaintance with a small set of data is enough to see the larger picture, in many cases.