Intent vs. Inference by David Loshin.
David writes:
I think that the biggest issue with integrating external data into the organization (especially for business intelligence purposes) is related to the question of data repurposing. It is one thing to consider data sharing for cross-organization business processes (such as brokering transactions between two different trading partners) because those data exchanges are governed by well-defined standards. It is another when your organization is tapping into a data stream created for one purpose to use the data for another purpose, because there are no negotiated standards.
In the best of cases, you are working with some published metadata. In my previous post I referred to the public data at www.data.gov, and those data sets are sometimes accompanied by their data layouts or metadata. In the worst case, you are integrating a data stream with no provided metadata. In both cases, you, as the data consumer, must make some subjective judgments about how that data can be used.
A caution about “intent” or as I knew it, the intentional fallacy in literary criticism. It is popular in some legal circles in the United States as well.
One problem is that there is no common basis for determining authorial intent.
Another problem is that “intent” is often used to privilege one view over others as representing the “intent” of the author. The “original” view is beyond questioning or criticism because it is the “intent” of the original author.
It should come as no surprise that for law (Scalia and the constitution) and the Bible (you pick’em), “original intent” means agrees with the speaker.
It isn’t entirely clear where David is going with this thread but I would simply drop the question of intent and ask two questions:
- What is the purpose of this data?
- Is the data suited to that purpose?
Where #1 may include what inferences we want to make, etc.
Cuts to the chase as it were.