For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights – NYTimes.com.
A colleague sent this article to me and what follows is my response.
Read the article thank you. Everybody trying to understand analytics needs to understand this and the burden it puts on projects and coming up with results.
Unfortunately, the issue goes even deeper than the article describes. Transaction systems were designed for accounting and contractual fulfillment, not for data science. The designers of those systems weren’t too particularly savy about the way people work so the data entry became corrupted by laziness and short cuts and some just crazy sloppy validation and edits. Now we’re in a state where the data to model coming from these lousy data entry systems got loaded into data warehouses. The ETL performed on data again, was now maybe a bit better….supposed to make reporting and analysis easier. But the Transform logic just added another layer of poor hygiene to the data and/or illogical transformations. And the Load logic was all about reporting and not data science. So data warehouses are not great to facilitate data science.
Data science is unwinding all of that row by row and column by column in a brute force effort. We even try to get inside of the bugs by finding patterns in null values and unexpected 1’s and 0’s where there is supposed to be valid values entered.
Data science projects simply run out of time to correct all of this and end up throwing out half the data originally thought to be interesting. Also keep in mind that after the janitorial work, the data has to be preprocessed for the specific algorithmic approaches being used…..binning, log transformations, and a dozen other critical techniques to extract signal and not get fooled by the noise.
I don’t believe there is an automated approach beyond what we already have, because the source systems are so varied in the way the data collection was programmed, the ETL was programmed and the data entry actually happens. The first step is to perform statistical evaluation to ‘smell’ the data. These are pretty basic steps but need to be done on every column you are working with…sometimes hundreds or thousands.