After reading Jay Stanley’s ACLU article on “Eight Problems with Big Data,” it is worth reflecting on what could be construed as a fear-mongering indictment of the use of big data analytics and the implication that big data analytics and its implementation of data mining algorithms are tantamount to all-out invasion of privacy. What is interesting, though, is the presumption that privacy advocates have been “grappling” with data mining since “not long after 9/11,” yet data mining was already quite a mature discipline by that point in time, as was the general use of customer data for marketing, sales, and other business purposes. Raising an alarm about “big data” and “data mining” today is akin to shutting the barn door decades after the horses have bolted. Read more
The other day I received an email from one of the advisory service providers to which I subscribe, saying:
“We have been informed by our e-mail service provider, Epsilon, that your e-mail address was exposed by unauthorized entry into their system.”
By this morning, I had gotten another two emails from different companies that send me emails with the same news, that Epsilon had been hacked and my email address was exposed. Actually, the notes, which all shared the same wording, said this:
“We have been assured by Epsilon that the only information that was obtained was your first name, last name and e-mail address and that the files that were accessed did not include any other information.”
Filed under: Analytics, Business Intelligence, Data Governance, Data Quality, Performance Measures
This recent article about analytics suggests that IT executives worry too much about data quality. However, the provocative headline (well, at least provocative to me) is a conclusion drawn out of survey results, and that interpretation may reflect some flawed thinking.
We are defining a high level process flow for data preparation for data mining and analysis. One of the objectives is to use undirected data mining to identify the variables that are most suitable for classification. The idea is that if there are specific characteristics that distinguish what we have identified as “good customers,” then we can look for similar characteristics in other customers and see if they can be transitioned into the “good customer” category. Therefore, we are looking for discriminating variables and their corresponding value sets.