Cambridge Analytica’s wholesale scraping of Facebook user data is big news now, and people are “shocked” that personal data is being shared and traded on a massive scale on the internet. But the real issue with social media is not harm to individual users whose information was shared, but sophisticated and sometimes subtle mass manipulation of social and political behavior by bad actors, facilitated by deceit, fraud, and amplification of lies that spread easily through societal…

What most people call “analysis,” I refer to this as “guidance.”  It is not guidance in terms of guiding the company; but rather, I provide a narrative to help guide people through the data - of which there is a great deal.  I play the role of a tour guide.  I remember when I was a teaching assistant for a social science class - and there was a contentious area that would likely be the focal point for essays - I said that it didn’t matter to me what “opinions” people expressed.  Nobody had…

Missing data present significant challenges to trend analysis of time series. Straightforward approaches consisting of supplementing missing data with constant or zero values or with linear trends can severely degrade the quality of the trend analysis, which significantly reduces the reliability of the trend analysis. …

Six Sigma is a quantitative approach to problem solving - to solve certain types of problems. At the root of Six Sigma is an improvement methodology that can be described by the acronym DMAIC: define, measure, analyze, improve, and control [1]. Those interested in reading up on Six Sigma might consider the book for dummies, which I found fairly succinct. Those wondering what I mean by "certain types of problems" should consider how to apply the approach to their own business circumstances. I…

I have been writing about the Crosswave Differential Algorithm for a number of years. I described in previous blogs how the algorithm emerged almost by accident while I was attempting to write an application intended to support quality control. In this blog I will be discussing the event model that powers the algorithm. Events are the details and circumstances…

I found some leftover hamburgers in the fridge. I decided to stack a couple of them together to form a colossal “super-burger.” At the time, I didn’t appreciate how doing so would make it almost impossible to physically fit the burger in my mouth. I squished and squeezed the burger until it was flat enough to eat. Such are the problems of physics that become…

In this blog, I will be discussing some distinct types of data involved in feedback. The types that I will be covering are as follows: 1) structural; 2) event; 3) quantitative; 4) contextual; and 5) systemic. In 2014, I recall reading a number of blogs about three types of data: prescriptive, descriptive, and predictive. There was a data scientist apparently on tour lecturing extensively about these three types. I don't recall the individual's name. Well, prescription, description, and…

The role of statistics in data science is often debated. Despite rapid developments in technology giving access to algorithmically sophisticated approaches, I feel that statistics can still provide many worthwhile insights. If I have a database of sales figures spanning many years, I feel that I can become more aware of historic trends and seasonal patterns through the use of statistics. Statistics offers a sense of state, direction, pace, and progress. Statistics can also enable estimation…

Somebody once mentioned to me that there is a need for a standard method of performance evaluation that can be applied to all employees regardless of their exact duties: e.g. to compare a janitor to an accountant. In my jurisdiction, there is a regulatory requirement for "equal pay for work of equal value" that can affect companies with government contracts. I consider the concept of "equal value" complicated due to its subjective nature. Certainly two people handling exactly the same work…