This summer I took a working vacation to San Diego, California. Having spent a week there each year, for many years, I was impressed with the significant increase in number of app-based transportation rentals available all over the streets.

They often refer to Time as a female character. Indeed, she plays a significant role in the theatric drama we call Data Quality. A while back, I was in a hurry to get some updated retirement account balances and had to reset my password in order to get into my account. I provided my email account and requested the password reset, expecting an email to shortly follow with instructions and a hyperlink to change my password. The problem was that I didn’t get the email, with the link to reset the password, until the next day (long after I needed the information).

From January 23-25, the Los Angeles Homeless Services Authority (LAHSA) conducted their annual count of unsheltered homeless with the help of more than 8000 volunteers. On the evening of the 24th, I joined the #TheyCountWillYou effort in Los Angeles to count the homeless in the city of Cudahy. As you’ll see in the description of steps conducted the process is thorough and intentional.

During a family visit to a local pumpkin patch (Uesugi Farms) this fall, I was looking for a gas station. The corner, just across from the Farm (yellow arrow below), was identified as a gas station on my car's GPS, but that was just a dirt parking lot. Google's satellite imagery shows this as well in the image below. There was, however, a Valero gas station just down the street, but even that didn't show up in Google's list of local gas stations (blue arrow).

In October, during my travels to the International Conference on Information Quality (ICIQ), held at the University of Arkansas Little Rock (UALR), I rented a Toyota Yaris from the Dollar rental car company. When I returned the Yaris, the friendly staff member casually asked if I had just driven around town, and I said yes, but asked why they were asking me.

The best data quality examples are those that we encounter daily. Today I went to the dry cleaners to pick up my suit and what did I find? A great example of unclean data at the dry cleaners! I handed my ticket to the lady, who I later found out was the owner, and she initiated a friendly conversation about the name on the ticket while advancing the dry cleaner conveyor (the rack that holds hundreds of garments that have been cleaned and are ready for pickup). As #110 on the conveyor arrived, she verbally acknowledged that my suit was not there- which I clearly saw with my own eyes.

We all shop for goods and professional services at local supermarkets, restaurants, doctor's offices, etc. Recently, new Point-of-Sale vendors (companies that provide hardware and services for retailers to process credit/debit card purchases), allow customers the option to provide their email address in order to have the retail purchase receipt sent to them digitally rather than (or in addition to) a paper copy. If you've ever eaten out for instance at a restaurant that uses Square, a point of sale service (Squareup.com) you'll be offered this feature.

Sometimes in a digital world it can be confusing whether the lack of quality stems from poor programming (and lack of adequate testing) or from poor data quality. In this post we'll help you identify the possible reasons for quality breakdown that appear in popular social networking sites.

In 2016 it was reported that customers do more shopping online than in the store. Forrester estimates that Amazon accounted for 60% of total US online sales growth in 2016 (1). So with these changes in how we shop, the delivery of goods to our houses has become more frequent and common place. One of our readers found a really good example of data quality relating to these changes in our lives so let's take a look at this in detail.

Do you value data? Of course you do, otherwise you probably wouldn't be subscribed to this blog. So my guess is that you appreciate data without bias. In this age of "Fake News" and other obstructions to our desired level of information quality (think broader than data quality) we have to be weary of how information is interpreted and whether the data we use, to draw a conclusions, is without bias.