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Conformed Dimensions of Data Quality Blog

Homeless Count Offers Great Example of Data Quality Principles

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.

Fall Harvest Time Reveals Geospatial Data Quality Example

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).

Duplicate Profile Photos Show Integrity Issues

 

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.

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Shippers Foretelling Future Deliveries!

 

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.

Help Reduce Survey Bias- Take the 2017 Annual Dimensions of Data Quality Survey

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.

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