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.

If you are anything like me, you love a good cup of coffee/tea and your contribution to Starbucks’ last quarter (Q1FY17) report of $4 Billion USD net revenues¹ is more than you want to tell your spouse. So let me give you a few excuses to visit your local Starbucks in order to study some examples of data quality.

*Text in bold has additional explanation of the term which can be seen if you place your cursor over the text.

The IT department has just migrated 400,000 accounts from the legacy ERP system onto a new, bright and shiny, system sold by a large software vendor, but the Sales team is mortified. The Sales team has discovered that all of the Sales Notes fields (in addition to others) have been Truncated to 255 characters. The sales agents use the end of this text field to record all of the "juicy" (or current) leads and now this valuable information is no longer accessible...

When faced with a number of data quality issues, and urgent stakeholder requests to improve quality, it can be tempting to dive right in and clean the data. This however doesn't get to the root of the problem and one finds that many of the same types of problems have to be resolved over and over again. This can be not only ineffective, but also demoralizing when staff have to spend so much time to get back to where they started.