42-54349840-300x200

Five data quality lessons from Amazon

Five data quality lessons from Amazon

About a year ago on this site, I penned a post titled "Analytics lessons from Amazon." In it, I described the analytics lessons that employees and even entire companies can learn from the retail giant.

But there's so much more that Jeff Bezos et. al can teach us. Today, I'll focus on the data quality lessons we can glean from the largest Internet retailer.

By way of background, Amazon understands that all data is important whether it is:

As someone who has followed the company since its inception, trust me: this core belief cascades throughout the organization.

Amazon stocks a ridiculous number of items. One of my favorite stats: its clothing selection is now bigger than 250 Walmart supercenters combined. With that much inventory, from time to time there's going to be a data issue or two. (I wrote a post about one such issue back in July of 2014.)

Still, Amazon is constantly its improving the data around offerings, even if that means acquiring companies such as IMDb in 1998.

Read Also:
30 Top Videos, Tutorials & Courses on Machine Learning & Artificial Intelligence from 2016

With so many products and different lines of business, a top-down approach to data quality would never fly at Amazon. To this end, the company provides its customers, partners and vendors with tools to manage a great deal of their own data.;

 



Read Also:
11 Best Practices for Business Intelligence
Read Also:
Let's Resolve the Data Analytics Conflict in 2017
Read Also:
It’s data integration, but not as we know it
Read Also:
Let's Resolve the Data Analytics Conflict in 2017

Leave a Reply

Your email address will not be published. Required fields are marked *