The Beginner’s Guide to Pydantic by Ng Wai Foong via @BttrProgramming

 A Python package to parse and validate data.

This looks really useful and well worth investigation and some experimentation. Certainly I only did the basics with it but can already see the value in it's use.

3 best practices for improving and maintaining data quality by Maxim Lukichev via @infomgmt

Organisations are increasingly relying on insights generated by data analysis, and they realise that insights are only as good as the data they come from.

Maxim makes some very good points in here.  I think any data analysis with bad data is at best worthless and at worst destructive for your business as you will be making key decisions based on something which is not correct. It is important that you validate your data to make sure it is trustworthy and have a network of data stewards in your business to ensure that data is correct and processes and in some cases systems are updated to make sure that quality is improved and assured going forward.

5 Ways Businesses Can Cultivate a Data-Driven Culture by @Ronald_vanLoon via @LinkedIn

The pressure on organisations to make accurate and timely business decisions has turned data into an important strategic asset for businesses. In today’s dynamic marketplace, a business's ability to use data to identify challenges, spot opportunities, and adapt to change with agility is critical to its survival and long-term success.

Some interesting points on what to look out for.  As I often say, you need to make sure the data is clean, tidy and well understood.  If you can't guarantee that the data is up to data and clean I see little point in collecting it let alone using it.  You need to be able to guarantee it is clean in order to guarantee the results of any analysis or reporting is reliable.

Data validation with the assertr package by/via @tonyfischetti

The assertr package has some wonderful validation constructs: even if you don’t spend a lot of time in R, it’s worth reading this piece purely for its approach to scale-able data validation.

The code examples and the comments with replies are a must read too as I think if you add the article, the examples and the questions it adds to the complete value in looking at this.  Definitely worth a look for anyone who uses R but can also give some overall help for anyone who validates data.