Data modelling vs. data architecture: What's the difference? by/via David Loshin

Data modellers and data architects have distinctly different roles, but they work in a complementary fashion to help enterprises unlock and capitalize on data's business value.

It's a pretty important job definition difference and a key one for people to understand.

Data Rights Will Be The New Civil Rights by @VanRijmenam via @Datafloq

The Digital Speaker and his great conversation with Dan Turchin on how to use AI in an ethical and responsible manner and why AI = Math + Data.

This was a great video of the conversation between them and really a must watch even if you are not interested in AI as a subject on its own.

Coding habits for data scientists by David Tan via @thoughtworks

Code to train ML models can get messy fast. This article identifies the bad habits that add complexity in code and suggests good habits to cultivate in order to declutter your code.

Some great advice in this article and some great examples of python code - both good and bad.

42 countries adopt new OECD principles on AI by/via @OECD

The Organisation for Economic Co-operation and Development (OECD) and six partner countries formally adopted the first set of intergovernmental policy guidelines on AI, agreeing to uphold international standards to ensure AI systems are “designed to be robust, safe, fair and trustworthy.”

The document, described as a “political commitment,” is aimed at governments and offers general principles but no details or agreement on how to achieve them.

So glad that these countries have started to follow the guidelines and I really hope others start to follow them too. There is a link in the article so that you can download the entire guidelines.

How better standards can decrease data security spending needs by Anna Johansson via @infomgmt

Recently, standardisation at the highest levels has opened new doors for companies seeking cyber security solutions that don’t cost a fortune and work better than current approaches

Anna makes some good points - chaos with data, systems or interfaces add needless complexity to an organisation that can give areas that could be exploited. You need ordered data with tight controls.

Genomic Data Sharing Requires Standardization of Lab, Clinical Info by Greg Slabodkin via @infomgmt

To address a number of challenges, the group calls for the standardisation of laboratory and clinical information to enable data compatibility as well as interoperability between systems.

Like always standards are always needed for the format of the the data, the structure of the messages that shre the data, and how the data is treated.

Blockchain’s Backers Gather to Push Governance for Technology by Olga Kharif and Peter Coy via @infomgmt

Now that companies in finance and other industries are looking to adopt blockchain -- essentially a new kind of a database for recording transactions -- more standards-setting organisation is needed.

Definitely a standard is needed especially because the finance industry is adopting it.