Organizations gaining new benefits by automating data engineering by Jelani Harper via @infomgmt

A number of advancements have now decreased data preparation time while increasing the time available for exploration and applications.

I think there are several possible tools that enable users to do their own querying and this article talks about the one that the author is most familiar with.  Some organisations use Tableau, Pentaho or Power BI. I'm sure there are others I have not listed.

Security pros complain the cloud obscures compliance issues by Bob Violino via @infomgmt

Half of the organisations surveyed said existing tools aren’t effective in the cloud and an overabundance of tools makes it almost impossible to prioritise IT and security investments.

I can relate to their concerns - I'm not convinced that cloud is mature enough and all the tools are in place to manage all the areas that should be. I think we need more centralisation of security tools and especially those in the cloud area - yes there are some tools that exist as part of cloud management suites but they do not interface or fit in with the other sides of data security and we need something to make sure it can become more central.

Understanding Feature Engineering - 4 part article by Dipanjan Sarkar via @TDataScience

Great 4 part series that you really need to set some time aside so you can sit and read these:

1 - Strategies for working with continuous, numerical data

2 - Strategies for working with discrete, categorical data

3 - Traditional strategies for taming unstructured, textual data

4 - Newer, advanced strategies for taming unstructured, textual data

7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning by Ajit Jaokar via @kdnuggets

It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help

A great list of books to help with this area.  Certainly I know I need to be better at this.

Understanding fast data and its importance in an IoT-driven world by Kayla Matthews via @infomgmt

Processing high volumes and continuous streams of information in real-time with low to medium latency, it is scalable, has a high uptime and can quickly recover from failure situations.

I think for me you have to have :

- Clean Data - it has to be good data that is not rubbish.
- Data Management - you have to understand exactly what you have and what it means. It has to have consistent definition and there must be some sort of validation to make sure it is correct.
- Process Consistency - your processes have to be consistent too so everyone works off the same thing.

9 key mistakes organizations make when analyzing data by Larry Alton via @infomgmt

The accessibility and ubiquity of information has led to an increased number of amateur mistakes in analysis. Here are some of the most common, and how to overcome them.

I think this list needs to be bookmarked, printed out and more importantly referred to in order to try and check for all of these in order to improve the standard of your analytics.

Overcoming hidden data risks when managing third parties by Baan Alsinawi and Adriaen Morse via @infomgmt

Here are steps that will extend a risk management program to include outside vendors and reduce the likelihood of a breach due to factors outside an organization’s control.

I'd like to think that none of these are new or surprises but recent breaches and legislation (like GDPR) turn a much higher focus on this kind of thing. It's almost a master list for anything that is put out to an external organisation to complete for you.