Mastering MDM is mandatory for digital transformation success by Scott Taylor via @infogmt

Master data is the vital difference between scaling or failing in this time of business disruption and non-stop data.

I agree with Scott - you fundamentally need to change the company culture and processes in order to make sure that this in par of what you do and accurately reflects the organisation as a whole.

The 23 Most Influential Business Intelligence Blogs via @Better_Buys

This list is from Better Buys.

Some useful ones to add to your reading list.

Four fundamentals of workplace automation by Michael Chui, James Manyika and Mehdi Miremadi via @McKQuarterly

Automation occurs in stages. While full automation might still be a ways off, there are many workflows and tasks that lend themselves to partial automation. In fact, McKinsey estimates that “fewer than 5% of occupations can be entirely automated using current technology. However, about 60% of occupations could have 30% or more of their constituent activities automated.”

It's heartening to read that jobs will not be completely replaced - so modified I can live with.

A Guide to Decision Trees for Machine Learning and Data Science by @GeorgeSeif94 via @kdnuggets

What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.

Bookmark this so you can refer back to it when needed. Add him in Twitter for some great help and information too.

Consolidating data silos: What enterprises need to know to harmonise data by Miika Mäkitalo via @infomgmt

The more disparate silos an organisation has, the more vulnerabilities are likely to exist across the organisation.

This is an area that needs careful consolidation and integration into the rest of your data. If you invest the time and money to do that you will achieve some benefits.

What Great Data Analysts Do — and Why Every Organisation Needs Them by Cassie Kozyrkov via @HarvardBiz

Full stack data scientists and machine learning pros get all the glory. But this Harvard Business Review article argues that instead of asking your analysts to develop machine learning skills (risking mediocrity in two fields rather than excellence in one), your analysts should be encouraged to excel at analysis.

Cassie makes a very good point - do you really want a Jack of all trades who is not great at what they do or do you want an expert in the one thing (analysis) that can produce something that is worth risking your businesses future on?

Netflix machine learning director talks personalisation software by Holden Foreman via @StanfordDaily

“Applying machine learning to data solicited from users allows Netflix to proactively reach their audiences.” Netflix machine learning director, Tony Jebara, explains.

This all sounds really exciting - what a great use of all the data they readily have available to them.