Preparing For Machine Learning: 5 Questions Enterprises Must Consider by @IE_James via @IEGroup

Evidence of machine learning's potential to change the world is now everywhere, from lights-out factories through to Netflix's recommendation engine. However, adoption is still not reaching the levels many predicted.

James had it exactly right - there is absolutely no reason to implement machine learning unless you and your organisation are ready for it, and I fear that is no is many cases at the moment.  By all means design your infrastructure and systems to be ready for ML but don't try and implement it until/unless you are really ready.

Implementing Successful Big Data and Data Science Strategy by Ashish Sukhadeve via @DataScienceCtrl

Big Data and Data Science are two of the most exciting areas in the business today. While most of the decision makers understand the true potential of both the fields, companies remain sceptical on how to implement a successful big data strategy for their enterprises.

I liked this and thought it warranted a share.  He is right in saying a pilot project is necessary - you have to do something that gives you a proven quick benefit in order to justify the technology, the methodology and the benefits that can be achieved.

How Facebook's oracular algorithm determines the fates of startups by Burt Helm via @NYTmag

The platform is so good at “microtargeting” that many small e-commerce companies barely even bother advertising anywhere else.

I found this interesting and it suggests that they really have their algorithms and machine learning right and that many others could learn from that.

The 3 most important data metrics for retaining customers by Matthew Tharp via @infomgmt

In an era where every customer is valuable, it’s important to use information to more deeply understand the most profitable and high-growth segments and tailor the business to them.

Some great suggestions on metrics as keeping customers is definitely cheaper than getting new ones.

It’s time to solve deep learning’s productivity problem by Hillery Hunter via @VentureBeat

Deep learning is fuelling breakthroughs in everything from consumer mobile apps to image recognition. Yet running Deep learning-based AI models poses many challenges. One of the most difficult roadblocks is the time it takes to train the models.

Hillery makes some good points and gives a lot to think about.

What the world's central banks are saying about cryptocurrencies by listed authors via @infomgmt

Eight years since the birth of bitcoin, finance execs around the world are increasingly recognising the potential upsides and downsides of digital currencies.

I like this overview of how banks are handling the currencies which might help you to understand how to understand it yourself.

Three tips for mastering digital transformation by Michael Gale and Chris Aarons via @infomgmt

Organisations that saw real results had and built a unique digital DNA that enabled them to succeed where other similar companies failed or saw far fewer results.

I really liked this and agree with the points made in the article.  Organisations really need leadership with the vision and will to do all these things in order to ensure success.