Hadoop for Beginners by Aafreen Dabhoiwala via @kdnuggets

An introduction to Hadoop, a framework that enables you to store and process large data sets in parallel and distributed fashion.

A nice little overview of Hadoop although I do agree with the first comment  by Randy about relational databases

Why organisations should regularly assess the KPIs they track by Kayla Matthews via @infomgmt

KPIs alone are not enough. Instead, it’s necessary to regularly re-evaluate all applicable KPIs to ensure they’re still providing information that’s relevant to the business at large and in line with its data governance strategies.

I definitely agree that it is important to adjust KPIs so that they stay relevant to your organisation and actually achieve what you want them to.  Thing of all the time and money that could be wasted by not adjusting them so they are still relevant.

Artificial general intelligence: Dream goal, nightmare scenario or fantasy? by Herb Roitblat via @infomgmt

The quest for artificial general intelligence is the holy grail of artificial intelligence research, and, arguably, just as difficult to find. It may be a myth.

This is a really interesting piece by Herb and really makes you stop and think about what is and what is not possible. Definitely worth reading during a time when you have time to stop and think a bit about it.

Getting to ROI with AI in the enterprise by Tom Wilde via @infomgmt

Despite its promise, and its growing adoption, there is still too little we can point to in terms of real business results from artificial intelligence.

Some great thoughts from Tom although I'm certainly not convinced that many organisations have actually achieved something very tangible as a return on ther investment in AI.

Essential Math for Data Science: ‘Why’ and ‘How’ by Tirthajyoti Sarkar via @kdnuggets

It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.

This is really useful - you can teach yourself statistics if your own skills are not up to scratch.

5 top strategies to make development cycles more efficient by Charles Dearing via @infomgmt

Software development is fraught with all sorts of pitfalls. Adopting the principles of Agile software development is one way to combat these inevitable pitfalls.

Some useful advice.

WEBINAR: 4 Ways to Tackle Common Data Prep Issues - 25 September 2018

Anyone who's ever analysed data knows the pain of digging in only to find that  it is poorly structured, full of inaccuracies, or just plain incomplete. But "dirty data"  isn't just a pain point for analysts; it can have a major financial and cultural impact on  an organisation.  In this latest Data Science Central webinar, you will learn four actionable ways to  overcome common data preparation issues, including how to establish a company  standard for "clean data" and how to democratize data prep across your organisation.  Speaker: Louis Archer, London Manager -- Tableau Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Title:  4 Ways to Tackle Common Data Prep Issues Date:  Tuesday, September 25th, 2018 Time:  9:00 AM - 10:00 AM PDT

Register here

New open challenge seeks to promote ethics in the use of AI and the news by David Weldon via @infomgmt

Toward that goal, a new open call is offering $750,000 for ideas that will shape the impact artificial intelligence has on the field of news and information.

Something to think about entering - I'm sure we all have thoughts on this.

Key steps to ensure data protection amidst the growth of mobile apps by Nathan Sykes via @infomgmt

As data protection regulations grow and the laws become more stringent, it has also become much more difficult to follow them because of widespread mobile adoption.

Some useful pointers to steer you in the right direction.

WEBINAR: Columnar Databases: Best Choice for Real-Time Analytics - 19th September 2018

Business today often calls for analyzing millions or even billions of rows of data  on demand and in real time. And although relational databases are unmatched  for transactional workloads, columnar databases allow for faster and easier analytical queries. In this latest Data Science Central webinar, we'll explore how columnar databases can: Decrease the need to read from disk Massively compress your dataset Eliminate the need for indexes Support ad hoc, on-demand queries at any scale We'll use MariaDB AX, an open source columnar database for enterprises, to demonstrate  how column-based storage can make your analytics more efficient, flexible and scalable –  without sacrificing standard SQL. Speaker: Shane Johnson, Senior Director of Product Marketing -- MariaDB Hosted by: Bill Vorhies, Editorial Director -- Data Science Central Title:  Columnar Databases: Best Choice for Real-Time Analytics Date:  Wednesday, September 19th, 2018 Time:  9:00 AM - 10:00 AM PDT

Register here

AI Knowledge Map: How To Classify AI Technologies by Francesco Corea via @kdnuggets

What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.

I love the diagram and explanations in this article - it is worth printing and keeping to hand.

How blockchain technology could aid key data challenges by Kevin Peek via @infomgmt

A variety of healthcare provider organisations and health insurers are just beginning to deploy distributed information to solve vexing data issues.

Yes blockchain gives definite benefits and I think that organisations should be looking at it seriously to see if it solves some of the problems they are experiencing.

Master data management is not the answer to GDPR compliance by Aaron Zornes by @infomgmt

By themselves, neither data governance nor MDM offer sufficient capabilities to meet GDPR requirements. Together, we are much more empowered as an organisation.

This article contains some really interesting points that I had not realised. Worth reading and thinking about - maybe they are true in your own organisation and you are not aware and may need to make some changes.

Digital 'fixation' causing firms to throw good money at bad projects by Bob Violino via @infomgmt

Organisations risk wasting millions of dollars in the next 12 months, as they rush into flawed digital projects, according to a new study.

I have to agree - you must find a benefit from each project and it is also important that you look afterwards to ensure that the project actually DID give the benefits that was suggested - it might meant you have a few failures but in the long term it can only improve the process of providing evidence of potential benefits fr proposed projects.

Machine Learning Cheatsheet via @readthedocs

Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.

Definitely something to be bookmarked.

Data Visualisation Cheat Sheet by @jschwabish via @kdnuggets

Core principles for successful data visualisation, including tips on how to reduce clutter, preattentive processing and how to integrate text within the graph.

This is so very useful and is worthy of a bookmark for sure.

How advanced OCR found new life in big data systems by Anna Johansson via @infomgmt

Today, optical character recognition, in combination with natural language processing, allows businesses to perform complex data extraction tasks.

A great idea - use OCR to scan in old paper documents to fill the gaps in your online data - you will never get accurate results on analytics if you are missing data.

o succeed at digital transformation, do a better job of data governance by Darren Cooper via @infomgmt

To set the stage for initiatives like AI and machine learning, companies need a rock-solid governance framework.

Great suggestions by Darren in this article.

GDPR compliance the perfect opportunity to modernise data architecture by Amandeep Khurana via @infomgmt

Compliance with the data privacy and security mandate enables organisations to become more agile in their product and service development and rollouts, and more efficient and effective in their ability to respond to market trends and competitive threats.

Yes this is exactly right - everything has to turn onto it's head and be data centric not application centric. I think we need to concentrate on:

WHERE is the data created
WHERE is it also stored (so where is it interfaced to)
HOW it is updated
WHAT changes when it is updated
HOW do you delete the data in ALL systems?

I would suggest you do something like a data flow diagram so you can document all of this for every piece of data.

The bias problem with artificial intelligence, and how to solve it by Sanjay Srivastava via @infomgmt

AI bias may come from incomplete datasets or incorrect values. Bias may also emerge through interactions overtime, skewing the machine’s learning. Moreover, a sudden business change, such as a new law or business rule, or ineffective training algorithms can also cause bias.

I agree - you need good quality and representative training data if you want to get good results from any AI and ML you want to use. My advice would be:

1.  Take your time - rushing always leads to mistakes so be realistic with plans.
2.  Be careful with the methodology you use to create and split your data into Training and Data.
3.  Try to use separate teams to test the same piece of code - the hope being that it will help to avoid the bias. Think of it as a human version of a small parallel ML solution.
4.  Check, check and check again.

Community lenders tell big tech vendors to get up to speed by Nathan DiCamillo via @infomgmt

Small banks and credit unions say slow responses and outdated products from the establishment tech vendor can become a drag on their innovation efforts.

I partially agree with him - yes large organisations move slow (particularly when you are a small customer and therefore your business is not a big loss to them if you move on) but small ones are less stable and sometimes that can be an unacceptable risk to the business (particularly in the financial sector where you just cannot afford an issue). So do really careful risk management and have SLAs to protect yourself.