You Don’t Need a Data Scientist, You Need a Data Culture by @rahulbot via @wordpressdotcom

Most of the larger non-profit organisations we work with are scrambling to figure out how to deploy complex technologies like machine learning and “AI” in service of the social good.

This is a great article and I'm sure many of you will recognise some of the barriers described in the article - I know I do.

2018 will be the year of the chatbot by Patrick Nguyen via @infomgmt

In the coming year, AI will increasingly shape the customer experience.

I think this is an interesting development and although there are plenty of imperfect chatbots I'm hoping over time they will get better and better as their use if more established.

Central banks are turning to big data to help them craft policy via @infomgmt

Collection of micro data increased after the financial crisis, when policy makers realised they lacked the depth of information to make appropriate decisions.

I found this interesting to see comparisons of how the various central banks are all starting to use big data to make decisions and understand the impact of certain actions in the market.

7 Industries that will be taken over by Robots by @cbcandmore via @Datafloq

Robots are taking over. You can see it already happening at McDonald's with its automated ordering kiosks, or your nearest supermarket with its self-checkout machine. Soon, it will be normal to see driverless cars and people walking their robot dogs outside. And this is only the beginning. With advancements in technology, many jobs typically performed by humans are being replaced by artificial intelligence (AI) and robots. Here are seven industries that are significantly impacted by automation.

This is really interesting. I think we need to remember that there are also opportunities for us to have humans in these areas so there is nothing to panic about.

Artificial intelligence cracks 'unbreakable' Enigma code in just 13 minutes by Laura Stevens via @BT

A demonstration at the Imperial War Museum showed how the latest AI technology takes just minutes to decipher messages once thought unbreakable.

Proof on just how far we have come from those days.

Trustworthy Data: The Goal of Data Quality and Governance by Lindsay Stares via @TDWI

Is poor data governance and slow data prep really a problem? It is when it erodes confidence in the quality of your data.

I completely agree with Lindsay - you need to have complete trust in your data in order to make business decisions based on it. She has some good suggestions for areas to comcentrate on.

How blockchain will underpin the new trust economy by @lucasmearian via @computerworld

Over the next two years, businesses will increasingly turn to blockchain to establish trust among parties looking to transfer everything from money and movable goods to property.

I love the diagram, the way he has explained what blockchain actually is, and some of the potential uses which are great - so will companies actually implement blockchain - I hope so.

Predictions 2018: Organisations turn their attention from big data to smart data by Sandy Steier via @infomgmt

The new focus will be on the information that business users and business-focused analysts can utilise in their everyday decision-making.

I like these predictions and it looks like an interesting year we have to look forward to.

Understanding the role of information rights management by Larry Alton via @infomgmt

IRM focuses on restricting access and improving security for documents, spreadsheets, PDFs and other important files intended to preserve or share information.

Interesting thoughts from Larry. I have to say this is sadly an area that needs much more attention in many organisations. I think it is very sad as senior business managers appear to be unaware of the reputation damage and cost of any breach of data.

The Ascent of Blockchain-Based Trading Platforms by Vincent Stokes via @datafloq

Why is Blockchain gaining so much traction and how is blockchain-based trading platforms changing the finance world?

A real implementation of blockchain which has recently been written off as hype and a fad.

Four important best practices for assessing cloud vendors by Nick Sorensen via @infomgmt

It can be challenging to know how best to communicate the requirements of your assessment process and ultimately select the right partner to help your business move forward.

I think this is really useful and worth reading if you are about to do this.

Getting started with artificial intelligence using the Value Pyramid by Suffiyan Syed via @infomgmt

These guidelines span cost reduction, increased efficiency, enhanced insights and customer engagement, and new business automation for executives looking to invest in AI, but unsure where to start.

This is really useful and something worth reading - the pyramid is definitely worth keeping and referring to.

Incorporating weather data into IoT is a game changer by:Franklin Morris via @IBMIoT

Weather has long been a source of headaches for businesses across all industries.

This is a hugely beneficial development and I can imagine countless examples where weather data would have made a difference. As I write this comment in the UK we have snow, ice and low temperatures. If this could be predicted from  a weather forecast it would have a huge number of benefits.

Google Has Released an AI Tool That Makes Sense of Your Genome by Will Knight via @techreview

AI tools could help us turn information gleaned from genetic sequencing into life-saving therapies.

This is a great progression in knowledge and technology and I'm looking forward for it to be used to create some great results.

Leveraging data to elevate the customer experience by Marc Wilczek via @infomgmt

Many organisations are harnessing only a fraction of the potential value of analytics and thus missing out on the chance to turn insights into a competitive advantage.

Great areas pointed out by Marc in this article.  Worth a read if you are likely to have to do this in your organisation.

Using the scientific method in data analytics for better results by Erick Harlow via @infomgmt

Answers without questions are simply objects that exist. To really get answers out of your 'big data' you need the right questions.

I agree with Erick - you really need to do all the "normal" methodology work to make sure that you do this properly otherwise the result could be incorrect and you waste time, money and worse reputation by coming to the wrong conclusions.

4 ways small businesses can effectively use big data by Charlene Glidden via @infomgmt

There are lots of simple methods an organisation can use to effectively gather, analyse and make sense of information it already has to enhance business insights — without breaking the bank.

Great suggestions by Charlene.  Google Analytics is fairly easy to use and should be well within the capabilities of a Small Business. Google has a lot of help online and just searching should give you plenty of help.

Deep Learning and Google Street View Can Predict Neighbourhood Politics from Parked Cars by @BotJunkie via @IEEESpectrum

It's likely that your car says something about you. The make and model, whether it's foreign or domestic, and how expensive it is can provide information about who owns it.

This is very interesting.  I think this is not true for everyone, but you can certainly make all of these assumptions.

Google’s artificial intelligence built an AI that outperforms any made by humans by Dom Galeon and Kristin Houser via @Futurism

Google's AutoML project, designed to make AI build other AIs, has now developed a computer vision system that vastly outperforms state-of-the-art-models. The project could improve how autonomous vehicles and next-generation AI robots "see."

Wow - the future is definitely on the way and there are so many uses for this technology.

Fraud detection in retail with graph analysis by Jean Villedieu via @Analyticbridge

Fraud detection is all about connecting the dots. We are going to see how to use graph analysis to identify stolen credit cards and fake identities.

This is great and we can only hope that others are doing something similar to this in order to help us all avoid fraud.

Using data to drive a smarter way to faster insights by Maxim Lukichev via @infomgmt

Deep integration between master data management and analytics execution can provide schema synchronisation and offer new decision-making strategies.

A nice aim and something I hope sticks. At the very least lower paid people should be the ones looking at data cleanup as that should be easier to do.

Predictions 2018: The blockchain revolution will have to wait a little longer by Martha Bennett via @infomgmt

In the coming year, we expect to see a number of projects stopped that should never have been started in the first place.

It's a sad reality that if it is new and people or organisations don't fully understand it, that many projects will fail. A shame as I still think this technology has benefits, but maybe the wider business/IT world are not in the right place for it yet.

Backing Up Big Data? Chances Are You’re Doing It Wrong by @PeterSmails via @datanami

The increasing pervasiveness of social networking, multi-cloud applications and Internet of Things (IoT) devices and services continues to drive exponential growth in big data solutions.

I like that this contains two case studies and it makes perfect sense. You need to make sure that backups and recovery and always included in a big data project and that you treat it as a big data project not just a "normal" database.

WEBINAR: America’s data quality crisis - 12 December 2017

Dec. 12, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Your company may have grand ambitions for analytics and AI. But you can’t 

get anywhere with bad data. And research reveals that only 3% of enterprise data fulfils 

even the most basic data quality standards. Worse yet, poor quality costs companies like 

yours an astonishing 20% of revenue

Join this interactive webinar – featuring renowned data quality expert Thomas “the Data Doc” 

Redman – to learn how you can turn your data quality issues around effectively and sustainably.

You’ll learn:

• The cold, hard facts about enterprise data quality – and its business impact
• What it takes to permanently fix your data issues and your data culture
• How your data quality efforts generate quantifiable near- and long-term ROI

Dr. Thomas Redman President Data Quality Solutions (Speaker)	Lenny Liebmann Contributing Editor SourceMedia (Moderator)

Sponsored By:

Register here

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.