18 data sources for investigative journalists by Mădălina Ciobanu via @journalismnews

Government websites can be a starting point for many journalists investigating issues in the public interest, such as local planning and development, or spending. There are also many other data sources available that are often the result of other investigations and projects journalists and news organisations have worked on, or that have been compiled by civic groups and non-profits, which other reporters can use.

Some great sources that could also be used to provide test data for any practising you would like to do with any data science concepts or techniques.

With attacks soaring, India races to regulate cryptocurrencies via @infomgmt

A giant cyberattack that crippled the largest container port in June provided a costly wake up call for a country determined to digitise its mostly-informal $2T economy.

Good that someone is trying to regulate and get control of these currencies - I guess it could then become a blueprint for other parts of the world too.

The 5 top cybersecurity threats for 2017 by David Weldon via @infomgmt

Cyber attacks and data breaches are on the rise this year, thanks in large part to the proliferation of affordable, customisable and accessible tools for cyber criminals, says Accenture.

This is interesting and in some ways gives you a list of what to look out for and avoid.

That IBM blockchain? Northern Trust says it works by @pennycrosman via @infomgmt

Big Blue formally launched the platform Tuesday, but the custody bank has been using it for months, as has a group of Canadian institutions.

This is another great advance in the use of blockchain and definitely the way forward.

10 Artificial Intelligence (AI) Technologies that will rule 2018 by Biplab Ghosh via Knowstartup

Artificial Intelligence is changing the way we think of technology. It is radically changing the various aspects of our daily life. Companies are now significantly making investments in AI to boost their future businesses.

Great list of technologies that if you have not experienced them yet then you will experience them soon in your working life.

Cars Suck Up Data About You. Where Does It All Go? by John R Quain via @nytimes

Cars have become rolling listening posts. They can track phone calls and texts, log queries to websites, record what radio stations you listen to - even follow you and tell when you are breaking the law by exceeding the speed limit.

Something that we see to have accepted as normal but do we really know what happens to the data and how it is used?  I think this is a question that needs to be investigated further.  We are used to ticking boxes and giving explicit permission for our data to be shared and used - how is that done (if it actually is) for our cars in this increasingly connected world?

WEBINAR: Predictive Analytics for Supply Chain Management 22 August 2017

Overview

Title: Predictive Analytics for Supply Chain Management

Date: Tuesday, August 22, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Predictive Analytics for Supply Chain Management

The Retail and Consumer Packaged Goods industries are continuously looking to maximise margins across all aspects of their supply chain. Tight competition and increasing distribution costs can exert negative pressures on revenue, sink profits, or force companies to adopt unfavourable pricing strategies to keep pace with ever-evolving markets and consumer demands.

In this Data Science Central webinar, data experts from Alteryx and Keyrus will show how organisations can model their entire supply chain, identify cost and distribution bottlenecks, keep store shelves stocked with accurate forecasting, and model the cost-to-serve a product across dynamic and complex distribution systems.

Join us to discover how putting advanced analytics within reach of your organisation can lead to disruptive and actionable insights for your business.

Speakers:

Razvan Nistor, Head of Data Science & Retail Analytics Expert -- Keyrus
Scott Trauthen, Director, Product & Alliances -- Alteryx

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

The AI Hierarchy of Needs by @mrogati via @hackernoon

From stealth hardware startups to fintech giants to public institutions, teams are feverishly working on their AI strategy. It all comes down to one crucial, high-stakes question: ‘How do we use AI and machine learning to get better at what we do?’

This is an article which is both a must read, a must bookmark and a must share.  Thank you so much Monica for writing this.

WEBINAR: Toxic analytics: How good technology leads to bad decisions - 22 August 2017

Aug. 22, 2017 | 2 PM ET/11 AM PT

Hosted by Information Management

Your business is demanding analytic insight. But seemingly helpful analytics can actually lead decision-makers in the wrong direction.

Fortunately, these common analytic errors are well-understood—so you can easily avoid them if you know what they are.

Attend this provocative webinar to learn:

• 3 main types of “toxic analytics”
• Proven best practices for avoiding negative analytic outcomes
• Why more information isn’t always better information

(Moderator) Lenny Leibmann Contributing Editor, SourceMedia

Sponsored By:

Register here

Hill for the data scientist: an xkcd story by Lucy D'Agostino McGowan via Live Free or Dichotomize

A how-to guide for data science - as told via xkcd comic strips!

I love, love, love this.  It also makes some important points.

Oracle Wants Its Cloud to Grow Inside Your Data Centres by Wylie Wong via @datacenter

Taking a different approach to hybrid cloud than some of its biggest competitors, Oracle has substantially beefed up the capabilities of its on-premises cloud product called Oracle Cloud at Customer. It gives companies the ability to use its cloud services but have them run inside their own data centres.

I can't make up my mind whether this is a good idea or not. I don't like the thought that there is still a cost to you relating to hardware and if the main players in the cloud market have different approaches it messes up the ability to change vendor - hence the nightmare of vendor lock-in feels very real.

Fears over vendor lock-in dampening cloud investments, study says by Bob Violino via @infomgmt

As more enterprises move to multi-cloud models, they are also becoming more aware of the potential negative consequences and implications, says Ariel Maislos.

I have to agree with some of the points in this - yes it seems imperative that organisations move towards the cloud to achieve all the associated cost savings and flexibility, but it also seems wise to have caution and try to protect yourself against vendor lock-in. It seems that you have to think ong and hard and find the mid-ground that you are happy to be in.

Russia's banks prepare to hit the `gas' on digital currency use by Ilya Khrennikov and Jake Rudnitsky via @infomgmt

The firms are hoping that by adopting the technology they will make payments safer and faster, while thrusting Russia to the forefront of a trend that’s transforming the financial industry.

Interesting development and something to watch.

Adopting AI in the Enterprise: Ford Motor Company by Medha Agarwal via @OReillyMedia

Dimitar Filev, executive technical leader at Ford Research and Advanced Engineering, explains how Ford is bringing cutting-edge computational intelligence to cars and the factories that build them.

Nice to understand the view from the car makers side as we all know it is coming to us.

IoT, automation projects drive rise in IT spending by Elliot M Kass via @infomgmt

Worldwide IT spending will rise to $3.5 trillion in 2017, a 2.4 percent increase from 2016, according to industry researcher Gartner

IoT is definitely a growth area at the moment - both in actual projects and articles being published about it.

How to Build a Data Science Pipeline by Balázs Kégl via @TDataScience

Start with y. Concentrate on formalising the predictive problem, building the workflow, and turning it into production rather than optimising your predictive model. Once the former is done, the latter is easy.

Interesting post which is more useful than the initial description of it suggests.

The 4 Types of Data Analytics by Thomas Maydon via @PrincipaD

This blog post focuses on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive.

This is a great blog post and contains some great charts and knowledge that could be used in your organisation to sell data analytics.

GDPR: About to Lose Your BI and Analytics? How to Save Your Investment by Majken Sander via @TimeXtender

Are you facing the risk of losing the investment you’ve made in your BI and Analytics Program? The first examples of companies choosing to shut down parts of their BI and analytics solutions have started to surface.

Interesting. I guess if you were better organised and had a Data Protection Officer (or similar) it could not stop the use of your data.

Why Big Data Enhances the Need for Enterprise Information Management by @s11ravindra via @datanami

Many EIM (enterprise information management) or data management programs do not live up to their potential, and the arrival of big data makes the need for enterprise information and data management even more significant.

The article makes a number of great points and I have to agree with him. It all sounds great and sounds like an ideal set of systems to work on. I think this could give you the start of a blueprint of what needs to be in place for success.

Artificial Intelligence Is Stuck. Here’s How to Move It Forward. by Gary Marcus via @nytimes @NYTopinion

There's a lot of hype around AI these days but even so, it has a long way to go. Gary Marcus, an expert in human learning, has an idea to speed things along.

Having read this I can agree with Gary - a CERN type approach would give great benefits to AI and would be able to tightly focus on particular areas - the current approach is a bit like a scatter gun.

How Artificial Intelligence Can Help Us Have a Better Vacation by @ricknotdelgado via @Datafloq

The next time you take a trip anywhere in the world, you should and can use artificial intelligence to assist you with planning your trip. Artificial intelligence completely revolutionises and improves the technology available for planning your next holiday, making it faster, cheaper and less stressful.

I think AI will improve a lot more areas than booking a vacation.

7 steps organizations can take to better protect data by Val LeTellier via @infomgmt

The rampant proliferation of easy-to-use hacking tools and number of bad actors willing to deploy them means that everyone needs to 'up their game' to stay safe.

Definitely agree with him - sometimes it feels like a free for all so it is vitally important to be on top of your game.

SLIDESHOW: 12 top products for data governance stewardship by David Weldon via @infomgmt

Alation, BackOffice Associates and Infomatica are among the leading providers in this space, according to a new report by Forrester Research.


Some interesting alternatives to look at.

Disruptive and 'intelligent' technologies driving IT investments by Bob Violino via @infomgmt

Firms are relying on advanced analytics and AI to interpret interesting patterns in data and to deliver meaningful insights, says Swapnadeep Nayak.

I think all the new technologies coming into use now will make a bigger and bigger impact on the wider wold as companies start investing in them and using them in anger. I'm hoping that they will be pleasantly surprised at the advantages that they can now get.

General Electric Builds an AI Workforce by @ewoyke via @techreview

Integrating artificial intelligence into an organisation founded in 1892 is a difficult task. It starts with training the technical brains behind the company, which employs 300,000 people across all its businesses worldwide. Here's a look at how General Electric builds its data and AI workforce by training from within.

This is really interesting and I found it fascinating how GE are filling the internal skills gap while implementing AI.

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Four analytic errors that can defeat data project efforts by Lenny Liebmann via @infomgmt

Not all data insights generate business value—and some can be downright counter-productive.

I completely agree - there has to be context and need to make analytics and their results useful - else it becomes destructive and more of a danger than a help.