The IoT Data Platform: Architectural Approaches by Mark Herring via @DZone

This breakdown of some of the leading IoT platforms covers their architecture and how to apply them toward solving business goals.

This looks really useful and definitely worth looking at.

5 Tips How to Write a Data Analysis Plan by Janet Anthony via @Analyticbridge

With a data analysis plan, you know what you’re going to do when you actually sit down to do the analysis of the data you’ve gathered.

Great article and definitely worth a read. Maybe if we all did a plan we would get everything right first time and work so much more efficiently?

Cloud tech is helping small firms tap into big data by David Nield via @TeleConnectSME

SMEs are using web-hosted software to manage and analyse data, enabling them to streamline operations and save costs at the same time.

This is a great article and I particularly love the fact that there are three examples in there so you can put some context into it too.

Microsoft Creates New AI Lab to Take on Google's DeepMind by Jeremy Kahn via @technology

The new Microsoft Research AI lab will employ more than 100 scientists from sub-fields of AI research, including perception, learning, reasoning, and natural language processing. Its goal is to create more general-purpose learning systems.

They must think there is a market and money to be made.  I think it will be good for Google and the market if there is some competition.

Artificial or Augmented Intelligence: Talks with Intel’s Chief Data Scientist, Bob Rogers by @ronald_vanloon via @Datafloq

Artificial Intelligence: Intel's Chief Data Scientist, Bob Rogers, gives his insights on the future of AI.

I have to agree with his vision of the future. I think over time organisations will begin incorporating AI into their processes and code and as they start to see the benefits they will strive to find more uses.

7 Unusual Uses of Big Data by @AndrewDeen14 via @Datafloq

Within the last decade, we’ve seen companies in every industry leverage big data to become more efficient, save money, and connect with customers. However, the most common uses of big data aren’t the only exciting developments in the field—the massive potential of big data lies in its diverse applications. There are so many opportunities for the technology to improve our lives in many different ways—some of which may surprise you. Here are just 7 of the countless unusual ways big data can make a big impact.

Definitely some surprising uses which makes me wonder how someone had that Eureka moment to work out that it would be a good idea.

Machine-Learning Solar Tracking Technology Nudges PV Field Production Nearer Optimum Levels by Andrew Burger via @theSolarMag

Solar energy products and services developers and vendors continue to leverage the latest in distributed information and communications technology (ICT) in bids to drive further declines in the cost and boost the productivity of solar energy systems.

This is really interesting and a great way to maximise on the amount of energy generated fro Solar panels.

WEBINAR: Analytics for All: How to Make Your Entire Business Smarter - 25 July 2017

Web Seminar  Analytics for All: How to Make Your EntireBusiness Smarter

Jul. 25, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Today’s highly motivated, tech-savvy knowledge workers are demanding more access to more data so they can make better fact-based decisions—and contribute more effectively to your business.

But how can you empower more users with greater analytic insight? Just as important, how can you democratize analytics while controlling costs and mitigating risk?

Join our data management experts on this informative, interactive webinar to learn:

• Why it’s so important to satisfy Millennials’ appetite for data and analytics
• How top performers safely democratize data via intuitive, secure “catalogs”
• 3 top risks associated with pervasive analytics—and what do to about them

Democratized insight is a significant competitive business advantage. And it drives higher ROI from your existing technology investments. So let our experts point your in the right direction by registering now!

Sponsored By:

Register here

Solving the Bee Crisis with Machine Learning by @BigCloudTeam via @Datafloq

Without the natural pollination bees provide, global food supply would deplete so rapidly, the effects would be disastrous. They’re an essential part of our ecosystem, a part of a delicate tapestry that works to naturally pollinate our crops. Unfortunately, their populations are rapidly declining. Could machine learning help in solving the bee crisis?

I really hope we can solve this and save the bees as they are so necessary for our world.

WEBINAR: A Language for Visual Analytics - 25 July 2017

Overview

Title: A Language for Visual Analytics

Date: Tuesday, July 25, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

A Language for Visual Analytics

In this Data Science Central webinar hear from Jock Mackinlay, VP of Research and Design at Tableau, as he describes how he used a linguistic approach inspired by the work of Jacques Bertin to influence the development of visual analysis. He will share the journey from his days at Stanford while studying for his PhD, continuing right up to present day with his current work as the VP of Research and Design at Tableau Software. Jock will share his thoughts about the future of visual analysis.

Speaker: Jock Mackinlay, VP of Research and Design  -- Tableau 

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

Register here

What is the Benefit of Modern Data Warehousing? by Ronald Van Loon via @simplilearn

Access to relevant customer and industry information is the primary competitive advantage businesses have over their direct and indirect competitors today. It’s the smartest approach to remaining vigilant in a business environment where competition is at an all-time high.

There is definitely still a home for a data warehouse, however you need to really understand why you are using one and what benefits it will give you.

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.

A good list to review to see if there are any surprises to you in there - there were certainly a few new ones to me that I'm now going to work from and investigate them further.

The ex-cop at the center of controversy over crime prediction tech by Joshua Brustein via @infomgmt

Brett Goldstein was a commander at the Chicago Police Department, in charge of a small unit using data analysis to predict where certain types of crimes were likely to occur at any time.

He has some good points. I can see if you focus purely on the numbers it can blind you to the softer elements..

Exploring the risks of artificial intelligence by @danfaggella via @techcrunch

“Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.”

I really enjoyed reading this and he makes some really great points that if you are like me you'll miss one.

Finding new actionable insights in old data research by Hazhir Rahmandad via @infomgmt

A new information management method enables researchers to combine, contrast and build on multiple previous studies.

This is really interesting and could be quick qin which is also cheap as you already have the data research.

9 Mistakes to Avoid When Starting Your Career in Data Science by/via EiteDataScience

If you wish to begin a career in data science, you can save yourself days, weeks, or even months of frustration by avoiding these 9 costly beginner mistakes.

Very useful list of tips that I think could be part of your blueprint on how you are going to convert to Data Science.

WEBINAR: Self-Service Machine Learning - 18 July 2017

Overview

Title: Self-Service Machine Learning

Date: Tuesday, July 18, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Self-Service Machine Learning

In today’s demanding market, Machine Learning capabilities have become a basic requirement you need to support. Self-service BI solutions are no different. Users need machine learning capabilities as an integral part of the provided solution.

The specific challenges of integrating ML capabilities in a self-service BI platform include the supply of ML algorithms that do not rely on specific data and can be easily applied and fit to customer specific use cases (and data).

In this Data Science Central Webinar we will demonstrate how to implement a classification model in a generic manner that can be used by many customers, without relying on specific data, and by automating the validation process ensuring minimum overfit introduced. It will outline the challenges in such a scenario and ways to mitigate them. Specifically, the case study will demonstrate implementing a Decision Tree model and visualize it using a dynamic UI component. 

Speakers:

Nir Regev, Senior Data Scientist, Data Scientist Team Leader -- Sisense
Evan Castle, Product Manager -- Sisense

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

Facebook Will Use Artificial Intelligence to Find Extremist Posts by Sheera Frenkel via @nytimestech

Facebook is using AI, along with human moderators, to find and stop extremist posts, such as photos and videos of beheadings or other gruesome images.

I just hope this approach works - partly because I don't want anything extremist on anywhere and partly because I'm fascinated as to if it works and how successful it will be.

5 Ways Businesses Can Cultivate a Data-Driven Culture by @Ronald_vanLoon via @LinkedIn

The pressure on organisations to make accurate and timely business decisions has turned data into an important strategic asset for businesses. In today’s dynamic marketplace, a business's ability to use data to identify challenges, spot opportunities, and adapt to change with agility is critical to its survival and long-term success.

Some interesting points on what to look out for.  As I often say, you need to make sure the data is clean, tidy and well understood.  If you can't guarantee that the data is up to data and clean I see little point in collecting it let alone using it.  You need to be able to guarantee it is clean in order to guarantee the results of any analysis or reporting is reliable.

Do More! What Amazon Teaches Us About AI and the “Jobless Future” by @OReillyNext via @Medium

Worried about robots taking jobs? Tim O'Reilly argues that good applications of AI don't take jobs; they allow us to do more. Drawing on Amazon as an example, he points out that by using technology Amazon is constantly upping the ante.

Great article that takes a good look at the whole area of AI, robotics and jobs.  If you think about it, this article gives you hope that there is life after AI and that it is not going to kill jobs.

Europe joins forces to create largest ever shared data repository for researchers by Benedict O'Donnell via @HorizonMagEU

The idea is to give every scientific user access to the data resulting from research carried out with public funding, using a single login.

A great idea - lets hope it results in sme great results.

Conversion rates — you are (most likely) computing them wrong by/via @fulhack

I brilliant article by Erik Bernhardsson about how to correctly calculate conversion rates - especially when you are looking at them over time.

Something we should all read and understand.  Essential reading.

China is outsmarting America in artificial intelligence by Paul Mozur and John Markoff via @FinancialReview

Soren Schwertfeger finished his post-doctorate research on autonomous robots in Germany and seemed set to continue his work in Europe or the United States, where artificial intelligence was pioneered and established. Instead, he went to China.

Very interesting and I can completely relate to his observations (although I obviously don't have his experience).  China seems to be making great strides in many areas.

Overcoming 3 top challenges to MDM success by Aaron Zornes via @infomgmt

A master data management project typically faces many hurdles. MDM Institute Chief Research Officer Aaron Zornes offers advice on how to beat them.

Interesting points.  With the second point I think that you really need to think about what it is you want or even think you are going to achieve with the MDM system. Yes I think they are necessary and great, but you need some very quantifiable advantages from your system.

Master data management driving better business decisions by Aaron Zornes via @infomgmt

MDM Institute President Aaron Zornes assesses the state of the market and how organizations are modeling their programs.

I think MDM is definitely a growth area as organisations start to see the benefit of having good MDM.

AI, business process automation the hardest skills to find by David Weldon via @infomgmt

The most difficult to acquire talent this summer are IT pros with experience in emerging and growing technology areas, including AI and the Internet of Things.

A good list of areas to try and train and get some experience in.

7 must-have traits for a successful data team by Daniel Mintz via @infomgmt

The need for technical prowess is a given, but there are also several personal qualities that analysts should have as a group.

Interesting set of skills.  I think the trick is to find a way to balance the team in such a way that they not only complement each other with skills, bu as in temperament so they really do gel and identify themselves as a team.

SLIDESHOW: 10 tips from the HHS on how to boost data security via @infomgmt

Legacy system shortcomings, a lack of trained staff and connectivity issues put healthcare data at especially high risk, a new study reveals.

Interesting list. I think legacy systems is a general cybersecurity problem which needs to be addressed by everyone.

How to build a highly effective AI team by @PMPhacks via @CIOonline

Four organisations share real-world insights into staffing a successful AI effort from the ground up.

I found this really interesting and it was nice to understand the process that can be followed to build an effective team.