WEBINAR: Are you ready for IoT analytics? - 4 April 2017

Web Seminar  Are you ready for IoT analytics?

Apr. 04, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Everything—cars, utility meters, security systems, wearables and more—is generating data. And your company can potentially use that data to its advantage.

But IoT analytics presents many new challenges—including data collection, transport, integration and heuristics. And those challenges will escalate dramatically as IoT grows from initial pilots to a pervasive Internet of Everything.

Make sure you’re prepared for this impending tsunami of thing-driven data by attending our informative free webinar on IoT analytics.

You’ll learn:

• Why and how businesses of all kinds are engaging with the IoT economy
• 3 pitfalls to avoid as you plan your company’s IoT initiatives
• How to drive IoT conversations with LOB and C-level management

You can get ahead of the IoT curve, instead of behind it. Register today to discover how.

Featured Presenters:

Moderator: Lenny Liebmann Contributing Editor SourceMedia

Sponsored By:



Register here

The robots are coming for a third of U.K. jobs, PwC says by Lucy Meakin via @infomgmt

Education, healthcare and social work are least likely to be affected due to the relatively high proportion of tasks that are difficult to automate.

Interesting - makes me wonder how we are all going to have jobs and earn a living.

Big data and analytics see double digit growth through 2020 by Bob Violino via @infommt

The greatest investments will be in banking, healthcare, insurance, securities and investment services, and telecommunications.

I'm surprised that other areas like manufacturing are not there.

Making the Future Starts with Focus on AI by Navin Rao via @intel

 Intel is combining its AI efforts into a single organisation, the Artificial Intelligence Products Group with a mission to bring down costs and help create standards.

This definitely makes sense.

17 More Must-Know Data Science Interview Questions and Answers by Gregory Piatetsky via @kdnuggets

17 new must-know Data Science Interview questions and answers include lessons from failure to predict 2016 US Presidential election and Super Bowl LI comeback, understanding bias and variance, why fewer predictors might be better, and how to make a model more robust to outliers. Part 2 overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms. Part 3 overs A/B testing, data visualisation, Twitter influence evaluation, and Big Data quality.

Worth reading and checking if you could answer these or even if you understand them. I certainly found a couple of areas I need to concentrate on.

Part One

Part Two

Part Three

Ideas on interpreting machine learning by Patrick Hall, Wen Phan and SriSatish Ambati via @OReillyMedia

Detailed article that presents several approaches for interpreting machine learning models and results. This goes well beyond the usual error measures and assessment plots.

This is quite simply brilliant - I advise everyone to bookmark it and have a printout of it for easy access

Industry 5.0 – When man meets machine through AI by Gal Horvitz via @infomgmt

We are in the fifth industrial revolution, with more collaboration between advanced technologies and humans, says Gal Horvitz.

I found this interesting and it helped me to think about the areas and consolidate my thoughts around them.

SIDESHOW: 25 top master data management providers by David Weldon via @infomgmt

Informatica, Enterworks and IBI are among the leading MDM software vendors, according to the MDM Institute.

There is no shortage of tools there to help you with MDM - you just need to choose which one carefully and make sure it connects to the other tools that you already or are planning to use.

Split into two - part one and part two

SLIDESHOW: 7 forces shaping modern BI and analytics by David Weldon via @infomgmt

The rapidly evolving business intelligence and analytics market is being influenced by seven dynamic trends, according to Gartner Group.

I like slide 3 in particular - if you prepare the data right you can join it with what you want to and it wil give better results.

WEBINAR: Best practices for cloud-based analytics - 30 March 2017

Web Seminar  Best practices for cloud-based analytics

Mar. 30, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

With the majority of organisations now having some presence in the cloud, that is driving increased interest in could-based analytics. That brings a unique set of challenges, since your data is not being housed on in-premises systems that the organisation controls. This webinar will explore best practices for cloud-based analytics. Topics to be covered include:

• What are the most significant challenges with cloud-based analytics?
• How does an organisation control data governance in a cloud-based system?
• What lessons can be learned from organisations that have mastered cloud-based analytics? 

Sponsored By:

Register here

Did Alexa hear a murder? We may finally find out by @dmkravets via arstechnica

Arkansas authorities requested data from Alexa Voice Service that they say might be evidence in a murder prosecution. Amazon refused, claiming that the data was protected by the First Amendment. Had it gone to court, precedent-setting decisions about IoT data privacy might have been made, but the defendant has agreed to allow Amazon to release the data, presumably answering the question "Is Alexa a witness to murder?" but leaving open the more complex legal questions about AI and privacy.

I found this very interesting and hadn't really thought about all the legal implications this case is suggesting.

Visualisation, analytics and machine learning - Are they fads, or fashions? by Gary Cokins via @infomgmt

These three “hot” managerial methods and tools are essential, but they need to be properly designed and customised.

I agree with him - they are all essential and we all cannot do with out them.

How Healthcare Technology Improves Information Security and Puts Patient Care Front and Centre by David Pritts via @WorkflowOTG

Every industry is affected by technological advancements and the healthcare industry is no different. Healthcare providers – small and large – see the value in implementing technology and are continuing to explore ways to use it to improve operations, boost patient data security and meet industry regulations.

Nice article that gives a good summary of the challenges facing the health industry.

Every Intro to Data Science Course on the Internet, Ranked By David Venturi via @kdnuggets

For this guide, David has spent 10+ hours trying to identify every online intro to data science course offered as of January 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings.

A must read for anyone who wants to train to become a Data Scientist.

IBM Machine Learning brings Spark to the mainframe by @andrewbrust via @ZDNet

IBM has announced support for machine learning on Z-series mainframes. If a lot of your transactional processing is still happening on a mainframe and you want to build predictive models on their data it's definitely worth a look.

This is great news and I think can deliver big results for IBM and their customers.

Artificial intelligence: A tipping point for digital business by Mohamed Kande via @PwC_LLP

Is AI the shot in the arm that today’s transformation efforts need?

I really enjoyed reading this. I recommend you sign up to their Next in Tech blog here.

How to Search for Data Science Jobs by/via @BobMuenchen

This article describes the technical details of how to search for jobs in the field of data science. Some software used for data science is also used by a wide range of other tasks.

I love this article as it not only helps you with the search terms to use but also can be useful on the crossover between different roles and the terms used by organisations to describe them.  The world is full of synonyms you just have to find a way of moving between them.

WEBINAR: Mastering master data management - 20 March 2017

Web Seminar  Mastering master data management

Mar. 20, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Organisations today are desperate to get value out all the date they collect on their customers, their suppliers, and their partners. But having lots of data, and having lots of insights are two vastly different things. Turning data into effective action requires a strong understanding of what that data has to say. That requires excellent data analytics, data management, and governance. But it also requires quality data that can be trusted and accessed by everyone that needs it. In short, it requires excellent master data management.

• What are the essential elements of an effective MDM program?
• What is the role of data governance in an MDM program?
• What are the leading causes of MDM efforts when they fail, and how can they be avoided?

Sponsored By:

Register here

Battling superbugs with Big Data by Shruti Sharma via @livemint

Antibiotics that once cured ailments across the spectrum are now turning into a potential source of prolonged illness, disability and death. India, sitting at the cusp of a digital revolution, is well placed to address the antibiotic resistance problem.

This sounds like a great use of Big Data and I really hope they can get something useful out of their analysis.

Why IoT should have an artificial intelligence layer by Rick Delgado via @JAXenterCOM

Combining artificial intelligence with the Internet of Things opens the world to unlimited technological potential. In this article, Rick Delgado, freelance technology writer and commentator, explores why it makes sense to add an AI layer to IoT, possibly adding new possibilities and capabilities to both technologies.

I agree with him - if you sit and think about this there are so many possibilities if these two technologies are combined.

What's in a Name; 7 Blockchain Benefits for the Finance Industry by @VanRijmenam via @Datafloq

A few days ago, The Merkle ran a story that R3CEV, the largest blockchain consortium of banks and technology firms, admitted that the technology they are developing does not use a blockchain and as such they admitted defeat.

I found this just amazing - how is anyone else going to either believe in them or in blockchain after this?

AI Is Going to Change the 80/20 Rule by Michael Schrage via @HarvardBiz

Vilfredo Pareto's 80/20 principle says that 80% of effects like sales, revenue, etc. come from 20% of causes, such as products or employees. But as machine learning and AI transform analytics, this may change.

I found this interesting and wonder what else might change that we consider to be standard theory.

Will Democracy Survive Big Data and Artificial Intelligence? via @sciam

Everything is becoming "intelligent." Soon we will not only have smart phones, but also smart homes, smart factories and smart cities. Should we also expect these developments to result in smart nations and a smarter planet?

This is a great article that is very well written and thought out. Worth the time to read.

Have We Allowed Big Data To Become Too Powerful? by @IE_George via @iegroup

Big Data seems to be everywhere. In this great article George Hill looks at whether we have allowed it to become too big and have too much power.

I found this really interesting. It kind of reminded me of the statement about some banks during the financial crisis that they were too big to fail and I wonder if that is the case with Big Data now.

Beyond the Black Box in Analytics and Cognitive by Thomas H. Davenport via @Data_Informed

There is a growing crisis in the world of analytics and cognitive technologies, and as of yet there is no obvious solution. Tom Davenport examines this problem in his most recent article.

Interesting and a good article to read.

Metrology And Quality Assurance In Industry 4.0 by Ian Wright via @IotOneHQ

Gisela Lanza has a unique perspective on the IIoT, Industry 4.0 and the new role of metrology for quality assurance they engender. For four years, she worked simultaneously as the first incumbent of the shared professorship of Global Production Engineering and Quality at the Karlsruhe Institute of Technology (KIT), and at Daimler AG in strategic planning.  Lanza shared her insights with journalist Nikolaus Fecht in a recent interview.

I found this fascinating and it taught me lots of new terminology.

The Advantages of The Cloud for Big Data Storage by Ivan Widjaya via @cloudbizreview

You’re probably had enough of hearing about big data and its relevance to your business. It’s 2017, if you’re not collecting AND analysing big data, you’re getting left behind. No, the conversation is no longer about whether or not you should be investing in big data analysis, it’s how.

I think cloud has brought down the cost and also has made it so much easier to do something quick and easy to at least prototype due to the easy availability to use the cloud for testing first (no matter where the final solution ends up).

Why Manufacturing Stands to Gain the Most Through Artificial Intelligence by Adeesh Sharma via @pcquest

Everyone thinks first about robots on factory lines, but combining the IoT, big data, and AI can impact manufacturing in multiple ways—and that impact can be even more profound in certain industry verticals.

I agree - more technologies can be pulled into this list but data security and standards need to be made before this spreads too far in any technology.

WEBINAR: Communicating Analytic Insights Visually - 14 March 2017

Overview

Title: Communicating Analytic Insights Visually

Date: Tuesday, March 14, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Communicating Analytic Insights Visually

There’s more to your dashboard’s appearance than meets the eye. A well-designed dashboard isn’t just a pretty picture – it’s a powerful tool for communicating data-driven, actionable insights to your audience. How can we use the science of perception to make our visualisations more effective, compelling, and delightful? Join this Data Science Central webinar to learn how to build intuitive dashboards that inspire users to interact with and learn from their data.

In this webinar we will cover the following topics:

• How visual cues can obscure or reveal critical information
• The impact and proper deployment of data types
• How to ask the right questions to properly focus your dashboards
• Common mistakes
• Visual best practices
• Hands-on dashboard critique, revision, and creation

Speakers:
Jake Kinstler, --  Product Consultant -- Tableau 
Alex Cortez, --  Product Consultant -- Tableau 

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

Prophet: forecasting at scale by Sean J. Taylor and Ben Letham via Facebook Research

Facebook is open sourcing Prophet, a forecasting tool available in Python and R. Forecasting is a data science task that is central to many activities within an organisation. For instance, large organisations like Facebook must engage in capacity planning to efficiently allocate scarce resources and goal setting in order to measure performance relative to a baseline.

This is fascinating.

Inside Facebook's AI Machine by Steven Levy via @backchnnl

This new article by Steven Levy explores the work of Facebook's Applied Machine Learning group and shows how Facebook could not exist without AI.

This was really interesting and was good to read as a user of the system.

SLIDESHOW: 10 top ways IT pros are boosting their careers via @infomgmt

What do IT pros hope to accomplish in their careers in 2017? To find out, job site Spiceworks polled more than 1,000 of members of the Spiceworks Community for their IT career resolutions.

I don't think that there is anything in this slideshow that I disagree with.

Fueling the Gold Rush: The Greatest Public Datasets for AI by Luke de Oliveira via @StartupGrind

Though not at the forefront of the AI hype train, the unsung hero of the AI revolution is data — lots and lots of labelled and annotated data—curated with the elbow grease of great research groups and companies who recognise that the democratisation of data is a necessary step towards accelerating AI....Most people in AI forget that the hardest part of building a new AI solution or product is not the AI or algorithms — it’s the data collection and labelling. Standard datasets can be used as validation or a good starting point for building a more tailored solution.

There is a wealth of data out there and it is a good starting point as long as you are careful and don't rely on data that you cannot guarantee is correct and accurate.

WEBINAR: Open Data Science in the AI Era - 8 March 2017

Wednesday, March 8th, 2017
2PM Central | 3PM Eastern | 12PM Pacific

The vast open data science landscape is full of choices but can be overwhelming to the uninitiated. However, organisations that can distil the landscape to the key players are making giant strides in leveraging the unending innovation — including Artificial Intelligence — to drive new business value.

How can you navigate the landscape to identify the key players and the emerging ones? Can your enterprise harness open source without descending into anarchy? How can you embrace R, Python and their thousands of powerful analytic packages without the accompanying legal risks? How do you see through the legacy vendor FUD and make open source work?

We're here to help—Continuum Analytics EVP Anaconda Business Michele Chambers and Computational Scientist Ian Stokes-Rees will help you embark on your enterprise's journey to Open Data Science in this webinar on March 8th at 2pm CST.

In this webinar, you will learn to:

Navigate the open data science landscape

Champion open data science projects for success in your organization

See a practical AI demonstration for enterprises

Register here

How to determine who will be a good data scientist by Eric Larson via @infomgmt

Skilled technologist have a number of key attributes, including a passion for solving problems and the ability to think ‘strategy first.'

Interesting to read this opinion piece and assess yourself against this list.

Informatica announces industry’s first intelligent healthcare data lake by Kyt Dotson via @SiliconANGLE

Data management solutions and big data company Informatica LLC has announced the debut of the industry’s first intelligent data lake, a repository for raw data, that’s made specifically for healthcare.

Interesting new development which proves how common this approach is becoming.