How Machine Learning Boosts Personalisation in Travel by/via @7wData

This article walks through a scenario and points out how machine learning can help to personalise travel and in effect help both the customer and the seller.

I liked this and can imagine the kind of analytics that could be produced off the data they obtained from the OTA clients.  You can also apply the learning and method from this example into other areas just as easily.

WEBINAR: What's big in big data: Predictions for 2018 - 4 December 2017

Web Seminar  What's big in big data: Predictions 
for 2018

Dec. 4, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Information assets, especially big data, are increasingly seen as a 

critical corporate asset. Not surprisingly, more organizations are 

implementing big data projects and they are looking for methods to best 

measure ROI and insights on where big data is heading. To answer that, on December 4, 

Information Management will present a webinar on “What’s Big in Big Data: Predictions for 2018.”

Participating in the event will be Gartner vice president and distinguished 

analyst Doug Laney, who will offer examples of leading organizations at big data 

projects, provide Gartner research on what to expect with big data in 2018, and 

share highlights from his new book, Infonomics: How to Monetize, Manage, and Measure 

Information for Competitive Advantage. Also participating will be Tyler Kleykamp, 

chief data officer for the State of Connecticut, and Mark Stange-Tregear, director of 

analytics at Ebates, who will both share their experiences with big data projects, 

what they have planned with big data in 2018, and lessons they have learned to 

help guarantee success with those efforts.

Douglas B. Laney VP & Distinguished Analyst Chief Data Officer Research Gartner (Speaker)	Tyler Kleykamp Chief Data Officer State of Connecticut (Speaker)	Mark Stange-Tregear  Director of Analytics Ebates (Speaker)	David Weldon Editor-in-Chief Information Management (Moderator)

Sponsored By:

 Register here

Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey by/via @kdnuggets

This is a great analysis and list of Masters courses to learn much more about Analytics or Data Science  so you can get a qualification in it.

Yes there are expensive full time options where you have to be there physically, but there are also fully online options that can be done fairly cheaply. You also need to factor into that how respected the qualification might be.

WEBINAR: Getting the EDGE on data governance - 5 December 2017

Dec. 05, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Data governance ensures that an organization’s data assets can be discovered, understood, governed and then socialized for greater visibility, control and value. It enables an enterprise to get a handle on how it handles its data in the context of achieving larger business goals – from regulatory compliance to growing topline revenue.

Such comprehensive and effective data governance requires a shift from isolated IT program to strategic initiative, with a persona-based approach that ensures everyone – from executives on down – invested in and accountable for data use.

Join Information Management and erwin, Inc. for this webinar on how to create an enterprise data governance experience (EDGE) to mitigate risk, improve operational performance, and accelerate growth.

We’ll look at the five factors of an EDGE that join IT and business functions in managing data risks while maximizing its opportunities.

Jamie Knowles Product Manager, Data Governance Products erwin, Inc. (Guest)	Mariann McDonagh Chief Marketing Officer erwin, Inc. (Guest)	Eric Kavanagh CEO Bloor Group (Moderator)

Sponsor Content From:

Register here

Mastering change management to drive digital transformation by Elliott Hultgren via @infomgmt

When faced with the task of altering the entire technology infrastructure of an organisation, CIOs should adopt a mission-oriented mindset.

He makes some good points.  I would add that it is important to find a few quick wins for the new strategy to help you get the rest of the organisation on board.

Why Blockchain and Analytics Don't Mix Well by @billfranksga via @iianalytics

Blockchain and analytics might not mix as analysing data within a blockchain environment would be different from how we analyse data within other platforms.

Bill has some great points in this article and it's only when you sit and think hard about this that you realise that this is not the same as any traditional database and that you could get lost in processing and searching the chains to get the anwser to all the questions you are faced with.

Dirty Data Is OK, How You Cleanse It Matters by Chirag Shivalker via @DZone

It has been an unsolved mystery for companies if they should get their data cleansed first to opt for data analytics or if they should opt for data analytics to conclude whether their data is dirty.

There are some really good points in this article.  I cannot emphasise enough the single source of truth point.  We must all have worked for organisations where department A's figures don't match department B's.  You cannot run an organisation if the numbers in your reporting don't match, and even worse you have no idea why they don't match. You need data management, agreed definitions for data, and just the one source of the truth across the entire company.

Why analytics will be the next competitive edge by Gary Cokins via @infomgmt

Organisations are drowning in data but starving for information. The application of data science is becoming commonly accepted, but will senior executives realise it?

I would add to this article that the data has got to be clean and of a consistent quality so that any analysis or decisions you make based on the data are correct. If you can't rely on the data or it is of a dubious quality you are better off not using it.

WEBINAR: Deep Learning: From Basic Principles Through Training Models - 28 November 2017

Overview

Title: Deep Learning: From Basic Principles Through Training Models

Date: Tuesday, November 28, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Deep Learning: From Basic Principles through Training Models for Deployment into Production

What are neural networks, but more important, how are they trained in practice?  How can data scientists design an optimal neural network when a single training run can take 2 weeks? In this Data Science Central webinar we will start from the foundation of what deep learning is then fast forward through what it takes to train a production quality neural network.  You won't be able to train a network when this talk is over, but you'll understand enough basics to start smart conversations about our customers' practice of deep learning.

Speaker: Anthony Stevens, Offering Manager, Watson Deep Learning -- IBM Watson and Cloud Platform

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

 

Register here

WEBINAR: 7 GDPR “Gotchas” and how to avoid them - 28 November 2017

Web Seminar  7 GDPR “Gotchas” – and how to avoid them

Nov. 28, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

As GDPR deadlines loom, most data managers are already working to ensure compliance. But 

the first wave of GDPR compliance efforts reveal some serious potential GDPR “gotchas.” 

And many of these “gotchas” are the result of basic misconceptions about what compliance really requires.

Protect yourself and your organization by attending this interactive expert-led webinar. You’ll learn:

• How European business partners can expose you to unanticipated risk
• What the “data portability” requirements of GDPR really require
• Why you need to select and empower an EU representative ASAP

William Beckler GDPR Compliance Lead ALICE (Speaker)	Lenny Leibmann Contributing Editor SourceMedia (Moderator)

Sponsored By:


Register here

4 Pain Points of Big Data by @DigitalMcKinsey via @Medium

Big data analytics is an amazing tool at the epicenter of the digital revolution. But it’s not foolproof. Here’s how successful companies deal with its potential drawbacks.

I really like this article - it's very clear and says all the things that I find important.  Worth a bookmark so you can refer back to it.

Personalising the retail supply chain with predictive analytics by Michelle Covey via @infomgmt

It has never been more critical for retailers to leverage data to create the convenience and the experiences that consumers crave.

I definitely think personalisation is a key way of differentiating your own organisation from the others in the marketplace.

The titans of AI are getting their work double-checked by students by @davegershgorn via @qz

In an effort keep AI research a science, Joelle Pineau, an associate professor at McGill University and head of Facebook's AI research lab in Montreal, is pushing back against unreproducible AI research. Her challenge, coordinated with five other universities, is to reproduce the work in submitted papers. Students are tasked with reproducing research from some of the world's top AI labs—from universities to companies like Google, DeepMind, Facebook, Microsoft, and Amazon.

I find it strange that not only is it almost impossible to reproduce the research, but that they publish papers with deliberate missing information.  When learning R and how to publish a paper it was made clear to me that I must publish my code so that others can reproduce my results.

WEBINAR: Advanced Tool for Dynamic Visual Analytics - 30 November 3027

Overview

Title: Advanced Tool for Dynamic Visual Analytics

Date: Thursday, November 30, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Advanced Tool for Dynamic Visual Analytics

As you journey through the age of self-service analytics it’s important to understand and explore your data in impactful new ways. There are critical drivers for making better decisions – can you see them? Why did something happen? Have you examined all options and uncovered opportunities hidden deep in your data? 

Join us for this Data Science Central webinar to see the new version of SAS® Visual Analytics. It introduces new data visualizations – including third party custom ones, a refined user experience to improve productivity, self-service data preparation, and the power of location analytics to visualize data in new contexts. These enhancements will help you automatically highlight and understand key relationships, outliers, clusters and more in your data, revealing vital insights that inspire action.  

Speakers:

Tapan Patel, Principal Product Marketing Manager, Business Intelligence and Analytics -- SAS
Steve Mellgren, Senior Solutions Architect, Business Visualization Practice -- SAS

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

5 mistakes to avoid when implementing data lakes by Matt Maccaux via @infomgmt

Many organisations are making debilitating mistakes that will ultimately hinder their ability to have a scalable, elastic data-monetisation platform.

I have to agree with his comments on too much Hadoop and not enough governance.  The data lake is only going to be useful if it is efficient and contains good and proper data - the old adage about garbage in and garbage out can definitely apply to a data lake without data stewards and some kind of of quality control.

Developing a successful data governance strategy by Federico Castanedo via @OReillyMedia

Multi-model database architectures provide a flexible data governance platform.

Great article by Federico that is worth reading. There is a free e-book you can download too.

The battle over bank customer data may finally be over by Penny Crosman via @infomgmt

Guidance from the CFPB and reduced friction between banks, fintechs and data aggregators are easing bank-fintech partnerships at Wells Fargo, Capital One and others.

I can see that this has a lot of benefits, but I can also see that there are a lot of potential drawbacks as it could become a security risk. I'm also interested in how it verifies that you are the right person.

Majority of firms now adopting AI; more investments planned by David Weldon via @infomgmt

One in three business leaders believe their company will need to further invest in artificial intelligence over the next 36 months to keep pace with competitors.

AI and ML are becoming much more mainstream and I can only see that expanding over time.

What Intelligent Machines Can Do, And What They Can't by @jessicadavis via @InformationWeek

From Alpha Go to cancer detection to data centre efficiency, we talk to analytics practitioner and SAS CTO Oliver Schabenberger about what AI can do and what it can't do.

Good to read something that talks about limitations and not all of the time promising things.

4 Ways Cities Can Change Their Data Game by @tnewcombe via @GOVERNING

From who they hire to how they share, adjusting municipal data use is a must for any city looking to improve its services.

An interesting look at a few possibilities of how data could help cities.

Top Big Data Skills To Help You Stand Out from the Crowd by Sarah Shannon via SmartDataCollective

Big Data is the latest buzzword hitting the technology sector with data analytics fast becoming the newest technique implemented by businesses to monitor their IT networks, and stop impending threats.

Definitely something to read and work out which skills you might be missing or would add to your offering if you worked on it.

How to tap into Splunk's big data analytics engine by @kenhess via @TechRepublic

Read this primer on using Splunk as a big data analysis tool. You'll learn how to analyse data and filter results, as well as create visual results from that data.

This is great and I think a great resource that should be bookmarked so you can refer back to it.

SLIDESHOW: 6 trends shaping the future of data analytics by David Weldon via @infomgmt

Machine and deep learning and natural language processing are among technologies that will help drive organisational success.

Interesting list.

Mastering data architecture to enable digital transformation by Joshua Satten and Nicolas Papadakos via @infomgmt

The information model is the gas powering the engine, enabling organisations to more effectively communicate and reach their specific goals.

Some good points. I would add to it that you need to try and balance the needs of the business, the availability of data and as mentioned in the article you need to focus of granularity in order to get that right.  Business knowledge is key.  Also be aware that documentation can sometimes we scarce so be prepared to have to generate your own for some older data/systems.

How to unit test machine learning code by @keeper6928 via @Medium

Unit tests can save you weeks of debugging and training time.

This article by Chase is really useful and makes a very valid point. I would reiterate that the earlier you find errors the easier it is to fix them and the less costly.

The top 10 technology trends for 2018 by David Weldon via @infomgmt

Artificial intelligence, the IoT, cloud computing and cryptocurrencies are among the technologies that will dominate new IT investments, according to Forrester Research.

I found this interesting.

WEBINAR: Govern first, then ask many questions - 16 November 2017

Govern first, then ask many questions

Nov. 16, 2017 | 1 PM ET/10 AM PT
Hosted by Information Management

Who did what with your corporate data, and when? Knowing that information is more than useful in today’s business world. It’s often a prerequisite, or even mandated by law. That’s why data governance has become a hot button issue. More and more organisations realise that they must design, manage and enforce appropriate rules and regulations regarding the creation, use and retirement of data.

Doing this property can keep your organisation in bounds, while also fuelling the kind of analytics that can genuinely improve the business. How can your company stay on top? Register for this episode of IM Live to find out! Host Eric Kavanagh will interview several guests.

Adam Famularo CEO erwin (Speaker)	Rick Sherman Managing Partner Athena IT Solutions (Speaker)	Eric Kavanagh  CEO Bloor Group (Moderator)

Sponsor Content From:

Register here

Pig vs Hive vs SQL – Difference between the Big Data Tools by Manisha Nandy Mazumder via @Hadoop360

Great article comparing the three tools.

This is great for understanding the differences and which one might be best for you.

WEBINAR: Building a Compelling Argument with Data - 14 November 2017

Overview

Title: Building a Compelling Argument with Data

Date: Tuesday, November 14, 2017

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Building a Compelling Argument with Data

Telling stories with data sounds like a great idea, but often you simply want to make a clear and compelling argument. How do you do that? In this Data Science Central webinar, Robert Kosara will walk you through a particular structure for stories that works well in news graphics and describe how and why it applies to data. He will also talk about the differences between classical story and story with data based on his research and experiences.

Speaker: Robert Kosara, Research Scientist  -- Tableau 

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

 

Register here

Create editable Microsoft Office charts from R by David Smith via @rbloggers

Embedding an R graphic into a Microsoft Office Document can easily be done. But can you make it editable in Microsoft Office? This post introduces two packages that allow you to do this.

This is a great article and could help you even if you didn't want to embed it into MS Office.

Object detection with TensorFlow by Justin Francis via @oreillymedia

How to create your own custom object detection model. (Includes Python code and iPython notebook.)

This is great and you can access the code on Github.

Evolving technology calls for more disciplined approach from auditors by Mohammed J. Khan via @infomgmt

As the maturity of our applications increases, how we code in our environments must adapt to keep up the pace.

Interesting - I guess auditors need to adapt all the time for all innovations no just in technology.

Machine Learning Algorithms: Which One to Choose for Your Problem by Daniil Korbut via @statsbotco

Intuition of using different kinds of algorithms in different tasks.

I like this as it clearly explains which machine learning algorithm to use.

Your Data Are Probably Biased And That's Becoming A Massive Problem by @Digitaltonto via @Inc

Nobody sets out to be biased, but it's harder to avoid than you would think.

I really liked this and he makes some very good points.  I believe if you are aware of bias and the ways it can happen you are part way to get around it as you can look out for it and adapt your ways of working to avoid it.  Peer reviews are also great as the more eyes on a problem the less chance you are all going to have the same bias - especially if you are a from different backgrounds and skillsets.

TensorFlow: Building Feed-Forward Neural Networks Step-by-Step by Ahmed Gad via @kdnuggets

This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.

Please note this is a 3 page article.  I love that this is so clear and I think easy to understand.

Navigating the future of IoT one step at a time by Wael Elrifai via @infomgmt

At stake are new ways of working, new skills and resources, new types of contractual arrangements and significant cultural change up and down the supply chain.

Great 8 points that you should read and make note of because it will save you pain.

How to master machine learning logistics by Ted Dunning and Ellen Friedman via @infomgmt

90% of the effort in a successful implementation is about proper model management, say authors Ted Dunning and Ellen Friedman.

I agree with them completely. There are some things that may appear boring and not as sexy as machine learning, but if you do them then your machine learning will be implemented more successfully and you will get the results that you intended.