WEBINAR: Say goodbye to the old ways of Master Data Management and embrace the future - 10th April 2018

Web Seminar  Make way for the agile single customer view: 
Say goodbye to the old ways of Master Data Management 
and embrace the future

Apr. 10, 2018 | 2 PM ET/11 AM PT
Hosted by Information Management

Did you get burnt? Are you one of the thousands that implemented MDM, only to find it too rigid, 

inflexible and ultimately failing to meet business expectations? Reboot your thinking, apply Graph and uncover the relationships between people, places and things.Join our webcast to learn about a new approach 

to mastering customer information. The agile Single Customer View helps organizations 

start fast and understand customers without replacing existing technologies.

Andrew Chumney Single View Solutions Manager Pitney Bowes (Presenter)	Aaron Zornes Chief Research Officer The MDM Institute (Moderator)

Sponsor Content From:

Register here

How to get more value from machine learning by Ben Schreck and others via @HarvardBiz

Spoiler alert: it's not the algorithms. It's the ease of use.

I have to agree with the authors of this - if it is so complicated no one understands it and can barely use it, how can anyone else use it, and how do you know that the result is good?

SLIDESHOW: 14 top platforms for governance, risk and compliance data management by David Weldon via @infomgmt

AI Global Compliance, LogicManager and Nasdaq BWise are among the leading products in the GRC software space, according to a new Forrester Research Wave report.

Some I know and some I don't - certainly this is a good place to start if you need to get a new platform for your organisation.

SLIDESHOW: Avoiding the 7 biggest threats to data backup by David Weldon via @infomgmt

As we approach World Backup Day on March 31, security expert Rod Mathews reviews the top vulnerabilities that organisations need to guard against.

Who knew there was a day for backing up data? Some really important points here. Definitely the backups need to be separate and not in the same system/place. 

For your own personal data it is really important that you do regular backups and that you do them onto some kind of external media.  I have several external hard drives that I backup to, always run all the scans possible first. and backup at least once every 2 weeks (at home I don't create that much data etc).  Get into a routine.

SLIDESHOW: 20 best practices of top chief digital officers by David Weldon via @infomgmt

Growth in digital transformation efforts in healthcare is driving the need for more CDOs, who need to be versed in strategy, governance and execution.

Slide 11 if really key - you need to get the C-level people behind you and seeing the benefits of the advantages of what you are trying to achieve.

Using Evolutionary AutoML to Discover Neural Network Architectures by/via @googleresearch

Updates from Google Research: they’ve successfully used an evolutionary algorithm to beat out reinforcement learning-based approaches to autoML. The most fascinating result is that the evolutionary approach is not simply higher performance—it requires far less computation to arrive there

This is really interesting.

WEBINAR: Model Risk Management with Automated Machine Learning - 29 March 2018

Webinar Details Register today Thursday, March 29, 2018      Presenter:  1:00 pm Eastern / 10:00 am Pacific      Seph Mard 45 minutes with Q&A      Head of Model Validation, DataRobot

Model Risk Management has recently become a very hot topic in regulatory and compliance-rich industries. But traditional model development methods are time-consuming, tedious, and subject to user error and bias.  Automated Machine Learning offers a much more robust framework for managing and minimizing model risk than traditional manual modeling.  It not only automates the building of highly-accurate predictive models, but also automates the documentation required for Model Risk Management.  On this webinar, you'll discover:  - The tools that Automated Machine Learning (AML) provides to optimize and accelerate your model risk management framework. - How AML increases the efficiency of your model development and validation processes, while also further aligning modeling and validation processes to regulatory expectations for model risk management. - How AML reduces model risk, while developing cutting-edge machine learning models.

Presenter: Seph Mard Head of Model Validation  DataRobot

A Tour of The Top 10 Algorithms for Machine Learning Newbies by James Le via @kdnuggets

For machine learning newbies who are eager to understand the basic of machine learning, here is a quick tour on the top 10 machine learning algorithms used by data scientists.

This is great and definitely worth a bookmark.  Please note this is over 2 pages.

Visualizing outliers by Nathan Yau via @flowingdata

Here's a clear and concise walkthrough of visualisation techniques to highlight outliers in a dataset.

I like some of these ideas so it'd worth a bookmark to keep this in case you need it.

Get the data basics right by Toni Sekinah via @TheDataIQ

Get the basics right, use incoming regulation as a stimulus to innovate, and choose the right tools to manage the data. Those are the three key conclusions drawn by credit reference agency Experian from a recent survey, "Embracing the data challenge in a digitalised world".

Definitely if you have to do the work anyway due to some kind of regulation you should use it as an opportunity to make some steps forward at the same time. This reduces the overall cost of any improvement as the regulation changes are under a different cost bucket.

6 IoT trends that are shaking up the business world by/via @estuate

Internet of things is leading us to smarter lives. Here are 6 real-world IoT applications that are driving businesses crazy.

Some great examples that might give you are idea on how IoT could be used in your own organisation.

Big data might translate into higher online prices by Noah Smith via @infomgmt

The more information a merchant gets about a customer -- where they live, what they buy, what websites they visit, etc. -- the better they can predict how much they’d be willing to pay for a certain product.

This is an interesting way to look at pricing and how big data could affect that going forward.  Lets hope it doesn't make too much of an impact on real pricing.

WEBINAR: Top 4 Excel Functions Done Better with Data Wrangling - 21 March 2018

Overview

Title: Top 4 Excel Functions Done Better with Data Wrangling

Date: Wednesday, March 21, 2018

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Top 4 Excel Functions Done Better with Data Wrangling 

Analysts have relied on Excel for decades, but as the size, variety, and pace of data accelerates, certain Excel functionality has begun to feel archaic. Today's data wrangling platforms offer analysts new approaches to preparing data that alleviate Excel bottlenecks. In this webinar, we’ll demonstrate how to leverage data wrangling techniques to execute common Excel functions more effectively, and what features to look for in a leading data wrangling platform. 

In this latest Data Science Central webinar, we will demonstrate how to leverage data wrangling techniques to execute common Excel functions more effectively, including:

• A side-by-side comparison of Excel vs. data wrangling platform 
• Examples of real-world applications for data wrangling techniques 
• Key functionality of a modern data wrangling platform

Speakers:

David McNamara, Customer Service Executive -- Trifacta

Tanya Cashorali, Founding Partner -- TCB Analytics

Hosted by:

Bill Vorhies, Editorial Director -- Data Science Central

  

Register here

4 key steps to building a comprehensive data strategy by Phil Bullinger via @infomgmt

Organisations that develop and implement a strategic plan are fundamentally better prepared to anticipate, manage and capitalise on the increasing challenges and possibilities of data.

I like this because it gives you a list of things that you need to consider when making a data strategy. I'm sure we can all think of something that is not on the list but it very applicable for our business.

Implementing a real-time catalogue of enterprise data assets by Suresh Chandrasekaran via @infomgmt

Beyond defining access privileges, effective data governance means that companies need to be able to document or label their data assets, much like the labels on pharmaceuticals.

Automatic data cataloguing is a great aim and could if done correctly save a lot of time.

SLIDESHOW: 20 best practices of top chief digital officers by David Weldon via @infomgmt

The growth in digital transformation efforts is driving the need for more CDOs. Forrester Research looks at how strategy, governance and execution separate the best from the rest.

Some great best practices in this article that I think can partially apply to a much wider range of people than a CDO.

Looming GDPR puts renewed focus on Sarbanes-Oxley compliance by Larry Alton via @infomgmt

SOX is a perfect bridge protocol for organisations undergoing a General Data Protection Regulation compliance audit in preparation for the May 2018 deadline.

Having worked on SOX I can understand the authors comment and the connection. It's certainly a good discipline that can be added to and expanded for GDPR compliance.

WEBINAR: Succeed with BI-on-The-Data-Lake - 20 March 2018

Mar. 20, 2018 | 2 PM ET/11 AM PT
Hosted by Information Management

Learn the best practices used by companies like Toyota, TD, and GSK as they rolled out Central Business Analytics Units to deliver full self-service analytics to all, while governing access to enterprise data. In this session, you’ll learn how:

• Toyota’s Center of Analytics Excellence uses Tableau and Microsoft PowerBI on Cloudera for Finance and IoT use cases.
• GSK delivered secured self-service access on 100% of its data, stored across hundreds of Cloudera nodes.
• Vivint delivered secured insights at unparalleled speed and scale, making smart homes smarter and saving money for customers (and for themselves).
• TDBank reduces data lake costs by 98% with AtScale, Tableau, and Cloudera.

Plus, we share best practices to achieve BI on the data lake nirvana: manage your big data while giving analysts self-serve access to the lake - on premise and/or in the Cloud, using your existing BI tools.

Register here

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SLIDESHOW: 16 top platforms for data science and machine learning by David Weldon via @infomgmt

Alteryx, KNIME and SAS are among the top vendors in this space, according to a new Magic Quadrant report from Gartner.

Definitely a few new names on this list. Interesting to know what is happening in the market.

Understanding the difference between machine learning and predictive analytics by Shailendra Kumar via @infomgmt

The two are closely related and both are focused on efficient data processing to enhance accurate predictions, but there are also many differences between them.

Great explanation from Shailendra and I particularly like his examples so that you can put it into context (maybe even if your own organisation).

WEBINAR: Practical Human-in-the-Loop Machine Learning - 15 March 2018

Overview

Title: Practical Human-in-the-Loop Machine Learning

Date: Thursday, March 15, 2018

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Practical Human-in-the-Loop Machine Learning

Curious about what human-in-the-loop machine learning actually looks like? Join CrowdFlower and learn how to effectively incorporate Active Learning, Transfer Learning, and Annotation Quality in your ML projects to achieve better results. 

Join us in this latest Data Science Central webinar, where we will cover the following topics:

• When to use the human-in-the-loop as an effective strategy for machine learning projects
• How to set up an effective interface to get the most out of human intelligence
• How to ensure high-quality, accurate training data sets
• How to use ML models from different domains to improve your own labeling

​This webinar will include an end-to-end look at setting up and running a job that generates high-quality training data, and shows how to incorporate that training data into human-in-the-loop machine learning systems that you can run in your own environment.

Speaker: Robert Munro, Chief Technology Officer -- CrowdFlower 

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

 

Register here

How to write a production-level code in Data Science? by Venkatesh Pappakrishnan via @TDataScience

The ability to write production-level code is one of the most sought-after skills for a data scientist role. If you are new to it, here are some tips.

I would add that comments are crucial not just for maintenance or reviews but to help remind you what you wanted to achieve. I also find statements to print out variables are various parts of the code that are then commented out are useful for debugging.

Many firms overlook the risks associated with emerging technologies by Bob Violino via @infomgmt

Top risks are often associated with the IoT, artificial intelligence, cloud computing and robotic process automation, says Phil Lageschulte

I think often these risks are what puts organisations off implementing these newer technologies and whilst I agree they need to be cautious and not damage their own business they do need to find a way to manage risk so they are not prevented from taking advantage of a possible advantage to their business.

SLIDESHOW: 20 top platforms for analytics and business intelligence by David Weldon via @infomgmt

Tableau, Olik and Microsoft are among the leading vendors in the data analytics and BI space according to a new Gartner Magic Quadrant report.

I've used a few of these - it's an important thing to understand the differences between these tools - I know there are often company wide decision about the preferred tool, but sometimes it is worth investing in a different tool if it gives you a much better result for what you need to do.

Big Data misuse can break your business by/via @itsvit

Correct use of the Big Data analytics and ML algorithms helps boost the customer satisfaction, secure the bottom line and increase the ROI. Quite opposite, the Big Data misuse results will be awful.

As this article points out, it is crucial that you use the data correctly so you make the right conclusions.

Taking advantage of applied AI in Manufacturing by Beena Ammanath via ‎@xplorexit

The manufacturing digital revolution is happening as the factory floor gets connected and we are able to achieve production at a scale with lower cost and increased quality.

Manufacturing is definitely an area that can benefit greatly from analytics, AI, IoT etc.  In order to survive these skills are becoming a necessity not a nice to have.

Making AI software smarter by adding human feedback by Kayla Matthews via @infomgmt

Developers can now really push the limits of their artificial intelligence platforms, giving them not just more human-like and life-like speech patterns, but also more accurate responses.

This sounds like a promising method of improving AI and I think it has to be the right way to go if you want to try and emulate human decision making.

Successful data management strategies start with quality control by Martin Doyle via @infomgmt

There is the obvious waste in data, but it’s the bad decisions - when based upon false information - made with a high degree of certainty that do the real damage.

Bad quality data gives you results that you cannot rely on - if you can't rely on it what is the point doing anything with it?  I certainly wouldn't risk my business on decisions made with bad data.

Three sectors being transformed by artificial intelligence by Matt Zeiler via @infomgmt

Retail, insurance and government are three areas where AI will be implemented this year in ways we haven't seen before.

I found this a really good summary of how both ML and AI are being used and the areas that are focusing on them.

Ethical Data Practices by/via @datadotworld

The data ethics discussion has been taken up a notch by the team at data.world. This crowd-sourced effort includes a set of principles for ethical data sharing and a manifesto for data teams. This is worth paying attention to and even better, get involved.

I encourage you to read and understand this as I think it is a great idea/ You can sign up for it on the site.