Most organisations slow to reap real benefits from analytics strategies by David Weldon via @infomgmt

Despite being the number one investment priority of CIOs today, few say they have reached a level of maturity in their data management efforts.

There are several reason for this - they don't have the staff, the knowledge of their data and they have no idea what could be achieved to start with. This is a prime example of a need for knowledge of what data you have and a background in analytics so you can think about what could be possible.

Data pros waste half of their work time chasing costly data by Bob Violino via @infomgmt

Analytics professionals are spending more time governing, searching and preparing data than they are on extracting business value, says a new study from IDC

I'm sure we have all spent time looking for data. This is definitely a justification for a MDM so you can understand and find your data better.

Gentle Introduction to Vector Norms in Machine Learning by/via @TeachTheMachine

Vector norms are often used as a regularisation method in regression and neural network algorithms. This post is a gentle introduction to vector norms.

This a great article and I strongly advise you to sign up for Jason's emails.

WEBINAR: Why your MDM single customer view will fail GDPR - 1 March 2018

Why your MDM single customer view will fail GDPR

March 1, 2018 | 2 PM ET/11 AM PT
Hosted by Information Management

With GDPR, you must now be able to pinpoint every single record pertaining to every EU customer and prospect across all your databases and applications – with extreme precision.

That’s an extremely tough challenge given the many nickname variations, address errors, digit transpositions, and other data quality issues so commonplace in today’s enterprise. And traditional “uncertain = no match” methods won’t enable you to avoid GDPR’s high-stakes downsides – especially when it comes to Article 17 “Right to Erasure.”

Attend this interactive expert-led webinar to learn:

• How to accurately assess your real risk of inadequate record-matching
• 3 best practices for bringing record-matching up to GDPR-readiness
• The importance of EU-centric third-party data validation

Ed Allburn Founding President & CEO Data Delta (Presenter)	Charles Gaddy Director of Global Sales & Alliances Melissa Data (Presenter)	Lenny Liebmann Contributing Editor SourceMedia (Moderator)

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AI may hold the key to success with predictive analytics by David Weldon via @infomgmt

Traditional machine learning techniques can be very labour intensive, but artificial intelligence can learn and improve performance results quickly, says John Crupi.

I agree with John - Python is a great way to use lots of open source software and tools and do some really great things.  If you don't know how to use Python there are lots of online training courses that can be used to get yourself up to speed.

Should Data Scientists Adhere to a Hippocratic Oath? by Tom Simonite via @WIRED

Last week in San Francisco, dozens of data scientists from tech companies, governments, and nonprofits gathered to start drafting a data science code of ethics. The general feeling is that it’s about time that the people with powers of statistical analysis woke up to their power, and used it for the greater good. This article by Tom Simonite at Wired explores the effort and why everyone's not on-board.

A great idea although I'm not sure how it could be policed or enforced.  Certainly there is no reason why you couldn't use your free time to use public data to work on something for the greater good. There are lots of sources of free data, plus you might find a competition on Kaggle that fits the bill and gives you some kudos for entering too.

The most in-demand trait for new data pro hires - passion by David Weldon via @infomgmt

What employers most need from analysts is enthusiasm for what the employee can do with data to drive business decisions and advance the corporate mission.

I find this article interesting but mostly as a way to encourage more discussion on skills needed.  I agree that "Passion" is needed, but you should have passion for your job otherwise I wonder why you are actually doing it.

You need to understand the data if you want to use it to make decisions about the business.  That could mean you need good Master Data Management,  You need some kind of business knowledge either through experience or Analysis.  There are so many other things that you need if you just sit down and think about it.

Where Moore’s Law Is Headed with Big Data by Mark Palmer via @datafloq

Moore's law is a fundamental idea of technology; Big data, however, might change that.

Yes I agree with Mark - 3D is the way to go and seems the only way to handle the potential increases that are going to multiply more in the future.

AI's Next Challenge: Predicting Natural Disasters by @kaylaematthews via @dataflloq

Artificial intelligence and related technologies have enabled massive leaps in the fields of marketing, customer service, transportation and industrial processes. AI has even composed music. Its next challenge? Predicting natural disasters and improving the way we respond to them.

This all sounds like an amazing opportunity to look forward to.  I can also see machine learning being used to predict better so we are more prepared, however I think a note of caution needs to be used - there are several downsides including insurance companies using it to adjust premium costs, misinterpretation of results, and over-reliance on something which considering we do not understand it well enough to have certainty, could prove dangerous.

The 5 Clustering Algorithms Data Scientists Need to Know” by George Seif via @TDataScience

This article does a great job of walking through the different clustering algorithms and ends with a really wonderful visualisation summarising the trade-offs.

I like this as it's clear, easy to understand and worth a bookmark to refer back to if necessary.

Why Data Scientists Must Know About Change Management by Jurjen Helmus via @kdnuggets

Change management may be seen as an opposite to data science, but in reality both are related. Without proper implementation, a data science project fails.

I think you need change management in everything no matter what it is that you are doing. How else are you going to keep track of what has changed?

WEBINAR: Machine learning & data discovery: You can't analyse what you can't find - 27 February 2018

Feb. 27, 2018 | 1 PM ET/10 AM PT
Hosted by Information Management

Corporate data teams are under intense pressure to leverage machine learning and other technologies to transform everything from customer engagement to supply chain management.

But data science doesn’t do you much good without good data. So if you want to successfully compete and innovate in today’s data-driven marketplace, you better be able to put the right information resources into the right hands — quickly, efficiently, and comprehensively.

Attend this interactive, expert-led webinar to learn:

• Why data scientists and analysts struggle to find essential “raw material”
• How poor data discovery adversely impacts IT and the business
• 3 practical ways to radically improve your source-to-insight data pipelines

Lenny Liebmann Contributing Editor SourceMedia (Moderator)

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Three trends that will help organisations modernise their data warehouse by David W. Thompson via @infomgmt

The adoption of machine learning and the need for access to data beyond just data science team is changing how many firms approach information warehousing.

Interesting thoughts from David. I don't think data warehousing is dead, but it'a certainly not in the same form as it was 10 years ago.

Customers qualify 16 top data quality tool makers by Elliot M. Kass via @infomgmt

BackOffice Associates, Informatica, Oracle and Pitney Bowes are among 16 leading vendors highlighted in Gartner’s Magic Quadrant report.

You might find some surprising or interesting vendors in this list.

WEBINAR: Dynamic visuals: The evolution of understanding data - 22 February 2018

Feb. 22, 2018 | 1 PM ET/10 AM PT
Hosted by Information Management

True self-service for business analysts requires next-generation data visualizations and built-in analytics features combined with a refined and easier user experience for more productivity and less tedium.

Join this session for an overview of SAS® Visual Analytics:

• Integrated Location Intelligence to bring out the geographic and demographic context
• Add open visualization objects - D3 graphs, Google Charts or C3 visualizations – to your reports or visuals for more flexibility
• New visualizations like Parallel coordinates plots, path analysis, etc. to explain important relationships in your data
• Attractive infographic-style reports that highlight compelling numeric and categorical values
• Automatic filters and linked selection, enhanced actions and calculations, etc. to improve interactivity

Tapan Patel Principal Product Marketing Manager, Business Intelligence and Analytics SAS (Presenter)	Varsha Chawla Senior Solutions Architect, Business Visualization Practice SAS (Presenter)	Jim Ericson Consultant, Editor Emeritus Information Management (Moderator)

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14 leading tools for predictive analytics and machine learning by Bob Violino via @infomgmt

KNIME, RapidMiner, Dataiku and Statistica are among the top vendors offering products in these technology areas, according to Forrester Research.

There are definitely some in the list I have never had experience of. When I have the time I'll have to look and see if you can download a demo version of them.

Will AI make data analytics jobs obsolete? by Anna Johansson via @infomgmt

If developers create an algorithm that can process vast volumes of data, present it in an easily recognisable form, and even draw basic conclusions from it, it could threaten many job positions.

Maybe data analysts need to start expanding and retraining ready for when they will no longer be needed?  I do think that at the moment you definitely need a human to interpret some results and their meaning.

4 key elements to successful data governance by Marc Wilczek via @infomgmt

The sad reality is that most data holds no value, yet it consumes valuable time and resources. Organisations need better ways to assess the information they have, starting with these criteria.

Some good points in here. I completely agree that often with data more is not more and can just waste time - better to have a small amount of perfect data that is reliable.

Avoid Analytics Mistakes by Being Aware of Misinformation Visualization by Nathan Sykes via @SmartDataCo

Learning styles have an impact on data visualisation and communication of business objectives. This data visualisation guide can help.

Useful read to remind you of some of the pitfalls to avoid.

WEBINAR: Gain more agility, context and business insight with graph-based MDM - 15 Feb 2018

Web Seminar  Gain more agility, context and business insight with graph-based MDM

Feb. 15, 2018 | 2 PM ET/11 AM PT
Hosted by Information Management

Businesses continue to collect volumes of information about their customers. 

When organised effectively - by integrating data from a variety of different sources 

through enterprise master data management (MDM) - this data can provide 

important and actionable buyer insights.

Some are tackling this challenge with a relational database approach to MDM, 

which limits what businesses can do, and how quickly they can do it.

Join this webcast to learn about developing a more agile MDM strategy 

around Graph databases to better serve business needs.

In this webcast, you will learn how to:

• Gain the flexibility you need to pull answers from a full range of customer information
• Add and remove new data sources quickly
• Identify new connections in data
• Explore connections that previously would not have been obvious
• Add Graph technology to connect more traditional relational databases - effectively create a “hub of hubs”

This flexibility and modern approach to data offers a number of unique, 

cross-vertical use cases, including sales optimization, fraud detection, 

anti-money laundering (AML), customer support, and more.

Join this webcast to learn about the power of knowledge graphs for a 

full 360-degree view of your customers, no matter where or how they are interacting with your enterprise organisation.

Sponsor Content From:

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How Poor Data Quality Can Spoil the Digitization Game for the Insurance Industry by Kaustubh Deshpande via @LexisInsureUK

This article focuses on the Insurance Industry and problems with how policies can be compared in India however many of the lessons and problems are global in nature.

Interesting and also nice to know it is a more global problem.

Convolutional neural networks for language tasks by Garrett Hoffman via @OReillyMedia

Though they are typically applied to vision problems, convolution neural networks can be very effective for some language tasks.

This looks really useful and it has snippets of code for you too.

How Blockchain is Making Data Predictions More Accessible by @abrahamnorah via @datafloq

Due to developments in big data, artificial intelligence (AI), and machine learning (ML), predictive analytics is starting to become highly reliable.

This is a very positive article that gives hope that all organisations can take advantage of the latest developments in data. As always you need to have good quality data if you want to have reliable results.

Five Ways in Which Artificial Intelligence Changes the Face of Web Accessibility by @dboudreau via @hackernoon

Artificial intelligence (AI) is all the rage right now. Chances are your news feeds and social media timelines are filled with articles predicting how AI will change the way we will interact with the world around us. Everything from the way we consume content, conduct business, interact with our peers, transport ourselves, and earn a living is going to be affected by AI-related innovations. The revolution has already begun.

I really like these ways listed by Denis.  I think the automated lip recognition would be good for the BBC to replace their subtitle generating software with so it was more correct.

The unexpected benefits of data analytics by Bob Violino via @CIOonline

Data analytics can uncover surprising insights that lead to unexpected new program or product ideas, as these six real-world examples show.

This is great as it gives some real world examples of the benefits of using analytics.

Does your business need a chief AI officer? by Olivia Krauth via @techrepublic

The field of artificial intelligence (AI) is booming. It's expected to create 2.3 million jobs by 2020, and around three-fourths of tech leaders plan on hiring more AI talent, according to a July report.

I'd hope in some organisations the CIO could do this role or even the Data Scientist.  Someone does have to keep an eye on AI and where/how it is being used.

How to transform from data survivor to data thriver by Mark Bregman via @infomgmt

When it comes to digital transformation, many organisations either don’t know where to start or they’re jumping off the wrong diving board.

Good advice in this article - worth a read and think about.