Breaking the data prep barrier by Eliot Knudsen via @infomgmt

Unification technologies, like those created at MIT’s Computer Science and AI Lab, are helping companies make better use of analytics.

I like this well thought out and written article. I love the concept of Data Unification and the potential benefits - just read this and think about how it could provide great benefits for your organisation.

Top 15 Python libraries for data science by @ibobriakov via @Medium

Here's a list of Python libraries for working with data, broken down by: core libraries (like NumPy and pandas) visualisation, machine learning, natural language processing, data mining, and statistics.

If you are learning or are into Python this is an essential list and worth checking in case you missed one.

The Guerrilla Guide to Machine Learning with R by Matthew Mayo via @kdnuggets

This post is a lean look at learning machine learning with R. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.

This is a great article and includes many instructional videos and links to find out more.

How You Can Improve Customer Experience With Fast Data Analytics by @Ronald_vanLoon and @jKoolCloud via @DataScienceCtrl

Using Fast Data Analytics you can take your data mining and analytics to the next level to improve customer service and your business’ overall customer experience faster than you ever thought possible.

This is a great article and really gives examples of what is possible if you use fast data analytics.  Definitely something that needs to be investigated and incorporated into your plans even if you aren't in a position to do this right now.

The information management challenges of data monetisation by David M. Raab via @infomgmt

Organisations must adjust to blurred lines between known and anonymous customer identity information.

I agree with this article - nowadays the data available will contain data that can but also cannot be connected to known data. All organisations need to have a strategy on this data . I recall in the past the unknown data was ignored and deleted.  Nowadays this is missing a valuable source of information and potential customers.  My advice would be to have default values for all fields so that you do not have any empty fields and do not find that joins will not work or have to be outer ones.  That way you can use everything as much as you can.

WEBINAR: Advancing your business intelligence with location analytics - 1 June 2017

Web Seminar  Advancing your business intelligence with location analytics

Jun. 01, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Nearly 70 percent of business data contains some level of location information. But business analysts rarely use this data within their BI and analytics workflows.

Location analytics is a powerful way to put this trapped information to use in visualisations and reports that identify patterns and opportunities to make better business decisions.

Esri and SAS will demonstrate the role that location analytics can play, including:

• The right uses for location analytics
• Skill sets and resources required to get the most from location data
• Best practices and strategies

Featured Presenters:

AJ Rice Global Alliance Manager Esri (Presenter)	Rick Styll Senior Manager, Visual Analytics Product Management SAS (Presenter)	Jim Ericson Consultant, Editor Emeritus Information Management (Moder

Sponsor Content From:



Register here

Cloud storage keeps data out of reach of criminals by @peternowak via @globeandmail

As chief information security officer for Amazon Web Services, Stephen Schmidt is surprised by how many businesses still fail to see the dangers of storing information on computers and servers in their offices rather than in the cloud.

Important points have been made in this well thought out article by Peter which I think can be used as another justification for moving data to the cloud.