The 5 Clustering Algorithms Data Scientists Need to Know by George Self via @kdnuggets

In this article we’re going to look at 5 popular clustering algorithms that data scientists need to know and their pros and cons.

This is a great article that needs a bookmark so you can refer to it.

Facebook's fight with fake news gets helping hand from robots by Natalia Drozdiak @business via @infomgmt

The firm is turning to machine-learning technologies to amplify the impact of human fact-checkers reviewing hoax news articles.

This is great news - I just hope it works properly.

SLIDESHOW: 7 top challenges to working with data by David Weldon via @infomgmt

Data pros are dealing with a skyrocketing amount of data, created and gathered by ever-more devices. Here are the top challenges this is creating, according to a new study by Nexla.

From my own perspective these are a good list of pain points to the use of data. I would add to this  list:

1.. Data Sources - do you know the best place to get your data from - there could be better alternatives do get the data from.

2. System of Record - related to 1. make sure you understand where your data really comes from and if the data is clean and pure of has been altered in some way.

3. Change control - I've been using a systems data to feed in some of the data I was using, but they have missed it in their change control and I've suddenly had different or no data arrive.

4.  Data Management - are fields with the same name really the same?

How to know when data is 'right' for its purpose by Annette Wright via @infomgmt

The key to evaluating the accuracy of data is more about understanding the eventual use of it than any arbitrary or independent measure.

I agree with Annette although I would bring your attention to some ways to try and make sure that data is correct. 

1..For codes always provide values to select from - yes you cannot guarantee the value chosen is the right one but it is a major step forward just to ensure that there are a finite list of values for that field.

2. For some fields use publicly available data to try and limit data entry to valid values - examples could be master postal code lists, master lists of registered companies, master lists of ISO values for items like a country number, language code, etc.  Yes you cannot guarantee that the correct value is selected but you can at least make sure that the value selected is from a finite master list AND is a valid value.

3.  Make sure that all customer facing systems give the customer a mandatory chance to check and correct their data.

Update your processes to ensure that system design takes all of these things into account - time for a culture change to make sure data quality is a top priority in your organisation.

WEBINAR: Embrace the Modern Analytics Lifecycle - 26th June 2018

Overview

Title: Embrace the Modern Analytics Lifecycle

Date: Tuesday, June 26, 2018

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

How can organizations quickly discover insights in their data and develop deployable data science models? First step: understand how various components of their analytics ecosystem work together to achieve unprecedented value. Join Radiant Advisors for a discussion surrounding new research that explores solutions and reference architectures for data science platforms on Azure – all in the context of a modern analytics lifecycle. Ready to dive in and learn how to craft an environment that optimizes capability, efficiency, and stability? Register for this latest Data Science Central Webinar and you will learn: The stages of the Modern Analytic Lifecycle that support enterprise analytics The conceptual model for a Modern Data Platform to sustain analytic capabilities The logical and physical architectures, including frameworks and solutions architecture components, that specify how technology components fit together in the data platform A demo of Alteryx-based ecosystem architecture patterns that enable enterprise architects to deploy Alteryx on Azure, among other ways to support the modern analytics lifecycle Register and learn how a leading analytics ecosystem and strategic partnership with IT can help permeate the value of self-service analytics throughout an entire organisation. Speakers: Hasan Hboubati, Solutions Engineer -- Alteryx John O'Brien, Principal Advisor and CEO -- Radiant Advisors Raman Kaler, Sr. Manager, Alliance Marketing -- Alteryx Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

 Register here

How change data capture technology drives modern data architectures by Kevin Petrie via @infomgmt

When designed and implemented effectively, CDC can meet today’s scalability, efficiency, real-time and zero-impact requirements. Without it, organisations usually fail to meet modern analytics requirements.

I like the use of case studies and it's very clear. I have to say like the article says where I have worked it's been a mish mash of different methods across the organisation.

Facebook said to have shared user data with select companies by David Weldon via @infomgmt

Some of these agreements were reportedly known as 'whitelists,' and enabled those firms to access information about a Facebook user’s friends.

It seems to me that this entire situation is just getting worse and worse.