New study looks at the top trends in data mining and analytics by Bob Violino via @infomgmt

Data mining can help enterprises identify anomalies, patterns, and correlations within large unstructured data sets to predict business outcomes.

I found this interesting and it gives you some pointers on things to think about including in your own plans.

Steps organisations can take to quantify qualitative experiences by Anna Johansson via @infomgmt

Subjective experience tends to produce qualitative data if they produce data at all, which makes it notoriously hard to form the same calibre and a number of conclusions from those data.

I really like the three steps she suggests - numerical values for qualitative data is very important - it is efficient to create fields in the table or database, is a very efficient key for anything (be it a primary key or a foreign key) and can help you if you want to modularise code so that you can share it.

How to banish silos, consolidate data and avoid errors in the process by Fredrik Forslund via @infomgmt

Data silos tend to arise naturally in large businesses because each organisational unit has different goals, priorities and responsibilities, as well as different technical systems or platforms in place.

One of the keys to reducing silos is to have a strong data management team but you also need a strong team of data stewards too.  Something I have found is not only do you have silo's of data, but in those silos you have the same names data fields either in different formats or have a completely different name. You need to sort that out before you can think about getting rid of the silo.

Data veracity challenge puts spotlight on trust by Pat Sullivan via @infomgmt

The data veracity challenge is one that most businesses have yet to come to grips with, but if we’re to fully harness data for the full benefit to businesses and society, then this challenge needs to be addressed head on.

I think automation of reports are great for businesses yes, but as this article from Pat says/suggests, you absolutely have to be confidence in your data, that you can rely on the quality of that data, that you know the journey of that data from the original source into wherever you use it from in your reporting, that you understand the meaning of the data (data management), that you can join it with other data and produce something useful and that any data analysis/visualisations/algorithms are correctly defined and are not biased if your business is going to be run using it and investment that is based on it is not wasted.

Growing skills gap taking toll on data management strategies by Bob Violino via @infomgmt

Nearly half of firms recently surveyed struggle to identify between data truths and manipulations. This indicates an urgent need to upskill and support workers, the report said.

I agree - you have got to understand your data if you are going to use it and make good decisions based from reports using it.

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?

Using Big Data Analytics To Improve Production by Rob Consoli via @MBTwebsite

Manufacturing remains a critically important part of the world’s economic engine, but the roles it plays in advanced and developing economies has shifted dramatically. In developing countries, manufacturing operations deliver unprecedented new employment opportunities that are transforming societies.

I definitely think that manufacturing is going to be improved greatly as soon as there is a larger use of IoT and the big data and analytics is covering far more of the manufacturing process. Hopefully the efficiencies can be vastly improved. Anything that can be automated is good - I remember having to take snapshots of data from a source system and importing it into a spreadsheet so I could use sheets, pivot tables, etc to work out where the largest delay was in the whole passage of orders from input to delivery to the customer - took hours and the benefit was reduced just because of the time to produce and timing.

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.

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.

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 Data Mining Improves Customer Experience: 30 Expert Tips by Angela Stringfellow via @ngdata_com

How does data mining actually work to enhance customer experience? What ways are the most successful companies utilizing data to improve processes, capture a broader audience, convert prospects to buyers, and create exceptional experiences that keep customers coming back for more?

This looks like a short article, but you need to cick on the names to see their tips.  Some helpful tips in these - you just need to mine down through them.

How the random forest algorithm works in machine learning by @saimadhup via @dataaspirant

This is a great article by Saimadhu Polamuri which is a good explanation of how Random Forest works.

Definitely work reading. Contains some great diagrams.

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.

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.

Top 10 Amazon Books in Data Mining, 2016 Edition by Matthew Mayo via @kdnuggets

Given the ongoing explosion in interest for all things Data Mining, Data Science, Analytics, Big Data, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the Data Mining category.

Definitely worth considering these books if you look at the contents list and can see areas you been to learn or brush up on.

Data Mining Tip: How to Use High-cardinality Attributes in a Predictive Model by Julie Moeyersoms and David Martens via @kdnuggets

High-cardinality nominal attributes can pose an issue for inclusion in predictive models. There exist a few ways to accomplish this, however, which are put forward here.

This is useful and is a summarisation of what goes on in my head when doing this kind of model.

We need to talk about AI and access to publicly funded data-sets by @riptari via @techcrunch

This is a thoughtful article about who should own the value that's locked up in our data.

This is really interesting and points out a few things that I never know or thought about it.

SLIDESHOW: Data Pay Days: The 12 Top Paying Noncertified Data Skills via @infomgmt

Data Pay Days: The 12 Top Paying Noncertified Data Skills by David Wedon via +Information Management - There seems to be no end in sight when it comes to high demand for data professionals. But just how well an individual can cash in on that trend depends on their job experience, location, and acquired skills. This week we look at how all those factors impact the paycheck. Today we review the pay premiums being paid in 2016 for the top noncertified data skills.

How Hadoop Revolutionised IT via SmartDataCollective

How Hadoop Revolutionised IT by Martyn Jones via SmartDataCollective - This is the story of how the amazing Hadoop ecosphere revolutionised IT. If you enjoy it then consider joining The Big Data Contrarians.

Great story and look back and what happened.

Automated Data Science and Data Mining via @kdnuggets

Automated Data Science and Data Mining by Gregory Piatetsky via +KDnuggets  - Automated Data Science is becoming more popular. Here is his initial list of automated Data Science and Data Mining platforms.

I'm sure over time the list will grow and grow.