How big data is revolutionizing corporate training by @jenmcknzie via Big Data Made Simple

The days of basing huge business decisions on the “gut feeling” of the CEO are quickly disappearing. Instead, many businesses are turning to big data to help them make better decisions.

I like this and think it's a well thought out article. I think big data is going to revolutionise a lot of areas not just corporate training.

Database skills fetching top pay, study says by David Weldon via @infomgmt

Professionals with the right skillsets are earning the highest pay premiums according to new research from Foote Associates.

Worth reading this so you know the kinds of skills to develop going forward.

SLIDESHOW: 7 ways AI and machine learning will remake finance by Thomas Zipperle via @infomgmt

Accounting will be one of the first areas to see the impact of these technologies on day-to-day activities—from automating payments to calculating risk and maintaining records.

I could definitely see 2,3 and 6 being implemented fairly easily and that it could give some major cost savings.

Facebook enlists AI, human experts in new push against terrorism by Jeremy Kahn via @infomgmt

The firm has hired more than 150 counterterrorism experts and is using artificial intelligence that can understand language and analyze images to try to keep terrorists off the site.

Sounds like a great thing to aim for - no idea how successful they will be to sort this out.

WEBINAR: Maximize Value of Your IoT Data - 29 June 2017

Overview

Title: Maximize Value of Your IoT Data

Date: Thursday, June 29, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Maximize Value of Your IoT Data

The digital universe is expanding. Not just the data collected, but also the devices that generate that data. It is estimated there will be over 20x connected devices per person on the planet by 2020, and anything from 50-200 billion IoT devices. That’s a lot of data being generated from IoT ecosystems. The challenge will be making all that data accessible and understandable to start extracting value from it all. In this Data Science Central webinar learn how you can discover more value in your IoT data.

Speaker: Adam Mayer, Senior Technical Product Marketing Manager -- Qlik 

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

Register here

Data Modelling and E-R Diagrams by Nagesh Kumar G via @DataScienceCtrl

We can subdivide the process of designing a database into three separate phases: Data Analysis, System Design, and Technical Design.

I would also draw you attention to normalisation.

And slowly changing dimensions.

Why has Keras been so successful lately at Kaggle competitions? by François Chollet via @Quora

François Chollet, creator of Keras, answered the Quora question "Why has Keras been so successful lately at Kaggle competitions?" It's not the smartest people or the best ideas that win competitions, he says. It's just iteration. Lots and lots of iteration.

I found this interesting.  If you wish to join Kaggle please note there is an examination to gain access.

WEBINAR: Do the graph: Achieving next-gen MDM - 29 June 2017

Do the graph: Achieving next-gen MDM

Jun. 29, 2017 | 3 PM ET/12 AM PT
Hosted by Information Management

The value proposition for Master Data Management is well known, and many organizations have improved their sales, marketing and operations with effective solutions. But the old way of doing MDM was hard and costly. The new way of doing MDM involves graph technology, which greatly expedites time to value. How does this technology work, and what can it do for your company? Check out this episode of IM Live to find out! Host Eric Kavanagh will interview several MDM experts in this live, interactive roundtable webcast!

Eric Kavanagh CEO Bloor Group (Host)	Dr. Robin Bloor Chief Analyst The Bloor Group

Sponsor Content From:

Register here

Rice U. scientists slash computations for ‘deep learning' by Jade Boyd via @RiceUNews

In a recent study, Rice University researchers adapted a widely used technique for rapid data lookup to slash the amount of computation required for deep learning. "This applies to any deep learning architecture, and the technique scales sublinearly, which means that the larger the deep neural network to which this is applied, the more the savings in computations there will be," said lead researcher Anshumali Shrivastava.

This is very interesting.  Some of us can already relate to hashing as  remember using the technique on Oracle and Teradata.

WEBINAR: SPSS Statistics to Predict Customer Behavior - 27 June 2017

Overview

Title: SPSS Statistics to Predict Customer Behavior

Date: Tuesday, June 27, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

SPSS Statistics to Predict Customer Behavior

In today’s world, every organization is collecting and storing massive amounts of data about their customers. In order to take full advantage of this data, you should be equipped with the right tools that are powerful, easier to use and able to draw accurate conclusions in understanding the motivations behind customer behaviors. These tools will allow you to derive new insights, aiding in the decision making process. 

In this Data Science Central webinar, you’ll see firsthand how IBM SPSS Statistics will enable you to: 

• Quickly understand large and complex datasets using advanced statistical procedures ensuring high accuracy to drive quality decision-making
• Reveal deeper customer insights and provide better confidence intervals via visualizations and new analytical techniques
• Build a predictive enterprise making the business more agile and maximizing return on investment

Speakers:

Taylor Perez, Client Technical Specialist - IBM Software -- IBM Analytics 
Murali Prakash, Product Manager - IBM Global Markets -- IBM Analytics 

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Reister here

Beware the next wave of cyber threats: IoT ransomware by David Balaban via @infomgmt

This malware may stop vehicles, disconnect the electricity or even halt production lines. Such programs can do more harm, and demand much larger ransoms.

Something we may not have thought about in the excitement of IoT and what it can do for us.

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.

5 Reasons That Business Intelligence on Hadoop Projects Fails by Remy Rosenbaum via @DZone

Need to build an application around your data? Learn more about dataflow programming for rapid development and greater creativity.

Some great points by Remy. Make sure you read this if you need to do something like this.

Data governance vital to digital transformation efforts by Mithun Sridharan via @infomgmt

Organizations need to ensure that proper controls are in place without having to trade off speed, agility, flexibility and performance.

I completely agree with him - you need good data to make good business decisions.  Just remember the old saying "garbage in, garbage out" and have that as your mantra.

Three steps to achieving real big data value by Jeff Goldberg via @infomgmt

These tips will help provide some key benefits early on and convince the business to fund the work going forward.

I cannot stress enough the need to produce a benefit for your big data as soon as you can. You need to be seen to make it pay for itself and show the success too.

5 top platforms for data management by David Weldon via @infomgmt

Gluent, iguazio and Rokitt Astra are among the top products in Gartner's 'Cool Vendors' report that enable organisations to effectively access and use their information

Interesting list and definitely a few new companies for me to investigate.

WEBINAR: The Myth of the Machine Learning Black Box - 21 June 2017

Overview

Title: The Myth of the Machine Learning Black Box

Date: Wednesday, June 21, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

The Myth of the Machine Learning Black Box

Critics describe machine learning as a "black box," where data goes in and a prediction comes out, without visibility into how the prediction was derived. This lack of transparency makes it difficult to evaluate and update predictive models as conditions change or new sources of data become available. But today's machine learning systems are not black boxes, allowing data scientists and business professionals alike to understand how a model makes its predictions.

In this Data Science Central webinar, DataRobot will discuss how today's automated machine learning systems provide the information and visualisations that deliver deep insights that break out of the black box.

Speaker: Greg Michaelson, Director of DataRobot Labs -- DataRobot 

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

  

Register here

7 Techniques to Handle Imbalanced Data by Ye Wu & Rick Radewagen via @kdnuggets

This blog post introduces seven techniques that are commonly applied in domains like intrusion detection or real-time bidding, because the datasets are often extremely imbalanced.

Good blog post worth reading.

Why so many data lakes turn into swamps by Mark Wilczek via @infomgmt

The majority of stored information is stale, ancient, unmodified or orphaned. Yet, most strategies are focused on volume rather than value.

I agree that quantity is not useful if the data quality and governance is not there.  I can understand the temptation to hoard data on a just in case basis, but if it is not guaranteed as quality data there is no point keeping it as you can't rely 100% on the answers and conclusions you get from that data.

How artificial intelligence will transform financial services by Harphajan Singh via @infomgmt

From bots and virtual assistants, to biometrics and digitization, firms must embrace new technologies that provide rewarding customer experiences.

Interesting set of predictions.  I can see what Harphajan means - companies in the UK like Nutmeg are using robo-advisors and I can see a lot of the pensions and investment world increasingly using them in order to cut costs, reduce commission and attract customers.  Just like discount supermarkets are taking the food retail area by storm, robo-advisors are taking the financial world in the same way.

The machine learning paradox by Mike Loukides via @OReillyMedia

Nothing says machine learning can't outperform humans, but it's important to realize perfect machine learning doesn't, and won't, exist.

Some very good points here and I have to agree with him.

WEBINAR: Level Up Your Data Science Team - 20 June 2017

Overview

Title: Level Up Your Data Science Team

Date: Tuesday, June 20, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Level Up Your Data Science Team

Tired of the lone wolf data scientist myth? Figured out that data science pulls people and expertise from multiple domains? Want to learn more about how to pull data science teams together to solve tough data challenges and explore the possibilities of machine learning? Join this Data Science Central webinar, “Manage Data Science in the Enterprise: Level Up Your Data Science Team" and learn data science best practices, tools, and techniques to increase collaboration and productivity. 

Speakers:

Carlo Appugliese, Big Data and Data Science Evangelist -- IBM Analytics, Watson and Cloud Platform
Alex Jones, IBM Offering Manager, Data Science Experience Local -- IBM Analytics, Watson Data Platform

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

 

  
Data Science Central, IBM and its affiliates may use the provided contact information to keep you informed on related topics, products and services

Register here

Google now knows when its users go to the store and buy stuff by @lizzadwoskin and @craigtimberg via @washingtonpost

You see an ad online. You go into a brick-and-mortar store and buy the item advertised. But the advertisers have no way of tracking your in-person purchases. At least, until now. "Google will begin using data from billions of credit and debit card transactions—including card numbers, purchase amounts and time stamps—to solve the advertising juggernaut's long-standing quest to prove that online ads prompt consumers to make purchases in brick-and-mortar stores, the company said."

Interesting.  I can see so many privacy holes and risks in this.

What is an Ontology? The simplest definition you’ll find by Jo Stichbury via @GraknLabs

This post takes the concept of an ontology and presents it in a clear and simple manner, devoid of the complexities that often surround such explanations.

I would say that it is important to have standards as to how to name the items in your ontology and that it is consistely enforced.  It may appear appear to be a pain, but consistency ensures useful results and it is easier to understand across the entire organisation.

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.

How to use Machine Learning to Sell Better by Cate Trotter via @insidertrends

As the shopping experience becomes more and more integrated, retailers tend to adopt an omnichannel sales approach. This means that a customer may seamlessly switch across the multitude of sales channels, shopping online from a desktop or mobile device, by telephone or in a bricks and mortar stores.

It's definitely an environment where if you don't use ML and AI your company is not likely to survive.

AI-powered dynamic pricing turns its gaze to the fuel pumps by Andrew Orlowski via @theregister

Analysis "AI" could soon be making petrol more expensive at times of peak demand like the start of a bank holiday weekend or the school run.

Wow - now that would be a very unpopular move with customers.

SLIDESHOW: 5 top vendors for performance analytics by David Weldon via @infomgmt

Indeni, Loom Systems and SignalFx are three of the leading companies for this technology according to Gartner.

It was interesting to look through this list and look further into the companies listed.

Machine Learning and AI: When to Start? by @shellypalmer.via @adage

When to start using machine learning in your business is not a hypothetical question; it's a question you must answer today because your competitors are working on their answers as you are reading this.

There are links to some great ways you can try machine learning using existing software for free, so my advice is to play a bit - do it after work or during your lunch break and you will be surprised what you could achieve.  I think if you do this, a potential use will come to you so you can plan doing it on a larger scale.

What to tell the board about the blockchain – Gartner by Kasey Panetta. via @bizEDGEnz

It’s entirely possible that, in the next few weeks or months, your CEO will pull you into a meeting and ask “What do we tell the board about blockchain?”

This could be used to explain it to anyone not just senior management.

SLIDESHOW: 32 top service providers for master data management by David Weldon via @infomgmt

Affecto, BackOffice Associates and High Point Solutions among the leading vendors in Gartner’s market guide to MDM.

Great list - have a look and see if you can see your favourites or even some new ones to try.

How to Pick the Perfect Colour Combination for Your Data Visualisation by @bhopecart via @HubSpot

Choosing any colour scheme -- whether for graphics, websites, brands, etc. -- is a challenge in and of itself. That choice of colours sets the mood for anything and everything you create.

Great blog by Bethany Cartwright that certainly made me thing about the colours I use differently and I hope makes any output I produce easier to read and understand.

Surprising repercussions of making AI sound human via @WIRED

Amazon recently upgraded its speech synthesis markup language tags...[allowing] Alexa to do things like whisper, pause, bleep out expletives, and vary the speed, volume, emphasis, and pitch of its speech. This means Alexa and other digital assistants might soon sound less robotic and more human.

A definite step forward as I can't bare to use them at the moment because of the way the speech sounds.

AI Playbook by/via @a16z

Andreessen Horowitz has unveiled its AI Playbook, a microsite intended as a resource for newcomers (both nontechnical and technical) to explore what's possible with AI.

I love, love, love this microsite which, as it says, is an amazing resource on AI.  It is great for newbies or experts and they have a great repository on GitHub that you can fork containing code and data. I strongly suggest that you bookmark this microsite.