VIDEO: AI, Deep Learning, and Machine Learning: A Primer by Frank Chen via @a16z

After multiple AI Winters, why is Silicon Valley buzzing about artificial intelligence again? What’s the breakthrough? In this presentation, Frank Chen from a16z explains why we're finally in an AI Spring.

Great video and worth a watch to make sure you really understand it.

Big Data and the Role of Data Governance via @infomgmt

Big Data and the Role of Data Governance by Stan Christiaens via +Information Management - Companies need to have a data governance process that enables data to be governed across all silos and presented in a meaningful way that generates the most value -- just the way a business user needs it.

I agree data governance is key.

Machine learning is the new Big Data via @SiliconANGLE

Machine learning is the new Big Data by Marlene Den Bleyker via @SiliconANGLE - You can almost hear the whooshing sound as the technology industry is sprinting to the marketplace with new solutions to help the enterprise garner useful and intelligent insights from their data. Analytics, Machine Learning (ML) and Artificial Intelligence (AI) delivered in a simplistic form is what companies are demanding.

Definitely I see ML everywhere.

Infinite Data Overlap Detection Arrives to Speed Business Insights via @KDnuggets

Infinite Data Overlap Detection Arrives to Speed Business Insights Tim Howes vi +KDnuggets - Infinite Data Overlap Detection(IDOD) is a new, Spark-based technology that empowers non-technical business users to automatically discover data patterns and blend any data type for any set of values from multiple sources – both inside and outside the enterprise.

This is very interesting,

Why business intelligence is no longer a luxury via BigDataMadeSimple

Why business intelligence is no longer a luxury by Paco Darcey via BigDataMadeSimple - Once thought of as something that was only available to Fortune-500 companies, there are fewer and fewer reasons for companies to not have a strategy for their business insights. Not only has mobile device security improved greatly in the last five years, there are a variety of BI vendors available which offer native mobile applications of their own.

Interesting.

How to Win your Customers for Life with Predictive Analytics via @Datafloq

How to Win your Customers for Life with Predictive Analytics via Mark van Rijmenam via @Datafloq - Winning your customer for life is a challenging task for organisations. How can you connect with your customer and how can you ensure that they stay with your organisation for a long time? Questions that many organisations face. Fortunately, with the advance of big data and analytics, it has become a little bit easier for organisations. ​Here are several ways how you can win your customer for life with predictive analytics.

Interesting.

Ten Simple Rules for Effective Statistical Practice via Plus

Ten Simple Rules for Effective Statistical Practice by Robert E. Kass, Brian S. Caffo, Marie Davidian, Xiao-Li Meng, Bin Yu, Nancy Reid via Plos - Under growing pressure to report accurate findings as they interpret increasingly larger amounts of data, researchers are finding it more important than ever to follow sound statistical practices.

A must read and great rules to ensure you calculate and report right.

Customer Data, Your Key to a Personalized Experience via @MyFeelBack

Customer Data, Your Key to a Personalized Experience by Daniel Archer via @MyFeelBack - Consumers are inundated each day with new products and brands, making it increasingly difficult for them to differentiate one business from another. Therefore, to stand out, you must figure out how to distinguish yourself. One way to do this is by focusing on ways to provide customers a unique experience, one which has been tailored to appeal to their exact needs and wants.

Interesting.

How to Capitalise on the Data Landscape of Tomorrow via @Data_Informed

How to Capitalise on the Data Landscape of Tomorrow by Marshall Daly @Data_Informed - Tableau’s Marshall Daly examines where organisations are storing their data, choices and innovations based on today’s business demands that are shaping the data landscape of tomorrow, and how organisations can build a data workflow to keep pace with that innovation.

Interesting.

Unlock the Value of the Internet of Things with Data Storytelling via @Data_Informed

Unlock the Value of the Internet of Things with Data Storytelling by Stuart Frankel via @Data_Informed - Narrative Science CEO Stuart Frankel writes that IoT connected devices hold tremendous potential for helping businesses understand markets and customers, but that this potential can be realised only if the insight from IoT data is communicated in a comprehensible way.

A Complete Tutorial to Learn Data Science with Python from Scratch via @AnalyticsVidhya

A Complete Tutorial to Learn Data Science with Python from Scratch by Kunal Jain via +Analytics Vidhya - It happened few years back. After working on SAS for more than 5 years, I decided to move out of my comfort zone. Being a data scientist, my hunt for other useful tools was ON! Fortunately, it didn’t take me long to decide, Python was my appetiser.

Interesting viewpoint.

Driving New Business Value with Big Data and Data Science via @infomgmt

Driving New Business Value with Big Data and Data Science by Roy Wilds via +Information Management - The combination of big data and modern data science can empower you to ask questions in entirely new ways, and uncover answers locked away in your data to questions you hadn’t thought to ask.

Definitely a valid possibility.

Using Predictive Analytics to Maintain Physical Assets via @infomgmt

Using Predictive Analytics to Maintain Physical Assets by Ashish Tyagi and Jay Rajagopal via +Information Management - Companies today are much better equipped to remotely monitor assets and put in place a more intelligent system that senses the state of various components and predicts the type of maintenance required.

Something I have seen already for companies as diverse as coffee chains to manufacturing plants.  This is a three page article.

Power of Teamwork In Data Science via @infomgmt

Power of Teamwork In Data Science by Ruurd Dam via +Information Management - The lack of graduate programs delivering ‘ready to analyse’ data scientists underpins the importance of looking deeper into the skills of a data scientist.

Interesting viewpoint.

SLIDESHOW: Gartner’s Top 10 Technologies for Information Security in 2016 via @infomgmt

SLIDESHOW:  Gartner’s Top 10 Technologies for Information Security in 2016 by David Weldon via +Information Management - Information security threats are growing in frequency, duration, and impact. In response, Gartner, Inc. has identified the top 10 technologies for information security and their implications for security organisations in 2016, announced at the research firm’s Gartner Security & Risk Management Summit.

SLIDESHOW: 20 Top Master Data Management Solutions for 2016 Parts 1 and 2 via @infomgmt

SLIDESHOW: 20 Top Master Data Management Solutions for 2016 Parts 1 and 2 by David Weldon via +Information Management - As organisations looks to get more value from their data, master data management is getting increased attention this year. This newly-released listing from The MDM Institute offers great insights on 20 top products you should consider.

Part 1

Part 2

Big Data Terminologies You Must Know via @acadgild

Big Data Terminologies You Must Know by Brundesh R via @acadgild - In this blog, we will discuss the terminology related to Big Data ecosystem.

Worth reading to make sure you understand everything.

Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016 via @Forbes

Five Ways Data Analytics Will Shape Business, Sports And Politics In 2016 by George Matthew via +Forbes - Over the past few years, we’ve seen a steady rise in the importance of data analytics, in organizations as varied as consumer goods companies, professional sports franchises, political consultancies, medical research institutions, and financial firms.

Interesting observations.

AI And Cognitive Computing - What's The Hype All About? via infomgmt

AI And Cognitive Computing - What's The Hype All About? by Michele Goetz via +Information Management - Artificial intelligence and cognitive computing have captured the imagination and interest of organisation large and small but does anyone really know how to bring this new capability in and get value from it?

Interesting.

Microsoft taps into Apache Spark to drive its Big Data & analytics services via @siliconangle

Microsoft taps into Apache Spark to drive its Big Data & analytics services by Mike Wheatley via @siliconangle - Microsoft is making what it claims is an “extensive commitment” to the Apache Spark Big Data processing engine, launching several new offerings out of preview and into general release.

Real or virtual? The two faces of machine learning via @InfoWorld

Real or virtual? The two faces of machine learning by Galen Gruman via +InfoWorld - The combination of big data, predictive analytics, AI, machine learning, and the Internet of things together powers two very different technology paths

This is really interesting and worth reading.

Boosting Sales With Machine Learning via @Xeneta_AS

Boosting Sales With Machine Learning by Per Harald Borgen via +Xeneta AS - If your company has an inside sales team, you know just how big of a deal lead generation and lead qualification is. Frequently, this is a human-powered endeavour requiring tons of time from tons of junior reps. A developer at Xeneta build an entire workflow to qualify their sales leads—really fascinating, practical read.

What Do Predictive Analytics Consultants Do? via Gladwin Analytics

What Do Predictive Analytics Consultants Do? by Jeffrey Strickland via +Gladwin Analytics - great 4 part blog series which is well worth reading.

Part one
Part two
Part three
Part four

The 7 Deadly Sins of Quantitative Data Analysts via @Datafloq

The 7 Deadly Sins of Quantitative Data Analysts by Raquel Sapnu via +Datafloq - Quantitative analysis: It’s such an appealing phrase. It provides a sense of order and rationality in an often unordered and irrational world. When done right, quantitative analysis can give you a sense of satisfaction and accomplishment. You’ve quantified, you’ve analysed, and as a result you now have a nicely working model with which to grow your business.​ Unfortunately, it doesn’t always work out that way. There are actually quite a few ways to screw up quantitative analysis and here are 7 examples of it.

Interesting and worth taking heed of the warning - these are easy to do and should be avoided.

WEBINAR: A Day in the Life of a Citizen Data Scientist - 21 June 2016

Are more and more employees across your organization being asked to make data-driven decisions on a regular basis? Join our June 21 webcast to see how the new tools of Dell Statistica 13.1 respond to this trend, making it easier than ever for these "citizen data scientists" to be productive on a daily basis.

With demonstrations and walk-throughs, attendees will learn how Dell Statistica 13.1:

- Empowers business users to leverage advanced analytics
- Helps users tap the power of collective intelligence
- Reduces redundancy and increases the efficiency of your analytics platform

The future belongs to citizen data scientists!

Date: Jun. 21, 2016
Time: 12:00 PM – 1:00 PM CST
Location: online
Event: Online

Register here

How to Use Data to Tell a Stronger Story via @convince

How to Use Data to Tell a Stronger Story by Jon Gatrell via @convince - Stats, facts, and figures—we all love data, and it certainly helps make content all the more powerful. Data, when used properly, makes an argument more compelling, underscores a position, and adds relevancy and authenticity to a story.

Very interesting and worth reading.

Is it necessary to learn Hadoop to become a Data Scientist? via @dezyreonline

Is it necessary to learn Hadoop to become a Data Scientist? via @dezyreonline - Is it necessary to learn Hadoop to become a Data Scientist? A study of more than 100 data scientists by Paradigm4 found that only 48% of data scientists used Hadoop or Spark on their jobs whilst 76% of the data scientists said that Hadoop is too slow and requires more effort on data preparation to program.

Interesting.

Free Advice For Building Your Data Science Career via @kdnuggets

Free Advice For Building Your Data Science Career by Nathan Brixius via +KDnuggets - Got hired as data scientist, where to go now from here? Understand how you can make the most of your career by following the different paths like managerial, consulting, or as a domain expert.

Worth reading and taking note.

20 Free 'Open Big Data' Sources You Should Know via @gladwinalytics

20 Free 'Open Big Data' Sources You Should Know by Anandh Shanmugaraj and Saranya Anandh via @gladwinalytics - Open data is based on the idea that some data should be freely available to everyone to use and republish as they wish, without restrictions from copyright, patents or other mechanisms of control.

6 page article - interesting sources.

5 Machine Learning Projects You Can No Longer Overlook via @kdnuggets

5 Machine Learning Projects You Can No Longer Overlook by Matthew Mayo via +KDnuggets - We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.

Great resources and very interesting.

Analytics Living on the Edge of Cloud Computing via @Data_Informed

Analytics Living on the Edge of Cloud Computing by John Schmidt via @Data_Informed - CommScope’s John Schmidt discusses how the growth of data stores and the era of big data are driving the evolution of cloud computing.

I agree with his conclusion.

Eliminate Silos, Converge Data, and Improve Results via @Data_Informed

Eliminate Silos, Converge Data, and Improve Results by Scott Etkin via @Data_Informed - For many organizations, data silos are the result of separating analytical and operational, or production, systems for performance reasons and to control costs.  But advances in enterprise computing, including falling costs, are eliminating the need for these silos and changing how organizations architect and deploy applications.

Interesting and worth reading.

7 Ways Machine Learning Is Already Affecting Your World via @respondrio

7 Ways Machine Learning Is Already Affecting Your World by Pam Neely via @respondrio - What do you think of when someone says “AI” or “Artificial Intelligence”? For most of us, it conjures up an image of the future. Of movies and robots and technological magic. It doesn’t much evoke the here and now. But that’s not so. Artificial intelligence is already out of the box.

Good article.

Tools to scrape Instagram without any coding by Geert Verhoeff

Tools to scrape Instagram without any coding by Geert Verhoeff -  this is a short list of tools you can use to extract data or pictures from Instagram.

Very useful blog by Geert.

The Rise of Channel Data Management and What It Means For You via @infomgmt

The Rise of Channel Data Management and What It Means For You by Dan Blacharski via +Information Management - Better visibility, created when data-driven channel optimization strategies are applied, offers more than the efficiencies and cost savings you might expect in logistics and supply chain management.

Interesting. Specialising and processing data and decisions by channel is definitely the smart way to go.

Top 10 Open Dataset Resources on Github via @kdnuggets

Top 10 Open Dataset Resources on Github by Mathew Mayo via +KDnuggets - The top open dataset repositories on Github include a variety of data, freely available for use by researchers, practitioners, and students alike.

Very useful for practising with.

IBM Launches Cloud Development Environment for Apache Spark via @infomgmt

IBM Launches Cloud Development Environment for Apache Spark by Bob Violino via +Information Management - Available on the IBM Cloud Bluemix platform, the Data Science Experience provides 250 curated data sets, open source tools and a collaborative workspace.

Sounds fun to use.

What is the Difference Between Deep Learning and “Regular” Machine Learning? via @kdnuggets

What is the Difference Between Deep Learning and “Regular” Machine Learning? by Sebastian Raschka via +KDnuggets - Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.

Great article.

Agile In Data Science: Ensuring Assurance Scoring Answers the 'Ask'via @infomgmt

Agile In Data Science: Ensuring Assurance Scoring Answers the 'Ask' by Toby Gamm via +Information Management - The first key to delivering a solution that adds not only immediate business value but also achieves maximum potential is in the appreciation of the exact requirements of the range of stakeholders.

Good blog by Toby.

Intel’s internal IoT platform for real-time enterprise analytics via @oreilly

Intel’s internal IoT platform for real-time enterprise analytics by Moty Fania via +O'Reilly - Intel's internal IoT platform is a single, multitenant platform built with open source technologies, based on an understanding of basic common needs.

Great explanation.

The preoccupation with test error in applied machine learning via @oreilly

The preoccupation with test error in applied machine learning by Patrick Hall via +O'Reilly  - "The technology exists now, be it purchased or built in-house, to directly measure the monetary value that a machine model is generating. This monetary value should be the criterion for selecting and deploying a commercial machine learning model, not its performance on old, static test data sets."

Great article.

Big Data in 2016: The 11 Biggest Professional Services Companies via @infomgmt

Big Data in 2016: The 11 Biggest Professional Services Companies by David Weldon via +Information Management - When it comes to the big data market, Big Blue is clearly the dominant player – IBM holds a 9% total market share. Here’s a look at the 11 top big data professional services companies, according to the Wikibon 2015 Big Data Market Shares report.

There is still a sizeable portion in the others category.

SIDESHOW: Big Data in 2016: The 10 Biggest Software Companies via @infomgmt

Big Data in 2016: The 10 Biggest Software Companies by David Weldon via +Information Management - By 2026, software will account for almost half of all big data revenues, according to the 2015 Wikibon Big Data Market Shares Report. Here’s a look at the top players in this segment you need to know about.

There is still a sizeable proportion in the others category.

Four Features Needed for Extreme Archiving via @infomgmt

Four Features Needed for Extreme Archiving by Jeroen Van Rotterdam via +Information Management - Archives that handle both structured and unstructured data need to understand the data in order to make smart decisions based on the information.

This is part two of a two part article - link to part one.

Time to Analytics: The New Metric for Data Management via @infomgmt

Time to Analytics: The New Metric for Data Management by Prat Moghe via +Information Management - Now the challenges are more about data access, movement, security and governance as enterprises struggle to get the right data to the right people in the right form -- in time to make a measurable difference.

Yes this has always been an issue but now it is far more of an issue that it used to be.

Total Value of Big Data Expected to Increase By 50% Over Next 3 Years via @infomgmt

Total Value of Big Data Expected to Increase By 50% Over Next 3 Years by Bob Violino via +Information Management - Worldwide revenues for big data and business analytics will grow from nearly $122 billion in 2015 to more than $187 billion in 2019, according to a new report from International Data. Corp. (IDC).

The Benefits of Decentralizing Analytics Talent via @infomgmt

The Benefits of Decentralising Analytics Talent by Lewis Tierney via +Information Management - In Lewis Tierney's opinion some of the most talented analytics professionals he's managed were ones that had intimate knowledge of the system limitations required to meet customer needs.

I have to agree with him - with the growth of easier user created reports the best reports come directly from the experts in the actual area you want to analyse.

SLIDESHOW: 7 Guiding Principles of Insights & Data via @informgmt

7 Guiding Principles of Insights & Data by Capgemini via +Information Management - Capgemini has developed 7 Guiding Principles to help drive the transformation to an insights-driven enterprise. Through leveraging these principles, organizations can generate real value from their data.

Definitely good advice and worth following.

Apologies for the lack of posts recently

Apologies for the recent lack of posts - this was due to illness - hopefully I can get myself back to reading and updating this blog again although I am still not able to spend as much time at the computer as I would want.

SLIDESHOW: 19 Tips To Help You Land a CDO Job via @infomgmt

19 Tips To Help You Land a CDO Job by David Weldon via +Information Management - Information Management asked several top data executives what it takes to get and keep their job. Here is their collective advice on what skills you need to rise to the top in data management.

I think these are useful not just for a CDO role.