Data Security Demands Are Stifling Innovation for Many Firms via @infomgmt

Data Security Demands Are Stifling Innovation for Many Firms by David Weldon via +Information Management - Data security and data privacy have become such top-level concerns at many organisations that daily tasks around them are crowding out the ability to pursue technology innovation.

It's definitely a tightrope balancing security and innovation, but security is not something you can afford to get wrong.

Four Reasons to Consider Extreme Archiving via @infomgmt

Four Reasons to Consider Extreme Archiving by Jeroen van Rotterdam via +Information Management - Recently we introduced the phrase “extreme archiving” which means that there are more extremes to consider when it comes to managing structured and unstructured data.

Part one of a two part article.  Interesting discussion.

Modern CRM Drives Engagement, Relationship And Revenue via @infomgmt

Modern CRM Drives Engagement, Relationship And Revenue by Kate Leggett via +Information Management - Modern CRM strategies enable good customer experience. They support customer easy, effective customer engagement, that leaves the customer feeling good about the interaction.

Interesting blog.

How to Perform RDBMS CRUD Operations with Hadoop MapReduce Integration via @Datafloq

How to Perform RDBMS CRUD Operations with Hadoop MapReduce Integration by Ethan Millar via +Datafloq - This article introduces the way to perform RDBMS operations with Hadoop integration. Hadoop is a trending technology these days and to understand the subject, you need to clear some basic facts about this technology. In this post, experts will explain how to read the RDBMS data and manipulate it with Hadoop MapReduce and write it back to RDBMS.​ They are introducing a way to perform simple RDBMS read and write operations using Hadoop.

Great article and highly recommended.

WEBINAR: Top Five Technical Tricks to Try when Trapped - 7 June 2016

Overview

Title: Top Five Technical Tricks to Try when Trapped

Date: Tuesday, June 07, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Top Five Technical Tricks to Try when Trapped

There's no better source for tricks of the analytics trade than Dr. John Elder, the established industry leader renowned as an acclaimed training workshop instructor and author -- and well-known for his "Top 10 Data Mining Mistakes" and advanced methods like Target Shuffling.

In this latest Data Science Central Webinar, Dr. Elder, who is the CEO & Founder of Elder Research, North America's largest pure play consultancy in predictive analytics, will cover his Top Five methods for boosting your practice beyond barriers and gaining stronger results.

Speaker: John Elder, Founder and CEO -- Elder Research

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

Register here

Project Management Methodologies for Big Data Analytics via @MyTechLogy

Project Management Methodologies for Big Data Analytics by Mario via +MyTechLogy  - Many companies today are in the very early stages of adopting Big Data analytics. They are likely to go down a road ahead filled with experimentation and discovery. From many decades of IT history it is known that a significant number of large projects or new initiatives just end up in failure, or do not deliver all the results.

Interesting thoughts.

Data Lakes: Safe Way to Swim in Big Data? via @infomgmt

Data Lakes: Safe Way to Swim in Big Data? by David Menninger via +Information Management - A data lake combines massive storage capabilities for any type of data in any format as well as processing power to transform and analyse the data. Often data lakes are implemented using Hadoop technology.

Interesting.

WEBINAR: Accelerating Customer Analytics with Alteryx at Verdict Retail - 1 June 2016

Wednesday, June 01, 2016
2:00 PM BST

Effective analysis of customer data can give you a competitive advantage in retail. But with large amounts of data being collected, how do you get the details to predict buyer behaviour?

Register for this webinar and see how Verdict Retail, a consulting firm with deep expertise on the UK and European retail industry, uses a repeatable analytic workflow in Alteryx to:

	Prepare and blend data from 100 reports
	Automate data analysis to reduce a 30-day process to just 6 days – an 80% savings!
	Provide better insight to clients about store performance and customer buying patterns

Register now and see how a non-technical data analyst became a master of self-service data analytics by automating data cutting and generating deeper insights with Alteryx.

Register here

How to Hire ‘Trilingual’ Data Science Talent

How to Hire ‘Trilingual’ Data Science Talent by Scot Etkin via @Data_Informed - Vijay Murugappan of Health Care Service Corporation talks with Data Informed about how his organisation’s multifaceted and cross-disciplinary approach to hiring and training data scientists is paying dividends.

I like the concept of being trilingual.

What Data Scientist Shortage? Get Serious and Get Talent via @Data_Informed

What Data Scientist Shortage? Get Serious and Get Talent by Tom Davenport via @Data_Informed - Tom Davenport discusses two approaches to acquiring data talent and how companies have found success with each.

I like the thought of training up your own people although there is the risk you train them and then they leave fr much more money.

Big Data Analytics for the Retail Industry via @DZone

Big Data Analytics for the Retail Industry by Balaji Kandregula via +DZone, Inc. - How big data analytics have changed the brick-and-mortar and online retail industries for the better.

Good summary of the situation.

Is Fast Data the new Big Data? Why Big Data is No Longer Enough via @A2BData

Is Fast Data the new Big Data? Why Big Data is No Longer Enough by Ava Carmichael via @A2BData - What if the average time and effort you currently spent in your data extract processes for each object, per file or table could be reduced from thousands of hours to nearly zero?

Interesting points if you ignore the slant for their own product.

Data Minimization In Big Data: Benefits and Risks via @suyatitech

Data Minimization In Big Data: Benefits and Risks by Sahana Rajan by @suyatitech - The question “Do we really need all the data?” is pertinent in in case of Big Data. Data Minimisation rises as a solution to this question.

Interesting.

The 'Internet of Things' Will Be The World's Most Massive Device Market And Save Companies Billions Of Dollars via @BI_Europe

The 'Internet of Things' Will Be The World's Most Massive Device Market And Save Companies Billions Of Dollars by John Greenough via @BI_Europe - The Internet of Things (IoT) is beginning to grow significantly, as consumers, businesses, and governments recognize the benefit of connecting inert devices to the internet.

Old article but still very relevant.

The Data Supply Chain will become the greatest enabler or inhibitor of a cognitive world via Gladwin Analytics

The Data Supply Chain will become the greatest enabler or inhibitor of a cognitive world. by Marie Wallace via Gladwin Analytics - Everyone tells us that we are drowning in data, and yet we can never seem to find the data we need; either it's not being collected or its being collected but its not consumable, we don't have access, it's missing stuff, or we just can't find it.

9 Data Collection Methods Every Manager Should Know

9 Data Collection Methods Every Manager Should Know by +Bernard Marr via @Data_Informed - Bernard Marr discusses several sources of insight-rich data for businesses and offers tips for collecting and getting the most from each.

Good article.

The Essential Core Python Cheat Sheet via @TradePub

The Essential Core Python Cheat Sheet by DZone via +TradePub.com - very useful Python cheat sheet.

Requires email address and other details.

Why the Answer to Your Big Data Problem is Actually a Question via @infomgmt

Why the Answer to Your Big Data Problem is Actually a Question by Joanna Schloss via +Information Management - From transactional data and customer service calls to city traffic patterns, GPS, robotic sensors, emails, texts and social media -- we’re capturing as much data as we possibly can, without ever really knowing why.

Definitely a good summary - there has to be a reason and therefore a question behind a need for data and it's use.

3 Ways Every Enterprise Can Make Data-Driven Decisions via @infomgmt

3 Ways Every Enterprise Can Make Data-Driven Decisions by Kevin Roberts via +Information Management - Whether a company builds out a data team or has one data-savvy employee, turning metrics into meaningful insights is key to business success today and in the future.

It's all about focussed analytics looking at the right areas.

Navigating the Cybersecurity Threat Landscape via @infomgmt

Navigating the Cybersecurity Threat Landscape by Vilius Benetis via +Information Management - What is missing in all of this are the connections between actual attack techniques, vulnerabilities, threat actors and further detailed analysis of the domain. So how to fill this gap properly?

Interesting and something to think about.

Top Talent Wants Access to Data that is Fast, Flexible, Fun via @infomgmt

Top Talent Wants Access to Data that is Fast, Flexible, Fun by David Chao via +Information Management - From startups to the enterprise, it’s clear that lots of businesses are making very cool technology aimed at today’s digital economy. So why does our workplace technology still suck?

Should the NHS share patient data with Google's DeepMind? via @Wired

Should the NHS share patient data with Google's DeepMind? by Subhajit Basu via +WIRED - In giving Google access to the healthcare data of nearly 1.6 million patients, the NHS has used a loophole around "implied consent".

I have no contact with that NHS Trust but it is a good point as to if the people gave specific permission for it.

WEBINAR: Shaping Data Stories with Neuroscience - 24 May 2016

Overview

Title: Shaping Data Stories with Neuroscience

Date: Tuesday, May 24, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Shaping Data Stories with Neuroscience

Whether told through words, images, or sounds, a good story creates physiological responses in our brains and bodies, from unforgettable memories to overwhelming tears. In the next Data Science Central webinar, we will examine the neuroscience principles of vision, memory, and attention in storytelling. We will review scientific data and draw on examples from film, oral storytelling and Tableau to leave attendees with a deeper understanding of the brain and the stories it forms every day.

Speaker: Rawi Nanakul, Senior Consultant -- Tableau

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

Register here

Unlimited Possibilities: SAP HANA In the Non-SAP World via @infomgmt

Unlimited Possibilities: SAP HANA In the Non-SAP World by Detlev Sandel via +Information Management - In this digital age, business models are built upon data. Currency and accuracy of information define success or failure.

I've never considered using HANA for non-SAP things so I guess something to think about.

Most Organizations Lack Long-Term Strategy for Data Protection via @infomgmt

Most Organizations Lack Long-Term Strategy for Data Protection by David Weldon via +Information Management - A majority of organisations are required to store certain data for a specified number of years, but most lack a coherent long-term strategy for how to preserve and protect that data, a new study reveals.

I have to agree - there is more to it than archiving and forgetting about the data.

Gartner Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics: Key Takeaways via @BigData_Review

Gartner Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics: Key Takeaways by Timothy King via @BigData_Review - Parody among the vendors seems to be the only constant in the 2016 Gartner Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics.

Interesting.

CIOs Play Key Role in Organizational Success With Data via @infomgmt

CIOs Play Key Role in Organizational Success With Data by Bob Violino via +Information Management - CIOs must be “champions of change” for organisations to fully benefit from the cloud, mobility and collaboration, and data, according to a report from communications services provider BT.

I agree - who else is as knowledgeable about IT and what is possible.

Internet of Things Requires Operational Intelligence via @infomgmt

Internet of Things Requires Operational Intelligence by David Menninger via +Information Management - The evolution of operational intelligence, especially in conjunction with IoT, is encouraging companies to revisit their priorities and spending for information technology and application management.

Interesting.

Firms Have Plenty of Desire, But Little Trust, for Advanced Data Initiatives via @infomgmt

Firms Have Plenty of Desire, But Little Trust, for Advanced Data Initiatives by David Weldon via +Information Management - A new study reveals that there is a significant lack of confidence in data quality management despite the desire by many organisations to implement more advanced data-reliant initiatives.

To do proper data quality management and governance requires investment, but many want quick answers which can seem against that.

Why Data Analytics Governance Is More Important than Ever via @infomgmt

Why Data Analytics Governance Is More Important than Ever by Mark Owens via +Information Management - Despite all the hype over predictive analytics, we're still very much in an environment “where data science cowboys operate in a wild west of innovation and deployment," notes one data science expert.

I agree completely.  You cannot rely on or base major decisions on the equivalent of something designed on the back of a notebook.

12 Qualities Your Next Chief Data Officer Should Have via @Datfloq

12 Qualities Your Next Chief Data Officer Should Have by Mark van Rijmenam via +Datafloq - The Chief Data Officer is on the rise and is here to stay. It is/will be an important role for organizations, but it is also a difficult role since the CDO has to have the right balance of Big Data skills and business skills in order to be able to deal with all aspects related to big data. Since many organizations are on the lookout for a suitable Chief Data Officer, here is a list of the 12 key qualities and characteristics that your next CDO should have.

Great list.

Is Java Important For Hadoop Developers? via Soft Tech Solutions

Is Java Important For Hadoop Developers? by Ethan Millar via Soft Tech Solutions - Hadoop experts get frequent inquiries from people who want to know the importance of java to become a hadoop developer. Is it really necessary to learn java to become a big data hadoop developer?

Do You Know If Your BI Supports Actual Verifiable Facts? via @infomgmt

Do You Know If Your BI Supports Actual Verifiable Facts? by Boris Evelson via +Information Management - To win and keep customers in an increasingly competitive world, firms need to take advantage of the huge swaths of data available and put it into the hands of more users.

But it HAS to have good data quality and data management.

SIDESHOW: The 10 Biggest Big Data Hardware Vendors by Market Share via @infomgmt

The 10 Biggest Big Data Hardware Vendors by Market Share by David Weldon via +Information Management - The fastest growth in the big data vendor market is among Original Design Manufacturers (ODMs), which dominate the hardware segment. The Wikibon Big Data Market Shares Report reveals that ODMs see revenue increasing by $553 million year-over-year. Here’s a look at the top big data companies in the hardware segment.

Interesting.

Introduction to Apache Beam via @Talend

Introduction to Apache Beam via +Talend - This blog is the first in a series of posts explaining the overarching goal and purpose of the Apache Beam project.

Great start in explaining what it is.

WEBINAR: 5 Key Self-Service Analytics and How to Get the Data - 17 May 2016

Overview

Title: 5 Key Self-Service Analytics and How to Get the Data

Date: Tuesday, May 17, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

5 Key Self-Service Analytics and How to Get the Data

Organizations need the right data for decisions. But many organizations don’t have the ability to perform self-service analytics, diving into the data to get key insights quickly.  

In this latest Data Science Central Webinar event, you will learn how you can use Alteryx and Tableau to implement self-service analytics to:

• Blend data from your databases, applications and spreadsheets 
• Cleanse and prepare your data for visualization
• Create dashboards and visualizations to drive data insights

Join us for this live event and see how hundreds of organizations are getting the right data and the right visualizations through self-service analytics.

Speakers:
Brian Dirking, Director -- Alteryx 
Gene Rinas, Senior Solutions Engineer -- Alteryx 
Todd Talkington, Sr. Technology Partner Manager -- Tableau

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

SLIDESHOW: The Biggest Big Data Companies By Revenue via @infomgmt

The Biggest Big Data Companies By Revenue by David Weldon via +Information Management  - Everything about the big data market is big. According to the Wikibon 2015 Big Data Market Shares report, big data market revenues grew by 22 percent last year alone. Here’s a look at the top players by revenues and market share.

Interesting - not sure I realised all of the members in that list were doing so well.

Analytics Success Should Be Measured by Business Users via @infomgmt

Analytics Success Should Be Measured by Business Users by David Weldon via +Information Management  - Organizations are creating an enormous amount of data, from a growing number of sources. The task of managing it all falls on IT, but determining success with any analytics project starts with business users.

I completely agree - my version of success may not be the same as a business user.

Algorithms and architecture for job recommendations via @OReilly

Algorithms and architecture for job recommendations by Preetha Appan via @Oreilly - With 200 million unique visitors every month, Indeed.com relies on a recommendation engine that processes billions of input signals every day. Preetha Appan describes the evolution of the recommendation engine, from the initial minimum viable product built with Apache Mahout to a hybrid offline and online machine learning pipeline and describes the incremental modifications to algorithms, system architecture, and model format required.

Very interesting and related to your own situation if you are about to go down that route.

How to Remove Duplicates in Large Datasets via @KDnuggets

How to Remove Duplicates in Large Datasets by Suresh Kondamudi via +KDnuggets - Dealing with huge datasets can be tricky, especially the data cleaning process. One of such processing is de-duplication, find out how you can solve this using the statistical techniques.

Useful.

12 Algorithms Every Data Scientist Should Know via @Datafloq

12 Algorithms Every Data Scientist Should Know by Mark van Rijmenam via +Datafloq - Algorithms have become part of our daily lives and they can be found in almost any aspect of business. Gartner calls this the algorithmic business and it is changing the way we (should) run and manage our organizations. There are all kinds of algorithms and for each aspect of your business, there are different algorithms. Big Data scientists should be able to deal with a wide variety of algorithms to create this algorithmic business, but what are the 12 algorithms that should be in the repertoire of every big data scientist?

Great list and handy to print out and keep to remind you which ones to use and when.

Top 19 Free Apache Hadoop Distributions, Hadoop Appliance and Hadoop Managed Services via @PredAnalytics

Top 19 Free Apache Hadoop Distributions, Hadoop Appliance and Hadoop Managed Services via @PredAnalytics - Apache Hadoop project develops open source software for reliable, scalable, distributed computing. Apache Hadoop is an open source software for storing and analyzing massive amounts of structured and unstructured data terabytes and Hadoop can process big, messy data sets for insights and answers. Top Free Apache Hadoop Distributions provides enterprise ready free Apache Hadoop Distributions.

Great list.

10 Most Frequently Asked Data Science Questions Answered via @learnunbound

10 Most Frequently Asked Data Science Questions Answered by Preetham Varma via +LearnUnbound - Amazing as it may sound, the term Data Science the way it is understood today does not have a very long history. In fact the term “data scientist" became a bona fide job title not so long ago in 2008.

Interesting questions and answers. Contains to relevant articles in the answers.

Top 5 misconceptions about Big Data via @HPE

Top 5 misconceptions about Big Data by Walter Maguire via @HPE - Having been working with big data since the early days, Walter has had the privilege of watching many organizations work through the opportunities, challenges, and changes it has driven and has observed these misconceptions.

SLIDESHOW: 5 Steps for Securing Your Data In Hadoop via @infomgmt

5 Steps for Securing Your Data In Hadoop by Reiner Kappenberger via +Information Management - Data security remains a top concern for data professionals. To help organizations put up a best defense, Reiner Kappenberger, senior executive focused on big data and Hadoop at HPE Seecurity-Data Security offers five steps on how to best secure data in the Hadoop

Good advice.

Tool to proofread your data via @dataproofer

Tool to proofread your data via @dataproofer - "Before you can make use of any data, you need to know if it’s reliable. Is it weird? Is it clean? Can I use it to write or make a viz?" Dataproofer is open source software designed to automate this process of checking a dataset for errors.

Not used it myself but may be worth a play.

Approach Big Data Analytics Like A Lego Kit via @Datafloq

Approach Big Data Analytics Like A Lego Kit by Bill Franks via +Datafloq - In the era of big data, we have ever larger data sets, increasingly complex analytic requirements, and constantly increasing demand for new analytic processes. Without applying discipline and focusing on increased reusability and adoption, organizations won’t be able to reach the scale required for their analytics. If you want to successfully adopt big data within your company, you should start approaching big data like the Lego kit, since Lego give us some interesting insights.

This makes sense just like using reuseable components in code for repeatable actions.

How Credit Card Companies Use Big Data to Improve Industries via @Datafloq

How Credit Card Companies Use Big Data to Improve Industries by Nate Vickery via +Datafloq - Credit card companies are among the most data-savvy organizations in the world and they have been ever since their birth. The reasons for this are very simple and arguably the biggest one of these is the fact that credit card companies operate with enormous numbers of both credit card holders and merchants. In order for this machine to keep running the way it should and for it to keep making money for the credit card companies, it needs to rely on pure data.

I can see the credit card industry as a great home for big data due to the quantities involved.

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.

Practical Guide to Principal Component Analysis (PCA) in R & Python via @AnalyticsVidhya

Practical Guide to Principal Component Analysis (PCA) in R & Python by Manish Saraswat via +Analytics Vidhya - Do you get stuck when a dataset has too many variables? Then you must work your hands on PCA to overcome these. Here's a simplified practical guide on this, with implementation in R & Python.

Great guide and worth reading as a reminder.

WEBINAR: Bring the Benefits of Cloud Managed Services to Your Oracle Applications - 12 May 2016

Webinar Event Details

Date: May 12, 2016
Time: Noon ET/ 9:00 am PT
Duration: 60 minutes (including Q&A)

What You'll Learn The value of cloud for many data-intensive applications is clear. But what about running Oracle workloads on cloud? Is it safe to move mission-critical business applications like Oracle E-Business Suite, PeopleSoft, JD Edwards, Oracle Retail, Hyperion, Flexcube, etc. to the cloud? How long will it take you to prepare Oracle instances for development, test, or production? What about Oracle Database, Middleware, and Application license implications? Join Jonathan Miller, Global Oracle Cloud Leader at IBM, and Lucas Wager, IBM Cloud Sales Lead in the UK and Ireland, to learn why and how CxOs and LOB users are reaping benefits of a managed cloud by: • Migrating existing Oracle infrastructure to the Cloud at low risk, low cost, and in short time • Reducing costs by using a managed cloud infrastructure and experienced Oracle staff • Realizing faster service delivery and time to value for Oracle-based business processes • Improving resiliency with high-availability and disaster-recovery options • Leveraging global fiber-optic communications network for Cloud to deliver low-cost, low risk, and timely ROI options to global expansion.

Presenters

Jonathan Miller, IBM Cloud, Global Oracle Leader, Business Line Management worked for Oracle Corporation in Consulting, Global Account Management, Software Development, and as the Vice President of Embedded Technologies and Internet of Things. Moving to IBM, he pioneered Cloud-Based SaaS solutions and now a leader of worldwide sales in Cloud Enterprise Applications.

Lucas Wager, IBM Cloud Sales Lead, is an experienced IT sales leader with over 22 years experience in the IT Marketplace. For the past 2 years Lucas has led the Cloud Services Sales Division in IBM, driving multiple deals across different offerings areas including enterprise applications such as Oracle.

Register here

Comprehensive Guide to Learning Python for Data Analysis and Data Science via @KDnuggets

Comprehensive Guide to Learning Python for Data Analysis and Data Science by Martijn Theuwissen via +KDnuggets  - Want to make a career change to Data Science using python? Well learning anything on your own can be a challenge & a little guidance could be a great help, that is exactly what this article will provide you with.

Useful guidance and advice.

When Does Deep Learning Work Better Than SVMs or Random Forests? via @KDnuggets

When Does Deep Learning Work Better Than SVMs or Random Forests? by Sebastian Raschka via +KDnuggets - Some advice on when a deep neural network may or may not outperform Support Vector Machines or Random Forests.

Useful advice.

WEBINAR: Get To Business Insights Quicker With In-Database Blending 10 May 2016

Overview

Title: Get To Business Insights Quicker With In-Database Blending

Date: Tuesday, May 10, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Get To Business Insights Quicker With In-Database Blending  

Analysts in Sales, Marketing, Finance and Operations have to prepare and analyze data quickly to answer important business questions. Time spent blending data takes away from time spent analyzing and acting on it. Analysts need an easier way to work with data in a database in order to quickly go from data blending to producing insights.

Join our webinar and see how to:

• Prepare your data in 1/10th of the time, and automate the process for ongoing updates
• Utilize a drag-and-drop interface that does not require SQL coding to prepare, blend, and analyze data 
• Push the processing steps into the database rather than pulling the entire dataset to the processing location

See how you can leverage the power of in-database blending to get to business insights quicker.

Speakers:
Gene Rinas, Senior Solutions Engineer, -- Alteryx 
Dan Ganancial, Alliance Manager -- Alteryx 
Ron Ortloff, Senior Program Manager -- Microsoft

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

 
Register here

10 new exciting features in Apache Hive 2.0.0 via BigDataMadeSimple

10 new exciting features in Apache Hive 2.0.0 by Kumar Chinnakali via BigDataMadeSimple - We should be excited that Apache Hive community have released the largest release and announced the availability of Apache Hive 2.0.0. It brings great and exciting improvements in the category of new functionality, Performance, Optimizations, Security, and Usability.

9 must watch big data technologies along with Hadoop! via BigDataMadeSimple

9 must watch big data technologies along with Hadoop! by Kumar Chinnakali via BigDataMadeSimple - Today emerging big data technology firm focused on helping enterprises build breakthrough software solutions powered by disruptive enterprise software trends like Machine learning and data science, Cyber-security, Enterprise IOT, and Cloud.

Good list of what to watch.

How to Build a Big Data and Analytics Team by @BernardMarr via @Data_Informed

How to Build a Big Data and Analytics Team by +Bernard Marr via @Data_Informed - Where does a business start to build a big data and analytics team? Here are some criteria to consider.

Great summary of roles needed by Bernard.

Data governance is nothing without data quality via @SASsoftware

Data governance is nothing without data quality by Dylan Jones via +SAS Software - Great blog pointing out that one without the other is a waste of time - you need both to make any result useable.

How To Become A Machine Learning Expert In One Simple Step via @kdnuggets

How To Become A Machine Learning Expert In One Simple Step by Daniel Thomas via +KDnuggets - This post looks at perhaps the most important, and often overlooked, step in learning machine learning, an aspect which can make the biggest difference in one’s skill set.

He's right - you have to practice, practice, practice and Kaggle is a great source of data and competitions.  Good luck passing the entrance tests  ;-)

How to Grow Your Own Data Scientists via @kdnuggets

How to Grow Your Own Data Scientists by Amy Gershkoff via +KDnuggets - How Zynga is “home growing” its own data science talent from the inside, by retraining some of our top analysts and engineers to become data scientists.

Sounds obvious but I think very few are are committed to doing that as Zynga.

Big Data Discovery: Five Easy Steps to Value via @OnTechNowBlog

Big Data Discovery: Five Easy Steps to Value by OnTechNowTeam via @OnTechNowBlog - “Big data” could really be called “big frustration.”

Eight predictions about the future of Big Data via @OnTechNowBlog

Eight predictions about the future of Big Data by OnTechNowTeam via @OnTechNowBlog - Real-time data, data from connected everything, actionable vs. big… where is all this big data heading, and what should marketers be aware of this year?

Interesting and makes you think.

Top 15 Frameworks for Machine Learning Experts via @KDNuggets

Top 15 Frameworks for Machine Learning Experts by Devuendra Desale via +KDnuggets - Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.

Useful list.

SLIDESHOW: Data Pay Days: The 18 Top Paying Big Data Certifications via @infomgmt

Data Pay Days: The 18 Top Paying Big Data Certifications by David Weldon via +Information Management - Data professionals continue to be among the most in-demand in the job market, but what they earn -- or you should be paying -- depends on industry experience, location, technical skills, and contribution to the bottom line. This week we look at how data professionals can earn top dollar, depending on job focus, certifications earned, and skill concentrations...

Tells you what you should be aiming for.