Manulife Continues Exploration of AI in Innovation Lab by Danni Santana via @infomgmt

Indico’s platform will enable the Canadian insurer to evaluate data from news articles and analyst reports and recommend investment decisions to portfolio managers.

A Recipe for Cooking with the Hadoop Ecosystem by David Menninger via @infomgmt

The open source model has had a major impact on the big data market, yet in some ways, the open source approach has succeeded despite its shortcomings.

Open source is definitely here to stay.

The Biggest Barrier for Companies That Want to Leverage Their Own Data by Simon Owens via @infomgmt

When leveraged effectively, the data that companies have collected can allow them to gain customer insights, increase efficiency, and reduce costs.

I agree - mindset is a major barrier that is not easy to overcome.

Personalizing travel search: How data leads to happier travels by @MargaretAdy via BigDataMadeSimple

An overdue evolution is happening in travel search, especially in the hotel sector. Doesn’t searching for a “hotel in downtown Miami” seem archaic when the travel industry has so much more information to work with?

Surely the more detailed information with the more dimensions the easier it is to tailor the search.

Machine learning becomes mainstream: how to increase your competitive advantage by @Ronald_vanLoon via BigDataMadeSimple

First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency.

Interesting and definitely right - machine learning is pretty much everywhere.

Facebook's New Open Source Software Can Learn 1 Billion Words in 10 Minutes by @wheresKR via @inc

FastText is the tool that powers Facebook's AI--and now it's available for anyone.

Sounds worth accessing and having a bit of a play.

Data Partitioning in Big Data Application with Apache Hive by Vijay Aegis via CodeInnovationsBlog

Big data consulting company professionals are introducing the concept of partitioning in big data application. You need to read the post completely to understand how to do partitioning in such app using Apache Hive. If you don’t know how to do it, experts will help.

Useful blog.

Blockchain’s Backers Gather to Push Governance for Technology by Olga Kharif and Peter Coy via @infomgmt

Now that companies in finance and other industries are looking to adopt blockchain -- essentially a new kind of a database for recording transactions -- more standards-setting organisation is needed.

Definitely a standard is needed especially because the finance industry is adopting it.

SLIDESHOW: The Top Companies for Metadata Management by David Weldon via @infomgmt

Gartner has released its Magic Quadrant for Metadata Management Solutions. The report looks at nine players in the metadata product space, including leaders, visionaries, challengers, and niche players. Here’s a look at who made the quadrant, and why.

Interesting to see who they think are the visionaries and challengers.

Man Who Introduced Millions to Bitcoin Says Blockchain Is a Bust by Matthew Leising via @infomgmt

Industries from finance to healthcare to utilities are working with blockchains to radically change how payments are tracked, securities and derivatives trades are processed, and health records are stored,

Very interesting considering how many people and implementing it.

How Campaigns and Companies Use Data to Win the Race by Amir Orad via @Data_Informed

Sisense CEO Amir Orad discusses how political campaigns are leveraging data analytics to target individual voters and guide their advertising spend, how campaigns’ data challenges mirror those of enterprises, and how the analytics efforts of current candidates compare.

Definitely food for thought.

Data without Context is No Data At All by Rajan Chandras via @Data_Informed

Reference data provides meaning to data and the information that enables data integration, analytics, and governance, as well as applications such as machine learning and natural language processing. But the market lacks a focus on skills and solutions for governing this data.

I agree - if there is no context data is just data.

WEBINAR: Combining IBM SPSS Statistics and R for competitive advantage - 1 September 2016

Overview

Title: Combining IBM SPSS Statistics and R for competitive advantage

Date: Thursday, September 01, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Combining IBM SPSS Statistics and R for competitive advantage

In today’s world, the data is flowing from all directions: social media, phones, weather, location and sensor equipped devices, and more. Competing in this digital age requires the ability to analyse all of this data, and use it to drive decisions that mitigate risk, increase customer satisfaction and grow revenue.  Using a combination of proprietary software and open source technology can give your data scientists and statisticians the analytical power they need to find and act on insights quickly.

IBM® SPSS® Statistics provides all of the data analysis tools you need, and integrates with thousands of R extensions for maximum power and flexibility.  In this next Data Science Central Webinar event, we will show how SPSS Statistics can help you keep up with the influx of new data and make faster, better business decisions without coding. 

Speakers:
Murali Prakash, Portfolio Manager -- IBM Analytics 
Alex Oftelie, Subject Matter Expert -- IBM Analytics

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

Cloud computing as a utility is going mainstream by Bob O'Donnell via @Recode

The idea is to leverage power, storage space and fast network connection pipes to deliver computing much like power or electricity.

Interesting vision of the future.

A CIO writes (sort of): I’m terrified of cloud lock-in - what should I do? by Mat Keep via @cloud_comp_news

I’m in a three-year relationship which I don’t think I can get out of. It started off great, but now I’m getting bled dry and I don’t have the freedom I thought I would have. What should I do?

I'm sure this is an increasing problem unless we are careful.

Understanding Bias: A Pre-requisite For Trustworthy Results by @akelleh via Medium

Algorithms are never neutral. They routinely reflect (often unintentionally) the perceptions of their creators. In this post, Adam Kelleher explores what causes bias and how to correct it.

I really enjoyed reading this and it is worth reading and remembering.

WEBINAR: Modern infrastructure with NoSQL and containers - 31 August 2016

 

Modern infrastructure with NoSQL and containers

DATE: Wednesday, August 31, 2016

TIME: 1:00PM EDT | 10:00AM PDT

The best way to deploy and scale a NoSQL database is with elastic infrastructure, with containers being one of the most efficient platforms – deployed on-premise or in the cloud. In this webinar, we’ll discuss how development and operations teams can benefit from running NoSQL databases on containers, as well as the options for doing so.

We will discuss how running NoSQL on containers:

• Improves business agility
• Increases developer productivity
• Simplifies deployment and scaling

And, we will highlight options for running a NoSQL database with:

• Docker Swarm, Docker Machine, and Docker Compose
• Docker on Kubernetes or Apache Mesos (DC/OS)
• Docker on AWS, Azure, Google Cloud

FEATURED SPEAKER:

Shane K Johnson

Manager of Product Marketing, Couchbase

Register here

Bring Your Dark Data into the Light by Mika Javanainen via @Data_Informed

These days, people are accustomed to easily finding the information they need. They simply type keywords into a search engine and access material from any corner of the digital universe.

Interesting.

How Artificial Intelligence Can Improve Automated Customer Care by Tara Kelly via @Data_Informed

There are notable exceptions but, in general, people are dissatisfied with the level of customer service they receive today. The American Customer Satisfaction Index shows that customer-satisfaction rates have been trending downward for eight consecutive quarters.

Anything that improves customer satisfaction is worth trying.

Data Science for Beginners: Fantastic Introductory Video Series from Microsoft by Brandon Rohrer via @kdnuggets

A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.

Part one

Part two

Parts 3,4,5

Recent breakthroughs in deep learning by Yann LeCun via @Quora

Yann LeCun, director of AI research at Facebook, recently had an interesting Q&A session on Quora. (Spoiler alert, AI is not an existential threat to humanity, unless we are very stupid.)

Improving operations using data analytics by Parviz Deyhim and Arti Garg via @OReillyMedia

How combining data and applying time-series techniques can provide insights into a company’s operational strengths and weaknesses.

Great article with good examples to help with understanding.  Much easier as they describe - I remember doing this exercise manually in Excel.

How to become a data driven business by @DQMartinDoyle via BigDataMadeSimple

A data driven business utilises data to inform every business decision they make. By analysing relevant data and evaluating it they are able to form a conclusion and predict trends. Data-driven businesses ensure their company culture evolves to encourage innovation and agility.

Interesting.  I think it is very difficult to become a data driven business as there are all those legacy systems with data data all over the place/

Why Data and Analytics Aren’t Enough to Change Healthcare by Rich Krueger via @infomgmt

Now that hospitals have updated to their first and even second generation electronic health records, the data exists for a similar revolution in healthcare administration. All that remains is the will to change.

I could do some analytics on almost anything, but that doesn't mean to say it has a point or is useful.

Understanding the Empirical Law of Large Numbers and the Gambler’s Fallacy by Mehmet Suzen via @kdnuggets

Law of large numbers is a important concept for practising data scientists. In this post, The empirical law of large numbers is demonstrated via simple simulation approach using the Bernoulli process.

Nice explanation

WEBINAR: Enterprise Security: Deploying Defense-in-Depth - 25 August 2016

Complimentary Web Seminar
August 25, 2016
2 PM ET/11 AM PT
Hosted by Information Management

As enterprises become more dependent on mobile workers, the cloud and the Internet-of-Things, they need new tools and techniques to make sure their information resources are secure.

One approach is to implement a defense-in-depth plan, one that covers:

perimeter security protection
network access and activity monitoring
threat identification and isolation
vulnerability management and remediation
and more.
Featured Presenters:

Moderator:
David Weldon
Editor-in-Chief
Information Management


Sponsored By:


Register here

3 Big Data Housekeeping Measures You Can No Longer Overlook by Dan Potter via @Data_Informed

As big data analytics matures, measures once considered a speed bump to analysts' progress must be taken to ensure the success of big data initiatives, writes Dan Potter of Datawatch.

These things SHOULD be important no matter what.

Five data science projects to learn data science by Kunal Jain via @AnalyticsVidhya

Tune up your system, refresh your skills - here we're providing you with five datasets, available for free over internet, to help you enhance your skills, improve on strategies and working on new techniques. Start practising here:

No one has an excuse not to use these.

WEBINAR: From Disparate Data to Tell-all Dashboards at Talkdesk -24 August 2016

Webinar Event Details

Date: August 24, 2016
Time: Noon ET/ 9:00 am PT
Duration: 60 minutes (including Q&A)

What You'll Learn Inefficient data pulls to update stale Excel spreadsheets. Manual SQL queries against your production database. Data locked away in apps with unusable analytics interfaces. When you don’t have a solution for data, you fall back on slow, suboptimal tactics for accessing data, denying business users and decision makers access to the most accurate, up to date, and relevant information. Attend this webinar August 24 to hear how Talkdesk went from a chaotic world like the one above to being a company where people can easily explore data and quickly get a 360° view of every account. Using FiveTran and Looker, Talkdesk’s product manager was able to tie all their now-centralized data together to create a powerful and stable environment for data discovery. In this webinar, you will learn: • What a centralized data store can do for you and how you can achieve that • Why you should be transforming your data at the time of query, not before you load it into your database • How one man created a company-wide data platform in one month Join us to understand how to take control of your data and use it to drive results.

Presenters Taylor Brown is one of the Cofounders at Fivetran where he oversees marketing, sales, and partnerships. He strives to help companies access and analyze their data, while also driving to create a data driven culture at Fivetran. When he is not focused on helping customers or optimizing marketing efforts, he can be found playing soccer, or cycling around Marin. Ernest Wong is a Product Manager by day and a Data Nerd by night - but sometimes he loses track of what time it is. When he's not prioritizing a roadmap or crunching some data, he is probably getting eaten alive by mosquitos on a hike, dining somewhere woefully overpriced, or sleeping in like a boss. Nouras Haddad is the Director of Technology Alliances at Looker. He helps Looker offer complete analytics solutions to our customers by partnering with best of breed technologies in the big data ecosystem. Originally from Croatia, Nouras received his B.A. in Economics from the Zagreb School of Economics and Management and MBA from The Wharton School at the University of Pennsylvania.

Register here

WEBINAR: The Inside Scoop on Apache Sqoop - 25 August 2016

Apache Sqoop is the standard tool for loading and exporting data between Hadoop and traditional data stores such as relational databases or SaaS applications, through a standard JDBC interface. Sqoop serves as the data access layer for the Hadoop ecosystem to connect external structured data. What could go wrong connecting a massively distributed data processing system with your core business data?

Join our JDBC experts, Idaliz Baez from Progress and Alex Silva from Pluralsight, to learn about the best practices for using Sqoop and interoperability with JDBC data sources from relational to cloud.

Title: The Inside Scoop on Apache Sqoop
Date: August 25, 2016
Time: 11:00 am ET

Speakers:
Idaliz Baez, Progress DataDirect
Alex Silva, Pluralsight

During this webinar, you’ll:

• Get an introduction to Apache Sqoop
• Learn which data sources are accessible via JDBC for Sqoop
• Receive the latest best practices and lessons from the field

Get in and get the Sqoop!

Register here

Design Better Data Tables by @ilikescience via Medium

We’ve all seen poor visual design of tables: left-aligned numbers? Tons of useless formatting? There’s a lot that goes into making tabular data easy to consume, and with all the attention that goes into data viz today, the UI of tabular data often gets overlooked.

Really nice post which points out many things which should be obvious but are not.

Asymmetric Information Is Economists' Little Secret by Noah Smith via @infomgmt

When today we debate issues like financial regulation or high frequency trading, it helps to think about financial markets as being driven by differences in how much people know.

Interesting.

The Emerging Data Design: Bitemporal Data by Mike Lapenna via @infomgmt

With Microsoft joining the club, we now have Oracle, IBM (DB2), Teradata and Microsoft supporting some portion or all of the bitemporal design.

Good to see it become more mainstream - I've been adding the field to support this in data warehouses for years, but to use it properly is clunky so more support for this has got to be a good thing.

SLIDESHOW: 15 Top Paying US Job Markets For Data Scientists by David Weldon via @infomgmt

Where are the top paying job markets for data scientists? That depends on the cost to live in a particular city or region. Job site Indeed has just released its report “Where are the Highest Paying Tech Jobs in the US?” which looks at best salary offers adjusted for the cost of living. Here are the top 15.

WEBINAR: Supersize Your Career with Big Data - 24 August 2016

Supersize Your Career with Big Data 

August 24, 2016 | 10-11 am PT



You’ve read the headlines and seen the quotes: “Big Data is Creating Big Career Opportunities” (CNBC), “Big data is becoming an effective basis of competition in pretty much every industry” (Michael Chui, McKinsey Global Institute), “It’s super hard to find the right talent” (Sherry Shah, LinkedIn head of data recruiting). Now learn how you can expand your technical skills and horizons by taking advantage of the online data science courses, certificates, and degrees offered via the Stanford Center for Professional Development. In this webinar Dr. Larry Lagerstrom will outline the variety of big data programs offered by Stanford to working professionals and answer common questions. Registration for Autumn classes is open, so don’t miss out on this opportunity to find the program that’s right for you. Explore more topics with Stanford faculty on how vast amounts of data can be harnessed to make a positive impact on business and society.

About the Speaker

 Larry Lagerstrom is the Acting Director of Academic Programs for the Stanford Center for Professional Development, where he oversees eleven Master’s degree and twenty-eight graduate certificate programs in engineering and related fields. He also conducts research applying data science methods to the study of online learning and education. Before coming to Stanford he taught computer science and engineering at U.C. Berkeley and U.C. Davis for sixteen years. His degrees include a Ph.D. in history of science and an M.A. in physics from U.C. Berkeley.



Presented By
Online Stanford Data Science Programs certificate programs

Questions?
Please contact us at scpd-customerservice@stanford.edu or 650-204-3984

Register here

How Does Big Data Analytics Help in Decision Making by @farooqSL via @Datafloq

Staying ahead in the game is paramount for any business organisation to survive in this competitive world. The future poses challenges that need tackling in the present. Every decision made today has a significant impact on the future of that organisation. The rate at which a company responds to challenges in the present and the future is what determines their rate of success. Data Science and Big Data analytics can help organisations in decision making and drive the company to a realistic future.​

It seems to say on top these days you HAVE to use analytics.

With Prevention Efforts Failing, IT Security Focus Turns to Reaction by David Weldon via @infomgmt

“Organisations are increasingly focusing on detection and response, because taking a prevention approach has not been successful in blocking malicious attacks,” noted analyst Elizabeth Kim.

I can understand that approach - if you can't beat them one way, try to take another approach and beat them another way.

Economics Without Math Is Trendy But Doesn't Add Up by Noah Smith via @infomgmt

The redefinition of the foundations of economics that is currently being done by economists who do not not agree with established beliefs or standards will inevitably result in many of the models beloved of academic economists becoming obsolete.

I don't see this as a bad thing - just progress - I'm sure what we see as established were new and established once.

Growing Acceptance Will Drive Advanced Analytics Market Growth By 22% by Bob Violino via @infomgmt

The growth of the market is due in large part to the increased acceptance of data analytics, which helps eliminate the work involved in understanding customers and data tracking processes.

I think analytics are now seen as a must have tool in business.

Machine Learning as a Service: How Data Science Is Hitting the Masses by @ledambrosio via @HuffPostTech

The world of machine learning and predictive analytics is opening up to developers and companies of all sizes, with machine learning (ML) providers offering their products through a subscription-based model or open sourcing some of their technology.

Great blog outlining the concept, availability and players.

More Companies Turn to Machine Learning to Leverage Analytics by Bob Violino via @infomgmt

ABI Research estimates machine learning-based data analytics tools and services revenues to hit nearly $20 billion in 2021 as machine-learning-as-a-service (MlaaS) models take off.

I can definitely see everything going that way.

Top Data Analytics Tools to Unleash the Potential of Big Data by R Bhargav via @simplilearn

There is a data revolution going on around the world and data analytics is the shiny new thing in the job market that has been alluring professionals. If you thought it’s just hyperbole, check out these statistics:

Germany enlists machine learning to boost renewables revolution by Quirin Schiermeier via @nature

Germany is in the top three nations for both wind and solar power, with renewables already providing a third of its power needs. But weather is erratic, and that's a challenge when trying to operate an efficient grid. In June, meteorologists, engineers, and utility firms began to test whether big data and machine learning can make these power sources more grid friendly.

Interesting use of machine learning.

Statistical Data Analysis in Python by Christopher Fonnesbeck via @kdnuggets

This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.

Useful but it does contain links to courses that don't exist.

Bayesian Machine Learning, Explained by Zygmunt Zając via @kdnuggets

Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.

Interesting.

Data and electric power by Sean Murphy via @oreillymedia

Utilities face a lot of interesting challenges that most people are completely unaware of. This report from O'Reilly explores how data science and cutting-edge tools are used to manage some of the most interesting of those challenges.

Interesting report.

SLIDESHOW: 4 Key IT Sectors To See Major ‘Cloud Shift’ via @infomgmt

More than $111 billion in IT spending has shifted to the cloud and that amount will increase to $216 billion in four years, according to just-released report from Gartner.

Interesting.

WEBINAR: Data Analysis to Predict Voter Turnout and Outcome - 16 August 2016

Overview

Title: Data Analysis to Predict Voter Turnout and Outcome

Date: Tuesday, August 16, 2016

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Data Analysis to Predict Voter Turnout and Outcome 

It goes without saying, we live in a very data-rich age.  In the political arena, sophisticated analytic firms like Deep Root manage and analyze an ever-growing list of data sources to project voter turnout and predict vote choice.  To do this, they must first access and acquire the data, and then build complex data blending and analysis workflows to turn a variety of unlinked data sources into a single, actionable database of information. Only then can they decide which voters to speak with, with what message and through which media.

Join us for our latest Data Science Central Webinar and learn how Alteryx, Amazon Web Services, and Deep Root Analytics work together to leverage numerous data sources to quickly deliver critical insights. 

You will learn how to:

• Quickly blend and analyse data from all sources - cloud and local 
• Apply predictive and geo-spatial analytics to big data
• Enable data analysts with the cloud computing power of Amazon Web Services 
• Empower the organisation at large with analytic visualisations from Tableau

Speakers:
Raman Kaler, Alliance Manager -- Alteryx 
Nick Tussing, Solutions Engineer -- Alteryx
Danielle Mendheim, Database Analyst -- Deep Root Analytics
Moselle Freitas, Sr. Partner Manager – Big Data ISV Segment --  Amazon Web Services
 

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

8 Things Your Company Needs to Know About Cyber Security by @AndrewDeen14 via @Datafloq

Understanding cyber-crime is an essential part of protecting valuable data, particularly when this data involves important business or organisational affairs. Cyber hackers have a variety of motives for hacking networks and most often cyber hackers seek financial gain through bribes, identity fraud or credit card information. These attacks cost firms dearly as cyber criminals take sensitive information and sell in the deep web where crime is primarily hidden. Therefore, here are 8 things to know about cyber security.

Interesting points.

R code for model-free, data-driven confidence intervals by Claudio Lucio do Val Lopes posted by Vincent Granville via @Analyticbridge

Great piece of code which I think many R users would find useful if they don't already have their own snippet.

Big Data Helps Courage Kenny Connect with Patients by Greg Gillespie via @infomgmt

The rehabilitation institute analyses data from multiple sources to better identify patients who may need help.

Interesting.

Apache Spark: The Future of Big Data Science? by Matthew Thomson via @infomgmt

Spark is different from the myriad other solutions because it allows data scientists to develop simple code to perform distributed computing.

Yes this is definitely more flexible so is more efficient.

Data Governance Interview - Nick Keen by Nicola Askham via @infomgmt

The DG lead at the Environment Agency talks about how the organisation looks after its data.

I find this really interesting.

What the Privacy Shield Approval Really Means by Patrick Salyer via @Data_Informed

Compliance certification began Monday for Privacy Shield, the new framework for transferring the personal information of European Union citizens from the EU to the United States. Gigya CEO Patrick Salyer discusses what companies need to know, including the costs of noncompliance and who will be watching.

7 Steps to Understanding NoSQL Databases by Matthew Mayo via @kdnuggets

Are you a newcomer to NoSQL, interested in gaining a real understanding of the technologies and architectures it includes? This post is for you.

This is incredibly useful and a great overview.  Recommended.

Data Science Statistics 101 by Jean-Nicholas Hould via @kdnuggets

Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has travelled the path.

Good advice.  I did a few courses with Coursera to try to plug the gaps with my own knowledge.

Why Big data Fuels Significant Change in the Real Estate Market by @davidglenn97 via @Datafloq

Big data fuels significant change in the real estate market. Multiple sites offer detailed real estate data to make informed decisions. Gone are the days when real estate brokers and agents had proprietary access to information the average buyer or seller could not easily find. Depending on your role within the real estate industry, there are definitely pros and cons associated with the growth of big data.​

I can definitely see how big data can hep real estate just like it can help a bank or insurance company.

How to Build the Internal Reputation of Your Insight Team by @LaughlinPaul via @Datafloq

Why do some insight teams have a better internal reputation than others? Did some leaders just get lucky, with a great culture & receptive directors?​ If so, many did not get so lucky. Lack of internal influence, being neglected or treated as just a service function are common concerns raised with insight leaders.​ Do you know how to build awareness or manage PR for your ‘insight brand’?​

Useful 6 points to help make this a success.

Why The Internet of Things is Getting Real Now by @ricknotdelgado via @Datafloq

Few things have gotten as much hype as the Internet of Things (IoT). Some say it will be the biggest technological revolution since the rise of the internet itself. To be honest, the predictions aren’t that far off. If the IoT manages to live up to expectations, the impact it will have dwarfs anything that’s come before.​ But what would the Internet of Things mean for the individual?

They seem to be appearing everywhere. I have no issue with something that keeps track of my exercise but I'm a little uncomfortable with the concept of my fridge being accessible over the internet.

Why Uber Engineering Switched from Postgres to MySQL by Evan Klitzke via @UberEng

Uber Engineering explains the technical reasoning behind its switch in database technologies, from Postgres to MySQL.

I loved this explanation and the level of detail behind it.

6 ‘data’ buzzwords you need to understand by Katherine Noyes via @NetworkWorld

Take one major trend spanning the business and technology worlds, add countless vendors and consultants hoping to cash in, and what do you get? A whole lot of buzzwords with unclear definitions. In the world of big data, the surrounding hype has spawned a brand-new lingo.

Not heard of all of these myself.

4 Ways to Shrink the Gap between Data Integration and Insight by Yaniv Mor via @Data_Informed

According to a study conducted last year by my company, Xplenty, nearly one-third of business intelligence professionals say they spend between 50 and 90 percent of their time just cleaning raw data for analytics. As a result of valuable time and talent devoted to preparing data, businesses are often slow to unlock its insights or to act on them.

Good to be reminded of these.

Google Buys Machine Learning Startup Moodstocks by @jessicadavis via @InformationWeek

Google has acquired Paris-based machine learning startup Moodstocks as part of its ongoing effort to improve visual recognition technology.

I know this is one of the harder ML elements to crack so maybe they can get further working together.

A New Take on Data Discovery, Data Management, and its Relationships by Jennifer Zaino via @Dataversity

Having herself held senior roles in IT at Wall Street companies including Deutsche Bank and Morgan Stanley Smith Barney, Oksana Sokolovsky is quite familiar with the challenge of Data Management and data discovery. As co-founder and CEO of ROKITT, her goal was “to build a product that solves that challenge,” she says.

I found this really interesting.

M&A in the Predictive Marketing Space: eBay Acquires SalesPredict by Allison Snow via @infomgmt

This extension would likely offer the right items to the right individuals at the right price – brought to the attention of eBay shoppers by predictive-enabled, contextual and customised recommendations.

This is definitely the way to go to increase sales.

The Making of a Data Scientist by Sarah Lukens via @infomgmt

I was lucky enough to find my calling in numerical analytics and scientific computing, but how can we inspire an entire generation to track along career paths which emphasise quantitative reasoning.

Interesting and worth reading.

WEBINAR: A Pragmatic Approach to Processing and Refining Big Data - 3 August 2016

A Pragmatic Approach to

Processing and Refining Big Data Wednesday, August 3, 2016 | 8 am PT/16:00 BST

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8:00 am PT/16 BSTOn-demand

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Want to learn how to approach your Hadoop data processing and analytics projects without sacrificing governance and control?
In a big data world, business users need on-demand access to governed data sets on highly diverse sources, regardless of scale.  By focusing on the right principles from both existing data warehousing approaches and emerging data lake use patterns, it is possible to drive automatic processing, refinement, and publishing of Hadoop data sets for immediate interactive analysis
Join this webinar to learn how:

• Enterprise data warehouse and data lake design patterns fit today's analytic landscape
• Organizations can approach Hadoop data processing and analytics without sacrificing governance and control 
• Pentaho provides an approach to delivering refined on-demand data marts to end users in a big data environment
• Pentaho customer FINRA was able to leverage Pentaho's big data capabilities to rapidly accelerate fraud detection 

Speaker: Ben Hopkins, Sr. Product Marketing Manager - Big Data, Pentaho

Register here