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Planet Big Data is an aggregator of blogs about big data, Hadoop, and related topics. We include posts by bloggers worldwide. Email us to have your blog included.

 

April 24, 2018


Simplified Analytics

How HR Analytics play in Digital Age

Today every company is acting on the digital transformation or at least talking about digital transformation. While it is important to drive it by analyzing customer behavior, it is extremely...

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Ronald van Loon

What Does GDPR Mean For Your Business?

The European General Data Protection Regulations (GDPR) will come into force on May 25, 2018. These regulations will have a significant impact on existing data collection and analysis methods.

Many businesses have become reliant on customer data collection for marketing and product designing. These businesses would need to formulate a new strategy on how to keep their business operations going while dealing with the EU regulations.

The GDPR Regulations

The main objective of GDPR is to ensure that organizations implement strict privacy rules and stronger data security when it comes to protecting personal data. The regulations will make it mandatory to obtain consent from users before acquiring or using their personal data.

Organizations will also be required to inform their customers and users about the personal data that they are collecting and using. Data subjects will have the complete right to withdraw their consent at any time, and organizations will be required to delete the record where consent has been withdrawn.

Noncompliance with the regulations will result in hefty penalties. A company can be fined up to €20 million or 4% of its annual global turnover in extreme cases.

The Complexity of Acquired Data

Data acquired by businesses through the normal channels is usually in a complex form, and the process is completely automated. This presents two major problems for organizations.

Locating Customer Records

In theory, business organizations can become compliant with the new regulations by letting their customers know about their information that is being held by the company. Any data that customers want removed could be deleted.

In reality, the problem is that a majority of businesses may not even be aware that they are holding customer data or how to track it. Many would find it difficult to locate the exact customer information in their massive database or even in their paper files.

Problems in Data Processing

Businesses often rely on built-in models that extract relevant data fields from incoming customer information. Managing these processes will be a challenge for organizations looking to become compliant with the new regulations.

An organization would need customer consent to acquire and use their information. While some customers might be willing to share one set of information, others might be willing to share a different set. A third group might refuse to give consent at all.

This would make the data inconsistent. Any attempts to derive meaningful results or market trends would be similarly useless.

Solutions Available to Organizations

In order to stay compliant with the new legislation, business organizations would need to apply new techniques for collecting, storing, and processing of data. Some of the steps that should be taken by businesses are the following.

  • Inform clients and obtain consent prior to acquiring any personal data.
  • Update the company’s existing or new databases with procedures that allow access, transfer, and deletion of specific client details.
  • Properly document the company policy on collection and processing of client data and have it communicated to clients.
  • Store and process all personal data in a manner which complies with GDPR guidelines.
  • Implement security measures that protect the database from breaches.
  • Continuously monitor and manage the data to ensure that GDPR standards are being met.

The new regulations will come into effect next month, and there is not much time left for businesses to update their systems. The sooner they get started on their data collecting techniques, the better.

Protecting Client Data

The new regulations have two main components to them. The first is about obtaining customer consent for data acquisition. The second relates to ensuring that the acquired data remains protected and secure.

Last year, the U.S. credit rating agency Equifax was hacked. Reports suggest that private and sensitive details of more than 143 million users were stolen by hackers. And everybody has heard about the Facebook Cambridge Analytica data breach that affected 87 million users .

Data breaches like these can severely shake the trust of users on private and public organizations. In the example of Facebook, a large number of users closed their accounts and Facebook lost $ 50 billion in stock value. This is why the EU has made organizations liable for the security of data that they collect.Adding security to the data can be achieved in two ways; Data minimization and use of pseudonyms.

Data minimization reduces the database by only retaining the information that is absolutely necessary for processing. Using pseudonyms involves translating data into numbers and unidentifiable strings through encryption. Both the methods add increase security to the database and reduce risk to the business and their clients.

Upgrading the Technical Infrastructure

The new technical infrastructure for organizations would need to be compliant with the regulations. Businesses would need to let their customers decide what information is shared and stored by companies.

A comprehensive data governance solution would let an organization quickly sort through its records and delete customer information for which consent has been withdrawn.

It would also allow businesses to review their current processes of data collection and processing. Updating to a unified governance model would also make it easier to create documentation on personal data used by the organization. A company would need to share this document with customers to stay compliant with the new regulations.

Benefits of a Unified Governance Model

A unified data governance model allows businesses to achieve better insights about their customers while staying compliant with the new regulations. Without applying a holistic approach, a business can become susceptible to oversight on regulatory compliance as well as data breaches.

Innovations are being led by unified data governance solutions. These techniques enable an organization to retrieve information about data objects, their physical location, characteristics, and usage. The technology is expected to help improve IT productivity while meeting regulatory requirements.

Bob Nieme, the CEO of Datastreams, has more than a decade of experience in data collection and frameworks. He is very optimistic about the new approach of governed access to data sources. He believes that companies would gain three benefits from a unified governance approach.

  • It will help organizations comply with the new GDPR regulations and avoid penalties.
  • Obtaining customer consent will improve their trust and willingness to share their personal data.
  • Data governance would also reduce risks and improve security.

Planning for the Future in a GDPR Environment

While some organizations have taken steps to adapt to the changes, most businesses are not prepared for the May 25th deadline when GDPR goes into effect. Many of them are either not aware of the effects the changes will have or simply don’t know what to do about them.

In order to avoid fines and a troublesome litigation process in court, companies would need to implement data transformation systems as soon as possible. Advanced data collection and analytics capability would allow them to support proper data governance and management.

Organizations that start the process of upgrade sooner will be at an advantage. It will allow them to build competitive advantage over rival businesses. Organizations that give their customers control over their personal data will also improve customer experience and stand out as reliable businesses.

About the Authors

Bob Nieme

For over 15 years, Bob Nieme has been a Digital Transparency protagonist. In 2014, Bob was recognized as a Privacy by Design Ambassador by the Information and Privacy Commissioner of Ontario, Canada, and in 2013, he was admitted to the Advisory Board of the Department of Mathematics and Computer Science of Eindhoven University of Technology. Bob Nieme founded three leading data-technology companies: Adversitement specializes in data process management, O2MC I/O offers a prescriptive web computing framework, and Datastreams.io empowers data-driven collaboration by providing governed access to trusted data sources.

Ronald van Loon

Ronald van Loon is Director at Adversitement, and an Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He contributes his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

If you would like to read more from Ronald van Loon on the possibilities of Big Data and the Internet of Things (IoT), please click “Follow” and connect on LinkedInTwitter, and YouTube.

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

More Posts - Website

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Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post What Does GDPR Mean For Your Business? appeared first on Ronald van Loons.

 

April 22, 2018

 

April 20, 2018


BrightPlanet

All websites are not created equal. BrightPlanet knows how to harvest the exact data clients need, whether it is Deep Web, Dark Web or Surface Web content.

BrightPlanet provides terabytes of data for various analytic projects across many industries. Our role is to locate open-source web data, harvest the relevant information, curate the data into semi-structured content, and provide a stream of data feeding directly into analytic engines, data visualizations, or reports. In this blog series, we are going to be diving […] The post All websites are not created equal. BrightPlanet knows how to harvest the exact data clients need, whether it is...

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Revolution Analytics

AI, Machine Learning and Data Science Roundup: April 2018

A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications I've...

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April 19, 2018

InData Labs

5 Things you Must Consider to Maximize the Value of your Company’s Predictive Analytics and Machine Learning Initiatives

Investigating company data for insights is a well known and widely adopted practice. However, using predictive analytics and machine learning is the next frontier in data analysis. The ability to predict future outcomes is what sets predictive analytics apart from other analytics used today, such as descriptive that gives answer to question “what happened” or...

Запись 5 Things you Must Consider to Maximize the Value of your Company’s Predictive Analytics and Machine Learning Initiatives впервые появилась InData Labs.

 

April 18, 2018


Revolution Analytics

Uber overtakes taxis in New York City

In an update to his analysis of taxi and ride-share trips, Todd Schnieder reports that the number of daily Uber rides exceeds the number of taxi rides in New York City, as of November 2017. The data...

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Datameer

GDPR Is Almost Here. Are Your Analytic Processes Ready?

The May 25, 2018 deadline for the General Data Protection Regulation (GDPR) is almost upon us. And the question many in management are asking is: Are we ready? In most organizations, the risk and...

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April 17, 2018

Ronald van Loon

Google Deepmind: The Importance of Artificial Intelligence

Developments in Artificial Intelligence (A.I.) are happening faster today than ever before. However, the nature of progress in A.I. is such that massive technological breakthroughs might go unnoticed while smaller improvements get a lot of media attention.

Take the case of face recognition technology. The ability of A.I. to recognize faces might seem like a very big deal, but isn’t that groundbreaking when you consider the nature of applied A.I.

On the other hand, suppose an A.I. is asked to choose between a genre of music, such as R&B or rock. While it may seem like a simple choice, the mathematical algorithm that must be solved before the A.I makes a decision could take hours and days.

General A.I. vs. Advanced A.I.

Most people get their idea of A.I. from Hollywood movies and science fiction. They assume that A.I. robots would work and think in the same ways as human beings do. They tend to think of the Terminator or Data from Star Trek The Next Generation (TNG).

These fictional roles give us examples of A.I. that behave in a very general manner. The A.I. that developers are working on is actually much more advanced. This A.I. will be able to perform very complex calculations, but in a very limited field, while it will still be unable to perform some of the basic functions that humans perform.

General A.I. Is Not Very Profitable

Take the example of a dishwasher that is programmable and cleans dishes very well. A dishwashing manufacturer may program it to respond to voice command and play music as well. The dishwasher may learn the general times when you have dinner and improve its washing quality based on your preferences.

But would it make sense or even be economical to teach this dishwasher how to recognize facial expressions and the mood of the operator?

A generalized A.I. would be able to perform many tasks, but it cannot be very good at each task. It would be more efficient economically to build machines that are specialized in particular tasks.

People would love seeing generalized A.I. machines in the store and find them entertaining, but no one would actually buy them for the chores at home. This is why A.I. advanced in specific tasks is far more useful and gets a lot more attention from commercial developers.

A.I. and Machine Learning

The real task that lies ahead for developers is to build A.I. that is capable of learning, thinking, and feeling without input from a human. This kind of independent A.I. will be capable of making decisions on its own, and can be considered truly smart.

Before this A.I. is completed and ready for practical applications, it will have performed millions of simulations on its neural network, which would help it improve its actions in the real world.

The A.I. does this by making repeated computations and recording the result on each stage of its learning process. Once the A.I. finds the first correct solution, it runs the test from the second stage, making repeated calculations until it finds the best solution. Once this solution is found, the A.I. begins testing solutions from the third stage.

Using this approach on some old video games, developers were able to get amazing results. They tested the model on old classics such as Hungry Snake, where the A.I. learned how to play the game by making the correct left or right movement to grow to the maximum level which would be very difficult to achieve for even the most expert of human players.

The model was tested on PC Snooker, where the A.I. was able to determine brilliant pool shots that gave it a near perfect score.

Google DeepMind

Deepmind is one of the leading A.I. development firms in the world. It started in 2010, and was acquired by Google in 2014. The firm has been at the forefront of technological breakthroughs in the world. Google’s access to a very large database has also allowed the researchers at the firm to test a number of Artificial Intelligence concepts.

Testing Game Choices with A.I.

Video games are incredibly useful in testing and improving A.I. learning. Most video games are developed for humans, and have a learning curve. While humans are able to quickly learn and become good at games due to our intuition, A.I. usually starts from scratch and performs poorly in the beginning.

The research team at Deepmind used its DMLab-30 training set, which is built on ID Software’s Quake III game. Using an Arcade learning environment that ran Atari games, the team developed a new training system for A.I called Importance Weighted Actor-Learner Architectures, or IMPALA for short.

IMPALA allows an A.I. to play a large number of video games really quickly. It sends training information from a series of actors to a series of A.I. learners. Instead of directly connecting the A.I. to the game engine, developers told the A.I. the result of its action from the controller inputs, just like a human player would experience the game.

The developers found the results to be quite good based on a single A.I. player’s interaction with the game world. How well the A.I. performs against human players is still under testing.

Most A.I. in games is disadvantaged against human players. Developers try to offset this by allowing A.I. certain advantages against humans. In arcade games this is done by giving the A.I. special powers that the human player does not possess. In strategy games, this is done by giving the A.I. extra resources.

A.I. that performs well against human players without any hidden benefits would truly be considered an amazing advancement.

Self-Teaching Robot Hands

Developments in neural networks have allowed robots to run millions of simulations or run complex calculations faster than humans can. Yet, when it comes to figuring out physical things, A.I. robots still struggle, because they have a number of infinite possibilities to choose from.

In order to counter this problem, DeepMind created an innovative paradigm for A.I. powered robots. Scheduled Auxiliary Control (SAC-X) gives robots a simple task such as “clean up this tray,” and rewards it for completion of the task.

The researchers don’t provide instructions on how to complete the task. That is something that the robot A.I. hand must figure out on its own.

The developers believe that progress in performing physical and precise tasks will lead to the next generation of robots and A.I.

Understanding Thought Process

Researchers at DeepMind are also looking at ways to making the AI understand how humans use reasoning and make sense of things around them.

Humans have the intuitive ability to evaluate the beliefs and intentions of others around them. This is a trait shared by very few creatures in the animal kingdom. For instance, if we see someone drinking a glass of water, we can infer that the person was thirsty and water can quench their thirst.

The ability to understand these abstract concepts is called the “theory of mind,” and plays a crucial role in our social interactions. Developers at DeepMind performed a simple task.

They first allowed an A.I., ToM-Net, to observe an 11-by-11 grid, which contained four colored objects and a number of internal walls. A second A.I. was given the task of walking to a specific colored square. It also had to pass by another color on the way.

While the second A.I. would try to complete the task, the developers would move the initial target. They then asked the ToM what the second A.I. would do.

ToM was correctly able to predict the actions of the second A.I., based on the information that was given to it.

About The Author

If you would like to read more from Ronald van Loon on the possibilities of Artificial IntelligenceBig Data, and the Internet of Things (IoT), please click “Follow” and connect on LinkedInTwitter and YouTube.

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

More Posts - Website

Follow Me:
TwitterLinkedIn

Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post Google Deepmind: The Importance of Artificial Intelligence appeared first on Ronald van Loons.

 

April 16, 2018


Revolution Analytics

News from the R Consortium

The R Consortium has been quite busy lately, so I thought I'd take a moment to bring you up to speed on some recent news. (Disclosure: Microsoft is a member of the R Consortium, and I am a member of...

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April 13, 2018


Revolution Analytics

Because it's Friday: The borders have changed. Film at 11.

There's a lot of stupidity in the US news these days, but at least these reviews of bad maps on TV are amusing rather than infuriating. (Click through to see the entire thread.) That's Iran in the...

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April 12, 2018


Revolution Analytics

The case for R, for AI developers

I had a great time this week at the Qcon.ai conference in San Francisco, where I had the pleasure of presenting to an audience of mostly Java and Python developers. It's unfortunate that videos won't...

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April 11, 2018


Datameer

Data Challenges for New Age Analytics in Retail

Having spent some time in the retail business, specifically apparel, and at a company that focused on helping e-retailers, I have an appreciation for the challenges these organizations face. With the...

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April 10, 2018


Revolution Analytics

Statistics from R-bloggers

Tal Galili's R-bloggers.com has been syndicating blog posts about R for quite a while — from memory I'd say about 8 years, but I couldn't find the exact date it started aggregating. Anyway, it...

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April 09, 2018


Revolution Analytics

In case you missed it: March 2018 roundup

In case you missed them, here are some articles from March of particular interest to R users. The reticulate package provides an interface between R and Python. BotRNot, a Shiny application that uses...

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April 07, 2018


Jeff Jonas

Democratizing Entity Resolution.

In August of 2016, my team and I spun the G2 technology out of IBM. Into stealth mode we went, again. We are now back out of stealth mode and set to democratize Entity Resolution (yes, I am starting...

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April 06, 2018


Revolution Analytics

Because it's Friday: Regex Games

I've been wrestling with regular expressions recently, so it was useful to give myself a bit of a refresher with Regex Crossword (with thanks to my colleague Paige for the tip). Little...

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Revolution Analytics

A few podcast recommendations

After avoiding the entire medium for years, I've been rather getting into listening to podcasts lately. As a worker-from-home I don't have a commute (the natural use case of podcasts, I guess), but I...

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Datameer

Four Ways to Overcome Data and Analytic Challenges in the Insurance Industry

The insurance industry, in particular the property and casualty, life and annuity, and re-insurance sectors, is fraught with very interesting data and analytics challenges. While there is vast...

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Datameer

Datameer and IBM Cloud Private for Data

Many CEOs see Artificial Intelligence (AI) and Machine Learning (ML) as a key component to gaining competitive advance in their respective marketplaces. A 2017 survey of Fortune 500 CEO’s found that...

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Datameer

Five Rules of Data Exploration

As always, one always learns something new at the Gartner Data and Analytics Summit (the 2018 North America version held last week in Grapevine, Texas). I attended a fascinating session with two of...

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April 05, 2018

InData Labs

How to Design Better Machine Learning Systems with Machine Learning Canvas

Machine Learning Canvas is a template for designing and documenting machine learning systems. It has an advantage over a simple text document because the canvas addresses the key components of a machine learning system with simple blocks that are arranged based on their relevance to each other. This tool has become popular because it simplifies the visualization of a complex project and helps to start a structured conversation about it.

Запись How to Design Better Machine Learning Systems with Machine Learning Canvas впервые появилась InData Labs.

 

April 04, 2018


Revolution Analytics

Not Hotdog: An R image classification application, using the Custom Vision API

If you're a fan of the HBO show Silicon Valley, you probably remember the episode where Jian Yang creates an application to identify food using a smartphone phone camera: Surprisingly, the app in...

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BrightPlanet

Interview with Mikhail Shengeliya of Eagle Alpha: OSINT Data Collection Challenges & Solutions

Will Bushee, BrightPlanet’s Vice President of Technology, recently sat down with Mikhail Shengeliya from Eagle Alpha to discuss various topics, including: Challenges which come with harvesting open-source content Solutions for those challenges Specific project use-cases, such as harvesting job postings Eagle Alpha provides a full-service platform enabling asset managers to obtain solutions from alternative data […] The post Interview with Mikhail Shengeliya of Eagle Alpha: OSINT...

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Revolution Analytics

Mathematical art in R

Who says there's no art in mathematics? I've long admired the generative art that Thomas Lin Pedersen occasionally posts (and that you can see on Instagram), and though he's a prolific R user I'm not...

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April 03, 2018

Ronald van Loon

How Industrial IoT is Influenced by Cognitive Anomaly Detection

There are about 6,000 sensors on an A350 airplane.

The average Airbus flight generates 2.5 petabytes per flight with over 100,000 flights per day!

Industrial Internet of Things, or IIoT, is a massive market.

It includes airplane and car manufacturers, power plants, oil rigs, and assembly lines, all of which contain sensors measuring thousands of different attributes.

But most IIoT companies let 80% of their data go unused. And this is a big challenge for businesses.

But there are other challenges too, like latency issues that affect the results from real time data, the failure to predict when parts will breakdown, and the expense of hiring data scientists.

A Cognitive approach to Anomaly Detection, powered by Machine Learning and excellent data and analytics, is providing IIoT businesses with solutions, and helping them to overcome the limitations of traditional statistical approaches.

Machine Learning is becoming a commonplace tool for businesses, accelerating root cause analysis.

Anomaly detection refers to the problem of finding patterns in data that don’t conform to expected behavior.

There are many different types of anomalies, and determining which is a good and bad anomaly is challenging.

In Industrial IoT, one main objectives is the automatic monitoring and detection of these abnormal events, or changes and shifts in the collected data, including all the techniques aimed at identifying data patterns that deviate from the expected behavior.

With the help of Data Scientist Taj Darra from DataRPM, we can understand the importance of a bottom up approach to anomaly detection, which you can see here:

When Machine Learning is enhanced with a cognitive IoT framework, it enables IIoT businesses to detect anomalies from the initial ingestion of sensor data to outputting predictions and determining whether or not something is an anomaly in just 2 days.

With cognitive predictive maintenance powered by Machine Learning, all of the sensors can be measured in parallel.

Let’s break down the phases of anomaly detection:

Cognition is giving businesses the means to gain control over enormous quantities of sensor data generating from every machine.

This means augmented asset failure management, reduction of unplanned downtime, improved failure prediction, and enhanced asset life.

As the IIoT industry moves into the future, there is an urgency for change because of the limitations of traditional machine learning approaches.

There are opportunities for businesses to take advantage of Cognitive Anomaly Detection now.

I would like to thank DataRPM and Taj Darra for their insights.

Watch and Subscribe here: http://bit.ly/2q4m2oj

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

More Posts - Website

Follow Me:
TwitterLinkedIn

Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post How Industrial IoT is Influenced by Cognitive Anomaly Detection appeared first on Ronald van Loons.

 

April 01, 2018

 

March 31, 2018


Revolution Analytics

Use Python functions and modules in R with the "reticulate" package

Since its inception over 40 years ago, when S (R's predecessor) was just a sketch on John Chambers' wall at Bell Labs, R has always been a language for providing interfaces. I was reminded of this...

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March 30, 2018


Revolution Analytics

Because it's Friday: Open Source in Lego

If (like me) you work with open source software, you've probably had to explain to non-technical coworkers or family members what "Open Source" actually means. At least in my experience, that rapidly...

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March 29, 2018

 

March 27, 2018


Revolution Analytics

Generate image captions with the Computer Vision API

The Azure Computer Vision API can extract all sorts of interesting information from images — tags describing objects found in the images, locations of detected faces, and more — but today I want to...

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Ronald van Loon

GDPR Drives Real Time Analytics

New reforms under the General Data Protection Regulation (GDPR) started as an attempt to standardize data protection regulations in 2012. The European Union intends to make Europe “fit for the digital age.” It took four years to finalize the agreements and reach a roadmap on how the laws will be enforced.

The GDPR presents new opportunities as well as difficulties for businesses, digital companies, data collectors, and digital marketers. On the one hand, these regulations will make it more difficult for businesses and data mining firms to collect and analyze customer data for marketers, while on the other, they will present an opportunity for data collectors to innovate and enhance their techniques. This will lead to better collection of more meaningful data, as customers will be directly involved.

Understanding GDPR

The GDRP will go into effect on May 25, 2018. It will apply to all organizations and businesses that process personal and marketing data from European residents.

There are six underlying principles of GDPR.

⦁ Organizations must ensure that the personal data of users is processed transparently, lawfully, and fairly.

⦁ Personal data of users must only be collected for explicitly specified and legitimate purposes.

⦁ Data collectors must only gather limited amounts of personal information that is adequate to the gatherer’s needs and relevant to their business.

⦁ It is the responsibility of data collectors to ensure that the personal data is accurate and kept up to date.

⦁ Data collectors must maintain personal data in a form where the data subject can be identified for only as long as it is necessary for processing.

⦁ Personal data must be processed in a way that ensures that it remains secure and cannot be stolen.

The regulations apply to organizations that are trading within the EU. However, this potentially includes organizations from every part of the world. The regulations would keep European organizations from working with companies and states that do not meet the requirements of GDPR.

Implications of Reduced Customer Data

The regulation aims to protect the personal data of natural persons, whatever their nationality or place of residence. The regulations have the potential to apply to citizens and businesses from the U.S., Asia, and other parts of the world.

EU organizations are bound by the regulation to protect the personal data of anyone from anywhere in the world, and not just the EU citizens. Data collectors from outside the EU are also bound to protect the personal data of European citizens as long as it is collected within the European borders.

Personal Data

The scope of the term personal data has been expanded in the new legislation. It now encompasses any information relating to an identified or natural person such as their name, location data, identification number, or employment, etc. Personal data also includes the physical, genetic, mental, physiological, economic, cultural, or social identity of that person.

Rights of the Data Subjects

The major implication of the GDRP is that it drastically increases the rights of subjects on their personal data held by organizations.

Data collectors must now clearly communicate to the subjects of their data gathering efforts about what data they are collecting and what purposes it will be used for.

The data collectors must also obtain consent from the data subjects for collecting most types of personal data. While consent is not strictly necessary, it can restrict the type of data that can be collected or used by organizations.

Right to be Forgotten

Perhaps the most interesting thing about the new regulations is the right of data subjects to have their data removed from an organization’s records. If a person removes their consent at any time or explicitly asks an organization to remove their personal data, the organization is bound by the new regulations to comply with the request.

Organizations will need to build a process that enables them to erase records. This could be especially tricky in situations where data becomes archived. Organizations might find it too costly to search through their records just to remove data that they no longer use.

Real Time Analytics

GDPR regulations will soon be put into practice, and the million dollar question that has been on everyone’s mind is how it will impact the data collection and processing industry.

The co-author of this article, Bob Nieme, the founder of Datastreams.io and a long-time data collection expert, pointed out that the new regulations will have a significant impact on data analytics. Collection, retention, and processing of data with the organization will become more difficult, and businesses will need to shift towards an approach of real time analytics.

Real time analytics involves the use and analysis of data as soon as it enters a system. The term real-time refers to a level of responsiveness from the server where processing is done while the user is still connected to the network.

Real time analytics of user data could remove the need to keep the personal data on organizational files.

Researchers have developed a number of technologies that make real-time data analytics faster and better compared to post-dated analytics. Some of these technologies include the following.

In-Database Analytics

With this technology, the analytical tools are built into the database itself. As soon as personal data is received from a data subject, the protocol performs the analysis to create new logical conclusions. This technology can allow businesses to process the data without keeping a record of the user on their systems.

Data Warehousing Appliances

Specialized hardware and software products can be designed that perform data analysis on the customer’s premises. Technically speaking, the data would remain in the possession of the customer and they would have complete control on what relevant information they choose to pass on to their vendor.

 Real Time Analytics Application

The GDPR allows for a new risk-based approach to data protection. It shifts the burden of risk for incomplete data security from individual data subjects to larger corporations and processors which have the organizational capacity to improve data security.

The new regulations recognize that static identifiers are not doing the task of privacy protection as intended. Static identifiers may be connected to Mosaic effects, which leads to unauthorized re-identification of data subjects. Continued use of these static modules places additional risk on data subjects.

Instead of using static identifiers, the use of dynamic identifiers will allow data gatherers to process information without linking it back to individual data subjects.

The new GDPR mandates Data Protection by default. While it may put additional pressure on data collectors and marketers in the short run, we can be sure that the regulations will lead to new innovations from businesses. This will make the process more secure for users and also increase trust that the data is being used for relevant purposes by businesses.

About the Authors

Bob Nieme

For over 15 years, Bob Nieme has been a Digital Transparency protagonist , being the most essential condition for long term relationships based on trust and mutual interest. In 2014 Bob was recognized as a Privacy by Design Ambassador by the Information and Privacy Commissioner of Ontario, Canada, and in 2013 he was admitted to the Advisory Board of the Department of Mathematics and Computer Science of Eindhoven University of Technology. As a Data Science Ambassador, he initiates and supports various start-ups and education programs. Bob Nieme founded three leading data-technology companies: Adversitement specializes in data process management, O2MC I/O offers a prescriptive web computing framework, and Datastreams.io empowers data-driven collaboration by providing governed access to trusted data sources.

Ronald van Loon

Ronald van Loon is Director at Adversitement, and an Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He contributes his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

If you would like to read more from Ronald van Loon on the possibilities of Big Data and the Internet of Things (IoT), please click “Follow” and connect on LinkedInTwitter, and YouTube.

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

More Posts - Website

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Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post GDPR Drives Real Time Analytics appeared first on Ronald van Loons.

 

March 25, 2018


Simplified Analytics

How Consumer Packaged Goods companies are adopting Digital?

Consumers are at the heart of the digital transformation, in every industry starting Retail, Financial services, insurance & Health Care.  Consumers are most concerned with getting quick,...

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March 23, 2018


Revolution Analytics

Because it's Friday: 952 in your head

Countdown is one of those quintessentially British shows that I can't imagine anywhere else. (Reading the Wikipedia article just now I learned that it started as a French show, which I can well...

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Revolution Analytics

The most prolific package maintainers on CRAN

During a discussion with some other members of the R Consortium, the question came up: who maintains the most packages on CRAN? DataCamp maintains a list of most active maintainers by downloads, but...

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March 22, 2018

InData Labs

3 Criteria for a Successful Machine Learning Project

Many companies are taking advantage of the latest AI and machine learning technologies to create better products and services. If you consider joining their ranks, you are wise to do so. However, before diving into a new machine learning project you need to make sure that you have identified the best opportunity for your company....

Запись 3 Criteria for a Successful Machine Learning Project впервые появилась InData Labs.

Knoyd Blog

Churn Prediction - Maximize Your Customer Retention

A 5% increase in customer retention produces more than a 25% increase in profit. In the past, there were lots of business articles and research describing the customer retention and all of them came to the same conclusion: It is cheaper to keep existing customers than gain new ones. Over the years, we have collected a lot of experience with churn prediction, from industries like telecommunication providers, banking or computer security. Today we would like to share some of our experience with you. 

 

The Underlying PROBLEM:

The main goal is to improve customer retention. The churn rate is the percentage of customers who discontinue their subscriptions to the service within a given time period. For a company to expand its customer base, its growth rate, as measured by the number of new customers, must exceed its churn rate. Furthermore, it is crucial to have a churn rate as low as possible to achieve a healthy growth.

 

Two CHALLENGES:

There are a couple of challenges in churn prediction. Mainly, there is no easy generic approach that will work for everyone. Something else works for each customer... for example, different types of discounts, products or simply, a more personalised company approach.

The second problem is that churners can't be identified too late, when they are already upset, or when they already know about good offers from the competition. Once a customer decides to take another offer, it is very hard to convince them to change their mind.

 

The SOLUTION:

Most of the companies turn to Machine Learning to solve this problem. There is more than one way to approach the problem.

The first way is to create a metric of the behavior of the customers, for example, the number of off-net calls in the last 30 days or how many times a customer visited the website of a competition in the last 10 days. Using features like these, we can create a Machine Learning model that predicts the probability of churn for each of our customers.

The second approach is the so-called “path to churn”. We can generate a number of events which follow a customer throughout their journey. Then we look for paths (sequence of events) which often lead to churn. Using this approach we can also predict what type of event will happen next for each customer.

The third approach is through influencing customers. The goal is to identify people, so-called Alpha Users or Influencers, who have a significant impact on the people in their network.

 

In A Business case:

Suppose we have a company with 1000 customers and a 5% churn rate. Using following assumptions we can compute the value of the churn prediction model.

  1. Every month the company loses 50 customers

  2. With their marketing budget the company can target 100 people

If they don't use Machine Learning to select people for their campaigns, they will target 100 people who will have 5 churners among them. Based on our experience, the lift of churn models can easily reach approximately 4 for the first decile of the customer base. That means when we target 100 people using a churn model there will be 20 churners among them. Using Machine Learning, the company will be able to keep more clients using the same campaign.

pexels-photo-398532.jpeg

 

Our Value proposition:

The biggest value consists of the increase in revenue and the improvement in the targeting of campaigns. If you send discounts to people who would stay anyway, you will use revenue, but if you send discounts to the right people and make them stay, your revenue will increase. If you are currently thinking about tackling a similar problem in your business, don't hesitate to contact us.

 

March 21, 2018


Revolution Analytics

AI, Machine Learning and Data Science Roundup: March 2018

This is the first edition of a monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software...

...
Ronald van Loon

The Future of IoT and Machine to Machine Payments

Companies like Amazon, and Facebook are setting the standard for customer expectations and customer experience.

This includes everything from understanding the whole customer journey, defining the context and personalizing it, to ensuring the payment experience is seamless and frictionless without compromising security.

Personalization and contextuality in the mobile payment domain is evolving with Machine to Machine payments. As the popularity of in-app experiences grow, like those used by Uber, there’s a corresponding need for a streamlined IoT enabled payment system. Billions of IoT devices are connected all over the world, and it won’t be long before almost all of our devices and technologies are connected through IoT.

Our technology is communicating with each other, or Machines are exchanging data with other Machines without the help of people. IoT improves this Machine to Machine interaction significantly, changing the experiences that we’re having as consumers.

The Machine to Machine, or M2M, connections market is predicted to reach $27 Billion by 2023, so we’re going to see an increase of IoT and M2M payment solutions. M2M is a term that is sometimes used interchangeably with IoT. But they’re actually different concepts. IoT technology is how devices communicate between diverse systems, and M2M refers to isolated systems that don’t communicate with each other.

When applied to M2M, Artificial Intelligence and Machine Learning enables systems to communicate with each other and make their own autonomous choices. So M2M payments can include a multitude of scenarios, like transactions based on customer behavior without our knowledge. Regulations, ethics, and business rules can be included in intelligent machines through smart contracts, which are stored on blockchain technology. This increases the security of M2M transactions and enforces contract performance. Device agnostic solutions, like automatic SIM activation for telecom, also helps to support M2M capabilities and communication, and optimizes network resources. Furthermore, contextualizing payments with data and analytics helps facilitate fraud detection and terminal tracking, defines customer profiles, and blocks stolen devices.

The M2M payment system is going to continue to significantly disrupt the payments industry, simplifying transactions in emerging markets. The combination of IoT, AI and Machine Learning, and smart contracts are creating opportunities for new, different purchasing behaviors. And integrating the user experience with apps like mobile wallets, will cause M2M financial activities to be even more commonplace in the future.

I’d like to thank Mahindra Comviva and Srinivas Nidugondi for their insight.

Watch and Subscribe here: http://bit.ly/2FZ2vfV

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

More Posts - Website

Follow Me:
TwitterLinkedIn

Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post The Future of IoT and Machine to Machine Payments appeared first on Ronald van Loons.

 

March 20, 2018


Revolution Analytics

R and Docker

If you regularly have to deal with specific versions of R, or different package combinations, or getting R set up to work with other databases or applications then, well, it can be a pain. You could...

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March 19, 2018

Ronald van Loon

Blockchain Potential to Transform Artificial Intelligence

The research on improving Artificial Intelligence (A.I.) has been ongoing for decades. However, it wasn’t until recently that developers were finally able to create smart systems that closely resemble the A.I. capabilities of humans.

The main reason for this breakthrough in technology is advancements in Big Data. Recent developments in Big Data have allowed us the capability to organize a very large amount of information into structured components that can be very quickly processed by computers.

Another technology that has the potential for rapidly advancing and transforming Artificial Intelligence is the Blockchain. While some of the applications that have been developed on Blockchain are nothing more than ledger records of transactions, others are so incredibly smart that they almost appear like AI.

Here, we will look more closely at the opportunities for A.I. advancement through the Blockchain protocol.

Blockchain Technology

Supporters of Blockchain believe that it can offer benefits in a large number of industries. The technology has already proved its usefulness in the financial and money exchange markets.

The mortgage lending industry can benefit from Blockchain application for loan origination, payment, and trading. Smart contracts allow automated contingencies that will be executed when stakeholders meet their respective obligations of the contract.

Major retail corporations such as Wal-Mart are working with IBM to apply Blockchain in their processes. They aim to improve inventory control and reduce wastage. A Blockchain-based supply chain can help retailers keep track of product batches and maintain a steady supply in stores.

Blockchain can also be useful in the healthcare industry, as it allows patients to create medical history records that are completely secure, yet easily accessible from the Blockchain network.

Some even believe that the technology will be used to hold elections in the near future.

Improvements in Artificial Intelligence through Blockchain

Researchers have also looked at ways to utilize Blockchain for improving Artificial Intelligence. Blockchain developers make a good case on why the distributed ledger system is the perfect platform for testing the next generation of developments in A.I.

The existing A.I. testing databases are operating in what can be called the red ocean. There is a lot of competition. Similar technologies and methods are being tested with many businesses competing for the same incremental gains.

A Blockchain-based database for A.I. represents the blue ocean of uncontested markets. This is because the technology is still new, secure, and transparent. It has the potential to achieve great things in the future.

Some of the characteristics that make Blockchain a good contender for testing and building Artificial Intelligence are outlined here.

Decentralized Control and Data Sharing

The Blockchain works on a decentralized network of nodes, working together to solve complex algorithms. The mining node on the network which finds the best solution first adds the entry to the blockchain ledger.

Artificial Intelligence works on a similar model. When a decision must be taken by an A.I. system, it tests the possible solutions and alternating branches of possibilities that emerge as a result of taking the first decision. Evaluations of all possible alternatives are tested to the end result before the A.I. chooses the best option.

What makes Blockchain exceptionally good is that instead of a single, central system testing all possible hypotheses, the task is divided among hundreds of nodes spread around the world, which makes the process much faster.

Additional Security

An A.I system being run on a single, central processor is prone to hacking, as any malcontent only needs to break into a single system to manipulate the instructions.

Entries to the blockchain platform must be authenticated by the majority of nodes on the network before they are accepted and processed into the ledger. The higher the number of nodes that are operating on the network, the more difficult it is to hack the system.

While a Blockchain-based A.I. platform would not be impossible to hack, it is still far more difficult to manipulate and break such a system.

Greater Trust

In order to be reliable, a system must be trusted by the public in general. Blockchain allows far greater transparency than a closed A.I. system. Records maintained on a Blockchain ledger can be reviewed and audited at any time by authorized people with access to the system. At the same time, users who have not been granted access would not be able to view anything, as the database is encrypted.

Take the case of Blockchain application in the healthcare industry. People with medical complications may not want their medical records to be accessed by unauthorized people. Keeping the medical history in an encrypted format instead of plain English ensures that their records could not be accessed by any individual.

On the other hand, keeping the record on a Blockchain also ensures that medical practitioners would be able to provide quick medical aid in case of emergency by accessing the files.

How Blockchain will Transform Artificial Intelligence

Developments in A.I. technology rely on the availability of data from a large number of sources. Organizations such as Google, Facebook, and telecommunication companies have access to large sources of data which can be useful for testing many A.I. processes. The problem is, this data is not accessible on the market.

This problem can be solved by Blockchain’s P2P connection. The Blockchain ledger is an openly distributed registry. The database becomes available to all the nodes on the network. The Blockchain may be the best thing to end the control on data from a few major corporations by allowing it to be freely available.

Modern A.I. & Data

The development of A.I. depends on access to data in much the same way that the construction of a building depends on materials, stone, and steel. This is because data is consistently needed to test and retest alternative solutions for A.I.

As an A.I. system continuously tests these hypotheses, rejects the wrong answers, and builds upon the right solution, it improves its capability to make sense of things. This is what we commonly refer to as Machine Learning.

Machines do not have the same sense of intuition that humans developed over millions of years. In order for A.I to one day reach a similar level of intelligence as humans, it would need to test the data of millions of transactions in a matter of years.

Control Over the Use of Data

This is perhaps the most important and limiting factor in the development of A.I., and the reason why Blockchain would work where centralized databases have not.

Think of Facebook or Google. When a user logs into their Facebook account, they don’t have the right to any content uploaded on their platform. The content on the platform belongs to the website.

What makes Blockchain different is that data on the Blockchain is not owned by the operators but the individual wallet holder. This gives each user the ability to share their data on the platform without requiring permission for the network operators.

The future of A.I. development lies on a network that allows free flow of information between connected users and operators. The decentralized nature of Blockchain technology means that this could be the platform where we see the most breakthroughs on A.I.

About The Author

If you would like to read more from Ronald van Loon on the possibilities of Artificial IntelligenceBig Data, and the Internet of Things (IoT), please click “Follow” and connect on LinkedInTwitter and YouTube.

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

More Posts - Website

Follow Me:
TwitterLinkedIn

Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post Blockchain Potential to Transform Artificial Intelligence appeared first on Ronald van Loons.


BrightPlanet

We harvest a lot of websites for our clients, but how do we know which sites to harvest in the first place?

BrightPlanet has provided terabytes of data for various analytic projects across many industries over the years. Our role is to locate open-source web data, harvest the relevant information, curate the data into semi-structured content, and provide a stream of data feeding directly into analytic engines or final reports. The first phase of all projects – […] The post We harvest a lot of websites for our clients, but how do we know which sites to harvest in the first place? appeared...

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March 18, 2018


Simplified Analytics

HR in Digital Age

Human Resources is all about the recruitment, development, and retention of talent. Across all industries, HR is one of the departments that are most affected by digitization – via big data...

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March 16, 2018

 

March 15, 2018


Revolution Analytics

R 3.4.4 released

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R rises to #12 in Redmonk language rankings

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March 14, 2018


Revolution Analytics

In case you missed it: February 2018 roundup

In case you missed them, here are some articles from February of particular interest to R users. The R Consortium opens a new round of grant applications for R-related user groups and projects, and...

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March 13, 2018

Ronald van Loon

Machine Learning Explained: Understanding Supervised, Unsupervised & Reinforcement Learning

Machine Learning is guiding Artificial Intelligence capabilities.

Image Classification, Recommendation Systems, and AI in Gaming, are popular uses of Machine Learning capabilities in our everyday lives. If we breakdown machine learning further, we find that these 3 Machine Learning examples are powered by different types of machine learning:

  • Image classification comes from Supervised Learning.
  • Recommendation systems comes from Unsupervised Learning.
  • Gaming AI comes from Reinforcement Learning.

How can we better understand Supervised, Unsupervised, and Reinforcement Learning?

Let’s start with Supervised Learning, which makes up most of the uses for Machine Learning today. In Supervised Learning, the machine already knows the output of the algorithm before it starts working on it. The algorithm is taught through a training data set that guides the machine, and the machine works out the steps from input to output.

Supervised learning is used for image classification or identity fraud detection, and for weather forecasting. But how is Unsupervised Learning different?

Well first off, with Unsupervised Learning, the system does not have any concrete data sets, and the outcomes are also mostly unknown. Unsupervised Learning has the ability to interpret and find solutions to a limitless amount of data. Now when you log onto Hulu or Netflix, you have personalized recommendations because of Unsupervised Learning.

Lastly, there is Reinforcement Learning. Reinforcement Learning is different, because it gives a high degree of control to software agents and machines, which are determining what the behavior within a context should be. People are helping the machine to grow by maximizing performance, providing feedback to the machine, helping it to learn its behavior.

Reinforcement Learning requires the use of tons of different algorithms, giving control to the agent as they decide the best action based on the current results. When you are gaming on PC, Xbox, Playstation, or Nintendo, and you witness AI in Gaming, this is because of Reinforcement Learning.

Watch and Subscribe here: http://bit.ly/2Dnzkks

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

More Posts - Website

Follow Me:
TwitterLinkedIn

Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post Machine Learning Explained: Understanding Supervised, Unsupervised & Reinforcement Learning appeared first on Ronald van Loons.

 

March 12, 2018

Ronald van Loon

GDPR: an Opportunity to drive Customer Experience & Create Digital Trust

With consumer data privacy becoming a top priority in the current age, regulating authorities have jumped into the conundrum to ensure that users get the privacy they need for their personal data. One such regulatory authority that has come into the mix to ensure rights for all users online is the European Union. The EU announced the General Data Protection Regulation or GDPR, that will be in full effect by May of this year. Although GDPR may be considered a regional regulation, its impact is far=flung and may be seen across the globe in the coming days.

While GDPR imposes regulations on many aspects of management and user protection, the main clause of the regulation is that users will now be able to control their own personal data online and organizations will be required to protect the data that users share with them. New protection methods for personally identifiable information or PII gives every EU citizen the right to approve the use of their personal data. Citizens can now allow the use of their data or can opt for the “right to be forgotten” as an alternative.

The enforcement of the GDPR by the EU will be done through the implementation of a series of sanctions, stiff fines, and compensations. These fines and compensations will range from to two percent of an organization’s revenue or 10 million Euros for minor infractions to four percent of an organization’s revenue or 20 million Euros for major infractions. The amount will be settled on the basis of whichever of the two figures is higher. The regulations haven’t been imposed just for organizations based in the EU, but will also be applied to any organization doing business with EU citizens, regardless of the industry it operates in and its size.

Competitive Differentiation

While complying with GDPR regulations is definitely a challenge for all organizations currently operating with EU citizens, success would lie in seeing these new regulations as an opportunity to achieve competitive differentiation rather than just a barrier or a challenge. This presents an exemplary opportunity for organizations to drive digital trust for their brands and ensure that they not only comply with these regulations, but also end up making a mark for themselves in this competitive environment.

Take organizations like Google, Apple and Microsoft etc. Consumer confidence has always been important for these organizations and they have always operated within law to get customer consent for using their data. This has allowed major corporations to stand out and gain a unique selling point that differentiates them from the others.

Some organizations have stored tons of customer data for which they did not acquire written consent. This means the data cannot be used for the purpose of analysis after the end of May 2018.

Data management platforms (DMP) are instrumental for digital marketers. These platforms help marketers find high value audience to advertise their products and services. Most of this data is collected by third parties and used by marketers. However, with the general data protection regulation taking effect from May, DMPs will have a difficult time to obtain third party data.

DMPs mostly get their data through cookies and consent isn’t necessarily required to use cookies. However, implementation of GDPR will change this as it demands that personal data, especially data collected through cookies, can only be used after obtaining explicit consent from individuals.

Data collectors are likely to face more legal obligations under GDPR, leaving DMPs to rely more on first and second party data. Use of 3rd party data should be reviewed depending on new GDPR regulations.

Most organizations will have to revert back to the core architecture on how they collect and manage customer data. Businesses would need to switch to a flexible, agile & compliant architecture to manage & analyse real time (customer behavior) data.

Businesses will need to re-organize the strategy on how to segment their audience if their ability to collect data is limited. While this limitation presents new challenges, it also brings new opportunities for businesses.

Each business will have to find their own way of dealing with the changes in regulation and we are likely to see creative ways to improve the customer experience to get in return the customer’s data.

Furthermore, business will need to provide improved security features to their give consumers. By giving their users the right over their own data, as suggested by the GDPR, these organizations can assuage the concerns of the customers regarding data theft.

By cashing into this opportunity, you will not only comply with the EU regulations proposed through GDPR, but will also create a unique identity for your brand.

About the Authors

Bob Nieme

For over 15 years, Bob Nieme has been a Digital Transparency protagonist, being the most essential condition for long term relationships based on trust and mutual interest.In 2014 Bob was recognized as a Privacy by Design Ambassador by the Information and Privacy Commissioner of Ontario, Canada and in 2013 he was admitted to the Advisory Board of the Department of Mathematics and Computer Science of Eindhoven University of Technology. As a Data Science Ambassador, he initiates and supports various start-ups and education programs. Bob Nieme founded 3 leading data-technology companies: Adversitement specializes in data process management, O2MC I/O offers a prescriptive web computing framework,  and Datastreams.io empowers data-driven collaboration by providing governed access to trusted data sources.

Ronald van Loon

Ronald van Loon is, Director at Adversitement, an Advisory Board Member and Big Data & Analytics course advisor for Simplilearn. He contributes his expertise towards the rapid growth of Simplilearn’s popular Big Data & Analytics category.

If you would like to read more from Ronald van Loon on the possibilities of Big Data and the Internet of Things (IoT), please click “Follow” and connect on LinkedInTwitter and YouTube.

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

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Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post GDPR: an Opportunity to drive Customer Experience & Create Digital Trust appeared first on Ronald van Loons.

 

March 11, 2018

Ronald van Loon

What is IoT?

IoT devices are everywhere, but they aren’t just used for Smart City related technologies.

From Google Home, Fitbit, Apple iPhone, and Laptops, you are already using IoT devices everyday. In fact, Ericsson predicts that there will be 30 billion IoT devices in use around the world by 2023!

IoT devices are spanning industries like Transportation, Retail, Healthcare, Agriculture, Smart Homes, Smart Cities, and Wearables… But what exactly are IoT devices? How do they work?

Aside from IoT devices that you already know, like your laptop, smart home devices, or your smartphone, there are some amazing IoT devices out there with very compelling, useful applications.

The Internet of Things is creating a connected network of data between people, devices, and businesses through digital devices that are connected to the internet.

Embedded sensors in objects connect to the internet and generate information, collecting and exchanging data in real time.

These sensors collect and send information, which help people to make more intelligent decisions, make improvements to that technology, or monitor conditions.

Watch and Subscribe here: http://bit.ly/2FrzOfb

Ronald

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

More Posts - Website

Follow Me:
TwitterLinkedIn

Author information

Ronald helps data driven companies generating business value with best of breed solutions and a hands-on approach. He has been recognized as one of the top 10 global influencers by DataConomy for predictive analytics, and by Klout for Data Science, Big Data, Business Intelligence and Data Mining and is guest author on leading Big Data sites, is speaker/chairman/panel member on national and international webinars and events and runs a successful series of webinar on Big Data and on Digital Transformation. He has been active in the data (process) management domain for more than 18 years, has founded multiple companies and is now director at a Data Consultancy company, leader in Big Data & data process management solutions. Broad interest in big data, data science, predictive analytics, business intelligence, customer experience and data mining. Feel free to connect on Twitter or LinkedIn to stay up to date on success stories.

The post What is IoT? appeared first on Ronald van Loons.


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A 7-step Guide to GDPR Compliant Software Development

The GDPR, or General Data Privacy Regulation, is coming into force already in May this year. The regulation requires businesses to protect the personal data and privacy of EU residents. And non-compliance could cost companies dearly. GDPR pertains to the full data life cycle, including the gathering, storage, usage, and retention of data. GDPR applies...

Запись A 7-step Guide to GDPR Compliant Software Development впервые появилась InData Labs.

 

March 05, 2018


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Five New Big Data Use Cases for 2018 — Insurance Pricing, Risk and Underwriting

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Five New Big Data Use Cases for 2018 — Personalized Omni-Channel Experience in Retail

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