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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.

 

February 21, 2018


Revolution Analytics

Machine Learning in R with TensorFlow

Modern machine learning platforms like Tensorflow have to date been used mainly by the computer science crowd, for applications like computer vision and language understanding. But as JJ Allaire...

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February 18, 2018


Simplified Analytics

How Millennials are driving the Digital Age?

Digital Transformation has brought several changes in our lives, changes in technology, processes, workflow, communication, and even overall services and products. But more changes will come in near...

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February 15, 2018


Revolution Analytics

Because it's Friday: Hold the Door

You probably saw Boston Dynamics' robots achieve another milestone this week: not only can one of their robots open and pass through a door, it will cooperate and politely hold the door as a fellow...

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Revolution Analytics

A fresh look for base graphics

While ggplot2 (and its various extensions) is often the go-to package for graphics in R these days, if you need to step outside the boundaries of what ggplot2 can do, you can always step back to base...

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February 13, 2018


Revolution Analytics

What does Microsoft do with R?

I was genuinely chuffed to get a shout-out in the most recent episode of Not So Standard Deviations, the awesome statistics-and-R themed podcast hosted by Hilary Parker and Roger Peng. In that...

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Revolution Analytics

Diversity scholarships for upcoming R conferences

One of the greatest things about the R community is its diversity. This is largely thanks to organizations like Forwards and R-Ladies, who have been instrumental in welcoming women and other...

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InData Labs

The Most Exciting Applications of Computer Vision across Industries

Computer vision has come a long way in terms of what it can do for different industries. Now that the technology has finally caught up the original ideas of computer vision pioneers from the 70s, we are seeing more exciting computer vision applications across different industries.

Запись The Most Exciting Applications of Computer Vision across Industries впервые появилась InData Labs.


BrightPlanet

We talk a lot about Data-as-a-Service, but what exactly does that mean?

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. These collections often contain data from dozens, […] The post We talk a lot about Data-as-a-Service, but what exactly does that mean? appeared first on BrightPlanet.

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February 11, 2018


Simplified Analytics

Facial recognition in Digital Age

Do you remember Hollywood movies Terminator: Rise of Machines or Ex Machina where facial recognition technologies are used in several ways? Today with digital technological advances, face recognition...

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February 09, 2018


Revolution Analytics

Because it's Friday: Bon Voyage, Starman

SpaceX's Elon Musk had a plan: to launch his original Tesla Roadster into orbit around Mars, atop the new Falcon Heavy rocket. And on Wednesday, the plan went into effect. The Falcon Heavy launched...

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Ronald van Loon

Cognitive computing: Moving From Hype to Deployment

Although cognitive computing, which is many a times referred to as AI or Artificial Intelligence, is not a new concept, the hype surrounding it and the level of interest pertaining to it is definitely new. The combination of hype surrounding robot overlords, vendor marketing and concerns regarding job losses has fueled the hype into where we stand now.

But, behind the cloud of hype that is surrounding the technology currently, there lies a potential for increased productivity, the ability to solve problems deemed too complex for the average human brains and better knowledge based transactions and interactions with consumers. I recently got a chance to catch up with Dmitri Tcherevik, who is the CTO of Progress, about this disruption and we had a healthy discussion which led to the following insights.

Cognitive computing is considered a marketing jargon by many, but in layman terms it is used to define the ability of computers to replicate or stimulate human thought processes. The processes behind cognitive computing may make use of the same principles as AI, including neural networks, machine learning, contextual awareness, sentimental analysis, and natural language processing. However, there is a minute difference between both of them.

Difference between Cognitive Computing and AI

Both AI and Cognitive Computing may look extremely alike, but like we mentioned above there is a small difference between both methods.

Firstly, artificial intelligence does not work at mimicking human thought processes. The concept behind AI is to not mimic human thought and processes, but to solve a problem through the use of the best possible algorithm. This can be illustrated through an example of a car, which stays on course and avoids a collision. The processes in AI are not looking to process data in the same way as it would be processed by humans, but they’re looking to process it through the best known algorithm present. Processing data the way humans do it is a far more fault-prone and complex algorithm. And, we all know that a self-driven car isn’t giving suggestions to the driver, it’s responsible for all the decisions in driving.

Secondly, cognitive computing is not responsible for making decisions for humans, instead it is responsible for complementing or supplementing our own cognitive abilities of decision making. AI in medicine would be all about making the right decisions pertaining to a patient or the preferred mode of treatment, and minimizing the role of the doctor. Cognitive computing, on the contrary, would be more focused on achieving evidence that could supplement the human expert into making more flawless medical diagnoses.

Emerging Use of Cognitive Computing in Industries

We can gauge the success of cognitive computing and the development through the opportunities it has across industries. Cognitive computing is currently in a research phase, where research is going into properly implementing the technology in the fields deemed appropriate for its use. One can assess the opportunities for cognitive computing by looking at industries and industry specific scenarios where cognitive computing could make a big difference.

Customer services

Companies offering customer services deal with a lot of data which they have to accommodate with large processing requirements and are required to be efficient and flawless in advising customers to the right outcome. With so much happening, one can think about the opportunities for cognitive computing in this specific industry. At a consumer level, we can take the aid of robo-advisors that assist staff in advising new customers about what they can do and how they can go about creating a new account. There is also the concept of automated document processing that will limit human involvement and the flaws that come with it to a large extent. According to Dmitri: ‘Customer services are up for disruption, and the use of chatbots while booking airplane tickets or checking your insurance claim will go a long way in the future.’

Healthcare

Whenever we talk about Big Data, Machine Learning, AI or Cognitive Computing, the services that will be rendered through these technologies in healthcare always spring to mind. Human healthcare is certainly not at 100 per cent efficiency nowadays, which is because of the fact that there are certain flaws in the process. These flaws can be eradicated by giving machines the cognitive abilities required for going through a report and forming a basic judgment regarding the condition of any patient. The results can then be communicated to humans through a virtual display.

Industrial IoT

Most of the Industrial IoT giants that we have in industries such as car manufacturing, transportation, etc., have implemented exemplary data collection methods. These data collection methods do their job well, and hand over the necessary input to their patron organizations. Now, when the data is collected and stored off, the real challenge of anomaly analytics arises. Despite having stringent data collection and storage facilities, these firms don’t know what to do with their data and how to find actionable results.

The biggest problem facing businesses in today’s myopia is that only 20 percent of all problems or anomalies that occur are predicted and understood beforehand. This means that around 80 percent of the problems that businesses face are unpredicted, and the business is not prepared to handle them because of below par anomaly detection.

The Cognitive Anomaly detection is different from the traditional method, as it is a machine and data-first solution.  The future for cognitive anomaly detection is seemingly bright, and it is now the time to move from a research phase to deployment.

How to Move to Deployment

The deployment of cognitive computing requires adhering to a certain set of levels for achieving the desired aims. The levels that should be used for proper deployment of the technique include:

  1. Scale and Automate: It is necessary that you determine the scale of the deployment and then automate the process towards achieving the necessary scale. By knowing the scale of the move and the automation that is required, you can seamlessly incorporate cognitive computing in your setup.
  2. Start Using APIs: The next level in the deployment of cognitive computing includes the creation of APIs or Application Programming Interfaces. Chatbots and natural language processing are added to the interface to make it effective.
  3. Automation Middleware: The stage of automation middleware already provides 75 per cent of the entire share that is going into achieving the solution. Application developers need to put together applications quickly here, according to Dmitri Tcherevik. The fast processing of applications at this middle stage defines the success of the levels.
  4. App Blueprints per Domain: Despite the thoroughness of the steps mentioned above, there is still a need for an application blueprint for each domain. Application blueprints are created by Progress for different domains. Dmitri mentioned that they have created several blueprints for domains in healthcare. The applications required are complex, which is why there is a need for blueprints. The applications can then be personalized based on clients.
  5. DevOps: Once you have deployed the cognitive applications, there is the need to look out and monitor. The monitoring is done to look out for possible updates that can be incorporated. Cognitive applications need to be updated on a continuous basis to remain smart and up to date with what is expected from them.

With cognitive computing gaining center stage, it is expected that the concept will develop over time and will be implemented over numerous industries. Industrial IoT is expected to benefit a lot from cognitive computing as it can be used for deriving meaning out of the data they work with. In short, cognitive computing is currently leading the wave of the future as it holds the key to not only making healthcare, AI and Industrial IoT better, but also providing human thought processing and behavior that was needed here.

 

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

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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 Cognitive computing: Moving From Hype to Deployment appeared first on Ronald van Loons.

 

February 08, 2018


Revolution Analytics

DataExplorer: Fast Data Exploration With Minimum Code

by Boxuan Cui, Data Scientist at Smarter Travel Once upon a time, there was a joke: In Data Science, 80% of time spent prepare data, 20% of time spent complain about need for prepare data. — Big Data...

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Curt Monash

Some things I think about politics

When one tries to think comprehensively about politics these days, it quickly gets overwhelming. But I think I’ve got some pieces of the puzzle figured out. Here they are in extremely...

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Curt Monash

Politics can be overwhelming

Like many people, I’ve been shocked and saddened by recent political developments. What I’ve done about it includes (but is not limited to): Vented, ranted and so on. That’s...

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February 07, 2018


Revolution Analytics

In case you missed it: January 2018 roundup

In case you missed them, here are some articles from January of particular interest to R users. Josh Katz and Peter Aldhous used R to analyze the content and presentation of the most recent State of...

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Ronald van Loon

Ronald van Loon’s Journey to the Intelligent World

The world is rapidly changing thanks to digital technologies.

Business are transforming along with it.

Join Ronald van Loon on his journey through the intelligent world, and have a deeper look into technological development that are shaping a world and transforming business.

Watch and Subscribe here: http://bit.ly/2C1F7vg

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 Ronald van Loon’s Journey to the Intelligent World appeared first on Ronald van Loons.

 

February 06, 2018


Revolution Analytics

The AI Show: Data Science Virtual Machine

The Data Science Virtual Machine was featured on a recent episode of the AI Show with Seth Juarez and Gopi Kumar. If you want a quick and easy way to spin up a virtual machine with all of the data...

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February 05, 2018

Ronald van Loon

Digital Transformation Requires a Data-driven Culture

The role of data in organizations is no longer a myth or a mystery that is difficult to comprehend. The transformation to the workplace of tomorrow is underway, and it is time that all organizations catch up and implement the necessary changes.

While in the past leaders were praised for their ability to display gut instincts, this quality is no more required in the contemporary work setting. With more data driving operations in a business than ever before, it wouldn’t be wrong to say that the leaders of today need to cultivate a culture that is data-driven, instead of believing in their gut instincts.

The Need for Infrastructure

When cars and other vehicles started rolling out back in the 20th Century, it was imperative that the infrastructure be provided to facilitate the move towards vehicular transportation. This infrastructure consisted of roads, filling stations, and maintenance checkpoints. It was only after the necessary infrastructure was ensured that we could see cars going around on a more consistent basis. Thus, for data to be implemented in an organization, there needs to be proper infrastructure and most importantly, a culture that is driven by the need for data.

The cultural change towards a data-driven model would not only facilitate the data transformation that we expect to phase out in the future, but will also ensure that organizations have the infrastructure necessary to delivery and access adequate data and make accurate analytics.

By implementing a data-driven culture in your organization, you would surely not be the first organizational CEO to walk down this road. Numerous organizations have gone down this path and have experienced profound success in getting the results they desired. A recent report by Aberdeen has mentioned that organizations which have implemented a data-driven culture have witnessed a seven percent annual increase in the revenues they make in a year. This increase in revenue has been achieved through the increase in efficiency when it comes to the operational activities of the business. 83 percent of the businesses that have made the transition to a data-driven culture experienced an improvement in the time taken for one operational cycle to complete. Moreover, the improvement in time taken per cycle was coupled with the fact that businesses that have implemented the data-driven model also witnessed a reduction of 12 percent in organizational costs that were going down as a waste before the transition.

Implementing a Data-driven Culture

With the basic benefits of making the transition towards a data-driven culture out of the way, we will now take a look at some of the steps an organization should take to ensure a culture that runs on data.

  • Establishing a Clear Vision: To successfully incorporate data into the DNA of any organization, it is necessary that you develop a vision that points towards the path needed for success on this front.
  • Ensure Easy and Secure Access: The whole point of the implementation of a data-driven mindset in an organization is that it is easily accessed by all employees and creates a change across the organization.
  • Keep Your Data Clean: The point with data is that it needs to be regularly maintained to ensure that data remains clean and crystal clear. Data analysts need to know exactly what constitutes a good data-set.
  • Create Agile Teams: It is people not tools who dictate the culture within an organization. Thus, to create a data-driven culture, it is necessary that you make and regulate teams.
  • Develop Rewards: Rather than just inputting data into the organization, it is necessary that you instil competition as well to get the desired results. A reward mechanism would motivate teams and will help them work better.

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 Digital Transformation Requires a Data-driven Culture appeared first on Ronald van Loons.

 

February 04, 2018


Simplified Analytics

What Chatbots are doing in the Digital Age

Our lives have changed for good due to the digital tsunami – it started with internet in 1995, then in 2004 social media stormed the world with Facebook, then came iPhone in 2007 and the whole world...

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February 02, 2018


BrightPlanet

Mapping Bitcoin Metrics Across Deep Web News and Sentiment with Tableau

Bitcoin, and other cryptocurrencies, have been a hot topic of conversation recently. The volatile digital currency rocketed from $1,000 to nearly $20,000 in 2017 before crashing back down to around $10,000 in January 2018. We thought it would be intriguing to query BrightPlanet’s Global News Data Feed to compare Bitcoin price trends with news mentions […] The post Mapping Bitcoin Metrics Across Deep Web News and Sentiment with Tableau appeared first on BrightPlanet.

Read more »

Revolution Analytics

Because it's Friday: Time for Sushi

Why not end your week with a strangely calming dose of the bizarre: the short film Time for Sushi, by David Lewandowski. That's all for us for this week. Have a great weekend, and we'll be back next...

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Revolution Analytics

How AI works ... and how it fails

There's been a lot written about AI in recent years, but it's rare to find an article that explains the basics in non-technical language, without dumbing down the concepts. It's definitely worth the...

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February 01, 2018


Revolution Analytics

R Consortium funding for projects and R user groups

If you're an organizer of an R-focused meetup group, or are planning a community-led R conference, the 2018 R Consortium R User Group Support Program is now accepting applications for sponsorship....

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January 31, 2018


Revolution Analytics

Analysis of Trump's State of the Union Speech, with R

President Trump's State of the Union speech was last night, and it seemed to me it dragged on a bit. That's because it was apparently the slowest SOTU speech in history, based on average number of...

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Ronald van Loon

IoT and 5G New Technologies Create New Opportunities

The world is adapting and shaping around IoT technologies. In combination with 5G network connectivity, companies are preparing themselves for this constant shift to the progression of these technologies.

Network operators, telcos, and IoT companies are coming together to promote an ecosystem where customers can have great experiences with your brand. Companies can’t deliver this customer experience all by themselves. They cooperate with each other in an ecosystem of companies. A lot of the reliable, low latency IoT connections run on Telecom networks.

With IoT, everything can be connected and measured. If you can measure something, then you can make a lucrative businesses model with it. So there will be a ton of new opportunities and business models developing to monetize these IoT opportunities.

This new approach has to be supported with a BSS, or Business Support System. Think of a Business Support System as a program on your computer that you use to manage your business, and all parties in the ecosystem can work together. From point of sales, to billing, to managing customer experiences and feedback, a Business Support System manages everything.

Whether you are logging in as a Product Manager configuring and managing commercial aspects or a Business Configuration Engineer handling all technical aspects, this Business Support System is streamlined and user friendly, and you can design product offerings and calculate costs in real time.

Telcos are positioned to change the experience for App developers with an App Ecosystem all the way down to improving the experience for the user by giving them a seamless experience across platforms.

Telcos can fully benefit from using IoT to develop new technologies enabled by 5G network connectivity. A Business Support System is going to put the digital consumer at the center of their strategies, creating an ecosystem that is prioritizing the Customer Experience.

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 IoT and 5G New Technologies Create New Opportunities appeared first on Ronald van Loons.

 

January 30, 2018


Forrester Blogs

Dead Brands And Living Brands

MIT Media Labs may have helped put a fork in the monolithic approach to branding in 2011. Today great brands are less recognizable by a particular color palette than by a tonal, or spiritual, or...

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Forrester Blogs

Celebrate Data Privacy Day By Learning More About Your Consumers’ Privacy Attitude And Behaviors

When growing demand for more transparent information and control over personal data meets new rights and safeguards that enable consumers and employees just to do that, it’s an event to celebrate...

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Forrester Blogs

Healthcare Industry Not Immune From Amazon Effect

Perhaps no company has been so consistently disruptive as Amazon. Their ability to step in to new markets and upend the status quo by delivering consistent customer experiences with digital...

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Forrester Blogs

SAP Acquires Callidus Software For $2.4B

The definition of CRM has changed. Yes, that is a big statement to make, but it’s an important one. CRM grew from a customer data repository into functional applications, first with sales...

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January 29, 2018


Revolution Analytics

Speed up R with Parallel Programming in the Cloud

This past weekend I attended the R User Day at Data Day Texas in Austin. It was a great event, mainly because so many awesome people from the R community came to give some really interesting talks....

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Ronald van Loon

AI’s Impact on Retail: Examples of Walmart and Amazon

Artificial Intelligence or AI is expected to be in major demand by retail consumers due to its ability to make interactions in retail as flawless and seamless as possible. Many of us do realize the potential of AI and all that it is capable of, along with the support of Machine Learning or ML, but don’t realize that the implementation of AI in certain segments has already begun.

AI in Retail

The future for AI and the complicated computer processes involved behind it is really bright in the field of retail. AI currently has numerous data sets working along with computer visualization methods to ensure that the users get the most seamless experience when it comes to AI in the workplace. There are some interesting facts that pertain to the use of AI in retail. Here we have some of them to build the insight into what you can expect during the feature;

  • It is expected that customers will manage 85% of their relationship with the enterprise without interacting with a human.
  • According to a report by Business Insider it is said that customers who interact in online opinions and reviews with retailers are 97 percent more likely to convert along with the retailer during this phase of change.

With such promising figures on the card, one cannot help but notice the wave of change that has already started in the field of retail. With work already in progress, major retailers such as Amazon and Walmart have made advances that are expected to dictate this transition to AI in retail. We will be looking at these advancements, and will see how they can work out in the future.

Walmart’s Shelf Scanning Robots

Source YouTube/Walmart

You might have heard of shelf-scanning robots being tested by retailers, but we’re just about to witness one of the most interesting advances in the deployment of these robots. Walmart, which is one of the biggest physical retail chains across the world, is planning to extend the tests for its shelf-scanning robots across 50 additional stores, including some from its native land of Arkansas.

The machines, which have been deemed to be the future of shelf scanning, will roam around the aisles to check all factors including pricing, misplaced items, and stock levels, to assess the level of stocks within the store. This would not only save human staff all the hassle of checking these trivial details by themselves, but would also mean that they can focus on other more important details. The machines will require technicians to be present on site to handle the situation in case of a technological impairment, but the robots are currently fully autonomous to handle their tasks themselves. These robots will be using the concepts of 3D imaging to roam around aisles, dodge obstacles, and to make notes about the blockages in their pathway.

Amazon Go

Source YouTube/Amazon

Amazon Go is the latest wave of technology in retail that is expected to lead the way to the future of AI in retail. The basic concept behind Amazon Go is that it is a new kind of store that flourishes on the concept of no checkout requirements. Consumers who walk into a store can take whatever they want without having to go through the hassle of lines and waiting for checkout.

The checkout free shopping experience in Amazon Go is only made possible through the use of the same technology that is currently in place behind computer vision, sensor fusion, and self-driving cars. The technology automatically detects all that is being taken and keeps track of them in a virtual cart. Shortly after the consumer leaves, they will be sent a receipt and charged through their Amazon account.

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 AI’s Impact on Retail: Examples of Walmart and Amazon appeared first on Ronald van Loons.

 

January 28, 2018


Simplified Analytics

How Digital is helping businesses with geofencing?

You are walking in the mall doing the window-shopping, you pass in front of an electronics store, and suddenly you get a discounted offer to buy a TV you dreamed for….you are amazed. You go in see...

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January 27, 2018


Jeff Jonas

Meet Senzing. Meet G2. Say Hello to Entity Resolution 2.0

[Re-posted in full from my LinkedIn blog post] Good morning world! We are Senzing and this is a big week for us. We’ve been operating in stealth mode since August 2016 when we signed a unique...

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January 26, 2018


Revolution Analytics

Speed up simulations in R with doAzureParallel

I'm a big fan using R to simulate data. When I'm trying to understand a data set, my first step is sometimes to simulate data from a model and compare the results to the data, before I go down the...

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Mario Meir-Huber

Имплантация зубов в Могилеве от лучшей стоматологии города

Имплантация зубов в Могилеве от лучшей стоматологии города

Появление частых проблем с зубами, как правило, связано с каким-либо заболеванием, к примеру, диабетом или артритом. Иногда причина кроется в наследственности, возрастных изменениях в организме или беременности.

Когда зубы крошатся, нельзя откладывать на неопределенное время визит к стоматологу, так как это станет причиной потери поврежденных зубов. В месте сколов эмаль повреждается, зуб становится незащищенным от попадания вредоносных бактерий, вызывающих кариес. В результате за считанные недели можно потерять зуб.


имплантация зубов в Могилеве

Избежать повреждения эмали и многих других проблем поможет использование неабразивной зубной пасты. Практически все отбеливающие и осветляющие пасты являются абразивными. Постоянное их использование приводит к тому, что эмаль становится очень тонкой, склонной к механическим повреждениям. Из-за этого даже съеденный сухарик может привести к отколу кусочка.

Профессиональная консультация никогда не будет лишней, тем более что стоматологии Могилева способны удовлетворить потребности клиента.
Если кариес был залечен, но после установки пломбы продолжают откалываться на кусочки, то нужно повторно посетить стоматолога, чтобы установить причину этого явления и устранить ее.

Крепкие зубы – это не только своевременные визиты к врачу, постоянный уход за полостью рта, а и правильное питание. В рацион обязательно нужно включить молочные и кисломолочные продукты. Причем отдавать предпочтение стоит не глазированным сыркам или пудингам, которые содержат незначительное количество молока, а обычному творогу, молоку, кефиру. А вот твердые продукты вроде сухариков и сушек лучше вообще исключить из рациона. Об открывании банок, бутылок и раскалывании орехов зубами вообще нужно забыть.

Витамины и минералы, содержащиеся в сырых овощах и фруктах, благотворно влияют на организм в целом, в том числе и на здоровье зубов. Также весьма полезна морская рыба, свежая зелень, гречневая крупа и печень.

Чтобы эмаль не портилась, не стоит сразу после употребления холодных продуктов кушать или пить горячие, и наоборот. Любителям ковырять во рту зубочистками желательно заменить эту процедуру чисткой межзубных промежутков нитью.

Если десна часто кровоточат, а зубы крошатся, то следует каждый день полоскать рот теплым отваром шалфея и ромашки. Эти лекарственные травы способны снять воспалительные процессы, устранить кровотечения и избавить полость рта от вредоносных бактерий, вызывающих кариес.

За зубами необходимо правильно ухаживать с детских лет, чтобы не ходить со вставной челюстью, не достигнув пенсионного возраста.

Диета, которой мы придерживаемся, влияет не только на наше самочувствие и физическую форму, но и на здоровье наших зубов.
Стать обладательницей идеальной улыбки поможет ограничение или полное исключение следующих продуктов:
1. Сладости, особенно карамель и жевательные конфеты
Сахар – злейший враг нашей зубной эмали, который провоцирует возникновение кариеса. Жевательные конфеты и карамельприлипают к зубам и проникают в межзубные промежутки, откуда их потом трудно удалить. После употребления сладостей нужно обязательно почистить зубы и воспользоваться зубной нитью.
2. Цитрусовые и маринованные овощи
Цитрусовые, также как и маринады на основе уксуса, способны размягчать и ослаблять зубную эмаль.
3. Лед
Слишком твердые и холодные вещества могут стать причиной появления микротрещин на поверхности зубов.
4. Кофе и чай
Танины в составе кофе и чая приводят к появлению темных пятен на зубной эмали. После употребления этих напитков необходимо удалить налет зубной щеткой.
5. Сладкие газированные напитки
Кислоты и сахар в составе газированных напитков способствуют разрушению зубной эмали и развитию кариеса. Рекомендуется прополоскать рот после употребления таких напитков.
6. Алкоголь и табак
Ощущение сухости во рту, кариес, предрасположенность к раку ротовой полости, необратимое пожелтение зубной эмали…
Эти два ядовитых вещества желательно навсегда вычеркнуть из своей жизни, чтобы сиять здоровой белоснежной улыбкой!

The post Имплантация зубов в Могилеве от лучшей стоматологии города appeared first on Techblogger.

Jean Francois Puget

Gold Medal Winning Solution to Sales Forecasting Kaggle Competition

image

 

I had the pleasure to team with Kaggle grandmaster Giba, aka Gilberto Titericz Junior, currently ranked 1st on Kaggle.  We teamed for a sales forecasting competition, namely the Corporación Favorita competition.  Corporación Favorita is a retailer from Ecuador.  The problem was to forecast sales for all stores and a large selection of products for the next 16 days.  We were given past sales figures, as well as a number of additional data on stores, products, and holidays in Ecuador.  Giba and I ended up at the 8th rank among 1675 competing teams.

Our solution is a mix of deep learning and gradient boosting models.  It is described in more details in our Kaggle writeup.  The most noticeable event for us is that we were never near the top during the competition, and we were around the 45th place on the public leaderboard at the end.  But we jumped to the 8th place on the private leaderboard.  This may sound abstract but its significance is clear when we know how the public/private test data split was done. The public leaderboard is computed on the predictions made for the next 5 days, while the private leaderboard is computed on the predictions made for the days 6 to 16 to come.  Therefore, our model was ranked 45th on the first 5 days predictions, and 8th on the longer term predictions.   Our model is getting relatively better as the prediction period grows, which means we capture trends better than most.

I think this is due to the way we did cross validation.  Cross validation is a general technique to evaluate a model performance when you have a rather small dataset.  This idea is to repeatedly split the dataset into a train dataset and a validation dataset, train a model on the train dataset, and evaluate it on the validation dataset.  When doing cross validation, depending on the split, a given piece of the dataset can be either part of the train dataset, or the validation dataset.

When dealing with time series forecasting, as in the  Corporación Favorita competition, we must be careful as time is not symmetrical.  The validation dataset should always be in the future compared to the train dataset.  We describe our cross validation approach in detail in our writeup, but the main idea is to create several train/validation splits based on time, then use all these splits as usual in cross validation.  

A side effect of this competition is that I became the only Kaggle discussion grandmaster so far.  It seems that some of my writing on Kaggle forums is of interest ;)

 


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January 25, 2018

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Tweet Wars: The Last Data Point

It has been two years since we have started Knoyd. We have helped quite a few companies using their data more efficiently and have learnt a lot during the process. It has also been two years since we published one of our very first blog posts, analysing tweets about Star Wars: The Force Awakens.  And since the new installment has hit theaters in December, we have decided to create a re-hash of this original blogpost.

We have (once again) tracked the activity on Twitter before and after the release date to gain insight into the reactions of people and their feelings about the latest episode of the most famous movie franchise in history. We will also comment on the changes in the original code we created.

 

DATA:

We have collected the stream of Twitter data containing search terms and hashtags related to Star Wars: The Last Jedi through the TwitterAPI. The data had been collected between 24th of November 2017 and 8th of January 2018 (with world premiere being held on Dec 15th). All together more than 40 million tweets were collected, with ~7% containing geolocation either in form of direct coordinates or human readable location (e.g. New York) and ~20% being in english. One change we have improved over the years was the deployment of the collection script. Using micro instance on AWS and a cronjob to periodically sync the data back to our local machine, we managed to make the script error proof, while the whole thing remained free thanks to Amazon's Free Tier program.

Since we have collected almost 4x the data than in the previous experiment, this came with its own set of challenges. The python library for detecting tweet language that we used before (langdetect) was just too slow to process this much data and so we used (langid) instead that gave us a ~100x speed improvement. 

 

TRAFFIC:

The first thing we looked at was the frequency of Star Wars related tweets in time. It is clearly visible that most of the tweets came from US and UK, which can be easily explained by popularity of Twitter itself in these countries. The next thing to see is the periodicity of day and night, where people tweet more at night than during the day. Also the timezone shift is clearly visible.

More interestingly, we can see the build up before the release, as the number of tweets is increasing for a few days before the world premiere and sky rocketing on this day.

Just like two years ago, we have used a service called Carto (previously CartoDB) to create this cool map. The guys from Carto came a long way in the two years and their product unfortunately doesn't have a free version anymore, but it is definitely worth checking out.

 

SENTIMENT ANALYSIS:

One thing is to see that people tweet, but what do they think? To look at this problem we used a sentiment analysis model, which assigned each tweet a score between -1 and 1 (-1 being a total hater, 1 someone willing to die for the movie). First we plotted the results in a hexbin map, visualizing the sentiment in the world split in little hexagons (aggregating by mean within the cell).  

There are clearly visible small areas of very strong and positive opinions in Canada and northern and eastern Europe, which was consistent with our previous findings. There is a new fanbase based out of Morocco. Also apparently people did not like Star Wars (on average) very much in South America, Turkey, Japan and Indonesia (just like 2 years ago). 

Hexbin map of Twitter sentiments: using coolwarm reverse pallet (dark red = -1, dark blue = 1)

Hexbin map of Twitter sentiments: using coolwarm reverse pallet (dark red = -1, dark blue = 1)

We have still limited the analysis to the tweets in English language. NLP came a long way in the last two years and with a bit of additional effort and use of deep learning based techniques, it wouldn't be too much trouble to extend this analysis to account for multiple languages.

Let's take a look at how the sentiment evolved with number of tweets over the course of time. The number of tweets follows the normal distribution pretty closely with median around the release date. The average sentiment hovers around the same levels throughout the experiment. 

number_tweets.png

Conclusion

It was a fun exercise to revisit the code we have written over 2 years ago. Even tough the analysis remains largely the same, there are a few points that are good to mention:

  • Document your code. You will thank yourself in a couple of years
  • The open source tools evolve quickly. Don't take the way you solved a problem as a given
  • Automate as much of your process as possible

 

May the force by with you!


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IIoT: How Cognitive Anomaly Detection Transforms Industrial Maintenance

The Industrial IoT sector is facing maintenance challenges related to their data processes. Traditional methods of anomaly detection aren’t providing the right solutions for every entity, which is why Cognitive Anomaly detection is filling the gaps in predictive maintenance.

Progress DataRPM has developed an innovative, effective new route of anomaly detection and prediction in Industrial IoT. A Machine Learning and Data-First solution is providing prevention and optimization for organizations while simultaneously driving data value and enhanced customer experiences.

Join us for a webinar on Wednesday, January 24, 2018 at 9:00 am PT where Ronald van Loon, ranked number 3 influencer in the world for Big Data and IIOT & Taj Darra, Data Scientist at Progress DataRPM will discuss:

  • An introduction to anomaly detection and prediction for Industrial IoT, and why this is an important opportunity for businesses to take advantage of now
  • The evolving landscape of Industrial IoT, and how Machine Learning is augmenting cognitive anomaly detection
  • Challenges in the market and why technologies are aligning to suit business needs
  • Traditional approaches to anomaly detection vs. cognitive anomaly detection, and the steps involved in the process
  • Use cases and demonstrations in real world scenarios

Ronald and Taj will also hold a Q&A session at the end of the webinar.

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 IIoT: How Cognitive Anomaly Detection Transforms Industrial Maintenance appeared first on Ronald van Loons.

 

January 23, 2018


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Smart Society: How to Trust Artificial Intelligence

The concept of a smart society has been around for a long time, but the progress we have seen towards achieving it in the last decade has really been a giant leap for mankind. For those of us who are unaware, the smart society which looms over us is the future of mankind. We are about to enter a phase where living smart is the baseline, and everything else just falls in the jigsaw to complement that lifestyle. In smart societies, we are blessed with smart cities that run through the application of smart accessories and smart buildings.

In smart societies, we have smart cars (also known as self-driven cars or autonomous vehicles). We expect a better flow of traffic with traffic management that is propelled through extensive and authentic data provided by these vehicles and analyzed with smart algorithms (e.g. based on AI). The most prominent detail about smart societies as we know them now is the pervasiveness of the Internet of Things (IoT) at the smallest level. The implementation of IoT at micro levels drives the need for self-learning algorithms, hence the emphasis on AI. Eventually, it all conflates together to form the bigger picture of a smart society.

AI and Algorithms

Smart society is way more than just an imagination now, and it is high time that we started answering the genuine questions pertaining to it. The role of Machine Learning in the overall implementation of the smart society is driven by the simple fact that humans are incapable of analyzing all that data in a classical way. The words “Machine Learning” already provide an indication of where the issues is with these algorithms. The machine is learning, i.e. it doesn’t necessarily follow logic easily understood by humans. That leaves us with the interesting challenge to understand how Machine Learning and algorithms impact us and the concept of the smart society. The term coined for this impact by technology philosopher Evgeny Morozov is the “Invisible Barbed Wire.”

The omnipresent systems in our smart society are driven by algorithms. That means they are directly impacting our decisions, our lives, so it would be worthwhile to understand exactly how that works. Here are a few examples:

  • Data centered applications are now heavily adopted by the general public. One specific mobile application gives its users the chance to detect the type of cancer, with high levels of accuracy.
  • “Simple” algorithms such as those predicting the weather already have an impact on how you want to plan your day for ages. Who hasn’t changed his or her plans to go to the beach at least once because the weather prediction suggested that there were thunderstorms expected?.
  • Navigation systems that we have in our cars and our mobile phones decide the best path that should be used. The decision is optimized with fresh data coming in from all alternative paths available for our journey. By assessing traffic updates and other implications, these navigation systems come up with what is best for us.

Humans are now depending heavily on these system, even without realizing it, which is why algorithms are termed “invisible barbed wire”.

Can You Trust AI?

The question that arises once we are done with evaluating the impact of algorithms on our lives is, “can we trust AI?” The first and foremost concern for most individuals and smart phone users is what companies across the world are doing with their data. Where is the data that is collected going and how does the analysis of that data match with your expectations? These are important questions for all users, and questions that we would like to be answered.

Although the market for AI and analytics is developing rapidly, the trust deficit is not showing any signs of decreasing. Some recent statistics that highlight this trust gap are:

Source: KPMG

Now that we are aware of the current trust deficit, what can we do about it? What are the anchors that build trust between the stakeholders? KPMG identified of them:

  • Is the data analysis process and the data itself of top notch quality? Of course, requirements depend on domain and application. Bank transactions and medical diagnostics put higher constraints on the quality of data and data analysis processes than marketing campaigns.
  • Does the analysis do what it is intended to do? This becomes especially important when data or algorithms are reused. Data or algorithms collected or developed for one purpose are not per definition suitable for another.
  • Is the use of the data and algorithms considered acceptable from ethical and regulatory perspective? Gender-based or age-based discrimination are typically prohibited by law. Data analysis must obviously comply with all regulations such as the GDPR, etc.

A trust deficit will occur when data analysis fails to adhere to the anchors mentioned above. Data analysis that fails to meet the anchors of trust is widespread and well-known. Death by GPS is a common term for people getting lost due to GPS interpretation mistakes and the reputation of Flash has led to an aura of unreliability and unpredictable behavior. Interestingly enough unpredictability is the last thing you would expect from a machine and not meeting expectations is the fastest way to a trust gap. Other quick ways to dissatisfaction and reputation damage are security breaches and the discovery of unintended bias.

The smart society has some interesting knock-on effects, e.g. in the area of liability. Research shows that more than 62 percent of D&A professionals believe that accidents caused by self-driving cares are the responsibility of the organization that creates the software and algorithms.

How to Trust AI?

The last result indicates that the data scientist has a crucial role in establishing trust in AI. The data scientist is responsible for developing the data analysis and the metrics, so they play an important role in building trust in this ecosystem.

There are already numerous controls and frameworks that exist as a way to regulate development. ISO for e.g. software quality and information security and FAST for financial modeling are some examples. The FACT principle has also been suggested for responsible data science, representing Fairness, Accuracy, Confidentiality, and Transparency; all important and necessary attributes.

Another factor that can generate trust is ethics by design, an extension of privacy by design. Design a system in such a way that the correct processes have to be followed, i.e. make it technically impossible to circumvent these processes. Only the right combination of organizational and technical measures can enforce data scientists to do the right things in the right way.

Finally, third party review is crucial for building trust. Here is where assurance firms such as KPMG come into the picture. KPMG has performed audits on financial statements for the last 100 years and has now stepped into digital assurance. We can learn valuable lessons from how the audit of financial statements works. Social value is crucial, as is the willingness to work on a trusted relationship with society. This calls for a balanced approach towards transparency. Opening the black box and creating trust in all stakeholders is what external auditors such as KPMG aim to achieve. The smart society is imminent, but we need auditors to keep a stringent check on all aspects related to data analysis.

For more professional intake on this issue, you can listen to our webinar here .

 About the Authors

Sander Klous is Professor in Big Data Ecosystems at the University of Amsterdam and Partner in charge of Data & Analytics at KPMG Nederland.

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 YoutubeLinkedIn and Twitter.

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 Smart Society: How to Trust Artificial Intelligence appeared first on Ronald van Loons.

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