Category: Data Analytics

Data privacy is a hot-button topic. Most people can agree that it’s important to keep personal data private, but are you really doing much to keep your data safe?

Consumers are fervent in their fight to protect their data, but they do little to maintain it safe. It’s known as the privacy paradox, and it may be hurting consumer efforts in order to keep their information out of third-party hands.

What makes consumers so willing to give up their personal data?

Data in Exchange for Something Valuable

According to recent research, most (75%) of internet users don’t mind sharing personal information with companies – as long as they get something valuable in return.

A recent Harris Poll also found that 71% of adults surveyed in the U. S. would be willing to share more personal data with lenders if it meant receiving a fairer loan decision. Lenders typically ask for information about the applicant’s personal financial history, but the particular poll suggests that borrowers may be prepared to give up even more information.

Research suggests that consumers are well aware of and understand that data exchange is a sensitive matter, and they’re willing to be participants in the “game. ” But they want the particular game to be fair. In other words,…

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The entire process associated with working is going through fast changes with every advance in technology. Top financial advisors and leaders now see the future completely reliant on data science.

Automation is occurring in all industries, plus while some jobs will become streamlined, that does not necessarily mean lowering the number of employees. With new technology, people need to reexamine software, information storage and even give up some responsibilities to Artificial Intelligence.

Statistics vs . Data Analytics

Statistics are a vital part of learning customer basis and seeing exactly what is occurring within the finance company and how it can be improved. There is a difference between analytics plus statistics.

Vincent Granville, information scientist and data software pioneer explains this in the particular simplest forms, “An estimate that is slightly biased but robust, easy to compute, and easy to interpret, is better than one that is unbiased, difficult to compute, or not robust. That’s one of the differences between information science and statistics. ”

Data science did evolve from a need for better data, and once big data arrived, the particular standard statistical models could not handle it. “Statisticians claim that their methods apply to big data. Information scientists claim that their methods do not affect small data, ” Vincent…

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Most modern technologies complement each other nicely. For example, advanced analytics and AI can be used collectively to achieve some amazing things, like powering driverless vehicle systems. Big data and machine learning can become used collaboratively to build predictive models, allowing businesses plus decision-makers to react and plan for future events.

It should come as no surprise, then, that big data and 3D printing have a symbiotic nature as well. The real question is not “if” but rather “how” they will influence one another. After all, most 3D prints come from a digital blueprint, which is essentially data. Here are some of the ways in which big data and THREE DIMENSIONAL printing influence one another:

On-Demand and Personalized Manufacturing

One of the things 3D printing has accomplished is to transform the modern manufacturing market to make it more accessible plus consumer-friendly. There are usually many reasons for this.

First, 3D printing offers localized additive manufacturing, which means teams can create and develop prototypes or concepts much faster. The technology can also be augmented to work with a variety of materials, from plastic and fabric to wood plus concrete.

Additionally, the production process itself will be both simplified and sped up considerably. One only needs the proper digital formula…

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If we think of the newest trends in IT service automation, or try to follow the recent research, or listen to the particular tops speakers at conferences and meetups — they all will inevitably point out that automation increasingly relies on Machine Learning and Artificial Intelligence.

It may sound like the case when these two concepts are used as buzzwords to declare that process automation follows the global trends. It is partially true. In theory, machine learning can enable automated systems to test and monitor themselves, to provide additional resources when necessary to meet timelines, because well as retire those resources when they’re no longer needed, and in this way to enhance IT processes plus software delivery.

Artificial Intelligence in turn refers in order to completely autonomic systems, that can interact with their surroundings at any situation and reach their goals independently.

However , most organizations are in very early days in terms of actual implementations of such solutions. The idea lying behind the need for AI and related technologies will be that many decisions are still the responsibility of the particular developers in spheres that can be effectively addressed by adequate training of computer systems. For example, it is the developer who decides what needs to be executed, but identifying…

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Building on our month focussed on controversial topics, let’s turn to what will set your team up for success.

Different contexts can require different types of the analytics team. A lot of the advice that I offer within the Opinion section of this blog is based on a lifetime leading teams in large corporates. So , I’m pleased to partner with guest bloggers from other settings.

So, over to Alan to explain why getting “fuzzy” is the way for an analytics team to see success in the world of startups…

Get fuzzy! Why it will be needed

My co-founders and I have recently had to face up to this challenge of creating a new data analytics team having set up our new firm Vistalworks, earlier in 2019. Thinking about this challenge, reflecting on what we know, and getting the right answer (for us) has been an enlightening process.

With 70-odd years of experience between us, we have plenty of examples of what not to do in data analytics groups, but the really valuable question has been what should we do, and exactly what conditions we should set up in order to give our new team the best chance to be successful.

As all of us talked through this issue my main personal observation was that successful data analytics teams, of whatever size, have…

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Airline big data, combined with predictive analytics is being used to drive up airline ticket prices.

As airlines and their frequent flyer programs gather more intelligence on your day to day lifestyle, flying and financial position – they begin to build an airline big data profile.

Consumer interests, goals, psychometric assessment, your motivations in order to engage with a brand at any given every point throughout the day, what has driven you to purchase in the past – and most importantly – where your thresholds are.

To illustrate how data is playing a growing role within today’s flight booking engines I’ve broken down play by play how each piece of data collected about you can be used, analysed plus overlaid with other datasets to paint a picture of who you are, exactly what motivates and drives you to purchase a particular product.

Every day – trillions of calculations are number-crunched to transform this goldmine of data opportunity into real, tangible high-revenue opportunities for the airlines and their frequent flyer programs.

“When armed with key insights, a holistic overview associated with yours, and other customers’ detailed profiled information can be applied to direct booking channels which are designed to customize pricing for your personal situation at that very given moment. Here is…

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Heavy learning is growing in both popularity plus revenue. In this article, we will shed light on the different milestones that have led to the deep learning field we know today. Some of these events include the introduction of the initial neural network model in 1943 and the first use of this technology, in 1970.

We will certainly then address more recent achievements, starting with Google’s Neural Machine Translation and moving on to the lesser known innovations such as the Pix2Code – an application that is used to generate a specific layout code to defined screenshots with 77% accuracy.

Towards the end of the article, we will briefly touch on automated learning-to-learn algorithms and democratized deep learning (embedded deep studying in toolkits).

The Past – An Overview associated with Significant Events

1943 – The Initial Mathematical Model of a Neural Network

For deep learning to develop there needed to be an established understanding of the neural networks in the human brain.

A logician and a neuroscientist – Walter Pitts plus Warren McCulloch respectively, created the first neural network mathematical model. Their work, ‘A logical Calculus of Ideas Immanent in Nervous Activity’ was published, and it put forth a combination of algorithms plus mathematics that were aimed at mimicking…

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The production of massive amounts associated with data as a result of the ongoing ‘Big Data’ revolution has transformed data analysis. The availability of analysis tools and decreasing storage costs, allied with a drive-by business to leverage these datasets with purchased and publicly available data can bring insight and monetize this new resource. This has led to an unprecedented amount of data about the personal attributes of individuals being collected, stored, and lost. This data is valuable for evaluation of large populations, but there are a considerable number of drawbacks that information scientists and developers need to consider in order to use this data ethically.

Here are just a few considerations to take into account before ripping open the predictive toolsets from your cloud provider:

1 . Contextual Integrity

Data is gathered over different contexts which have different reasons and permissions for capture. Ensure that the data you capture is valid for that context plus cannot be misused for other purposes. There could be unintended side effects of mixing public and personal data. An example is notifying other parties associated with location data without consent, as there are numerous examples of stalkers using applications to track others.

2 . History Aggregation

History is an important part of many efforts to defining…

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As artificial intelligence progresses and technology becomes more sophisticated, we expect existing concepts to embrace this change — or change themselves. Similarly, in the domain of computer-aided processing of natural languages, shall the concept of natural language processing give way to natural vocabulary understanding? Or is the relation between the two concepts subtler and a lot more complicated that merely linear progressing of a technology?

In this post, we’ll scrutinize over the ideas of NLP plus NLU and their niches in the AI-related technology.

Importantly, though sometimes used interchangeably, they are actually two different concepts that have some overlap. First of all, they both deal with the relationship among a natural language and artificial intelligence. They both attempt in order to make sense associated with unstructured data, like language, as opposed to structured data like statistics, actions, etc. However , NLP and NLU are opposites of a lot of other information mining techniques.

Source: Stanford

Natural Language Processing

NLP is an already well-established, decades-old field operating at the cross-section of computer science, synthetic intelligence, and increasingly data mining. The ultimate of NLP is to read, decipher, understand, plus make sense of the human languages by machines, taking certain tasks off the humans and allowing for a machine to handle them instead. Common real-world examples…

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