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Since 2009, the popularity of the cryptocurrency industry has increased rapidly, establishing a new type of digital investing. 

The Rise of Crypto

As many people know, cryptocurrencies are digital payment forms that people can use to buy online goods and services. The technology behind the crypto market is blockchain, a digital ledger that records transactions and validates them through proof of work or proof of stake. Cryptocurrencies are found in many industries today, including retail and online gambling. For example, in countries like Canada, you will find digital currencies such as Bitcoin accepted at retail stores like Birks Group, and there are many 

Crypto and Big Data Analytics

Despite so many industries capitalizing on the popularity of cryptocurrencies, the market still has its problems. Fortunately, big data analytics can solve many of these issues, such as by helping crypto users better identify the future of the market and the value of the thousands of digital currencies available to purchase and trade. That is because data analytics tools can 

Since 2009, the popularity of the cryptocurrency industry has increased rapidly, establishing a new type of digital investing. Reports now suggest more than 100 million people hold virtual currencies, and there are thousands of transactions per day for cryptocurrencies like Bitcoin and Ethereum. However, as cryptocurrencies continue to grow, so too does market volatility and the amount of transactional data. Because of this, it can be hard to maximize profits and identify future trends, which is why the crypto market can benefit from adopting techniques from big data chúng tôi many people know, cryptocurrencies are digital payment forms that people can use to buy online goods and services. The technology behind the crypto market is blockchain, a digital ledger that records transactions and validates them through proof of work or proof of stake. Cryptocurrencies are found in many industries today, including retail and online gambling. For example, in countries like Canada, you will find digital currencies such as Bitcoin accepted at retail stores like Birks Group, and there are many crypto casinos in Canada available in the North American country. These casinos have many benefits, such as providing crypto users (Bitcoin, Litecoin, etc.) with fast transaction speeds, higher bonuses, and the lowest possible minimum deposit and withdrawal limits. Even corporate giants like Microsoft, AT&T, and Starbucks have started accepting cryptocurrencies in Canada.Despite so many industries capitalizing on the popularity of cryptocurrencies, the market still has its problems. Fortunately, big data analytics can solve many of these issues, such as by helping crypto users better identify the future of the market and the value of the thousands of digital currencies available to purchase and trade. That is because data analytics tools can help uncover trends by looking at historical data, which crypto investors can use to make informed decisions about where the crypto market is going. Let’s say someone uses a crypto dashboard to monitor their coins and transactions. Dashboards are one of the best data visualization tools in the analytics market because they help bring visibility to your investments and finances. When someone connects their crypto wallet to a dashboard, they are able to track their performance in real-time and look at how much they have invested in different cryptocurrencies. Crypto dashboards also allow people to study ROI, acquisition cost vs. profit/loss, and the exchange rate of their cryptocurrencies. Examining a particular cryptocurrency’s exchange rate history will inform you of how stable the coin is compared to others.

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Big Data Definition & Analytics

The sheer volume of big data continues to grow as corporations and consumers depend on mobile devices, streaming, the Internet of Things (IoT), and other technologies that collect and use massive quantities of data. 

And with the growth of big data, the market is seeing a steady increase of tools and capabilities for storing and analyzing it. 

See below to learn about the top tools in the big data market and how enterprises are benefiting from the increased insights borne from big data.

“Big data” was first coined in the 1990s when technology experts began to recognize the quickly expanding pools of data in enterprises as well as the growing problem of processing and applying that data with existing technology.

Big data has grown across industries throughout the 21st century, and with that growth has come the development of different big data tools to handle the data.

Big data informs several business decisions and operations, but is especially helpful in the following categories:

Customer Analytics

Operational Analytics

Fraud Detection

Compliance

Data Warehouse Optimization

Read next: The Pros and Cons of Edge Computing

The global big data market reached $208 billion in 2023 and is projected for a steady compound annual growth rate of 10%, reaching $450 billion by 2026, according to Expert Market Research. 

The growth is mostly attributed to a growing desire to make all business data actionable in a competitive marketplace, with the growth of IoT devices contributing to the expansion of big data solutions.

Although North America is leading the market in big data software adoption and general strategy, China is the fastest-growing country in this predicted time period, with Australia, India, and South Korea also increasing their big data investments at a healthy rate.

In order for software to be classified as appropriate for big data management, it must meet the criteria of the “3 Vs”: variety, volume, and velocity:

Variety:

The tool is able to work with a variety of data types, whether structured, unstructured, quantitative, or qualitative.

Volume:

Big data consists of terabytes and petabytes of data to manage. Big data software must have the capacity to store/analyze higher volumes of data than traditional data tools as a result.

Velocity:

Especially in a fast-developing IoT marketplace, big data solutions have to be able to work with data quickly and in real-time in order to produce desired results.

More on data variety: Structured vs. Unstructured Data

More on data variety: Qualitative vs. Quantitative Data

Understanding big data provides huge benefits to organizations that take the time to set up, uncover and analyze their data. 

Some of the top benefits that companies realize when they tap into their big data potential include:

A better understanding of customer behavior at various stages based on large amounts of data that might have previously gone unanalyzed. This information is particularly helpful in the entertainment, e-commerce, and tourism industries.

For organizations that want to simplify their network security and operations practices, big data makes it possible to develop AIOps and automate different network functions, such as application performance management and network monitoring. This frees up time for network administrators to spend on more strategic projects.

In industries, such as finance, banking, government, healthcare, and others with frequent and large transactions, big data analytics improves fraud detection, risk management, and general compliance.

Customers across industries use big data tools to make sense of their customer and product data at scale. 

They frequently rely on the big data analytics in these tools, but perhaps more importantly, they use these tools for the data visualizations and reports that make big data digestible for non-data professionals:

“We have been using [Hitachi Vantara’s] Pentaho Business Analytics for the past 3 years in our department due to its best services for data visualization and data analytics. It is open and easily added to any platform. It provides such an easy UI that non-technical persons can get the use and analysis results. Easily configurable and deployed at our organization.” –Software manager, manufacturing industry, software review at Gartner Peer Insights

Some of the top big data solutions in the market are:

Amazon Web Services:

AWS’s solutions for big data include cloud storage, databases, data warehousing, analytics, and machine learning services.

Hitachi Vantara:

This lineup features big data storage, DataOps, IoT services, and big data analytics.

Tableau:

The Salesforce-acquired tool offers big data analytics, business intelligence, and data visualizations

Cloudera:

This big data platform offers a Hadoop distribution, plus data science and analytics tools.

Microsoft Azure

: The cloud platform offers storage, big data analytics, machine learning, data warehousing, and data lakes.

IBM:

IBM’s big data solutions include cloud services, database management, data warehousing, analytics, and machine learning.

Oracle:

The Oracle suite of big data solutions includes cloud-based and on-premises database management, data integration, and analytics.

Splunk:

This offering primarily focuses on analytics for log and security data.

Talend:

The solution features a set of big data integration tools.

RapidMiner:

The data science platform includes data mining, predictive analytics, and machine learning solutions

Finding the right DBMS solution: Oracle Database vs. Microsoft SQL Server

Learn more about what’s happening in big data here.

A Deeper Look At Google Signals Map And Social Data Hub

If you’re anything like me, you stumbled across a Google Signals Map and had no idea it even existed. However, Google Signals has actually been available for quite some time, reported first by MarketingLand and also recently featured on Social Media Today, and more and more companies are beginning to explore this feature and what it can do for their SEO. After all, when Google comes out with a tool we all know there must be some sort of value. With all of the information at their fingertips, Google Signals doesn’t disappoint.

How Google Signals Map Works

It’s first important to understand where you can find the Signals Map and how it was created in the first place. Believe it or not, it was in 2009 that Google created their PuSH (PubSubHubbub) protocol. This was designed as a way for different social networks to send their activity data to Google in order to help companies keep everything in one place. It wasn’t until January 2012 that Google took things to the next level.

In 2012, Google took this PuSH protocol and integrated all of this data with Google Analytics. This is where the Social Reports section of Google Analytics arose, and while not all social networks are on board just yet (more about that later), Google has enough data for companies to really begin seeing the big picture when it comes to social media. Google then created a map of this data to help companies even further:

As you can see from the photo above, Google looks at five different categories as the most important when it comes to analyzing your social “ecosystem” as they say. If a social network hasn’t taken the steps to feed its data into the protocol, then that information will be indexed as a post.

How Social Networks Can Get Involved

It is up to the social networks to integrate their stream of data into the social data hub. There are a few different benefits that Google outlines as to why social networks can benefit from integrating:

Visibility. Your social network will be visible to any businesses and publishers using Google Analytics. Google gives you more attention, and so it makes sense that these networks will get more visibility than those that haven’t integrated.

Cost. It’s completely free for a social network to sign up and get involved, and it doesn’t take much time. You can learn more here about how to get started.

Measurement. Being a part of the social data hub means that your social network is a part of promoting the global social media landscape by providing accessible measurement of all platforms involved.

At this point, there are over 30 integrated into the social data hub. You will notice networks like Facebook and Twitter missing from the list, but this doesn’t mean that there is not valuable data to be gathered from those that are involved. Some of the most popular networks include:

4 Big Data Strategies It Should Steal From Marketers

Big Data is on the rise, with analysts and pundits almost unanimously predicting rapid adoption and growth. IDC, for one, predicts that the market for Big Data products will reach $16.1 billion by the end of this year and hit $41.5 billion by 2023, growing six times faster than the overall IT market.

Investors are also pouring money into Big Data startups, with the biggest splash being Cloudera’s billion-dollar investment from its partnership with Intel.

As more and more companies jump on the Big Data bandwagon – a recent Gartner survey found that 73 percent of all businesses are already investing in or have plans to invest in Big Data – IT will begin to get pressure to help turn these investments into actual business initiatives.

Here are 4 Big Data strategies IT should steal from marketers:

We all hate high-pressure, high-B.S. sales tactics. We avoid the Glengarry, Glen Ross sales types, who think of selling as a competition – with you as the mark. In the age of social media and Big Data analytics, it’s pretty easy to prove that these techniques aren’t optimal.

Yet, many, many businesses still employ them. Many businesses still treat their prospects as flesh-and-blood piggybanks that they’re eager to crack open.

Similarly, IT all too often treats the people seeking its help as nuisances, rather than as business assets.

It’s become a cliché that the most important asset in any business is its people, yet the biggest complaint about IT support interactions tends to be that IT comes off as too arrogant and is too dismissive of the people it serves.

How do you counter this?

A good place to start is with all of those calls that are supposedly recorded for training purposes. Rather than simply storing them for compliance purposes and then forgetting about them, now is the time to actually start analyzing them.

Lesson for IT: Tools from companies like CallMiner, Nexidia, and Utopy will help your organization apply text and sentiment analysis to calls to help you identify patterns and trends. Over time, effective techniques will stand out, as will effective members of your IT support staff.

Developing a proprietary audience involves nurturing and engaging with a person from the minute they become aware of you on through to when they follow you, sign up for your email newsletter, share your messages, and, eventually, become a loyal repeat customer. The key, though, is to tailor your communications, so they match where people are actually at along that path.

“If you send people the wrong message at the wrong time, you can do more harm than good,” Rohrs told me when we sat down at the Connections conference earlier this fall to discuss his book.

Marketers have woken up to the fact that they need to focus, in granular detail, on the needs of their audience, needs that evolve over time. Yet, the only way to better target your audience is to figure out who, exactly, these people are.

“Most companies have not taken the time to differentiate their audiences,” Rohrs said. “Contacts are strewn across different channels, databases, and teams, and there is no real strategy for engaging them.” As a result, audiences are regarded more like resources to use up, rather than business assets to cultivate and serve.

Rohrs identifies three main audience segments that marketers should focus on: seekers, amplifiers, and joiners.

Seekers are looking for information or distractions. This is what pretty much all of us do when we browse social media looking for interesting articles. Amplifiers are on the hunt for things they want to share with their own followers. Amplifiers have their own large audiences, and they are the fuel that powers any viral campaign. Then, finally, there are joiners, or the people who actually purchase your products or services.

Of course, people inhabit different roles at different times, so those roles can and should evolve, but it’s important to tailor your message to where people are now, not where you’d like them to be.

Lesson for IT: Start studying every interaction you have with the people you serve to see if you can segment them into more discrete audiences. After all, the person you need to remind to check to be sure their power strip is turned on will have much different needs than a tech-savvy person frustrated by some software glitch. Yet, all too often, each person is queued up the same way, which is wildly inefficient.

How Big Data Analytics Is Transforming The Casino Industry

As one of the world’s most cutthroat markets, it is no surprise that the casino business has jumped on the big data analytics bandwagon. Casinos may improve their tracking of consumer behaviour, learn more about their patrons’ requirements and preferences, and streamline their operations using big data analytics. Notably, some of the casinos reviewed on SF Gate are utilizing big data analytics to tailor their offering to suit specific customers. The article takes an in-depth look into how casinos utilize big data analytics to streamline their operations. 

Identifying customer preferences and trends

With big data analytics, casinos may better understand their customers’ tastes and behavior by gathering, analyzing, and drawing conclusions from massive volumes of data. Customer profiles, purchase histories, gaming tendencies, loyalty program participation, and other types of information may be collected. Casinos may use this data to understand their patrons better and cater to their needs. For instance, they may be able to tell which games are the most well-liked among people of a certain age or gender. In addition, they could ascertain which campaigns are most successful in generating client engagement or loyalty. Now that they have this information, they may make educated guesses about improving their business for optimum profit.

Promoting responsible gambling practices

There has been a rise in the importance of big data as a tool for responsible gaming. Operators may learn a lot about their customers’ habits by gathering and analyzing a lot of data. The data may help identify problem gamblers before they fall too far into debt. One other way operators may benefit from big data is by gaining a deeper understanding of client preferences and using that knowledge to improve the quality of their offerings. The operators may provide more customized incentives and promotions for each player by, for instance, monitoring their spending habits.

Ensuring customer privacy using big data analytics

There are several measures casinos use to protect their client’s personal information. Encryption is one of the most vital methods. Casinos utilize sophisticated encryption methods to protect all player information, making it almost difficult for unauthorized parties to decrypt player records. To further ensure the privacy of their patrons, casinos often use anonymization methods. Before using data for analytics, it must be cleansed of any identifying characteristics. To round things out, casinos have stringent procedures limiting who may access consumer data and how it can be utilized. To guarantee that the most up-to-date measures always protect the privacy of their customers, casinos examine and revise their policies regularly.

Automated decision making using big data analytics

Implementing fully automated decision-making might drastically alter the future of online gambling. Casinos can now make better, more efficient judgments regarding gameplay thanks to the use of algorithms and AI. The casino’s bottom line and the patrons’ overall satisfaction both stand to benefit from this. By identifying questionable behavior and taking action, automated decision-making might also assist in eliminating fraud and cheating in online casinos. Further, player data might be analyzed using automated decision-making to learn about preferences and design games appropriately. Lastly, automated decision-making might be used in developing new games and enhancing current ones to maintain player interest and satisfaction. As a result of all these positive implications, it’s evident that automated decision-making merits additional exploration for its possible uses in the online gambling sector.

Challenges arising from implementing big data analytics

The massive volume of data is a significant obstacle for casinos attempting to use big data analytics. Customers’ gaming habits, financial transactions, and preferences contribute to the massive amounts of data generated by casinos. Gaining valuable insights into client behavior requires collecting, analyzing, and storing this information which is challenging. Casinos also must safeguard this information against hacking attempts. Validating the accuracy and consistency of the analytics is another obstacle to overcome. Last but not least, casinos should think about how they will put the results of big data analytics to use to boost their business. 

Predictive analysis using big data analytics

Lastly, casinos may use predictive analytics to jump on the competition and maintain long-term profitability by predicting industry trends. Using predictive analytics, casinos can see how varied incentives affect patrons’ propensity to return and spend. It enables businesses to optimize their offerings for maximum profit with little sacrifice to client satisfaction.

A Fascinating Look At How Artificial Intelligence And Machine Learning Work At Apple

Respected journalist Steven Levy has scored another nice exclusive with a new write-up over at Backchannel, a Wired Media Group property, giving us a rare inside look at how artificial intelligence and machine learning work at Apple.

Welcome to The Temple of Machine Learning

Apple has “a lot” of engineers working on machine learning—some of which weren’t necessarily trained in the field before they joined Apple—and any results of their work are shared and readily available to other product teams within the company.

“We don’t have a single centralized organization that’s the Temple of Machine Learning in Apple,” says Apple’s boss of Software Engineering, Craig Federighi. “We try to keep it close to teams that need to apply it to deliver the right user experience.”

Of course some talent comes from acquisitions as Apple’s recently been buying 20 to 30 small companies a year. “We’re looking at people who have that talent but are really focused on delivering great experiences,” said Federighi.

Making Siri sound more like an actual person

Before we get to it, there are four aspects to Siri:

Speech recognition—turning speech to text

Natural language understanding—understanding what you’re saying

Execution—fulfilling a query or request

Response—talking back to you

Although Apple was still licensing much of its speech technology for Siri from a third party (likely Nuance), that’s “due for a change,” Levy writes, as Apple explores in-house speech capabilities for the digital personal assistant.

In fact, it’s deep learning that enables Siri to talk back to you more naturally while machine learning techniques smooth out recordings of individual words, making Siri responses sound more like an actual person than when it relied on Nuance.

All of that also helps with bringing Siri to your favorite apps in a way that doesn’t require you to use specific language to access third-party skills on iOS 10 and macOS Sierra.

Siri’s brain transplant

On July 30, 2014, Siri had a brain transplant.

That’s when Apple began moving Siri’s voice recognition to a neural-net based system following initial complaints from users regarding erroneously interpreted commands. They next trained the neural net for Siri using lots of data and GPUs.

“We have the biggest and baddest GPU (graphics processing unit microprocessor) farm cranking all the time,” says Siri’s Alex Acero. “And we pump lots of data.” According to Acero, Siri began using machine learning to understand user intent in November 2014, and released a version with deeper learning a year later.

By applying machine learning techniques such as deep neural networks (DNN), convolutional neural networks, long short-term memory units, gated recurrent units and n-grams, Apple was able to infuse Siri with deep learning and cut her error rate by a factor of two in all the languages.

“That’s mostly due to deep learning and the way we have optimized it — not just the algorithm itself but in the context of the whole end-to-end product,” says Acero.

The iBrain is here and with iOS 10, it’s already in your phone thanks to machine learning, artificial intelligence, the power of Apple-designed silicon and Differential Privacy techniques.

The iBrain is here and with iOS 10, it’s already in your phone thanks to machine learning, artificial intelligence, the power of Apple-designed silicon and Differential Privacy techniques.

The size of Apple’s user base allowed Apple to quickly train Siri.

Steve Jobs said you’re going to go overnight from a pilot, an app, to a hundred million users without a beta program. All of a sudden you’re going to have users. They tell you how people say things that are relevant to your app. That was the first revolution. And then the neural networks came along.

But where else does machine learning help Apple?

Apple and machine learning

Apple uses deep learning to detect fraud on the Apple store, to extend battery life between charges on all your devices, and to help it identify the most useful feedback from thousands of reports from its beta testers. Machine learning helps Apple choose news stories for you. It determines whether Apple Watch users are exercising or simply perambulating.

It recognizes faces and locations in your photos. It figures out whether you would be better off leaving a weak Wi-Fi signal and switching to the cell network. It even knows what good filmmaking is, enabling Apple to quickly compile your snapshots and videos into a mini-movie at a touch of a button.

If you were to argue that Apple’s competitors do many similar things, you’d be right.

Clockwise, from upper left: Tom Gruber, Siri’s Advanced Development Head; Eddy Cue, Apple’s SVP of Internet Software and Services; and Alex Acero, Siri’s Senior Director.

Clockwise, from upper left: Tom Gruber, Siri’s Advanced Development Head; Eddy Cue, Apple’s SVP of Internet Software and Services; and Alex Acero, Siri’s Senior Director.

But, you must keep in mind that Apple’s way of providing these features also protects your privacy. In using the power of in-house designed A-series chips that power iOS devices and Differential Privacy techniques, Apple’s software is able to pull those things off directly on the device, without dumping significant user data on its servers to let the cloud do the heavy-lifting.

RELATED: A closer look at Differential Privacy on iOS 10 and macOS Sierra

In a rare revelation, Apple told the journalist that the dynamic cache which enables machine learning on the iPhone takes up about 200 megabytes of storage space.

The system deletes older data, but the actual size of the cache depends on how much personal data is used, stuff like information about app usage, interactions with other people, neural net processing, a speech modeler, natural language event modeling and data for the neural nets that power object recognition, face recognition and scene classification in the Photos app.

Here’s Apple’s marketing boss Phil Schiller:

Our devices are getting so much smarter at a quicker rate, especially with our Apple-designed A- series chips. The back ends are getting so much smarter, faster, and everything we do finds some reason to be connected. This enables more and more machine learning techniques, because there is so much stuff to learn, and it’s available to [us].

These techniques are also used to power some “new things we haven’t be able to do,” he added.

But it’s not just the silicon, says Federighi:

Apple isn’t just dipping its toes in Differential Privacy, it’s fully embracing it.

“It’s a technique that will ultimately be a very Apple way of doing things as it evolves inside Apple and in the ways we make products,” said Schiller.

Less visible AI-driven features

Artificial intelligence is also used to improve other aspects of the iOS experience.

Here are some of the examples of AI-driven features:

Identifying who a caller not in your Contacts, based on their emails

Populating the iOS app switcher with a shortlist of the apps you’re most likely to open next

Getting reminders from appointments based on emails

Surfacing a Maps location for the hotel you’ve reserved before you type it in

Remembering where you parked your car

Palm rejection for Apple Pencil

Recognizing ‘Hey Siri’ hot word in a power-friendly way

Improving QuickType keyboard suggestions

And, of course, lots more.

Software boss Craig Federighi listens to Siri’s speech guru Alex Acero discussing the voice recognition software at Apple headquarters. Oh, how I’d like to be a fly on that wall!

Software boss Craig Federighi listens to Siri’s speech guru Alex Acero discussing the voice recognition software at Apple headquarters. Oh, how I’d like to be a fly on that wall!

“Machine learning is enabling us to say yes to some things that in past years we would have said no to,” says marketing honcho Phil Schiller. “It’s becoming embedded in the process of deciding the products we’re going to do next,” says Schiller, adding:

The typical customer is going to experience deep learning on a day-to-day level that [exemplifies] what you love about an Apple product. The most exciting [instances] are so subtle that you don’t even think about it until the the third time you see it, and then you stop and say, “How is this happening?”

Thanks to the article, I also learned that some of the AI code that recognizes hand-scrawled Chinese characters into text on iOS or power the new letter-by-letter Apple Watch input on watchOS 3 actually draws from Apple’s work on handwriting recognition for its failed Newton message pad in the early 1990s.

The piece provides additional insights into how Apple approaches artificial intelligence and machine learning work versus its competitors. It’s a fascinating article and you’ll wholeheartedly recommended to give it a read.

Source: Backchannel

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