Page 41 – DataToBiz

Sulaksh More
March 03, 2020
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AI Edge Computing Technology: Edge Computing and Its Future

After Industrialization in the 20th century, Digitalization is one hot topic and an ever-changing environment. From your smartwatches to Android-powered TVs and wonderful IoT applications. Out of all important aspects for emerging technologies, Data is one of the deciding factors. We now have dedicated teams and departments to utilize the Data, for the purpose of improvement, along with a massive amount of supported computing. What is Edge Computing? Imagine a number of machines, connected internally, sharing data, space and computing, now that’s simply Distributed Computing. Edge Computing, similar to Cloud Computing is built on the same Distributed Computing Architecture but differs largely when it brings Data Storage and Computing handy to the end-user. Edge Computing simply implements decentralization, making sure to abolish the need to send the data back and forth from user to centralized data storage. Processing and analyzing the user data is happening right where data is closest, at the end user. Why does Edge Computing Matters? There are always many reasons why any technology is introduced and implemented. Edge computing enables you to safeguard your sensitive data at the local level, by not sending every data part to centralized data storage. Latency is impressively reduced by not having to make roundtrips to the centralized data storage. Though Cloud and Edge Computing share their Distributed Computing Architecture, edge computing overcomes issues of Latency and bandwidth happening over cloud. Many of the operations happening will largely depend on the hardware capacity of the end-user device instead of centralized data systems. Also increases the chances of reaching out to remote or low network locations. Advantages of Edge Computing To begin with, Edge Computing has a great ability to enrich network performance. Latency in the network has been a major cause for delay and edge computing solves it with its architecture to provide data near the user. From a security perspective, it is a genuine concern that with making the network available to the user, it could be used as an easy entry point for attacks and malware insertions. But the Edge Computing architecture of Distributed Computing prevents such attacks, as it does not transfer data back and forth to the central storage or data center. And it is easier to implement various security protocols at the edge and not compromise the whole network. Most of the data and operations are performed on local devices. The need to establish private centralized data centers for collecting and storing data is a past concern now. With Edge Computing, companies can harness the storage and computing of various connected devices at low cost, resulting in immense computing power. As we understand that edge computing brings the enterprises or the solutions to the end-user, the opposite perspective will be that these large enterprises can easily reach their specific markets on the local level. With local data centers, chances of network crash or shut down are way reduced. With a number of local data centers, most of the problems can be detected and solved at the end-user level and the need to engage centralized systems will be not required. Industries Utilizing Edge Computing With every new technology in the market, many industries have their shares of benefits. Edge computing is set to help Customer Care Industry widely. There has been an impressive attempt to implement artificial Intelligence with customer support and voice assistants like Apple’s Siri and Google Home. Cisco, a company well known for its communication tools has begun experimenting with an edge on their cloud networks. IBM now offers you to combine your edge computing experience with WATSON. Other than that, IBM scientists are working on developing a technology to connect mobile devices without Cellular networks or Wi-Fi. Drones are being used for various purposes and edge technology can be used with drones for functions like visual search and image recognition, object tracking and detection. With AI, drones can be trained to function as human search psychology does in matters of identifying objects and faces. Industries will benefit from more and more computing devices being connected to IoT networks, which will help these industries in reaching wider networks, providing flexibility and reliable services. At DataToBiz, we have built custom digital solutions for businesses in various industries. The AI services that we offer not only help the organizations to scale but also have an ‘edge’ in their market. What could be AI’s role in Edge Computing? What is AI Edge Computing? To put it simply, AI on Edge Computing will have an incredible ability for AI Algorithms to be executed locally, on end-user devices. Most of the AI algorithms are largely based on neural networks which required a massive amount of computing power. Major companies manufacturing Central Processing Units (CPUs), Graphics Processing Units (GPUs) and many higher-end processors have pushed the limits and made AI for edge computing possible. These algorithms will function effectively with local data collected and stored. Another factor will be the requirement of training data for such algorithms, which is a lot smaller for edge computing devices. There have been subtle attempts to implement such AI models on edge computing, which results in impressive benefits to the enterprise as well as to the end-user. To Wrap It Up Edge computing has a wide scope and will be implemented for betterment with end-user as well as enterprise perspective. Along with AI, edge computing will attempt to push the traditional limits of edge and several factors like end-user privacy, data storage, security over usual data transmission, and latency will be improved. Edge Computing as a new approach has uncovered opportunities to implement fresh ways to store and process data. Edge computing has many stored-in answers for many enterprises for multiple problems and will be a real-time efficient solution. We at DataToBiz have been solving a few problems with Jetson Nano, Raspberry Pi, Android Devices & a few other AI edge developer kits. Talk to our AI developer today who will understand your business hurdles and will come up with the ideal solution

Vaishali
March 03, 2020
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What Is Facial Recognition, How It Is Used & What Is It’s Future Scope?

Few biometric innovations cause creativity, like facial recognition. Equally, its launch in 2019 and early 2020 has caused profound doubts and unexpected reactions. But later on, something on that. Within this file, you can uncover the truth and patterns of seven facial detections, expected to change the landscape within 2020. Impact of top innovations and suppliers of AI-Often developing industries in 2019-2024 and leading usage cases Face recognition in China, Asia, the United States, the E.U. and the United Kingdom, Brazil, Russia… Privacy versus security: laissez-faire, enforcement, or prohibition? New hacks: can one trick face recognition? Going forward: the approach is hybridized. How Does Facial Recognition Work? For a human face, the program distinguishes 80 nodal positions. In this case, nodal points are endpoints that are used to calculate a person’s face variables, such as nose length or width, eye socket size, and cheekbone form. The method operates by collecting data on a composite picture of an individual’s face for nodal positions and preserving the resultant data as a faceprint. The faceprint is then used as a reference for contrast with data from faces recorded in a picture or photo. Since the facial recognition technology requires just 80 nodal points, when the circumstances are optimal, it can quickly and reliably recognize target individuals. Nonetheless, this form of algorithm is less effective if the subject’s face is partly blurred or in shadow rather than looking forward. The frequency of false positives in facial recognition systems has been halved every two years since 1993, according to the National Institute of Standards and Technology (NIST). High-quality cameras in mobile devices have rendered facial recognition both a viable authentication and identification choice. For example, Apple’s iPhone X and Xs include Face ID technology, which enables users to unlock their phones with a faceprint mapped by the camera on the phone. The phone’s program, which is designed to avoid being spoofed by images or masks utilizing 3-D mapping, records, and contrasts over 30,000 variables. Face ID can be used to authenticate transactions in the iTunes Store, App Store, and iBookStore via Apple Pay and. Apple encrypts and saves cloud-based faceprint data, but authentication takes place directly on the computer. Smart airport ads will now recognize a passer-by’s gender, race, and estimated age and tailor the advertising to the profile of the user. Facebook utilizes face recognition tools for marking images of people. When an individual is marked on an image, the program stores mapping details regarding the facial features of that individual, once appropriate data has been obtained, and the algorithm may use the information to recognize the face of a single person as it occurs in a new picture. To preserve the privacy of users, a function named Picture Check notifies the designated Facebook user. Many forms of facial recognition include eBay, MasterCard, and Alibaba, which, usually referred to as selfie pay, have carried out facial recognition payment methods. The Google Arts & Culture software utilizes facial detection to recognize doppelgangers in museums by comparing the faceprint of a live individual with the faceprint of a portrait. Step 1: The camera can identify and remember one object, either alone or in a crowd, to begin. The face is easily recognized while the individual is staring at the camera directly. The scientific advancements have often rendered it more comfortable to figure out minor differences from this. Step 2: First, they take and examine a snapshot of the nose. Some face recognition is based on 2D photos rather than 3D since it will more easily align a 2D object with the public or archive photographs. Each face is comprised of distinctive landmarks or nodal points. Each human face has 80 nodal dots. Technology for facial recognition analyzes nodal points such as the distance between the eyes or the outline of your cheekbones. Step 3: Your facial examination is then translated into a statistical model. Such facial features are numbers in a database. This file numeric is considered a faceprint. Every individual has his faceprint, similar to the specific structure of a thumbprint. Step 4: Your code is then matched to another faceprint database. This website has images that can be paired with identifiers. More than 641 567million files are open to the FBI through 21 state repositories such as DMVs. Facebook’s images are another illustration of a website millions had exposure. All images which are marked with the name of an individual are part of the Facebook archive. The code instead finds a fit in the supplied database with the exact apps. This returns with the match and details, including name and address added. Developers will use Amazon Recognition, an image processing tool that is part of the Amazon A.I. series, to attach functionality for face recognition and interpretation to a device. Google has a similar functionality through its Google Cloud Vision API. The platform used to track, pair, and classify faces through machine learning is utilized in a broad range of areas, including entertainment and marketing. For starters, the Kinect motion game device makes use of facial recognition to differentiate between participants. Uses of Facial Recognition You Must Know! Face detection may be used for a broad range of purposes, from defense to ads. Any examples in usage include Smartphone makers, including Apple, for public protection. S. Government at airports, by the Homeland Security Agency, to recognize people who can meet their visa criteria. Law enforcement by gathering mugshots can evaluate local, national, and federal assets repositories too. Social networking is used for identifying individuals in photos, which also includes Twitter. Business protection, as businesses may use facial recognition to access their buildings. Marketing, where advertisers may use facial recognition to assess particular age, gender, and ethnicity A variety of possible advantages come with the usage of facial recognition. There is no need to directly touch an authentication system relative to other touch-based biometric identification methods such as fingerprint scanners, which could not function well if a person’s hand is soil. The safety standard

Vaishali
February 29, 2020
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Search Engine Optimization Using Data Mining Approach | Become A Smarter Digital Marketer!

You have probably heard, used, and perhaps even overused those buzz words as you persuade consumers how to take their business to the next stage. The terms are more than just the effective aspect of your sales pitch. However, their apparent popularity is an indication that in pursuing digital marketing and search engine optimization, we have entered a new age. In this article, we are going to dive in to unpack the words, research their importance to SEO, and go over some best practices to say a data-driven SEO story. Defining Data Mining And Its Place In Business Decisions Big Data and data mining have become, to some degree, umbrella words that sum up a modern reality: now, all digital activities are both data moving and data induced behavior. Data mining activity is focused on evaluating vast knowledge sets to discover trends and values that can then be leveraged to generate improved efficiencies or new opportunities within an enterprise. Google Mapping, your path, uploading on Twitter, buying from Seamless, watching your Netflix favorites – all these behaviors cause new data sources. Further, these systems collect, interpret, and use to forecast your next breath, so the internet will anticipate your next appetite for sushi better than you can. Once the prerogative of computer scientists, quants, or model-risk researchers, the methods of data mining now get used by almost every sector or occupation that has access to large data sets. As a gold digger during the Klondike Gold Rush, the task is to wade across knowledge sources in pursuit of a little nugget of evidence that really can benefit you. Amazon transformed the way businesses incorporate big data storage and processing into their processes and DNA, providing automated data warehousing tools, clickstream analytics, fraud detection, recommendation engines, event-driven reporting. Their innovation paved the way for companies to audit their data access levels, as well as the marketing possibilities that access offers. Consumers of today are not only comfortable with having their online behavior recorded, but they want the organizations with which they communicate to automate such experiences by data mining. For many consumer-facing organizations, this predictive ability is now the Holy Grail, with the quality of their data analysis being a key component of maintaining a competitive edge in their industry. With so much information available to the companies, there’s no reason to focus on assumptions or reflex judgments. Internal stakeholders now have to band together not only to unravel patterns of data but also to advocate their path through bureaucratic hold-ups and into actionable status. Organizations need to harness their enhanced consumer understanding to drive customer service, product satisfaction, successful marketing, and ultimate growth. Optimizing The Relation Between SEO And Data Mining Search engines are the most consumer-oriented entity, with consumers having driven the development of the business model since the web search first launched. Google’s goal, Bing, and their equivalents are there to provide meaningful answers to their customers. It is because, like any other company, they need to sustain a competitive model to keep traffic running, which in their case depends on driving traffic to the most relevant information at the exact moment when consumers need it to make a decision. Google dubs the zero moments of truth (ZMOT) on this point. Unsurprisingly, this business model has a sudden impact on how we treat search engine optimization as digital marketers, as well as also on how we interpret the data from analytics platforms. Data Mining SEO operation can be described as reviewing large data sets to identify new traffic patterns and to uncover possibilities for niches. These niche trends then get leveraged to market a service or product to a user segment in a better way. Abnormalities that you want to look for include traffic sources, simple and long-tail keywords that drive people to your site, and trends in traffic over time. For example, growth year-over-year, seasonality, and how all these factors relate to the traffic sources. Having revealed overarching patterns from large data sets, you need to adjust your SEO approach to say the true story based on the findings. Quality data mining can open up a wealth of possibilities for storytelling, but it’s not always a good thing to have all those options. To further set yourself up for success, make sure that you have set up key performance indicators (KPIs) to benchmark your performance against goals that matter to your customers and that remain relevant to the organic acquisition realm. Then make sure you monitor your progress as well as revising strategy consistently when it does not seem to be measuring up. While researching and posting on Google Analytics, stay away from bi-weekly duration data, or even month-over-month analysis. Unless you want to calculate the short-term effect of an on-page shift or determine that seasonality is at risk, you should always look at the larger picture–and, therefore, the more significant timeline. That is when the data gets large enough to be useful as well as actionable. How Can You Get Help From Data Mining With SEO (Search Engine Optimization)? That applies most in Big Data mining is what follows after in SEO and business analytics: to increase ROI by using smart data. If you have been thinking about how to achieve that goal but have not yet found a satisfactory answer, then it is high time to get in contact with experienced data miners. One of the Search Engine Optimization strategies that have proved to work well in the past has been allowing other websites to connect to the material of another website. It indicated that Google had a better ranking of the site whose advertising was linked to high-quality content. Google recently appears to be using fear to combat this technique. They want to make sure, according to Google, that websites with poor content, but use spam links to rank high in the SERP, no longer rank highly. High-quality link building techniques, such as guest blogging, should be used. You may want to consider

Vaishali
February 26, 2020
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DataToBiz Proud to be Named a Top AI Firm in India by Clutch

At DataToBiz, we are aware of how difficult it is for a new company to balance a cutting edge devilment with all sorts of business challenges. This is where our highly skilled team fits in. We are an AI & Data Science service company that helps clients make data-driven decisions to derive meaningful results in the long run that will benefit your business. We have a team of seasoned experts to help our clients manage their data assets, and find the best way to surface insights that help take their efforts to the next level! In light of our dedication and impact, we’ve been ranked among the leading artificial intelligence developers by Clutch, a verified and globally recognized review platform. They employ a unique rating methodology to compare leaders across a gamut of service industries. Their findings help interested buyers connect with qualified vendors for projects. We couldn’t have gotten this award without the help of our wonderful clients. They took time out of their day to engage with Clutch analysts to assess our impact across a variety of sectors. We were graded along with the basis of quality, attention to deadlines, and overall project management skills. We’re happy to report we’ve maintained 4.5 out of five stars! Take a look at a recent review below: “We are honored to receive this reward as one of the leading AI & BI service providers in India by Clutch” –DataToBiz Development Team We’re proud to receive this recognition and look forward to helping even more clients exceed their expectations with cutting edge technology. Contact us today if you’d like to collaborate with us on a project.

Vaishali
February 26, 2020
1 Comment

Don’t Fall For Frauds | Here Is How To Hire AI & Data Analytics Company?

Data Science, AI, and Machine Learning have now, become an integral part of the technology revolution in all industries. Capabilities of predictive analytics for all kinds of businesses have led it to become a hot topic of discussion. With more and more discussion going on about AI & Data Analytics, it has been attracting several business owners to hire AI & data analytics companies to help them get the best solution to their data-related problems. However simple it seems, it indeed is one of the important decisions for a business as they will provide access to all their data to the data analytics consulting company they hire. Before you decide to hire a data science company, you must understand what you need them for. This question can be answered by a simple consultation with experts, which every good data science company like DataToBiz provides for free or you can use a technique of isolating your question to figure out a specific problem you need to be resolved. This way you will know exactly what you want from a data science & AI consulting company for your business. To make it more simple, we are here sharing all the things you should consider before hiring an AI & data analytics company. Points To Check Before Hiring Data Analytics Company Being data analytics experts, we are here to share in detail the points to consider before you select a data analytics company. So, let’s start with the list of points to consider. 1. PinPoint The Problem & See If They Provide Possible Solution When it comes to data science, it is all about gathering useful information out of the collected data. There are many things for which a data analytics company is hired for. Some hire them to build products that use machine learning, for example, the product that helps an application to transform speech to text, etc while some might need to develop a custom analytical as well as visualization platform to make strategic decisions on the basis of insights. This is not all, you can also hire the data science experts like DataToBiz to gain insights about the business you do and use those insights to further improve the business operation. In addition to all this, you can also hire data science and AI experts to develop AI-based applications for your customers. Where the former is for the business end there, there the later one is developed for the customer end. Let’s discuss both these ends one by one. Business & Statistical Analytics For those who don’t know what is business analytics, you will get to know now. BA that is business analytics is a process of exploring the data using statistical & operational analysis. What is the purpose of Business Analytics? Business Analytics is a process designed for the purpose of monitoring the business processes and using the insights from data that can help you make a well-informed decision. What Are The Best Business Analytics Techniques You Should Know About? There are two groups of business analytics techniques that every efficient data analytics company like DataToBiz must know about. These two groups include – business intelligence and statistical analysis. The AI and data analytics company with expertise in business intelligence work efficiently on analyzing and reporting the historical data insights which in turn help companies make informed strategic decisions regarding current business operations and developments. However, the companies with specialties in statistical analytics bring on table more elaborate digging. Where Can You Use Business Analytics? Before you hire a data science company, you must know where business analytics can be helpful. Below is the list of issues where business analytics might come in handy. Types of Business Analytics include – Prescriptive Analytics, Predictive Analytics, Descriptive Analytics, and Diagnostic Analytics. So, before you hire data analytics & AI consulting company, you must know the basics of what business analytics is about. Customer End Applications & Fraud Detection Mostly every customer end application is powered by the machine learning algorithms and is designed with the sole purpose of providing a solution to any of the problems faced by customers. Every good AI and Data Analytics company must have knowledge of what customer-end applications need. Some of the Cases In Which You Might Need Customer Facing Solutions – Along with these applications, the customer end data analytics can also be used in fraud detection systems. 2. Check For The Off-The-Shelf Solutions Or Products Before Hiring Data Analytics Company! Before you start hunting for the best data analytics company make sure that you have gone through every possible off-the-shelf solution for the problem you need to be resolved. There are several websites and platforms that list analytics as well as SaaS solutions like KDnuggets & PCMag. In some cases where one uses CRM systems to collect customer insights, you must check with the vendor if they provide additional modules to resolve your problem. What Is The Catch? The catch in this off-the-shelf solution is that most of them do not support the functionality that you might need. This is where data science and AI companies jump in. 3. Check The Company’s Portfolio & References! Once you have shortlisted the company, you must check out for the portfolio of the AI & data analytics consulting company. Note that the data science consultancy company that vouches on having the domain knowledge not just delivers a solution but can also refer to product development and doesn’t need a huge time to study and figure out the problem. References – When you decide on hiring someone, it should be based on the references they get from their present and past clients. Not only this, the news articles, and press releases can also help you gather insight on how good the data science consultancy company is. 4. One-on-One Interview With Data Science Consultancy Company Finally when the data analytics and AI company has crossed all these aspects, what you have to do is have a one-on-one conversation

Vaishali
February 25, 2020
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5 Awesome Benefits of Big Data in Business Invoicing System

Invoicing system has undergone some major changes since the introduction of big data in them. We, being a big data analytics company with expertise in big data, data science, and machine learning are here to share how you can improve your invoicing system. Along with that in this piece of article, we are going to share how big data has helped improve the invoice system. There are many ways in which big data has improved invoicing applications which you can check in the detailed report by Spend Matters. Big Data Revolutionizing The Invoicing Software Applications Before the invoicing system was upgraded using big data, there was a debate on its application between many SME owners. The reason for this debate is that it is considered not very challenging to handle it manually. However, after running the invoicing system, software or application, everyone regrets not using it from the very start. There are many reasons to support why the invoicing system or software is perfect for businesses. Most of these reasons are because of the introduction of big data in them. Benefits of Switching To Invoicing System With Big Data Below are some of the advantages of using the Invoicing system rather than going with the traditional invoice template system. 1. Save Time & Money We have seen that the invoice template works fine for many businesses, however, there are many features and functionalities that are missing when it comes to this old invoicing system including the fact that these systems don’t ensure that you get paid or not. Using the invoicing software resolves this issue. Almost every invoicing system uses big data to connect clients and payment providers. This, in turn, streamlines the payment process for the companies. What is even more interesting is that this software also provides multiple payment gateways to pick from which takes only a few clicks. In addition to this, with the help of invoicing software, the receipts and accounts are automatically updated. 2. Can Be Used From Anywhere The following reason is that it is time-saving, these invoicing systems designed using big data can be used on the go. Thanks to this software, you do not have to sit in front of your computer to send the invoice. This feature comes in handy for those who find it difficult to spare time to process invoices. This robustness of the invoicing applications has made them more efficient and useful. Big data has helped advance the invoicing system to introduce this feature which allows you to not only send the invoice while you are on the go but can also allow clients to pay from wherever they are at that moment, making the entire system more efficient. 3. Customization Feature In addition to the above two points, the most important benefit these big data modified invoicing systems include is the option of customization. When you are using the invoice template method, there is no option for customization. However, big data is all about personalization. With invoicing software, you can easily customize the invoices as per the customers or and clients with simple settings, making the entire system flawless. 4. Detailed Reporting In Invoicing System The best part of these big data-modified invoicing systems is their ability to track all your financial transactions with every client. Not only this, but this software also includes the feature of generating detailed reports on what has been paid/received and when exactly to which client. So, instead of following up with every client, with the modified invoicing system, you can easily track payment history using an automated for you. With this reporting system, you can not only make your life simpler but can also ensure that your clients pay and on time. 5. Multiple Invoicing System When it comes to discussing the advantages of the invoicing system, multiple invoicing definitely comes up. Unlike the traditional invoice template method where one has to send a ton of invoices, through this software you can send multiple invoices for different services simply using the software feature. In all these points, it is clear that big data has helped dynamically improve the invoicing system for business owners. Every point we have discussed above makes it obvious why one should opt for the invoicing system. Through this blog, you have understood the functioning and benefits of big data in invoicing systems. Implementing these technologies can not only improve your revenue but also increase the efficiency of your business operations. Partner with leading big data analytics companies like DataToBiz to leverage big data to turbocharge the business operations. Talk to an expert today!

Vaishali
February 22, 2020
1 Comment

Revealing The Success Mantra Of Netflix! Role of Big Data & Data Analytics.

Today, Netflix is one of the most loved streaming apps in the market. With the number of users increasing every second from 115 million users, there is no doubt that this streaming channel has won the hearts of millions of people becoming the kind of streaming world today. Most of you must be thinking about how they have managed to be this successful, and we are here to reveal their secret today. You can also become a new rising star in the streaming world with our data analytics services. It has been established that Netflix has taken over the entire Hollywood which indeed is raising huge questions on how? The answer is simple, the secret is “Big Data”. As per the Wall Street Journal, Netflix has been using Big Data Analytics to optimize the overall quality and user experience. Through big data analytics, Netflix is targeting users through new offers for shows that will interest them. Not only this but through big data analytics, they also are playing the ground with relevant preferences. All these efforts all together have led to the success of the Netflix streaming platform. The Secret Behind Netflix, The Streaming Platform By now, we have established that Netflix has become one sensational streaming platform of today that has millions of subscribers from all across the world. Now, if we go deep, these million subscribers derive a humongous amount of data that can and has been used by Netflix to grow even more. Although there are many challenges that one faces when it comes to including data analytics in your business, still after reading this, you will understand how important it is. Right from the prediction of the type of content to recommending the content for the users, Netflix does it all through big data analytics. Netflix started collecting data from the time they were distributing the DVDs which later when they started their streaming service in 2007 shaped into something more. It took them 6 years to gather proper data to analyze find the result-driven data from it and use it. This big data analytics lead to the launch of their first show – “House of Cards” which they estimated to be a success through data analysis, proving how beneficial big data analytics has been for them. This also gives another reason why you should consider adding big data analytics to your business. Thankfully, there are many experts in the market like us at DataToBiz, who can help you through it. Netflix also invested a million dollars in the development of the algorithm for data analysis to improve the efficiency and accuracy of the process, helping then increase the retention rate. Why Has Netflix Become so Popular? Netflix has worked on a combination of factors to reach the current stage of being at the top. And now, why is Netflix so successful? Because it worked on its core aspects of providing users with content they want to watch and kept the pricing at an affordable range. Moreover, Netflix has such a vast collection of shows, movies, documentaries, etc., that users could keep watching and never worry about running out of content to consume. How Netflix Uses Big Data Analytics to Ensure Success Around 80% of the content streamed on Netflix comes from the recommendation engine. The platform has developed a series of algorithms that consider an array of factors to deliver personalized recommendations to every user. Netflix built new data pipelines, worked on complex datasets, and invested in data engineering, data modeling, heavy data mining, deep-dive analysis, and developing metrics to understand what the users want. Netflix innovation relies on- Netflix hasn’t limited the use of big data analytics only to curate content for users. It uses algorithms to estimate and predict how much a new project would cost and find alternate ways to optimize the production and operations. By reducing bottlenecks in daily operations, Netflix could streamline the workflow and make better decisions about the projects. This is how Netflix used big data and analytics to generate billions and has won 22 Golden Globe awards in 2021 while having 42 total nominations. Make Sure What Users Need! With the help of Big data analytics, Netflix knows what you want and what you would like to watch next. Now, this might seem scary but the science behind it really simple. Knowing and understanding the preferences of the users have proven to be the two pillars of success for Netflix. With the help of which they understood the viewing habits of viewers which help the prediction system that is powered by the algorithm designed by the developers. In short, big data analytics helped Netflix to gather insights which further helped in the optimization of the algorithm and then adjust the algorithms. In addition to studying the behavior of the users, Netflix also uses tagging features that allow consumers to suggest as well as recommend different movies and series they think a user will enjoy. This feature encourages more views, clicks, and raises engagements. This magic formula took 6 years of Netflix which has paid off really well as it has become the no.1 streaming app today. What Makes Netflix Different from its Competitors? Netflix has around 231.6 million paid subscribers around the world in the third quarter of 2021. The maximum of them come from the US, with Canada next in the line. There are around 5 million Netflix subscribers from India (as of Jan 2021). But why is Netflix a great product? How has it set itself apart from its competitors? Aggressive data mining has helped Netflix offer customers the exact kind of shows, movies, etc., they prefer to watch. The data is analyzed to sort through the genres, most-watched episodes, most-searched-for shows/ movies, and so on. Another advantage Netflix has created for itself is the pricing. With a flat fee per month, users have access to unlimited content streamed on the platform. Netflix also provides the first month free for subscribers. Even though Netflix

Sulaksh More
February 22, 2020
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Incredible evolution journey of NLP models !!

There have been ground breaking changes in the field of AI, where many a new algorithms and models are being introduced and worked up. The pace that we have been moving at, is sure to bring many more rapid developments in various industries with AI as major change maker. Here we are going to talk about the incredible evolution journey of NLP Models. While Google’s BERT and Transformer did set some amazing records, Facebook’s RoBERTa, which is based on BERT, surpassed many previous records and then Microsoft’s MT-DNN exceeds the benchmarks set by Google’s BERT almost of nine NLP tasks out of eleven. How Google’s Bert did: Google implemented two major strategies with BERT – Mask Language Model, where an amount of words was masked, kept hidden in the input and the BERT was to predict the hidden word. Refer the below example for better understanding. Referring to the above example, the masked words were kept hidden as part of training and model was to anticipate the words. For BERT, it was essential to understand the context of the sentence based on the unmasked words and predict the masked one, failing to produce expected words like Red and Good, would be a result of failed training techniques. Moving ahead, Next Sentence Prediction (NSP) was the second technique used with BERT, where BERT learned to establish a relationship between various sentences. Major task for NSP was to choose next sentence, based on the context of current sentence, to make proper pairs of sentences. Referring to the above sentences, BERT would have to choose the Sentence3 in order to complete the Sentence1 as its successor. Choosing Sentence2 instead of Sentece3 would result in failed training of the model. Both of the above techniques were used in training the BERT. Real Life examples of BERT can be seen with Gmail app, where replies are being suggested according to the mail, or when you start typing a simple sentence, further words to complete the sentence can be seen in Light Grey Fond. How were past models improved: Everything we have today is better than yesterday, but the tomorrow will demand many improvisations, “There’s always some room for Improvement”. When Google broke the records with BERT, it was exceptional but then Facebook decided to implement the same model of BERT, but with a slight change. The changes here were improved methods for training, with massively added amounts of data and a whole lot more of computation. What Facebook did was, simple carry forward the Language Masking Strategy of BERT but decided to replace the Next Sentence Prediction Strategy with Dynamic Masking. Masking: Static vs Dynamic: When Google fed its model, BERT, with massive amount of data with masked words, it was Static Masking, has it happened only at time of insertion. But what Facebook did was, tried to avoid masking same word multiple times and so training data was repeated 10 times and every next time, the masked word would be different, meaning the sentence would be same but the words masked would be different and this made RoBERTa quite exceptional. What’s with lesser parameters: Parameters, a very important part of training data, has to be accurate and useful for the model and must be in vast amount for the model to learn every possible scenario. NVIDIA, exceeded every past record for maximum parameters when they trained world’s largest Language Model named as Megatron, which is based on Google’s Transformer, with 8.3 BILLION Parameters. Amazingly, NVIDIA trained the model in 53 minutes and happily made it accessible for other major players like Microsoft and Facebook, to play with its State-of-the-Art Futuristic Language Understanding Model. BUT BUT BUT, what DistilBERT did was, stood up with almost matching results as BERT but with using almost half the number of parameters. DistilBERT meaning Distillated-BERT, released by Hugging Face uses only 66 million parameters while BERT base uses 110 million parameters. Along with Toyota Technological Institute, Google released a Lite version of BERT, ALBERT. While BERT xLarge uses 1.27 billion parameters, ALBERT xLarge uses only of 59 million parameters, now that’s reducing parameters to almost half. Smaller and Lighter compared to BERT, ALBERT might be BERT’s successor. Sharing Parameters is one of the most impressive strategy implemented with ALBERT, which works on hidden level of model. With Parameters Sharing, loss of accuracy happens but also reduces the number of parameters required. Google Again: Google, with Toyota brought out ALBERT, and as described above, it uses less parameters and implements a strategy to convert the words into numeric one-hot vector, which are later passed into an embedding space. But it is essential for embedding space to have same dimension as of the hidden layer and this was surpassed when ALBERT team factorised the embedding, meaning the earlier created word vectors were first projected into smaller dimension space and then pushed into higher one with the same dimension. What could be Next? Next is probably to harness the Language Intelligence that made English Language quite interesting for machines to understand and learn, to be implemented with various languages. Every language has its own roots and varies a lot with multiple factors, but possibility is to train next languages just as it was done with English Language. Many improvements are being made with added computation or by increasing data, but one factor that will be considered to be ground breaking in the field of AI is when models are efficiently being trained and improved with smaller amount of data and less computation. To talk to our NLP expert on how to use the NLP Models for your business contact us

Vaishali
February 22, 2020
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A Complete Guide To Data Warehousing – What Is Data Warehousing, Its Architecture, Characteristics & More!

With the aid of an in-depth and qualified review, the study extensively analyses the most crucial details of the global data warehousing industry. The study also provides a complete overview of the market based on the factors that are expected to have a substantial and measurable impact over the forecast period on the market’s growth prospects. Specific geographical regions such as North America, Latin America, Asia-Pacific, Africa, and India were evaluated based on their supply base, efficiency, and profit margin. This research report was examined based on various practical case studies from different industry experts and policy-makers. It makes use of various interactive design tools such as tables, maps, diagrams, images, and flowcharts for readers to understand quickly and more comfortably. Global Data Warehousing Market Report contains highly detailed data, including recent trends, market demands, supply, and delivery chain management approaches that will help identify the Global Data Warehousing Customer Industry’s workflow. This Report provides essential and comprehensive statistics for research and development estimates, row inventory forecasts, labor costs, and other funds for investment plans. This sector is enormous enough to build a sustainable enterprise, so this Report lets you recognize opportunities for each area in the global data warehousing market. What is Data Warehousing? Data Warehousing (DW) is a process for collecting and managing data from diverse sources to provide meaningful insights into the business. A Data Warehouse is typically used to connect and analyze heterogeneous sources of business data. The data warehouse is the centerpiece of the BI system built for data analysis and reporting. It is a mixture of technologies and components which helps to use data strategically. Instead of transaction processing, it is the automated collection of a vast amount of information by a company that is configured for demand and review. It’s a process of transforming data into information and making it available for users to make a difference in a timely way. The archive of decision support (Data Warehouse) is managed independently from the operating infrastructure of the organization. The data warehouse, however, is not a product but rather an environment. It is an organizational framework of an information system that provides consumers with knowledge regarding current and historical decision help that is difficult to access or present in the conventional operating data store. Characteristics of data warehousing Here is the list of some of the characteristics of data warehousing: 1. Subject oriented A data warehouse is subject-oriented, as it provides information on a topic rather than the ongoing operations of organizations. Such issues may be inventory, promotion, storage, etc. Never does a data warehouse concentrate on the current processes. Instead, it emphasized modeling and analyzing decision-making data. It also provides a simple and succinct description of the particular subject by excluding details that would not be useful in helping the decision process. 2. Integrated Integration in Data Warehouse means establishing a standard unit of measurement from the different databases for all the similar data. The data must also get stored in a simple and universally acceptable manner within the Data Warehouse. Through combining data from various sources such as a mainframe, relational databases, flat files, etc., a data warehouse is created. It must also keep the naming conventions, format, and coding consistent. Such an application assists in robust data analysis. Consistency must be maintained in naming conventions, measurements of characteristics, specification of encoding, etc. 3. Time-variant Compared to operating systems, the time horizon for the data warehouse is quite extensive. The data collected in a data warehouse is acknowledged over a given period and provides historical information. It contains a temporal element, either explicitly or implicitly. One such location in the record key system where Data Warehouse data shows time variation is. Each primary key contained with the DW should have an element of time either implicitly or explicitly. Just like the day, the month of the week, etc. 4. Non-volatile Also, the data warehouse is non-volatile, meaning that prior data will not be erased when new data are entered into it. Data is read-only, only updated regularly. It also assists in analyzing historical data and in understanding what and when it happened. The transaction process, recovery, and competitiveness control mechanisms are not required. In the Data Warehouse environment, activities such as deleting, updating, and inserting that are performed in an operational application environment are omitted. What are the Basic Elements of Data Warehousing? The following are some of the basic elements of data warehousing that should be considered by the data engineering team. ETL Toolkit with Screens ETL is to extract, transform, and load data to the DW. Quality screens are not always used as they are an additional requirement. But these screens process and validate data and the relationship between different data columns or sets. External Parameters Table Using an external parameters table will make it easy to add/ delete/ modify the parameters without affecting the configuration table in the data warehouse or changing the code. Team Roles and Responsibilities The team includes builders, maintainers, miners, analysts, and others who take care of data cleansing, data integrity, metadata creation, and data transportation. Warehouse administration, loading and refreshing data, information extraction, etc., are some functions performed by the team. Data Connectors The data connectors need to be updated and linked to external data sources. Legacy systems may not work with the latest software. Every connection and integration has to be checked and updated regularly. Architecture Between Environments The development environment, production environment, and testing environment should be in sync and align with each other. Differences in this could lead to defective results and loss of time and money for the enterprise. DDL Repository Having a backup is considered essential, at least during the initial phase. However, it is important to carefully consider the structure of the DDL (Data Definition Language) repository for the long term. Tests Building a test environment in advance will help in running a test, even before the data warehouse is fully functional. This helps catch errors and

Vaishali
February 21, 2020
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9 Ways Amazon Uses Big Data To Stalk You! [Leaked]

Many shoppers may find it odd when a shop knows a lot about them purely through the products they buy. Amazon.com, Inc. (AMZN) is a pioneer in gathering, saving, sorting and reviewing your and every other customer’s personal information as a means of determining how consumers are spending their money. The company is using predictive analytics for targeted marketing to boost customer satisfaction and build loyalty to the company. While big data has also helped Amazon to evolve into a giant among online retail stores, what the company knows about you might feel like stalking. Below we are going to discuss how Amazon uses Big data and predictive analysis to improve user experience. 9 Ways Amazon Uses Big Data to Collect Your Data 1. Personalized Recommendation System Amazon is a leader in the use of an integrated, collaborative filtering engine (CFE). This analyzes which goods you have recently bought, which are in your online shopping cart or on your wish list, which things you have checked and valued and which items you are most searching for. Such knowledge is used to suggest additional products bought by other consumers as they order those same things. For example, anytime you attach a Movie to your online shopping cart, it’s also advised that you buy similar movies bought by other consumers. Amazon uses the power of recommendation to allow customers to order on-the-spot as a way to further fulfill your shopping experience and spend more money 2. Recommendation Through Kindle Highlights Following the acquisition of Goodreads in 2013, Amazon has integrated the social networking service of around 25 million users into some Kindle functions. Kindle users can, therefore, highlight terms and comments, and exchange them with others as a way to discuss the text. Amazon checks the terms displayed in your Kindle frequently to decide what you’re interested in learning. The organization may then give you more suggestions on the e-book. 3. One-Click Ordering Because big data shows you shop elsewhere, Amazon created One-Click ordering unless your products are delivered quickly. One-Click is a patented feature that is enabled automatically when you place your first order and enter a shipping address and method of payment. You have 30 minutes by selecting one-click shopping in which you can change your mind about the transaction. After that, the product will be paid automatically through your payment method and delivered to your address. 4. Anticipatory Shipping Model Amazon’s proprietary anticipatory delivery model uses big data to predict the goods you’re likely to buy, when you can buy them, and where the items might be required. The goods are sent to a local distribution center or distributor so once you order them, they will be available for shipment. Amazon employs predictive analytics to boost retail sales and profit profits, thus rising delivery times and overall costs. 5. Supply Chain Optimization Since Amazon needs to easily deliver its purchases, the organization works with the suppliers and records their inventories. Amazon uses large data systems to pick the warehouse nearest to the retailer and/or to the shipping costs by 10 to 40%. In fact, graph theory helps to decide the best delivery schedule, path, and groupings of goods to further reduce shipping costs. How does Amazon use data analytics for supply chain optimization? Amazon offers two fulfillment options to sellers. One is FBA (Fulfillment by Amazon), where the responsibility lies with Amazon to deliver the order to the customer. The supply chain logistics are handled by Amazon. The second is FBM (Fulfillment by Merchant), where the merchant is responsible for shipping the products to customers. The shipping address and whether the customer writes reviews are analyzed to speed up the delivery process by urging the sellers to reduce the shipping time. This ensures that customers don’t feel irritated by the slow processing of their orders. 6. Price Optimization Big data is also used to monitor the costs of Amazon to attract more customers and increase profits by an average of 25 percent per year. Prices are set according to the website operation, pricing of rivals, quality of merchandise, expectations of customers, the background of sales, anticipated profit margin and other considerations. When big data is modified and evaluated, the product prices typically change every 10 minutes. As a consequence, Amazon usually gives best-selling product prices and receives larger profits on less popular items. Of example, the cost of a novel on the New York Times Best Sellers list maybe 25% lower than the retail price, whereas a novel not included in the chart costs 10% more than the same book sold by a company. 7. Alexa Voice Recordings Another answer to the question ‘how does Amazon use big data’ is in Alexa’s voice recordings. So what happens here? When you have an Echo or Echo Dot at home, it works as eyes and ears for Amazon. The tiny device sits in your house and takes voice orders with ease. It gives information from the internet, orders items on your behalf, and acts as a virtual assistant. But where do the voice recordings go? They are stored in the Amazon servers. This data is used to provide better and accurate results to users. Amazon uses your voice recordings to make Alexa’s speech recognition suit the diverse range of users and understand different tones and dialects. 8. Amazon Web Services Using Amazon Web Services (AWS), the cloud computing company launched by Amazon in 2006, organizations may build flexible big data systems and protect them without the use of equipment or infrastructure maintenance. Big data applications like data warehousing, clickstream analytics, fraud detection, recommendation engines, Internet-of-Things (IoT) processing and event-driven ETL processing are usually via cloud computing. Companies can take advantage of Amazon Web Services by using them to evaluate profiles of consumers, spending habits and other relevant information to more efficiently cross-sell client goods in ways similar to Amazon. Some companies can also use Amazon to harass you, in other words. 9. Safe with Virtual Cash With our FREE Market Simulator,