Page 43 – DataToBiz

Vaishali
February 26, 2020
No Comments

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
No Comments

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
No Comments

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
2 Comments

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
No Comments

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,

Vaishali
February 20, 2020
No Comments

Everything You Need to Know About Computer Vision

To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision. There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to the detection and labeling of objects has been able to surpass humans. One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision. What is Computer Vision? Computer vision is a field of computer science that develops techniques and systems to help computers ‘see’ and ‘read’ digital images like the human mind does. The idea of computer vision is to train computers to understand and analyze an image at the pixel level. Images are found in abundance on the internet and in our smartphones, laptops, etc. We take pictures and share them on social media, and upload videos to platforms like YouTube, etc. All these constitute data and are used by various businesses for business/ consumer analytics. However, searching for relevant information in visual format hasn’t been an easy task. The algorithms had to rely on meta descriptions to ‘know’ what the image or video represented. It means that useful information could be lost if the meta description wasn’t updated or didn’t match the search terms. Computer vision is the answer to this problem. The system can now read the image and see if it is relevant to the search. CV empowers systems to describe and recognize an image/ video the way a person can identify a picture they saw earlier. Computer vision is a branch of artificial intelligence where the algorithms are trained to understand and analyze images to make decisions. It is the process of automating human insights in computers. Computer Vision helps empower businesses with the following: Computer vision is largely being used in hospitals to assist doctors in identifying diseased cells and highlighting the probability of a patient contracting the disease in the near future. Computer vision is a field of artificial intelligence and machine learning. It is a multidisciplinary field of study used for image analysis and pattern recognition. Emerging Computer Vision Trends in 2022 Following are some of the emerging trends in computer vision and data analytics: One of the most vigorous and convincing forms of AI is machine vision that you’ve almost definitely seen without even understanding in any number of ways. Here’s a rundown of what it’s like, how it functions, and why it’s so amazing (and will only get better). Computer vision is the computer science area that focuses on the replication of the parts of the complexity of the human visual system as well as enables computers to recognize and process objects in images and videos in the same manner as humans do. Computer vision had only operated in a limited capacity until recently. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to the detection and labeling of objects has been able to surpass humans. One of the driving factors behind computer vision growth is the amount of data we generate today, which will then get used to train and improve computer vision. In addition to a tremendous amount of visual data (more than 3 billion photographs get exchanged daily online), the computing power needed to analyze the data is now accessible. As the area of computer vision has expanded with new hardware and algorithms, the performance ratings for the recognition of artifacts also have. Today’s devices have achieved 99 percent precision from 50 percent in less than a decade, rendering them more effective than humans in reacting quickly to visual inputs. Early computer vision research started in the 1950s, and by the 1970s it was first put to practical use to differentiate between typed and handwritten text, today, computer vision implementations have grown exponentially. How does Computer Vision Work? One of the big open questions in both neuroscience and machine learning is: Why precisely are our brains functioning, and how can we infer it with our algorithms? The irony is that there are very few practical and systematic brain computing theories. Therefore, even though the fact that Neural Nets are meant to “imitate the way the brain functions,” no one is quite positive if that is valid. The same problem holds with computer vision— because we’re not sure how the brain and eyes interpret things, it’s hard to say how well the techniques used in development mimic our internal mental method. Computer vision is all about pattern recognition on an individual level. Also, one way is to train a machine on how to interpret visual data is to feed. It can get supplied with pictures, hundreds of thousands of images, if possible millions that have got labeled. Also, later on, they can be exposed to different software techniques or algorithms. Further, these can enable the computer to find patterns in all the elements that contribute to those labels. For example, if you feed a computer with a million images of cats (we all love them), it will subject them all to algorithms. Further, that will allow them to analyze the colors in the photo, the shapes, the distances between

Vaishali
February 19, 2020
1 Comment

16 Amazing Benefits of Data Analytics for Healthcare Industry

Digital innovation and data analysis will and have been shaping the direction of healthcare. Analytics technologies will be a top priority for health CIOs in 2023 , especially as health information systems try to use big data to provide better care, prevent diseases, and automate all aspects of the continuum of care. Moving to a new decade, let’s go over the fundamentals of healthcare data analytics and why opting for data analytics services are beneficial for the healthcare sector: what it entails, what it can do, and how healthcare systems should continue. In the field of healthcare, we better understand what big data is and how the 3 Vs work within our environment than most businesses do. EMRs also improved by exponential factors the amount and quality of the data available to us. At the light speed-literally-the rate at which data is collected and transmitted into the networks, we are accountable for communicating from occurs. It is obvious that healthcare data analytics operates in a world of big data. The question for BI teams is how we leverage the data to transform it into something useful for our clients and actionable. Big data is capable of giving clinical professionals and physicians the opportunity to gain actionable insights into the enormous amount of data at their fingertips, with the right tools in place. It can allow them to: What Is Healthcare Data Analytics? Data analytics for healthcare is the processing and analysis of data in the healthcare industry to gain insight and improve decision-making. Through key areas such as medical costs, clinical data, consumer behavior, and pharmaceuticals, macro-and micro-level healthcare data analytics can be used to effectively streamline processes, optimize patient care, and reduce overall costs. Healthcare data is the most dynamic of all fields. Including electronic health records (EHR) and real-time recording of vital signs, data comes not only from multiple sources but must conform with government regulations. It is a complicated and complex operation, which requires a level of protection and accessibility that can only be supported by an embedded analytics system. Importance of Data Analysis in Healthcare Analytics is considered the way forward in the healthcare industry. The Covid-19 pandemic has increased the dependence on data analytics, artificial intelligence, and computer vision to provide healthcare centers and doctors with the necessary information to speed up the treatment process and increase the patient’s chances of survival. Early adoption of data analytics in healthcare helped hospitals provide quality treatment and care to patients while also reducing the pressure on doctors, nurses, and administrative staff. Data analytics in healthcare can also be termed healthcare analytics. It helps streamline and automate recurring tasks, assists the medical personnel in making a correct diagnosis of the patient’s condition, and provides care even remotely. Doctors can rely on the data-driven model to make medical decisions based on the patient’s health history. Data analysis in healthcare plays a prominent role in the following: Benefits of Data Analytics for Healthcare Industry A Business Intelligence (BI) and monitoring system, like any business, will significantly improve operational efficiency, reduce costs and streamline operations by evaluating and exploiting KPIs to recognize gaps and guide decision-making. Unlocking the usefulness of the data helps everyone from patients and caregivers to payers and vendors. Let’s look at all the aspects in which a data analytics system will affect the healthcare sector. 1. Analytics for Health Providers While healthcare organizations switch from fee-for-service to value-based payment models, the desire to maximize productivity and treatment renders data processing a key component of routine operations. Organizations can use an embedded analytics and reporting solution to: 2. High-Risk Inpatient Care Treatment for those needing emergency services can be expensive and complicated. While the costs increase, the patients do not always enjoy better care, there is a need for significant change in-hospital procedures. Patient behaviors and experiences can be detected more effectively using digitized healthcare data. Predictive analytics will identify patients at risk from chronic health problems for crisis situations, allowing doctors the ability to provide intervention measures that will reduce access to hospitals. It is impossible to monitor these patients and deliver personalized treatment plans without sufficient data, hence the use of a Business Intelligence (BI) system in healthcare is of paramount importance to safeguard high-risk patients. 3. Patient Satisfaction Most healthcare facilities are worried about patient satisfaction and participation. Through wearables and other health tracking tools, doctors may play a more active role in patient preventive care and consumers can become more mindful about their role in their own health. Not only does this information strengthen the interaction between doctors and their patients but it also reduces hospitalization levels and identifies serious health concerns that could be avoided. 4. Human Error Most preventable health concerns or appeals of insurers stem from human error, such as a doctor prescribing the wrong medication or the wrong dose. This not only increases the risk of patients but also increases the cost of premiums and the cost of paying hospital facility lawsuits. A BI tool can be used to monitor patient data and medicine taken and corroborate evidence to alert consumers of irregular medications or dosages to reduce human error to avoid patient health problems or death. This is particularly useful in fast-paced situations where doctors handle multiple patients on the same day, which is a scenario that is ideal for mistakes. 5. Health Insurance Health insurance companies undergo constantly changing regulations. And as one of the biggest family expenditures, health insurance relies on success efficiency. By collecting and interpreting data through a solution for analytics, the payers can: 6. Personal Injury Claims for personal injury are a particular concern of insurance companies, particularly in the case of fraud. But the best tool for healthcare BI will evaluate these incidents and fix the redundancies that contribute to these issues. Cases of personal injury are more effective and productive, with claim course descriptions that can be aggregated and analyzed according to typical patterns of behavior. Then, personal injury lawyers and

Vaishali
February 17, 2020
No Comments

Best Data Mining Techniques You Should Know About!

In this piece, we are going to discuss why one must study data mining and what are the best data mining techniques and concepts. Data scientists have a history in mathematics and analytics at their heart. Also, they are building advanced analytics out of that math history. We are developing machine learning algorithms and artificial intelligence at the end of that applied math. As with their colleagues in software engineering, data scientists will need to communicate with the business side. It requires a sufficient understanding of the subject to get perspectives. Data scientists often have the role of analyzing data to assist the company, and that requires a level of business acumen. Eventually, the company needs to be given its findings understandably. It requires the ability to express specific findings and conclusions orally and visually in such a manner that the company will appreciate and operate upon them. Therefore, you should practice data mining. It is the process where one constructs the raw data and formulates or recognizes the various patterns in the data via mathematical and computational algorithms. It will be precious for any aspiring data scientist, which allows us to generate new ideas and uncover relevant perspectives. Why Data Mining? Current technologies for data mining allow us to process vast amounts of data rapidly. The data is incredibly routine in many of these programs, and there’s enough opportunity to exploit parallelism. A modern generation of technologies has evolved to deal with problems like these. Such programming systems have been designed to derive their parallelism, not from a “super-computer,” but from “computing clusters”— vast arrays of commodity hardware, whether traditional Ethernet cable-connected processors or cheap switches. Data Mining Process Data mining is the practice of extracting useful insights from large data sets. This computational process involves the discovery of patterns in data sets using artificial intelligence, database systems, and statistics. The main idea of data mining is to make sense of large amounts of data and convert/ transform it into useful information. The data mining process is divided into seven steps: Collecting & Integrating Data Data from different sources is consolidated in a single centralized database for storage and analytics. This process is known as data integration. It helps detect redundancies and further clean the data. Cleaning the Data Incomplete and duplicate data is of little use to an enterprise. The collected data is first cleaned to improve its quality. Data cleaning can be done manually or automated, depending on the systems used by the business. Reducing Data Portions of data are extracted from the large database to run analytics and derive insights. Data is selected based on the query or the kind of results a business wants. Data reduction can be quantitative or dimensional. Transforming Data Data is transformed into a single accepted format for easy analytics. This is done based on the type of analytical tools used by the enterprise. Data science techniques such as data mapping, aggregation, etc., are used at this stage. Data Mining Data mining applications are used to understand data and derive valuable information. The derived information is presented in models like classification, clustering, etc., to ensure the accuracy of the insights. Evaluating Patterns The patterns detected through data mining are studied and understood to gain business knowledge. Usually, historical and real-time data is used to understand the patterns. These are then presented to the end-user. Representation and Data Visualization The derived patterns can be useful only when they are easily understood by the decision-makers. Hence, the patterns are represented in graphical reports using data visualization tools like Power BI, Tableau, etc. Data Mining Applications Data mining plays a crucial role in various industries. It helps organizations adopt the data-driven model to make better and faster decisions. Let’s look at some applications of data mining. Finance Industry: From predicting loan payments to detecting fraud and managing risk, data mining helps banks, insurance companies, and financial institutions to use user data for reducing financial crimes and increasing customer experience. Retail Industry: From managing inventory to analyzing PoS (Point of Sale) transactions and understanding buyer preferences, data mining helps retailers manage their stock, sales, and marketing campaigns. Telecommunications Industry: Telecom companies use data mining to study internet usage and calling patterns to roll out new plans and packages for customers. Data mining also helps detect fraudsters and analyze group behaviors. Education Industry: Colleges and universities can use data mining to identify courses with more demand and plan their enrollment programs accordingly. Educational institutions can improve the quality of education and services through data mining. Crime Detection: Data mining is also used by crime branches and police to detect patterns, identify criminals, and solve cases faster. Best Data Mining Techniques The following are some of the best data mining techniques: 1. MapReduce Data Mining Technique The computing stack starts with a new form of a file system, termed a “distributed file system,” containing even larger units in a traditional operating system than the disk boxes. Spread file systems also provide data duplication or resilience protection from recurrent media errors arising as data is spread over thousands of low-cost compute nodes. Numerous different higher-level programming frameworks have been built on top of those file systems. A programming system called MapReduce is essential to the new Software Stack that is often used as one of the data mining techniques. It is a programming style that has been applied in several programs. It includes the internal implementation of Google and the typical open-source application Hadoop that can be downloaded, along with the Apache Foundation’s HDFS file system. You can use a MapReduce interface to handle several large-scale computations in a way that is hardware fault resistant. All you need to write is two features, called Map and Reduce. At the same time, the program handles concurrent execution, and synchronization of tasks executing Map or Reduce, and also tackles the risk of failing to complete one of those tasks. 2. Distance Measures A fundamental problem with data mining is the analysis of data for