Data Engineering & Analytics Archives - Page 8 of 9

Vaishali
April 09, 2020
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Data Analytics Helping Accountant Excel! Role Of Data Science In Accounting.

If the C-suite were to shape a rock band focused on the standard positions, the guitarist would be the ambitious CEO, and the resourceful COO would play lead guitar. The level-headed CFO will possibly be positioned as the guitarist, a significant band member, but put in the background and tasked primarily withholding the band on track to help the other members shine. This perception of the CFO as a back-office number cruncher who controls schedules monitors costs and maintains the lights on might have been accurate in the past, but the new CFO is squarely at the heart of the corporate strategy. Data’s core position in today’s business climate is the impetus for this transition. Today the CFO is the company’s co-pilot, finding the most successful clients, evaluating risk through scenario preparation, measuring customer loyalty through data collection, and designing new KPIs. Corporate boards are continually considering a future CFO in terms of whether he or she will take over as CEO eventually. CFOs should have global and diverse experience, be up-to-date on technology, be able to recognize and recruit the right talent and, most importantly, know how to lead, as per one of the KPMG Global CEO Survey. The study also found that 85 percent of CEOs agree that the most significant strategic advantage a CFO can bring to a company is to use financial data to achieve sustainable growth. CFOs need new enterprise performance management (EPM) tools to serve this strategic role— and many see the cloud’s ability to unleash the power of their data and turn their business into an analytics powerhouse. CFOs and the finance department will need a live view into all business areas, with resources that allow them to provide real-time analyzes of changing situations, suggest actions, and offer effective strategic planning and forecasting. In a recent CFOs survey of Oracle as well as other business leaders, 90 percent of the executives said that the ability to create data-based insights is very crucial to the success of their organization. Still, more than half questioned the strength of their organization to handle large data inflows. So the more data an organization uses, the more reliable the research will be. So, almost half of the financial decision-makers in Europe and the Middle East, for example, expanded the number of data sources they evaluate to better understand the effect of this surprising change after the Brexit vote. How To End The Tyranny Of The Spreadsheet (Data Science In Accounting) In business, where you always stand depends on where you are seated, and the finance department is well placed to offer a holistic view of the company. The CFO’s ability to link key areas around the enterprise— marketing, supply chain, manufacturing, services, and human capital management — to build a holistic, real-time business image is vital to risk management and value creation. That calls for the right resources. The ubiquitous spreadsheet is one adversary of such real-time analytics. Consider how an annual budget is produced by the finance department of the business or any department within the organization. The budget process is mostly done through a series of spreadsheets that are sent to various stakeholders, with the usual concerns: Is this the latest version? Who made the most recent alterations? Was the data correct, or have the consolidation process made mistakes? Usually, the finance department spends most of its time tracking down and checking the data— and not enough time evaluating it. Due to the many data systems and reporting tools acquired over the years, organizations rely heavily on spreadsheets and Data Analytics in Finance to organize the information. Because data is siloed in their respective units, to build budgets and strategies, LOB members must first dig into the data. Finance then spends massive amounts of time testing and rolling this unconnected data into more detailed predictions and plans. Finance teams with Data Analytics in Finance need to build better models for financial and organizational improvements if businesses are to stay ahead of the market. Today’s digital finance team is moving from simple, traditional transaction analysis to more sophisticated predictive analysis, such as statistical-based modeling, dynamic market management, and risk-adjusted business simulations. To do so, they need access to a centralized data system that drills both intensely across transactional data, and broadly through core functional divisions of the organization. Finance companies need to use analytics that interacts with cross-functional drivers such as customer loyalty, process management, and business decision-making. And, unlike in the past, these observations are obtained in real-time, not just at daily reporting times— providing a continuous view of the company’s birds. Agile CFOs Measure Non-Financial Data, Too In addition to having a profound impact on existing business models, digitization and globalization have also changed the way we evaluate business performance. Today, intangible assets like brands, customer relations, intellectual property, and expertise have become the primary drivers of the overall success of a business. Measuring the success of a company in all of these fields involves data from around the organization. It is a challenge for finance to track these non-financial key performance indicators (KPIs) with the same degree of methodological rigor it gives to financial metrics— like productivity and return on investment. A new report by the American Institute of CPAs and the Chartered Institute of Management Accountants on financial leaders found that the most forward-thinking CFOs are more likely to monitor non-financial KPIs such as talent pool, customer experience, business process performance, brand credibility, and competitive intelligence; Therefore, sustainability and social responsibility are also increasingly relevant for consumers, workers, and the result, and are steps that CFOs will recognize What’s unique in monitoring this information is not just that the data is non-financial; it’s unstructured too. Many of the data regarding brand credibility and consumer loyalty may come from social media, for example. CFOs need to rapidly track, analyze, and evaluate unstructured data and collaborate with organization-wide subject matter experts to develop new performance metrics that incorporate this data. As a result, KPI

Vaishali
April 06, 2020
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Converting Big Data To Smart Data | The Step-By-Step Guide!

Over the last few years, Big Data has become one of the biggest buzzwords for businesses worldwide. With data of all sorts being generated in record amounts each year, capturing and analyzing this knowledge would give businesses greater visibility into their clients and their markets than ever before, and maybe even encourage them to foresee what may happen in the future. Here are just one of many amazing big data stats: They submit 204 million emails per minute, upload 2.5 million pieces of content on Twitter, send 277,000 tweets, and publish 216,000 photos on Instagram. There is a massive amount of data out there, just enough to learn. But it can be time-consuming as well as challenging to make sense of millions (maybe billions) of data points without powerful technology, particularly when this data becomes unstructured. That is often the case for digital online data in the form of news stories, social media messages, feedback from blogs, and much, much more. Besides, such is the difficulty of this cycle that a reaction towards big data has been somewhat current. Now there are concerns about the value of big data being overstated because it is too “huge” and unruly. There are two primary forms of Smart Information, which are often addressed by industry experts. Another type is information collected by a sensor, then sent to a neighboring collection point, and acted on before being sent to a database for Analytics. Such data comes from Intelligent Sensors, in particular within the Industrial Things Internet (IIoT) networks. The other kind of Smart Data is the Big Data stored and waiting to be translated into actionable information. Data heading to and from a Smart Sensor is “sensor data” for this report. The word, Smart Data, would apply to Big Data which was tested for useful information. Consumer Journey Analytics weaves hundreds of communications through multiple channels from the company internet. It incorporates thousands of activities to create a journey for the customers of a company. It is a data-driven methodology that is used to identify, interpret, and impact the experience of consumers. However, if the input is “false,” it is both annoying and offensive. Further, it may result in the loss of a client. The Customer Experience Assessment (or Customer Analytics Voice) utilizes tools and techniques to collect the perceptions, thoughts, and feelings of the customer. Consumer Analytics speech stresses the customers’ mental state. Machine Learning Smart Data Machine learning is often a method of preparation with Artificial Intelligence applications but can also be used as a system of understanding and decision making. While Smart Data’s use and prominence has grown, it has also been used with Machine Learning algorithms designed to find Business Intelligence and insights. Machine Learning allows companies to process data lakes and data centers, thus generating smart results. Traditionally, companies pursuing Big Data Business Intelligence have used Data Scientists who spend time searching for trends and correlations within the databases of an organization. Artificial Intelligence and Smart Data Decisions are made during the scanning and filtering process of creating Smart Data as to which data should be filtered and which should be released. During this method, Machine Learning and Artificial Intelligence (AI) employ specific criteria. AI is a continuous attempt to create wisdom inside computers, allowing them to function and act like human beings. Artificial Intelligence has provided autonomy and can address specific goals. Financial services companies, for example, can use AI-driven Smart Data for consumer identification, fraud detection, market analysis, and enforcement. Collecting Data Organizations with less knowledge of Big Data often gather everything and then archive it in a Data Warehouse, Data Lake, or often what a Data Swamp is. We obtain Big Data intending to use it “until we decide to use it.” While these companies may believe they are gathering quantitative data for years, the data may lose quality or quantity or may even be in the wrong format. Their money would be best used to collect data appropriate for their company. An enterprise can be knowledgeable about the data it collects and retains in a Data Lake. Data takes time and money to collect, compile, and manage. Collecting Intelligent Information can be an effective strategy for small and medium-sized organizations, rather than “pure” Information. The emphasis on Smart Data collection helps a company to use cost-effective solutions to handle it. Collecting only the essential data will minimize the use of Self-Service BI systems, preventing workers from getting lost in the mass of irrelevant data. Smart data collection is not just about removing the excess data. Smart data can come from various outlets, and an agile enterprise can combine these resources to develop a highly focused business intelligence model. The point of view is right away. Big data is unusable, lacking order. It is just a collection of random knowledge that would take years to absorb, and may not provide any results even then. But when the form can be easily overlaid and evaluated, big data tends to become smart data. At Talkwalker, we have a way to explain just how this is going to happen, and how it can be a little like searching for a partner in life. From a machine, all social data is just words on a page through different sources, including Twitter posts, Facebook posts, news articles, websites, and discussion sites. The first step, as you would on Google, is to search for a particular subject in that data. Let’s claim we’re typing “Talkwalker” into our framework for social data analytics. At this point, we would have a very long list of URLs or post titles in no particular order, without any other criteria or filters. With such a narrow filter, the knowledge that we can obtain from such details is, as you can guess, also quite restricted. All we’d ever say is how many times a specific word has been listed online. The detail is by no way meaningless. It may, in turn, be important Information for businesses looking

Vaishali
March 22, 2020
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Integrating Data Analytics At Every Level Of Your Organization! professionals’ Guide.

What is data analytics and how it is used by large organizations to support strategic and organizational decisions? Senior leaders offer insight into the problems and opportunities involved. Most data and analytics (D&A) conversations begin by focusing on technology. It is critically important to have the right resources, but executives too often ignore or underestimate the value of the people and organizational components needed to create a productive D&A process. When that happens, D&A initiatives that fail — not deliver the insights required to move the organization forward, or inspire trust in the actions necessary to do so. The stakes are high with International Data Corporation predicting global D&A market spending to surpass $200 billion a year by 2020. A stable, efficient D&A feature encompasses more than just a stack of technology, or a couple of people isolated on one level. D&A will be the organization’s heartbeat, integrated into all leading sales, marketing, supply chain, customer service, and other core functions decisions. Why can I develop successful D&A capabilities? Start by creating an enterprise-wide plan that provides a clear picture of what you’re trying to achieve and how progress will be evaluated. One of America’s prominent sports leagues is a perfect example of an organization making the most of its D&A feature, applying it to cost management plans, for example, reducing the need for teams to fly back-to-back nights from city to city for games. Throughout the 2016–2017 season, thousands of travel-related restrictions, player exhaustion, ticket sales, arena capacity, and three major TV networks had to be taken into account. With 30 teams and 1,230 regular season games stretching from October through May, there were trillions of scheduling choices available. Companies should follow the lead of the league by understanding first that good D&A starts at the top. Make sure the leadership teams are entirely engaged in the company in identifying and setting goals. Avoid allowing the setting of objectives and decision-making to take place in organizational silos that can generate shadow technologies, conflicting versions of the reality, and paralysis of data analysis. Ask: Is the aim to help boost the company output before launching some new data analysis initiative? System jump start and cost efficiency? Drive policy, and speed up change? Growing market share? More successful innovation? Any of that? Leadership teams must understand that it takes bravery to be successful because, as they embark on the journey, data analytics observations will always point to the need for decisions that may entail a course correction. The leaders need to be frank about their ability to integrate the findings into their decision-making. Further, they should keep themselves and their teams accountable for that. Consider a large global life sciences company that spent a huge amount of money to develop an advanced analytics platform without knowing what it was supposed to do. Executives allowed their development team to buy a lot of items, but none understood what the developed tools were meant to do or how to use them. Luckily, before it was too late, executives identified the issue, undertaking a company-wide needs assessment and restoring the platform in a manner that inspired trust in its ability to drive productivity and promote business transformation. In another instance, a global financial services company, focused on stakeholder expectations, has developed a robust development infrastructure. But executives soon discovered after creating it, that they lacked the organizational structure and resources to use the platform effectively. When these requirements were met, the organization was able to use a great platform to generate substantial operating cost savings. Data analytics is the most in-demand technology skill for the second year running, according to KPMG’s 2016 CIO Survey. Still, almost 40 percent of IT leaders claim they suffer from skill shortages in this critical sector. Formal, organized structures, procedures, and people committed to D&A can be a competitive advantage, but this significant opportunity is lacking in many organizations. Companies who develop a D&A infrastructure to meet their business needs have in our experience teams of data and software developers who are experienced in using big data and data scientists who are entirely focused on a D&A initiative. Although processes vary, the team should be integrated seamlessly with existing D&A suppliers and customers in the sector, working in collaboration with non-D&A colleagues — people who understand both the market problems and how the business analytics functions — to set and work towards practical and specific strategic objectives. The teams will need full executive leadership support, and their priorities should be aligned entirely with the company plan. In an era in which data is generated on a scale well beyond the capacity of the human mind to process it, business analytics leaders need D&A that they can trust to inform their most important decisions— not just to cut costs but also to achieve growth. And the best would use D&A to predict what they want or need from their customers before they even know they want or need it. Volatility, sophistication, and confusion can better characterize today’s business analytics decisions governing the macroclimate. In this uncertain climate, forward-thinking companies are identifying and exploiting data as a strategic tool to improve their competitive edge. Data Analytics facilitates proactive decision-making by offering data-driven insights into products, consumers, competitors, and any aspect of the market climate. Today analytics are applied on a need-based basis in most organizations. Although most companies are still considering making investments in data analytics and business intelligence, they need to realize that the process of incorporating advanced analytics into the corporate structure requires far more than investing in the right people and resources. A data-driven culture is the core of this framework and is a crucial factor for the effective introduction of analytics into the organizational system. The integration cycle begins with a data-driven resolve. Big data analysis and advanced analytics must be accepted at the corporate level as an operational feature to be powered by the data. Projects and assignments undertaken must be analyzed from an analytical

Vaishali
February 26, 2020
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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
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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

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,

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

Kavika Roy
February 08, 2020
2 Comments

Data Analytics in Travel Industry: Stand Out in the Crowd

Data Analytics brings endless opportunities for the travel industry. A large amount of valuable data is generated at every stage of a trip and with a lot of people traveling around the globe, this data can offer significant insights. Travelers buy stuff online, create an itinerary, save dates on calendars, use GPS to reach their destination and so on. At every stage of their trip, they leave a data trail. The experts now analyze this data and infer various insights to enhance the customer traveling experience. However, collecting this data and connecting it is a bit of a challenging task for data analysts, but the discernments obtained can revolutionize the travel industry and make this venture a more profitable business than it was ever before. There are several ways in which data analytics services are currently assisting the travel industry to do better and meet its goals. Business Data Analytics in Travel Industry Machine Learning and Artificial Intelligence are picking up traction in the travel industry to help airlines and hotels make data-driven decisions based on accurate and actionable insights. Business analytics provide real-time insights about customers using data from multiple sources. Data like flight bookings, hotel stays, schedule patterns, repeat bookings, flight preferences, and so on is collected from websites, apps, social media, customer accounts, etc., and stored in a centralized database. This data is cleaned and processed to avoid redundancy. Analytical tools are used to analyze this data in real-time and share insights with the business to help make strategic decisions. Business analytics in the travel industry help airlines and hotels understand customer behavior and market trends. When the airline knows what a customer expects or wants, it can customize the services to enhance customer experience and thus inspire brand loyalty. Business analytics helps in the data-driven transformation of the travel industry. The pandemic has pushed the travel industry into losses and havoc. Business analytics is a way to bring the necessary change and empower the airlines to recover from the dire situation and come out stronger. Experts claim that the use of predictive and prescriptive analytics will be a game-changer in the industry. Data science and predictive modeling can help airlines uncover critical intelligence to provide real-time actionable insights that help in recovering and gearing up to make the most of new market opportunities. An important aspect to consider is automation. Even though many businesses are already using data analytics, they spend too much time, energy, and money on cleaning the data rather than running analytics and using the insights. Artificial intelligence and machine learning make automation possible by streamlining data collection, cleaning, and storage processes. This gives airlines more time to run queries and use the insights without delay. Designing an Effective Business Strategy To build an effective business strategy it is important for an organization to be aware of its customer base and its preference. With the help of data analytics and predictive analytics tools, the data collected in the form of feedback, customer reviews, social media posts, etc. is utilized to infer the behavior pattern of the customers. This, thus, causes the organizations to comprehend their customer’s needs and offer services that will bring them more benefits. The bits of knowledge are utilized by the firm to tailor customized plans for its clients. Better services offered by the firm not only help to retain loyal customers but also boost sales and improve reputation. Let’s You Stand Out in the Crowd When a traveling firm uses data analytics services to distinguish itself from its competitors, it is able to create lucrative offers that cater to the needs of the clients and at the same time help you to gain an edge over others. For instance, Amadeus (a Global Distribution System) allows its users to ask simple travel-related questions without entering any personal details. The questions can be as basic as – When will I get the cheapest flight to Italy? Or is it possible to travel somewhere for just 700 $? These types of services make it very convenient for users to clear their travel-related doubts and get reliable advice. Once a customer starts to trust the product, he/she will definitely return to use it and in the process become a loyal customer. However, retaining customers is another challenge that must be carefully dealt. Improve the Pricing Strategy While planning travel, money is one of the major concerns. People spend hours and days finding that one tour package that can offer them the best deals without cutting a hole in their pocket. But how would the firms know beforehand what the customer wants from them? The simple answer could be data analytics. All searches made by the visitors’ on a firm’s website can be used to infer the budget that a major chunk of people can afford to travel. Keeping this in mind a well-tailored travel plan can be devised and placed on the website. This is the strength of applying data analytics, you can predict what will happen next. For instance, firms like KAYAK have been using data analytics to forecast the changes in flight prices for the coming seven days. Taking Better Decisions Taking upright decisions is one of the key aspects of improving a business. With the help of data analytics, travel planners are able to draw better choices based on real predictions and not just intuitions. Data analytics allows the firms to develop customers’ profiles and thereby helping them to accurately target the market campaigns. As per Forrester, data analytics tends to increase customer responsiveness up to 36%. Being able to comprehend the demand of the customer and recent ongoing trends in the market lets the companies peep into the future and perform accordingly. The famous Nippon Airways uses data analytics to optimize its cargo management system thus making it one of the largest airlines in Japan. Making the Tough Easy Having data analytics at your disposal prevents painful losses and enhances revenue for the travel industry. To understand this,

Ankush Sharma
November 21, 2019
1 Comment

Kickstart data analytics for e-commerce business with unbelievable $299 Budget

How we at DataToBiz helped ecommerce start-up in kick-starting data analytics journey with unbelievable $99 budget by using freemium and open source tools