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

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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!

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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

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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,

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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

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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,

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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

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Easily Fixable Data Analytics Challenges Faced by Your Business Enterprise

Data analytics has become an indispensable part of the business world. Look all around and you will realize that everything is already data-driven. A bigger pool of organizations is moving towards executing this practice on their premises also. However, as per a 2016 report from Gartner, it was discovered that lone 15 percent of the aggregate businesses who attempt to execute data analytics, win the battle, and the rest stall out in the pilot phase of the venture. After running a background check on this problem, it was understood that there is a set of common issues that every one of the firms is confronting. In this article, you will discover the 10 most regular concerns upsetting the execution of data analytics ventures and the approaches to effectively resolve them. 10 Data Analytics Challenges 1. Large Volume of Data to Store The first and foremost challenge faced by the companies implementing data analytics is associated with data storage and analysis. Higher traffic websites such as the New York Times and Amazon may generate petabyte data or more in a single month. IDC in its Digital Universe reported estimated that the information stored in the IT systems of the world is doubling every two years. Another issue with all this immense data is that a major chunk of it is in the unstructured form. Documents, videos, audios, and photos are comparatively difficult to search, analyze and occupy a lot of space. To deal with these data problems, organizations are turning to various types of technologies. Technologies like tiering, compression and deduplication are being utilized to reduce the amount of space required to store the data. To manage the analysis part, firms use tools like Hadoop, NoSQL databases, Spark, BI applications, big data analytics software, ML, and AI to dig out the insights that they want. Data literacy is the solution to this challenge. Instead of collecting any data available from various sources, enterprises need to work on collecting meaningful data. Hiring data analysts and training employees to understand data literacy will help businesses collect data that is useful for decision-making.  Another method to overcome the challenge is to scale the data warehouses/ data lakes in stages rather than going for a complete upgrade. This allows enterprises to manage the incoming data without spending billions of dollars at once.  2. Timely Generation of Insights The data doesn’t have to be just stored, it has to be used to achieve the business goals. As per the NewVantage Partners Survey, there are some common goals that are shared by almost every organization that deals with data analytics. Some of which include All these goals when achieved help businesses gain an edge over others in the market. However, the success of which usually depends on how quickly the generated insights are being acted upon. In case, the action time is less the data and insights tend to lose their value in the market. In order to achieve faster speed, some companies are looking forward to using new generation analytics tools and at the same time investing in real-time analytics that will dramatically reduce the time taken to generate reports. Real-time analytics are ruling the industry, thanks to powerful tools like Tableau, Power BI, Qlik, etc. The best way to generate timely insights is to choose the right tools for data storage and analytics. Where should a business store the data? In-house servers or cloud solutions like Microsoft Azure? Which analytical tools can easily handle big data and deliver real-time results? Talking to an expert will help businesses choose the right tools and customize them for their requirements. 3. Less Understanding of Analytics Data analytics has the ability to bring in precise and accurate decisions for the organizations that tend to use it. It helps them in managing their finances, launching new products, understanding their customers and much more. However, there is still a lot that needs to be done so that people have a clear picture of data analytics and its importance in today’s world. NewVantage found that only 27% of organizations in 2020 called their businesses data-driven. Moreover, 73% of businesses felt that big data management is an ongoing challenge. Seminars, small workshops on the office premises, discussions, and real-life examples are some of the ways that organizations are using to improve the understanding of data analytics among their staff. Training and empowering employees is vital to getting desired results from the data-driven model. It’s not sufficient if the top management and C-level executives understand the need for analytics. Every employee in the organization who needs to work with the new tools and systems has to realize the importance of quality data and accurate insights.  4. Recruiting Skilled Talent Organizations find it a challenging task to both retain and recruit talent that can handle their data and utilize it to derive useful insights.  The 2017’s Robert Half Technology Salary Guide has suggested huge pay raise for the positions of data scientists and business analysts all over the globe.  Companies are also trying to train their staff to learn some of the tools and techniques that can help them handle their data needs. But, there is still a large gap in the understanding of this field. The trend is continuing even in 2022, with Revenue Cycle Analyst and Database Administrator being the top two positions with the highest pay increase. Also, there are many firms that solely deal with data analytics and all the related operations. In case, the organizations are unable to find a suitable recruit for their firm, they can consult the professionals and get their data needs satisfied. These data analytics firms have all the expertise that is required to accomplish the given task. As an added advantage, outsourcing the work to another firm proves to be more economical than setting up a whole new section in an already established company. Hiring offshore solution providers and dedicated teams to manage data analytics for the business is a cost-effective solution.  5. Integrating

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Why is Building a Data Strategy Important For Your Business Growth?

We all know that an immense amount of data is generated with every passing second. From our Uber ride to ordering a burger at McDonald’s or every transaction that we make at the ATM, everything is recorded and stockpiled for further analysis. In the past, data was perceived as nothing but a by-product of business activity, but today it has a value and is considered more of an economic asset. All the big enterprises generate numerous data, which they want to utilize for the benefit of their company but still struggle in managing, sharing, and turning it into useful information. If you are among one those business owners who are looking forward to utilizing the data that has been just stored in your systems, you have come to the right place. In this article, you will know all about why is data strategy important, how to strategically manage your data, and whom to consult by uncovering data strategy. What Are the Advantages of Implementing a Data Strategy? Listed below are some of the benefits of implementing a data strategy in your organization’s operations:- What is Data Strategy? In this modern world, we are bombarded with a continuous flow of data in our lives. The same can be said for business houses as well. But, having said so, the raw data will render useless unless we cleanse, sort, process, and churn out the insights out of it. Though we all understand the importance of it, unfortunately, most organizations are unable to leverage the benefit of the most powerful weapon in their arsenal – their data set. As per research, less than half of the organizations can leverage their data for decision making and only 1% of the unstructured data is being utilized as of now. Organizations need a proper data strategy to smoothen the operational flow. Data Strategy can be defined in simple terms as a complete and comprehensive strategy for collating, governing, analyzing, and identifying the relevant intel out of the raw data and putting it to use for making business decisions in a data-driven manner.  Data strategy is inherently driven by the organization’s goal and overall business strategy. Whether it is better decision making, understanding the pain areas of the customers, or designing a product–data strategy can make a paradigm shift in the organizations’ business approach. A well-defined data strategy will comprise of –  In this fiercely competitive world, having a well-defined data strategy puts a business in a better position than its competitors. A well-round strategy defines all the aspects and considers all the factors so the management can make effective data-driven decisions to drive the organization. What is a Data Strategy Framework? Any strategy can be only defined when put in a proper and systematic framework. A framework goes by as the supporting structure underlying the concept or strategy. The entirety of data strategy success is solely dependent on how properly defined the framework is. With sophisticated platforms and methodology for data retention, the organizations do get the half job done but the other half is completely reliant on the tactical and strategic understanding of the 360-degree data strategy. A framework comes here for the rescue.  A properly defined data strategy framework covers multitudes of disciplines from data management. It comprises five core components that collaboratively work together as the building block for the comprehensive data management strategy – Identify, Store, Provision, Process, and Govern. Identify No matter how many terabytes of data we possess, none of this would matter much if we don’t know the proper identification and representation of the relevant content. Whether it is structured or unstructured, modifying and processing wouldn’t be possible if the data doesn’t cater to a properly defined format and value representation. Identification comprises the establishment of pertinent data element naming and proper value conventions. Having precise and accurate metadata (data about data) for identifying and referencing purposes is the sole essence of this first stage.  Store Once the data is identified, the data needs to be stored somewhere safe and in a secure manner. In simple terms, putting data in a proper structure and safe storage so it can be retrieved, accessed, and analyzed whenever in need in the future, is the main agenda of data storing. Many organizations do effectively define the storage mechanism, but in practicality, there is a lot of scope for improvement that organizations need to focus on. Provision Previously organizations used to store data in silos and whenever needed, they used to retrieve the data for an individual business need. But now there is a complete shift in the business management process. Having data always ready for retrieval and usage is not an add-on capability, rather it is the need of the hour. Provision is defined as the packaging of data systematically so it can be shared and reused. Also, it provides the appropriate rules and access as the guideline for data usage. Process All other steps will fall apart if the processing of raw data into meaningful information is not done properly. Processing is the most complex part. From data cleansing to data formulating – it takes care of all the steps required to provide a unified data view. It hides all the complexities in the back end and gives a complete viewpoint for the users. Govern The last part lies with governance to ensure the efficacy and usability of the data remain high. It constitutes multiple steps such as managing data security, establishing data correction logic, setting up new data management rules, and many more. Data governance ensures that the data is consistently usable and adheres to standard data policies. Data Strategy Roadmap Once we understand what data strategy is, putting all the points together and making an actionable plan is what a data strategy roadmap does. It is the culmination of operation and strategy. Roadmap collates all the activities and puts a proper structure around them. In the initial phase, all activities look equally important, but it is crucial from

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Marketing Analytics: The Secret To Your Business Success

Monitoring websites to track the success of a business is a culture and has since become a practice. It is usually interpreted that if the graph is good, the business is on the road to success and this somehow leads to significant aspects being neglected. Now, you must be wondering why so? This is a fact though, that simply by analyzing your website, you might just never be able to reach potential leads or evaluate the conversion rate of your potential customers or maybe even understand what is actually desired of your product to fulfill the demands of the market. Marketing analytics fills in the blank here and is the answer to all the forecasting you have been doing to date based on the evidence only. Implementing marketing analytics is what is to be learned now. When it comes to analytics there are people who will behave as if it doesn’t matter. But it sure does. Smart people across the globe are investing in this science so as to improve campaigns and gain insights. Data from HBR (2015) shows various ways in which marketing analytics is being used by different companies. Given the importance of marketing analytics for business growth, let me take you through it so we can see if it actually does matter to make it a part of your everyday business operations. Customer Behaviour Prediction: The key to all questions Prediction of customer behavior has been a significant part of major e-commerce firms like e-bay and Amazon. It gives them an edge over other businesses and allows them to predict the demands and needs that may arise in the future. According to a Silicon Valley-based predictive marketing company, AgilOne, these three principal classes of predictive models are recognized: Segmentation models Algorithms for grouping customers on basis of factors like the type of products they buy or the amount of money they spend etc. Prediction models Insights like customer conversion ratio (the number of people who visited the website to the ones who got influenced by the marketing campaign and bought products from the company), the likelihood of engagement, propensity to churn, the probability to buy etc. are derived from these models. Prediction models built on customer analytics can make a significant impact on any business. Recommendation models Offering recommendations about the product that the customers may like depending upon their previous purchases. All these models collectively utilize all the data from the present as well as the past and transform it into useful insights allowing businesses to improve in lucrative terms like sales and the RoI (return on investment). Prioritize and Qualify Leads Ranking your leads based on the likelihood of the actions they might end up taking is another front marketing analytics could help you peep into. This further assists in identifying prospects with similar attributes. The leads thus generated become prospects that can be turned into permanent customers. In this scenario, it is important to consider that, to generate optimum results a large number of data sets are needed. This gives bigger firms an edge over the ones that might not own such huge data quantities, by improving their return on investment. But with the right approach and help, smaller industries may also be able to gain this insight as per their needs. Driving Better Products in Market To launch a product that will suit the present demands and patterns of the market, a deep and well-predicted insight is a must. Again, marketing analytics is the answer here, the predictive algorithms allow the companies to improve the new product based on customer data piled up while taking sales and feedbacks into consideration. All these factors integrate to form a part of the bigger marketing strategy and allow the business to enhance its earnings. Creating Better Marketing Campaigns We all are proof that marketing campaigns via emails or social media allow the firms to improve their customer base. Marketing analytics gives companies the power to monitor and optimize these campaigns so that sales and ROI can see growth. The integrated tools and techniques help rule out the lacuna that might hamper the campaigns and their implementation as decided. Also, it becomes possible to monitor the current outcome of any campaign, whether it works properly or needs to be altered for the future. Taking Wiser Decisions A company may find out that digital, print, TV and radio marketing takes up around 85% of its market spending. Now amongst these many of the activities can be directly measured but spending on digital marketing can be refined using marketing analytics tools. The company can then use the results to optimize its strategy. Using these types of approaches allows the marketers to track marketing performance on real-time marketing analytics metrics and make wiser decisions for its better implementation. How Is Data Analytics Used in Marketing? Data analytics helps the marketing teams to combine structured and unstructured data and derive real-time insights about customer behavior, purchase patterns, sales graph, market trends, and more. It helps enterprises with the following:  Customer Acquisition  A study said that 42% of marketers use data analytics to make decisions about customer acquisition.  Customer acquisition is a technique of winning new customers and convincing them to buy the products by the brand. The process includes several stages and is represented in a funnel-shaped design. It consists of targeted promotions to reach prospective customers and then provide them with relevant information to spark interest.  Attracting the audience, engaging them through promotional material, convincing them to buy, and keeping them happy with customer service can be called the customer acquisition flywheel. Data analytics helps marketers by providing actionable insights to attract customers.  Customer Insight  Customer insight is the process of understanding and interpreting the purchase behavior of customers. This helps in knowing which products are more popular and why. Businesses can suggest products to customers based on customer insight and increase sales.  Data analytics processes the unstructured data from the World Wide Web to share reports and data visuals about customers’ preferences.

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