Effective Big Data Analytics Use Cases in 20+ Industries

If we have to talk about the modern technologies and industry disruptions that can benefit every industry and every business organization, then Big Data Analytics fits the bill perfectly.  The big data analytics market is slated to hit 103 bn USD by 2023 and 70% of the large enterprise business setups are using big data. Organizations continue to generate heaps of data every year, and the global amount of data created, stored, and consumed by 2025 is slated to surpass 180 zettabytes. However, they are unable to put this huge amount of data to the right use because they are clueless about putting their big data to work. Here, we are discussing the top big data analytics use cases for a wide range of industries. So, take a thorough read and get started with your big data journey.   Let us begin with understanding the term Big Data Analytics. What is Big Data Analytics? Big data analytics is the process of using advanced analytical techniques against extremely large and diverse data sets, with huge blocks of unstructured or semi-structured, or structured data. It is a complex process where the data is processed and parsed to discover hidden patterns, market trends, and correlations and draw actionable insights from them.  The following image shows some benefits of big data analytics: Big data analytics enables business organizations to make sense of the data they are accumulating and leverage the insights drawn from it for various business activities.  The following visual shows some of the direct benefits of using big data analytics: Before we move on to discuss the use cases of big data analytics, it is important to address one more thing – What makes big data analytics so versatile? Core Strengths of Big Data Analytics Big data analytics is a combination of multiple advanced technologies that work together to help business organizations use the best set of technologies to get the best value out of their data. Some of these technologies are machine learning, data mining, data management, Hadoop, etc. Below, we discuss the core strengths of big data. 1. Cost Reduction Big data analytics offers data-driven insights for the business stakeholders and they can take better strategic decisions, streamline and optimize the operational processes and understand their customers better. All this helps in cost-cutting and adds efficiency to the business model.  Big data analytics also streamline the supply chains to reduce time, effort, and resource consumption. Studies also reveal that big data analytics solutions can help companies reduce the cost of failure by 35% via: 2. Reliable and Continuous Data As big data analytics allows business enterprises to make use of organizational data, they don’t have to rely upon third-party market research or tools for the same. Further, as the organizational data expands continually, having a reliable and robust big data analytics platform ensures reliable and continuous data streams.  3. New Products and Services Because of the availability of a set of diverse and advanced technologies in the form of big data analytics, you can take better decisions related to developing new products and services.  Also, you always have the best market and customer or end-user insights to steer the development processes in the right direction. Hence, big data analytics also facilitates faster decision-making stemming from data-driven actionable insights. 4. Improved Efficiency Big data analytics improves accuracy, efficiency, and overall decision-making in business organizations. You can analyze the customer behavior via the shopping data and leverage the power of predictive analytics to make certain calculations, such as checkout wait times, etc. Stats reveal that 38% of companies use big data for organizational efficiency. Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. 5. Better Monitoring and Tracking Big data analytics also empowers organizations with real-time monitoring and tracking functionalities and amplifies the results by suggesting the appropriate actions or strategizing nudges stemming from predictive data analytics. These tracking and monitoring capabilities are of extreme importance in: 6. Better Remote Resource Management  Be it hiring or remote team management and monitoring, big data analytics offers a wide range of capabilities to enterprises. Big data analytics can empower business owners with core insights to make better decisions regarding employee tracking, employee hiring, performance management, etc.  This remote resource management capability works well for IT infrastructure management as well.  7. Taking Right Organizational Decisions Take a look at the following visual that shows how big data analytics can help companies take better and data-driven organizational decisions. Now, we discuss the top big data analytics use cases in various industries. Big Data Analytics Use Cases in Various Industries 1. Banking and Finance (Fraud Detection, Risk & Insurance, and Asset Management) Futuristic banks and financial institutions are capitalizing on big data in various ways, ranging from capturing new markets and market opportunities to fraud reduction and investment risk management. These organizations are able to leverage big data analytics as a powerful solution to gain a competitive advantage as well.  Take a look at the following image that shows various use cases of big data analytics in the finance and banking sector: Recent studies suggest that big data analytics is going to register a CAGR of 22.97% over the period of 2021 to 2026. As the amount of data generated and government regulations increase, they are fueling the demand for big data analytics in the sector. 2. Accounting  Data is Accounting’s heart and using big data analytics in accounting will certainly deliver more value to the accounting businesses. The accounting sector has various activities, such as different types of audits, checking and maintaining ledger, transaction management, taxation, financial planning, etc.  The auditors have to deal with numerous sorts of data that might be structured or unstructured, and big data analytics can help them in: Using big data analytics will also improve regulatory efficiency, and minimize the redundancy in accounting. 3. Aviation  Studies reveal that the

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Business Intelligence in Financial Services: 10X Growth in BFSI

Business intelligence in the financial industry helps unlock the full potential of the establishment and its products and services. The real-time analytics provided by tools like Power BI help financial companies make accurate decisions. Let’s discuss the role of BI in bettering financial services.  Business intelligence is a combination of processes and strategies used to collect, clean, format, and analyze data. Historical and real-time data is analyzed to derive actionable insights that aid in decision-making. BI helps understand business operations and customers to increase revenue.  The financial industry comprises banking, insurance companies, credit card companies, credit unions, wealth management companies, and other relevant service providers. Business intelligence is used to help these establishments overcome the challenges in daily operations and provide enhanced customer service.  BI has been a part of the financial industry for a while. However, it’s only recently that the institutions began to realize the advantages of business intelligence in financial services. Be it collecting data from multiple sources or providing data visualization reports using Power BI, business intelligence has a prominent role in the financial industry.  Power BI consultants assist the financial establishments to integrate the platform with their existing systems and use it for day-to-day operations.  How is Business Intelligence Used in Finance?  Using BI in finance not only eases the way of doing business but also provides the institutions with a competitive advantage over others. Banks, credit card companies, insurance agencies, etc., need to constantly add new customers to their base and increase their market share. Business intelligence provides them with the necessary information to achieve their goals.  Easy Data Handling  Access to data is no more an issue in today’s world. However, the problem arises in handling the huge amounts of data and making sense of it. That’s where business intelligence helps finance companies. From collecting customer information such as complaints, suggestions, requests to gathering data about the resources allotted for solving problems/ providing services, BI tools continuously get data from multiple sources in real-time.  Understanding the daily volume of work and comparing it with the results will show the performance of the establishment and help identify areas of improvement.  Better Customer Retention  Banks don’t want to lose a customer to another bank. Insurance companies don’t want competitors to take away their clients. Retaining and attracting new customers becomes easier when you know what the customers want. BI tools help segment prospective leads and analyze the quality of financial products/ services to shorten the sales funnel.  By tracking the customers’ income and spending patterns, financial establishments can cater/offer services that align with their requirements. This helps increase customer loyalty towards the establishment.  Managing Risk  The finance industry works with large amounts of money. Considering the volatile nature of the market, it is crucial to have a proper plan to navigate through the ups and downs. Business intelligence for finance helps banks with risk management by analyzing historical and real-time data.  The data visualization reports aid in risk assessment, data management, audits, etc., to perfectly document all transactions and train employees to handle customers’ demands and mitigate risks.  Streamlining Work The finance industry has a lot of repetitive work across all departments. Gaining a deeper insight into daily operations will give you better control over the tasks and optimize the use of resources. It helps increase productivity by removing unnecessary processes and shortening the service cycles.  Generating Real-Time Reports  How is the branch’s performance this quarter? Does it take too much time to plan the budget? Where does the establishment stand in terms of assets and liabilities? What is the status of the customer credit pipeline?  BI tools provide answers to these questions and more. The reports are generated in real-time to include the latest information. This helps optimize the IMS (Information Management System) and increase collaboration between different departments.  There is more to how business intelligence is used in the banking and insurance industry. Analytics are slowly becoming an inherent part of the banking industry as more and more establishments invest in BI tools.  How is Analytics Used in Banking? Data analytics gives banks the necessary insights into customer behavior and market trends. The insights show banks how they measure against the KPIs and where they need to improve their services. Real-time analytics provides employees and management with vital reports to make changes to their operations, processes, and approach.  Here’s one question many banking professionals ask- Why do banks need analytics? The simple answer is to understand data and use it to increase revenue.  For example, using Power BI for understanding finance services makes banks flexible in managing daily activities. It helps determine whether a customer will be an asset or a liability. Reports are available at the touch of a hand and bring more transparency into the system. All this helps attract more customers to the bank, thus increasing its market share, revenue, and profits.  What are the Applications of BI in Banking?  Banking business analytics can help with the following aspects:  Fraud Detection and Analysis There have been 4,047 reported cases of online banking fraud in 2021 in India. NCRB reported that 60.2% of the total financial frauds were done for the sake of fraud (intentionally). India saw banking frauds worth 1,384 trillion rupees in 2021.  Using business intelligence tools like Power BI can help banks detect fraud in the early stages and prevent them from happening. Fraud detection has become a priority for many banks and financial institutions. The insights derived from BI tools are used to build robust fraud detection systems that warn banking professionals about the possibility of fraud and help them avoid the situation. It helps with:  Performance Management  What happens when employees have access to up-to-date information? It gives them more control over daily operations and aids with making the right decisions. Since employees can get real-time reports with just a couple of clicks, they can understand the bigger picture before offering a financial product/ service to a customer. BI tools empower banking professionals to be more productive and deliver better performance day after day.  Cross-Selling Financial Services  How do you know

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Smart Video Generation from Text Using Deep Neural Networks

Creating animated videos doesn’t have to be a laborious process anymore. Artificial intelligence and deep neural networks process datasets to create videos in less time. The blog details the different AI models and techniques used for smart video generation from text.  It’s no surprise that creating animated videos takes time. It’s hard work and involves several man-hours. Even with the use of technology, animated videos are still not easy to produce. However, the entry of artificial intelligence has brought new developments.  Researchers from the Allen Institute for Artificial Intelligence and the University of Illinois have worked together to create an AI model called CRAFT. It stands for Composition, Retrieval, and Fusion Network. The CRAFT model took text/ description (captions) from users to generate scenes from the famous cartoon series, The Flintstones. CRAFT is entirely different from the pixel-generation model where the pixel value is determined by the values generated by previous pixels to create a video. It uses the text-to-entity segment retrieval method to collect data from the video database. The model was trained on more than 25,000 videos where each clip was three seconds and 75 frames long. All videos were individually annotated with the details of the characters in the scene and information about what the scene dealt with. That is still labor-intensive as the team has to work on adding the captions to each scene. How can AI experts help generate video from text using automated video generation models? First, let’s take a look at the problems in creating videos from different POVs. Problems in Creating Videos The major problems in creating animated videos can be categorized into the following: Problems from the General Point of View Time Consuming and Effort-Intensive There’s a high demand for animated videos, leading to a gap between demand and supply. Kids and adults love animated videos, games, etc. But the supply isn’t as much as the viewers would like.  This is because the technology still hasn’t reached the stage where we can generate content in minutes and meet the increasing expectations. Video generation is still a time-consuming and laborious process that requires a lot of resources and input data. Computers are Not Enough It might seem that computers are an answer to everything. However, computers and the existing software are not advanced enough to change the video creation process. While researchers and experts are working on creating new applications to create videos in quick time, we still need to wait to experience a higher level of innovation. Problems from Deep Learning Point of View Manually Adding Text Artificial intelligence has helped develop video generation software to speed up the process. However, even AI doesn’t offer a solution to everything as yet. For example, some videos don’t have captions. But you still need to create a video from existing clips. What do you do? Well, you’ve got to manually add the captions so that the software can convert the text to video. Imagine doing that for thousands of video clips!  Improper Labeling The problem doesn’t end at manually adding captions. You’ve got to label the videos as well. Now, with so many clips to work on, it’s highly possible that you might mislabel something or give a wrong caption to a couple of videos. What if you notice the error only after the smart video is generated from the given text captions? Wouldn’t that lead to more wastage of resources, and poor-quality videos?  More than CRAFT Model While the CRAFT model is indeed a worthy invention, the world needs something better and more advanced than this. Moreover, the CRAFT model is limited to creating cartoons and cannot work with all kinds of video clips. Introduction to NLP and CV Well, we’ve seen the challenges faced by the video industries and AI researchers. Wouldn’t it be great to find a solution to overcome these challenges? Oh, yes! That’s exactly what we’ll be doing in this blog. However, we’ll first get a basic idea about the two major concepts that are an inherent part of smart video generation from the text. Yep, we are talking about NLP (Natural Language Processing) and CV (Computer Vision), the two branches of artificial intelligence. Natural Language Processing (NLP) NLP can be termed as a medium of communication between a human and a machine. This is, of course, a layman’s explanation. Just like how we use languages to communicate with each other, computers use their own language (the binary code) to exchange information. But things get complex when a human has to communicate with a machine. We are talking about how the machine processes and understands what you say and write.  NLP models can train a computer to not only read what you write/ speak but also to understand the emotions and intent behind the words. How else will a computer know that you’re being sarcastic? Applications like Sentiment Classification, Named Entity Recognition, Chatbots (or our virtual friends), Question- Answering systems, Story generations, etc., have been developed using NLP models to make the computer smarter than before.  Computer Vision (CV) Computer vision is yet another vital aspect of artificial intelligence. Let’s consider a scenario where you spot a familiar face in the crowd. If you know the person very well, you’ll mostly be able to recognize them among a group of strangers. But if you don’t? What if you need to identify someone by watching the CCTV recording? Overwhelming, isn’t it?  Now, what if the computer can identify a person from a series of videos on your behalf? It would save you so much time, effort, and confusion. But how does the computer do it? That’s where CV enters the picture (pun intended). We (as in the AI developers) provide the model with annotated datasets of images to train it to correctly identify a person based on their features.  Possible Approaches other than CRAFT model Researchers have been toiling on finding ways to use artificial intelligence and deep learning to facilitate video generation from text. The solutions involve using

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17 Most Important Data Science Trends of 2023

There’s nothing constant in our lives but change. Over the years, we’ve seen how businesses have become more modern, adopting the latest technology to boost productivity and increase the return on investment. Data analytics, big data, artificial intelligence, and data science are the trending keywords in the current scenario. Enterprises want to adopt data-driven models to streamline their business processes and make better decisions based on data analytical insights. With the pandemic disrupting industries around the world, SMEs and large enterprises had no option but to adapt to the changes in less time. This led to increasing investments in data analytics and data science. Data has become the center point for almost every organization. As businesses rely on data analytics to avoid and overcome several challenges, we see new trends emerging in the industries. AI trends 2023 by Gartner are an example of development. The trends have been divided into three major heads- accelerating change, operationalizing business value, and distribution of everything (data and insights). In this blog, we’ll look at the most important data science trends in 2023 and understand how big data and data analytics are becoming an inherent part of every enterprise, irrespective of the industry. Top Data Science Trends of 2023 1. Big Data on the Cloud  Data is already being generated in abundance. The problem lies with collecting, tagging, cleaning, structuring, formatting, and analyzing this huge volume of data in one place. How to collect data? Where to store and process it? How should we share the insights with others? Data science models and artificial intelligence come to the rescue. However, storage of data is still a concern. It has been found that around 45% of enterprises have moved their big data to cloud platforms. Businesses are increasingly turning towards cloud services for data storage, processing, and distribution. One of the major data management trends in 2023 is the use of public and private cloud services for big data and data analytics. 2. Emphasis on Actionable Data  What use is data in its raw, unstructured, and complex format if you don’t know what to do with it? The emphasis is on actionable data that brings together big data and business processes to help you make the right decisions. Investing in expensive data software will not give any results unless the data is analyzed to derive actionable insights. It is these insights that help you in understanding the current position of your business, the trends in the market, the challenges and opportunities, etc. Actionable data empowers you to become a better decision-maker and do what’s right for the business. From arranging activities/ jobs in the enterprise, streamlining the workflows, and distributing projects between teams, insights from actionable data help you increase the overall efficiency of the business. 3. Data as a Service- Data Exchange in Marketplaces  Data is now being offered as a service as well. How is that possible? You must have seen websites embedding Covid-19 data to show the number of cases in a region or the number of deaths, etc. This data is provided by other companies that offer data as a service. This data can be used by enterprises as a part of their business processes. Since it might lead to data privacy issues and complications, companies are coming with procedures that minimize the data risk of a data breach or attract a lawsuit. Data can be moved from the vendor’s platform to the buyer’s platforms with little or no disturbance and data breach of any kind. Data exchange in marketplaces for analytics and insights is one of the prominent data analytics trends in 2023. It is referred to as DaaS in short. 4. Use of Augmented Analytics  What is augmented analytics? AA is a concept of data analytics that uses AI, machine learning, and natural language processing to automate the analysis of massive data. What is normally handled by a data scientist is now being automated in delivering insights in real-time. It takes less time for enterprises to process the data and derives insights from it. The result is also more accurate, thus leading to better decisions. From assisting with data preparation to data processing, analytics, and visualization, AI, ML, and NLP help experts explore data and generate in-depth reports and predictions. Data from within the enterprise and outside the enterprise can be combined through augmented analytics. Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. 5. Cloud Automation and Hybrid Cloud Services The automation of cloud computing services for public and private clouds is achieved using artificial intelligence and machine learning. AIOps is artificial intelligence for IT operations. This is bringing a change in the way enterprises look at big data and cloud services by offering more data security, scalability, centralized database and governance system, and ownership of data at low cost. One of the big data predictions for 2023 is the increase in the use of hybrid cloud services. A hybrid cloud is an amalgamation of a public cloud and a private cloud platform. Public clouds are cost-effective but do not provide high data security. A private cloud is more secure but expensive and not a feasible option for all SMEs. The feasible solution is a combination of both where cost and security are balanced to offer more agility. A hybrid cloud helps optimize the resources and performance of the enterprise. 6. Focus on Edge Intelligence  Gartner and Forrester have predicted that edge computing will become a mainstream process in 2023. Edge computing or edge intelligence is where data analysis and data aggregation are done close to the network. Industries wish to take advantage of the internet of things (IoT) and data transformation services to incorporate edge computing into business systems. This results in greater flexibility, scalability, and reliability, leading to a better performance of the enterprise. It also reduces latency and increases the processing speed. When

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DataToBiz Reviews: New 5-Star Rating on Clutch

With the advent of technology, Digital Advancements have totally transformed our everyday lives, and one of the most significant impacts felt has been in the business world. Companies now have access to data-driven tools and strategies that allow them to learn more about their customers and themselves than ever before. Still, not everyone is taking advantage of them. And with DataToBiz, we came here to help you! Since 2018, DataToBiz has been providing data science and artificial intelligence solutions such as data mining, data visualization, and predictive analytics using learning and deep learning technologies. Our team is composed of skilled professionals who utilize cutting-edge technology tools to exceed the needs and requirements of our clients. We take pride in producing optimal solutions for every client we work with. We recently received a new stellar 5-Star rating on Clutch, an established platform in the heart of Washington, DC, committed to helping small, mid-market, and enterprise businesses identify and connect with the service providers they need to achieve their goals. Their team of analysts interview service providers’ clients to collect client feedback, analyze industry data, and compare competitors in a specific market to arm businesses with the information they need to connect with the right service providers. In this partnership, a transportation technology company hired our team to implement solutions to track their business processes. Our team immediately implemented intuitive dashboards to make the client’s performance tracking efficient in their analytics framework. Here’s what they had to say about working with us:  “They were always willing to contribute and ready to resolve issues.”  – Data Science Manager, TransSIGHT In addition to the Clutch review, DataToBiz was listed in the Top 100 Machine Learning Consulting Companies on Top Design Firm. Browsers on Top Design Firms can find company directories, survey reports, and blog-style content that focuses on the development, design, and marketing spaces. On that note, we extend our sincerest thanks to Clutch for giving us our latest 5-star review and Top Design Firms for including us in their reputable directory. We assure to continue giving 100% best in every project we do.  Do you want to explore the possibility of growing your business? Contact us today, and let’s get started.

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Top 10 Disruptive Big Data Trends for 2023

The global big data market revenue is projected to hit the 103 billion US dollar mark by 2027. And, the current BI and analytics software market are valued at 16 billion USD globally. Apart from the stats that speak a thousand words, big data, in association with AI, ML, and other technologies, is fueling what we call the Fourth Industrial Revolution. Big data analytics is one of the most powerful technology trends and is reshaping numerous business processes and operations across the world. Rapidly expanding IoT networks, Data as a product, quantum computing, and data use for hyper-personalization – there are many emerging trends in the big data segment. Big data is also being used with AI, ML, and other innovative processing technologies to analyze, process, and parse the massive datasets in multiple sectors, such as Healthcare, eCommerce, Government Data, Public Infrastructure, Banking & FinTech, Security, Manufacturing, Natural Resources Management & Harnessing, etc.  The latest studies reveal that in just 2 years, Big Data has spurred a change in the business perspective across the entire globe. It forced business organizations to rethink their: The following visual shows the major potential applications of Big Data, AI, and other technology landscapes: With more than 2.5 quintillion bytes of data being generated daily. it is more than safe to assume that Big Data is gearing up for changing the way we think! Here, we are sharing the top 10 Big Data trends in 2023 that are going to be the major change drivers.  Top 10 Big Data Trends for 2023: Revolutionizing the Core of Modern Business Landscape Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. 1. AI Will Continue to Improve, but Humans Will Remain Crucial The rise of artificial intelligence is empowering businesses and industries across the globe and empowering people with unprecedented capabilities. While the general perception is of “massive overlays and mass firings“, the experts say that humans are still going to be “crucial“. A recent Forbes feature outlined how robots are not going to wipe out the human task force. Further, an MIT study suggests that AI will continue to fuel massive innovation across the globe, and will create many sectors for growth and employment.  Hence, it will improve the human race and the way we live, work and grow.  However, in order to keep the social and economic divide contained, the stakeholders have to have some common goals to adhere to, such as: Also, humans have a distinct way of learning, perceiving, and responding to things, incidents, and people around them. Hence, while the future might have smart AI-powered customer staff, the overall management of operations will still require humans.  2. Business Intelligence in Every Walk of Life Business intelligence or BI is already bringing changes in multiple sectors, namely marketing, consumer services, customer experiences, and the entire eCommerce segment. The value of the global BI and analytics software market is expected to be 17.6 bn USD by 2024.  The flawless and efficient data processing capabilities of BI software help companies around the world to accomplish their corporate and data goals without any hassle.  Apart from the current sectors employing BI, such as supply chain management, resource management, and customer interaction management, it is expected to revolutionize the technology and manufacturing sectors as well. BI software draws its strength from: Take a look at some of the future applications of BI: 3. Predictive Analytics Big data is empowering business organizations and data analytics stakeholders with its fundamental approach for quite a time now. It helps them to gain a competitive edge and accomplish their goals, such as better services, more sales, more customers, happier customers, and so on. Business organizations use multiple tools to achieve these goals and predictive analysis is a common feature of these tools. Predictive methods use historical events and modern data to uncover hidden trends and present actionable insights, such as: All in all, predictive analysis offers you a realistic and data-driven future prediction for various things. With advancements in Big Data, AI, ML, and other technologies, predictive analytics is all set to gain more power and offer more crucial insights.  So, you will be able to predict customer response, churn, purchase behavior and optimize your brand offerings, services, and business agendas accordingly. The visual shows how predictive analytics can identify the meaningful patterns of big data and generate future predictions to identify the value proposition of various options. 4. Cloud-Native Analytics Will Become Necessary Gartner says that by 2023, public cloud services will assume a mandatory stance for 90% of data analytics innovation and processes. As data analytics will move to the cloud, cloud-native analytics will become a necessity for all the leaders and industry stakeholders.  Cloud-native analytics will empower the data analysts to align the right services with the right use cases, which might give birth to governance and integration overheads. Apart from an in-depth analysis of the cost and pricing models, the data and analytics leaders will also be required to prioritize workloads to exploit cloud capabilities.  Some other additional responsibilities will include: Some of the sectors that are in for a huge wave of change in the light of cloud-native analytics are: Because of the inherent advantages of cloud-native platforms and analytics, such as elasticity, self-service, monitoring, etc, industry leaders consider cloud-native platforms mandatory in order to succeed with Big Data.  5. Digital Transformation Digital transformation stems from the ability of an organization to combine both automation and digitization.  As the global business landscape becomes more competitive, more sophisticated, and extremely data-centric, Big Data emerges as one of the key drivers of digital transformation. Businesses across the globe utilize huge chunks of unstructured data to discover the hidden patterns in relation to their business models and Big Data becomes all the more important.  Take a look at some ways Big Data triggers digital transformation: 6. Climate

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Power BI for Banking: Dashboard to Analyze Financial Operations

Power BI is a product by Microsoft developed to help with business analytics. Power BI is an effective tool for banks to track KPIs and streamline their services. This blog elaborates on the role of Power BI in the banking industry. Let’s see how consultants help banks use Power BI. With the various technology solutions available and implemented in the banking industry, a lot of staple tasks are already eased with them. Banks are always bustling with activity. Customers come to deposit money, withdraw money, apply for loans, and so on. The multitudes of transactions per day are a part of the process. The yearly balance sheet with profit and loss is one way to measure a bank’s success. However, it’s not enough. Banks need to know what and how their customers feel.  How is the bank’s performance when measured against the KPIs? Are the procedures easy for customers to follow? How can a bank identify where the problem lies? Business analytics is the answer to these questions. Power BI is an effective data analysis tool developed by Microsoft. Banks can collect data from multiple sources and measure their performance using a single platform. Power BI consulting partners assist banks in using this effective tool to track KPIs and analyze the success of their financial operations.  What is Power BI? Power BI is a collection of software, apps, and connectors that gather data from multiple sources, convert it to a readable format and derive insights through data analysis. The insights help make the right decisions to boost the business and increase customer satisfaction.  It is a well-known product of Microsoft and has three major components:  Why should banks consider Power BI? Power BI is not limited to data analytics. It is also a data visualization tool that generates customized reports for the given information. Data is collected, processed, and monitored in real-time. Power BI can help banks track the profitability ratio of customers, products, and services, as well as the branch’s performance against all KPIs (Key Performance Indicators). Business enterprises and banks use Power BI to access the latest insights and get a complete perspective of their operations. The Power BI banking dashboard gives banking professionals a deeper and better understanding of their services and customers’ response to the services.  Power BI is mainly used for the following:  How to use Power BI for Banking? A banking dashboard is a business intelligence tool where data collected from multiple systems across the bank is presented on a single platform. The employees and management can access all information through the dashboard. From customers’ accounts to loans, deposits, and defaulters, the analytics dashboard shares the overall picture of the bank in real-time.  Power BI is indeed a powerful tool for developing the banking dashboard to track the firm’s performance based on factors such as customer trends, new process results, financial operations, and so on. The dashboard has various elements, ranging from KPIs assigned, KPIs comparison, customer acquisition ratio, etc. Banks can add or delete elements in the dashboard to customize as per their requirements.  Power BI is used in the banking industry to create the dashboard to manage data and derive insights. The BI tool gathers data from the given sources and analyzes them to share the comparisons and insights on the dashboard. Banking professionals can measure how well the bank is delivering the expected results and identify the areas for improvement.  Banking KPIs/Visuals that can be Tracked on Power BI Dashboard Financial transactions play a vital role in the banking sector. Customers are directly involved in the process and can impact the bank’s success.  Even though there is no definite list of KPIs a bank has to monitor, the following are commonly used by banks and financial institutions. Power BI service providers assist banks in designing and customizing the dashboard to include the necessary KPIs and track performance.  Financial KPIs Expenses Expenses are the costs incurred by banks for daily operations. These are divided into interest and noninterest categories.    Revenue  The inflow of cash to a bank is called revenue and can be classified as deposit fee, transaction fee, service fee, loan interest, and so on.  Operating Profit  This amount is obtained by subtracting expenses from revenue. For a bank to do well, the revenue should be more than the expenses.  Return on Assets The net income of the bank is divided by the total number of assets to determine the return on assets.  Assets Under Management  Usually termed AUM, it shows the total value of assets managed by the bank. This KPI is measured quarterly by most banks.  Return on Equity  The total bank income divided by the equity owned by shareholders gives the return on equity and is measured in percentages.  Operational KPIs  Total Deposits and Loans  A bank with high deposits is considered reliable and trustworthy. By measuring the loan growth against the overall progress, banks will know if they are on the right track.  Customer Satisfaction Score  The customer satisfaction score is calculated based on trends, demographics, industry sentiments, response rate, feedback, surveys, etc.  Net Charge-off Rate  The net charge-off rate is the ratio of recovered debt to gross charge-offs. It shows the unrecovered debt of the banks and helps in maintaining a lower rate by making better decisions.  Efficiency Ratio  It shows how well the bank is managing the assets and liabilities in the long term. The efficiency ratio is derived by dividing operational expenses by revenue.  Actionable Advice for Data-Driven Leaders Struggling to reap the right kind of insights from your business data? Get expert tips, latest trends, insights, case studies, recommendations and more in your inbox. Benefits of Power BI for Banks Why use Power BI for banking? What benefits does the data analytics tool offer banks and financial institutions? How does Power BI help banking professionals?  Tips for a Successful Power BI Implementation Power BI in the banking industry is no longer a theory. Many banks have successfully integrated Power BI into the existing systems.  Role of DataToBiz in Serving a Renowned

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How a Perfect AI Strategy can Help Your Business – 25 Industry Experts Share Their Pro Tips

A comprehensive artificial intelligence business strategy can boost business and make the enterprise an industry leader. Let’s look at the round-up of pro tips shared by leaders in the AI industry. The current market scenario is proof that businesses need to adopt the latest technology to survive the competitive markets. Artificial intelligence has become a necessary step in digitally transforming the business and creating new growth opportunities.  However, the success of an enterprise depends on how well it plans and implements the AI strategy within the business. Wouldn’t it be helpful to have pro tips and advice from experts who’ve been in the industry for years?  This round-up post contains the top tips from twenty-five AI experts known for their domain expertise in various subfields of artificial intelligence.  Worth noting strategies by 25 AI Experts Bernard Marr Internationally Best-selling Author | Business, Tech and Data Advisor A separate AI strategy is essential even if a business has a data strategy.  AI-enabled services are a better option for small businesses instead of customization. Don’t ignore change management challenges and HR issues. Managing company culture is a must.  Allie K. Miller ML Business Development Global Head, AWS  Start with the pain point and expected outcome.  Add a buffer amount to the budget and work with it. Focus on edge cases and minimize data noise. Don’t expect perfection the first time.   AI should be integrated within business workflow. Ankush Sharma CEO & Co-Founder, DataToBiz Start at the beginning and understand the requirements and challenges for AI adoption. Get feedback from customers and make necessary changes to the system.  The AI business strategy needs to be constantly evaluated and edited to fine-tune the systems. Dennis R. Mortensen CEO and Founder, x.ai AI should make users more capable while sounding as human as possible when interacting with customers. Automation is the primary goal of using vertical AI agents in the enterprise.  Bots and virtual assistants can increase the worth of the employee and the business.  Gregory Piatetsky-Shapiro Founder and President, KDnuggets | Data Scientist  Data scientists should stop overfitting the data, which defeats the purpose of using the algorithm. Identifying the right problems to tackle within the business is essential for AI implementation to be successful. AI will keep evolving, and data scientists and analysts need to learn and upgrade their domain knowledge.  Tamara McCleary CEO, Thulium  AI uses reactive and proactive user experience to provide real-time data analytics for marketers. The insights require a huge volume of accurate data to be fed to the algorithms.  Issues with business scalability can be solved using AI to structure the business model accordingly.  Marketing is much more than human-to-human interaction, and it’s necessary to seamlessly use algorithms, bots, and humans to get the desired results.  Andrew Ng Founder and CEO, Landing AI | Founder, deeplearning.ai No need to invest heavily in AI projects in the first stage. Bet on safer and smaller AI technologies that guarantee results. Businesses already have valuable data sitting in their systems. They only need an AI team to work on it.  Building AI systems is similar to building a rocket ship. It won’t take off unless the elements are in the right proportion. Ronald Van Loon Principal Analyst and CEO, Intelligent World AI works in combination with IoT (Internet of Things), Big Data, Augmented Reality (AR), and cloud computing to deliver results.  Align and organize data around the data layers for accurate insights. Move on from older data storage systems. Start by using ML solutions that are already integrated into the systems. New models can be developed later.  Kirk Borne Chief Science Officer, DataPrime Hire a group of data analysts to decide which AI and ML systems to use. Don’t rely on the judgment of one person. Cloud-based infrastructure will reduce analytical costs for the enterprise.  It will take time to make the most out of AI systems. Even the leading companies have a lot more to do. Andriy Burkov Director of Data Science, Gartner | Author Testing and perfecting an ML model takes months and years. Hire more ML engineers to speed up the process. Machine learning doesn’t mean the machine is learning. It only provides the necessary numbers at a great speed.  Implementation errors in ML can be impossible to find unless various baseline models are implemented which beat the new model and show the error. Hilary Mason Co-Founder, Hidden Door Focus on ‘boring’ business problems to know how much AI can help find an easy solution.  AI should be used to overcome challenges rather than as a prestigious investment for the enterprise.  The spend-first-plan-later approach by businesses will lead to a higher risk of AI failure.  Steve Nouri Chief Data Scientist and Co-Founder, iN2iTY Lab | Founder, AI4Diversity Chatbots and virtual assistants empower employees with self-servicing and increase customer experience.  Messy datasets are the ones that deliver results. Businesses should train their data analysts to work on similar sets.  The data-centric approach is a great way to train AI systems to be more accurate in their predictions.  Kristen Kehrer Founder, Data Moves Me | Senior Manager, Engagys LLC Data literacy training is compulsory for employees to ask the algorithms the right questions.  It should be continuous, individualized, and asynchronous.  Enriching the data in the enterprise will make it easier to build an ML model. Randy Olson Senior AI Scientist, Absci | Data Science and Machine Learning Advisor, FOXO Technologies Choosing the right machine learning model is the responsibility of humans.  GUI-based data visualization tools have become more popular over the last few years and deliver better reports.  ML algorithms can be used to analyze most types of data.  Ganapathi Pulipaka Chief Data Scientist and SAP Technical Lead, Accenture Chatbots, virtual assistants, and tellers are a great way to manage customer service in various industries like banking, retail, etc. Drones can be effectively used for smart services and linked with IoT to share real-time information.  Smart services are the way to drive the economy for an enterprise. That’s possible with AI technology.  Mark van

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Power BI: How to Implement Advanced Analytics with Data Integration?

SMEs are on the way to becoming data-driven enterprises by investing in data analytics and business intelligence. This move is to empower themselves to make better decisions based on reports generated by advanced data analytics tools.  Revamping the business systems and processes by integrating the latest AI, ML software allows enterprises to collect, store, process, and understand the vast amounts of data generated every day. Historical and real-time data from numerous sources is gathered in one place and processed using BI tools such as Power BI to gather valuable and accurate insights. In this blog, we will learn about the need for data integration and how Power BI can be used to implement advanced analytics with data integration.  What is Power BI? Power BI is a product of Microsoft used for data analytics and visualization. The layout resembles an advanced version of Excel, belonging to the same company. The main job of power BI is to bring coherence to your data and present it in a visually appealing and compelling manner. It guarantees data accuracy, consistency, and reliability.  Though the software belongs to Microsoft, many data analytics and BI consulting firms are Power BI partners and help enterprises integrate the software into the business systems. You can take the assistance of these firms to enhance your business processes and become a data-driven enterprise.  What is Data Integration? Data integration is an inherent part of advanced analytics. What will the tools process if there is no data available? But what exactly is data integration?  It is the process of bringing together data from various systems and sources within and outside the enterprise. Any data that is considered valuable for the business is collected from across the internet.  It is common for one department in the organization to ask permission to access data collected and stored by another department. Data integration makes it easier for employees in all departments to access any data they need to complete their work. The process is automated and streamlined to reduce human error and delays.  Advanced analytics is the process of examining data autonomously or semi-autonomously using tools and techniques that are beyond the traditional business intelligence tools. Advanced analytics is used to obtain deeper insights, make accurate predictions, and provide recommendations to the top management. What are the Types of Data Integration? Data integration is not as easy as it sounds. But it is not impossible either. There are different ways in which data integration services can be provided. Let’s take a look at the types of data integration in brief.  Data Consolidation It is the process of physically bringing together data from different sources and consolidating it in one place. This reduces the need for excess data storage at multiple locations.  Data Visualization It is the process of presenting data in an easy-to-understand and visually appealing manner so that it becomes easier to interpret data based on numerous factors.  Data Propagation It is used for real-time data processing where information from one place is copied to another (data sharing). This is usually two-way communication between two systems or departments and is used for large amounts of data. Data Warehousing  It is the process of cleaning, formatting, and storing data in data repositories such as in-house servers, colocation centers, or remote cloud storage servers. This is data integration.  Data Federation It is a form of data visualization that uses data abstraction to facilitate a unified view of data from multiple sources. This data is analyzed and presented using tools like Power BI. Data federation is used when data consolidation is too expensive for SMEs to invest in.  What are the Benefits of Data Integration? If data integration wasn’t beneficial, we would hardly see organizations willing to invest in it, right?  So what are the benefits of availing the Power BI consulting services? How does it help enterprises in becoming better than their competitors in the market? SMEs have to work with limited resources that make it rather difficult to compete with multinational enterprises. But data integration helps businesses become efficient in optimizing the resources to get more efficient outputs.  When data is seamlessly available across the enterprise, it improves communication and collaboration between different departments. It also leads to better customer services as the support staff has all the details required to deal with customers.  Any enterprise armed with data analytics tools is better equipped to deal with competitors in the market. It gives you an edge over others and lets you expand the business to newer markets.  When you can rely on reports and insights created by advanced software, you will naturally become a better decision-maker. You have a solid backing to support your decision. There is no need to work with outdated reports or copy a competitor without knowing how a decision will impact the business in the long term.  It is not easy to convince employees to adopt new procedures and systems at work. Power BI services from offshore consulting firms help train employees and make them understand the importance of working on advanced analytics. This increases the willingness of employees to adopt new technology and digitally transform the business.  Challenges of Data Integration  Do you know that about 2.5 quintillion bytes of data are generated every day? The US economy suffers losses of around $3.1 trillion per year due to the poor quality of data. Now is the right time to invest in data integration and advanced analytics. But it doesn’t come without challenges of its own.  So how do you find a way to work around these challenges? Power BI development services help you in overcoming the challenges by- Glossary of Data Integration While the actual list is extensive and includes hundreds of terms, we have listed a few terms regularly used by data and BI analysts.  Analytics The process of analyzing data to derive meaningful insights and patterns from it. Big Data Vast amounts of data are collected from numerous sources and processed at high speed to get real-time insights.  Data Connector A tool that works continuously to replicate data

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15 Best Big Data Tools That You Need to Check Out Today!

Big Data is a large amount of data collected in real-time in various formats and structures. Latest technologies have simplified data gathering from multiple sources. Data warehouses and data lakes can store this data on-premises or on the cloud. However, the collected data is of no use to the business until it is analyzed. Basic data analytics tools like MS Excel cannot process Big Data due to the excess volume and complex nature of data. Big Data needs tools designed explicitly for the purpose.  Big Data Analytics is a type of advanced analytics where statistical algorithms, what-if models, and predictive analysis are used to identify the patterns, trends, and correlations between different elements.  What Are Big Data Tools?  Big Data tool is a software used to clean, format, and process vast data in real-time. It is an analytical system capable of understanding complicated information and deriving actionable insights from it. Big Data tools help enterprises make data-driven decisions and increase returns.   Why Do We Need Big Data Tools? The US economy faces around $3.1 trillion yearly losses due to poor data quality. The losses can be minimized by adopting a data-driven model and investing in the right Big Data tools.  Organizations have begun understanding the importance of Big Data Analytics tools and technology. An Executive Survey report by New Vantage says that 97.2% of enterprises are investing in Big Data and artificial intelligence.  Big Data tools can help businesses with the following:  Picking the right Big Data tools for the business is crucial. The accuracy of Big Data analytics and derived insights depends on the tools used for the process. In this blog, our expert talks about the best Big Data analytics tools preferred by numerous enterprises from around the globe. There are numerous tools available in the market. However, our list has been compiled based on the data and usage details collected from enterprises. The Best Tools for Big Data Analytics 1. Apache Hadoop Apache Hadoop is one of the best open-source Big Data analytics tools in the market. It’s written in Java and is used to handle clustered file systems through the MapReduce programming model. Hadoop is cross-platform software used by more than half of the Fortune 50 companies.  2. Apache Storm Apache Storm is another open-source Big Data tool that offers the best real-time processing capabilities. The Storm has cross-platform abilities and provides distributed stream processing. It’s written in Java and Clojure and is fault-tolerant.  3. Atlas.ti Atlas.ti is known as a comprehensive all-in-one software for research. It is used to research markets, understand user experience, and help with academic research and qualitative analytics. The software is available in two versions- desktop for on-premises use and web version for cloud applications.  4. Tableau Tableau falls in the category of leading tools for Big Data visualization and is available in three versions- Tableau Desktop, Tableau Server, and Tableau Online for cloud solutions. The open-source version of the software is known as Tableau Public. The data visualization tool works with data of all sizes and formats and provides real-time reports through the interactive dashboard.  5. Apache Cassandra Apache Cassandra is a free, open-source software that deals with vast volumes of data on several servers connected to one another. The NoSQL DBMS uses CQL (Cassandra Structure Language) to share information with the databases in the enterprise. Low latency is one of the significant advantages of using Cassandra.  6. Rapidminer Rapidminer is an open-source Big Data analytics tool that SMEs and large enterprises alike can use. It’s a perfect choice to use with data science models, predictive analytics, and new data mining models in the business. Rapidminer helps with data preparation, implementing machine learning, and deploying models.  7. Knime Knime is Konstanz Information Miner, open source Big Data software used for analytics, reporting, and data integration. The tool helps integrate machine learning and data mining models. Knime is the best choice for research, BI, CRM, etc. It has a rich algorithm set and is still easy to use in the enterprise. It is a free tool that comes with GNU General Public License.  8. MongoDB MongoDB is written in C, C++, and JavaScript. It is a NoSQL and document-oriented database that works with multiple operating systems. It is a free open-source Big Data tool that processes massive amounts of data and develops file systems for storage.  9. Cloudera If you’re looking for quick and secure data platforms, Cloudera is the answer. Cloudera is free and open-source software that works with any data environment and encompasses Apache Hadoop, Spark, Impala, etc. Data collection, processing, managing, modeling, and distribution are easily performed using Cloudera.  10. Oracle Data Miner Oracle Data Miner is used by data scientists for business and data analytics. It provides the easy drag and drop feature to make changes to the editor interface and customize the reports. The Big Data tool is an extension of the Oracle SQL Developer and deals with graphical workflows.  11. Apache Samoa Apache Samoa stands for Scalable Advanced Massive Online Analysis and is an open-source software tool used for data mining and machine learning. It is a well-known platform that allows data stream mining of Big Data. Data classification, clustering, regression, and development of new ML algorithms can be performed using Apache Samoa. 12. Apache Spark Apache Spark is an open-source Big Data analytics tool that deals with machine learning and cluster computing. Spark has gained fame for being a lightning-fast analytics engine that can process massive amounts of Big Data with the utmost ease.  13. Apache Kafka Apache Kafka is a publish-subscribe messaging system that sends messages from one endpoint to another. It works online and offline and prevents data loss by replicating the messages on disk storage and within the cluster. Apache works seamlessly with Spark and Storm to process and distribute Big Data analytics within the enterprise.  14. Apache CouchDB Apache CouchDB is an open-source, document-oriented NoSQL database with cross-platform abilities. It stores data in JSON documents and responds to JavaScript queries. Fault tolerance and the ability to run a single logical database on numerous servers are the two advantages of using Apache CouchDB.  15. Apache

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