What is Big Data Analytics in Finance?

Big data has been a hot topic over the past few years, and for good reasons. The amount of data available to businesses is staggering, and this presents a massive opportunity for companies that can effectively use big data analytics to gain an edge over their competitors. In this blog, we will explore what big data analytics in finance is, its usage in the world of finance, and whether or not it is beneficial for businesses in this industry.  “Big data in finance” refers to the vast amount of daily data that financial institutions generate. This data can come from various sources, such as customer transactions, stock market data, and even social media data. The big challenge for businesses is collecting this data, making sense of it, and extracting valuable insights. This is where big data analytics comes in. How Is Big Data Being Used in Finance? These are all pertinent questions that need to be answered when discussing big data analytics in finance. Big data analytics uses advanced analytical techniques to make sense of large data sets and extract valuable insights from them. This can be done for various purposes, such as identifying trends, detecting fraudulent activities, or predicting customer behavior. Big data analytics has been used extensively in other industries, such as retail and healthcare, and is now starting to gain traction in the world of finance. There are a few key reasons big data analytics is becoming more popular in finance. Volume of Data The sheer volume of data that financial institutions generate has increased exponentially in recent years. This is thanks to the growth of digital channels such as online banking and mobile banking, which have resulted in a considerable increase in customer transactions. Risk Management big data analytics can help financial institutions understand and manage risk better. Managing risk has become more critical than ever for banks and other financial institutions with the current economic climate. Big data analytics can help these organizations to identify risks early on and take steps to mitigate them. Detection of Fraud Third, big data analytics can be used to detect fraudulent activities. With the increase in digital transactions, there has also been an increase in the number of fraud cases. Big data analytics can help financial institutions to identify fraudulent activities and take steps to prevent them. Finally, big data analytics can be used to predict customer behavior. This is important for financial institutions as it can help them target their marketing efforts more effectively and sell the right products to the right customers. What Are the Benefits of Big Data in Finance? As one of the most data-sensitive sectors in business, they present a unique set of opportunities and challenges for big data analytics. On the one hand, financial institutions have enormous data at their disposal. On the other hand, this data is often sensitive in nature and subject to strict regulations. In the last two decades, humans have left the task of analyzing large amounts of data to computers. And no sector other than the finance industry presents a promising opportunity for big data technologies to process large amounts of data and interpret them for better decision-making. Big data analytics can help financial institutions overcome these challenges and capitalize on the opportunities. Stocks Can Be Tracked Real-Time For example, big data can monitor the stock market for early indications of stock price movements. This can give financial institutions a significant advantage in trading. Big data can also be used to develop better financial products and services. Off-Beat Financial Modeling Using big data, companies can develop predictive models that can identify which customers will default on their loans. Better lending products that are less risky for financial institutions can be developed using this information. Analyzing Customer Behavior Big data can also be systematically used to analyze customer behavior. This information can be used to develop targeted marketing campaigns and improve customer service. Regulatory Compliance Lastly, big data can help ensure compliance with government regulations. For example, banks can use big data to monitor transactions for suspicious activity. This information can be used to prevent fraud and money laundering. In conclusion, big data analytics is a robust process that can be used to transform the finance sector. By capitalizing on the opportunities and overcoming the challenges, financial institutions can use big data to their advantage. How Does Big Data Analytics Improve Financial Decision Making? The primary goal of big data analytics is to help organizations make better decisions by uncovering patterns and trends that would otherwise be hidden in the data. As fascinating as this is, there are several ways that big data analytics can be used to improve financial decision-making processes. First, big data can develop better financial products and services. For example, big data can be used to develop predictive models that help identify which customers are most likely to default on a loan. This information can be used to develop better lending products that are less risky for financial institutions. Identifying Potential Customers Financial institutions are always trying to introduce several products to their customers. However, they face significant challenges in detecting the potential customer base for a new product and developing a market strategy. Big data analytics can help here as well. It can help create market segments based on customer behavior and then target these segments with specific products. Ruling Out the Traditional Processes Data integration solutions at large can speed up the process for businesses trying to consolidate their data from various big data sources. This is crucial for financial institutions because they need to be able to access data quickly to make decisions. Spotting Fraudulent Activities Fraudulent activities are a big problem for financial institutions. Big data analytics can help detect fraud by identifying patterns in the data that are indicative of it. Structuring Workflow and a Reliable System As big data analytics is still a relatively new field, financial institutions need to be careful when implementing it. They need to

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Customer Analytics in Banking: Understand Your Customers

Customer analytics is the process of understanding customers to streamline banking products and services. It is also an integral part of banks’ strategies to achieve their goals and increase revenue. Check out the role and effectiveness of customer data analytics in the banking industry. The banking sector is moving from the product-centric model to a customer-centric model. The changing socio-economic scenario around the globe has pushed banks and financial institutions to reassess their traditional systems and make the necessary changes to stay relevant in the current market.  Banks face the risk of decreasing revenue and increasing expenditure due to the reducing interest rates, defaulters, and competition from other banks. But there’s one thing that can help banks overcome the challenges and create a strong customer base with loyal customers.  Customer analytics is the analysis of customer data to understand their requirements and specifications. From attracting new customers to retaining existing customers and detecting risky leads, bank data analytics helps in financial product development, streamlining processes, and making the bank a customer-friendly establishment. How Is Data Analytics Used in Banking?  Every industry uses data analytics, and the financial sector is no exception. Data analytics is the practice of analyzing data to derive insights and predictions that help in decision-making. Many banks have begun the process of digital transformation by adopting the data-driven model to make decisions based on accurate and real-time data reports.  Data analytics in banks helps the management and the chief executives optimize the resources and streamline operations to maximize returns. The aim is to increase the capabilities and efficiency of the bank without increasing expenditure. Since banks have access to multiple sources to collect customer data, it is easy to run analytics and derive insights by using the right tools.  When banks know customers’ spending patterns, credit card usage frequency, investment preferences, and so on, they can provide financial products and services that align with what customers want. Risk analysis is another aspect banks take seriously. Knowing the probability of a customer turning into a defaulter will help determine if the bank can provide them with a loan or not.  We can summarize that data analytics in banking is used for demand, supply, and risk management purposes, and customer data plays a vital role in these analytics.  How Do Banks Evaluate Customers? Banks use 5 C’s to evaluate customers- Capacity, Character, Capital, Collateral, and Conditions. These factors are primarily considered to assess the creditworthiness of a prospective customer.  Details like credit score, loan history, repayment patterns, income, expenditure, etc., are collected and analyzed to evaluate the lifetime value of a customer. Various tools like Microsoft Power BI and Tableau are used to run data analytics and assess the worthiness of customers.  Banking data analysis uses modern business intelligence tools to process historical and present data in real-time. Using the latest technology ensures that the employees and management can access the reports in minutes instead of waiting for weeks and months. This increases the accuracy of the reports and helps in making the right decisions.  Customer Analytics in Banking & Financial sector Customer analytics includes analyzing the financial and behavioral patterns in the target market to have a comprehensive idea about customers’ preferences and tendencies. Understanding customers will help understand the market trends and be prepared to grab new opportunities.  Customer Acquisition  Banks need to acquire new customers regularly to keep the income flowing. However, it is important to know who the customers are and what they want. Data analytics helps in creating banking customer segments based on several factors such as demographics, income sources, spending ability, etc. By categorizing customers into specific individual segments, banks can create financial products for each category and plan marketing strategies to promote the right product/ service to the right category.  Banks also need to consider customer response to the marketing campaign and the financial product/ service to determine if they are on the correct path to achieve their goals. Fraud detection is another aspect at this stage. Does the bank want to take the risk of acquiring a customer who is likely to commit fraud or become a defaulter? This helps in filtering the prospective leads to avoid risky customers and focus on those who bring value to the establishment.  Get Customer Insights  Customer insight is an interpretation of the trends in customer behavior. It provides banks with the necessary information to understand whether or not the target customer segment will like the product/ service offered to them. It helps customize the financial products and schemes to suit the target market based on their reactions/ behavior.  Banks can get customer insights through different ways like asking for feedback, conducting surveys, creating focused groups, collecting data from browsers, and so on. This data is housed in a centralized database and processed to derive insights. Various analytical and statistical methods are used for this purpose.  Manage Customer Experience  The customer analytics dashboard helps employees in managing the day-to-day customer experience. For example, banks use Power BI to create financial dashboards that share actionable insights and reports in real-time. Employees can click on the intuitive dashboard to get the macro and micro view of the customer to determine the best possible way to take the issue forward.  By providing customers with what they want, banks can enhance customer experience and inspire brand loyalty. A crucial aspect of customer experience is the personalization of products and services. Make customers feel special by offering them a personalized financial product or scheme that suits their requirements.   Plan Effective Marketing Strategies  Banks and financial institutions are no strangers to marketing and advertising. But with an increasing focus on digital marketing, it is vital to create an elaborate marketing plan based on the target market and customer perspectives.  Building a brand image is necessary for banks to establish themselves as reliable and trustworthy financial service providers. The marketing strategies should be customized for each segment to increase customer response and participation. From choosing the right message to identifying the right channel for communication, customer analytics provides banks

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Impact of Big Data Analytics in Retail Industry (Simplified)

There is a huge demand for automating the traditional data management system for retail industries. The data generated online is increasing every second with the ever-changing shifts in customer behavior. In fact, about 40% of marketers study the consumer journey for crucial decision-making. The traditional methods might have been very useful and served you well but they are not as accurate as of the latest methods. To track the volume, velocity, and variety consequently is a complicated procedure but cannot be ignored in the digital era.  This is why big data has originated as an independent stream in data science. The first step of analytics is to collect data. The aim is to find out an easy way to process the data that will provide you with insights to take corrective actions. Big data analytics plays an important role in this situation, especially in the retail sector. Let us understand how? What is Big Data Analytics in Retail? Retail analytics is the concept that uses big data to optimize the price and supply chain while analyzing consumer behavior. Thus, a huge amount of data is used to determine the patterns, trends, human behavior and their interactions.  In the retail industry, big data analytics helps companies collect and analyze customer purchase history and preference data, which further helps them attract new customers. The retail industry needs to collect a large amount of data for the sales of their product and this includes a customers’ purchase history. The amount of the data collected proceeds to expand further due to the availability and range of the business in online mode.  Once this data is collected, stored and ready, big data analytics helps to identify customer behavior, shopping habits and patterns. It helps to improve the quality of service provided to the customers. Thus, leading to customer satisfaction.  No wonder, big data analytics in the retail market was estimated at USD 4.18 billion in the year 2020. The market is expected to reach USD 13.26 billion by the end of 2026. Applications of Big Data Analytics in Retail  The global retail analytics market is predicted to grow from $5.84 billion in 2021 to $18.33 billion in 2028. From a business standpoint, retailers will need to empower people across their organization to make decisions swiftly, accurately, and with confidence. The only way to achieve this is to harness big data and behavior retail analytics, to make the best plans and decisions, understand customers more deeply, uncover hidden trends that reveal new opportunities, and more.  Below is the list of applications of big data analytics in the retail industry.  Customer Segmentation This is one of the most important components in the retail organization. It provides you with various ways and shows how different sections of society respond to the shifts in demographics and trends. It helps to segment customers in the following manner:  Segment A: Customers who respond to the new promotions and advertisements  Segment B: Customers who acknowledge the new product commencement.  Segment C: Customers who respond to various discounts.  Segment D: Customers who show a tendency in purchasing specific goods. Segment E: Customers with varied purchasing power Campaign/ Promotion Effectiveness Analysis Once the campaign has launched, it’s necessary to study consumer response. The effectiveness of the campaign can be observed across various social media to study the ROI. This will greatly help you in understanding the main factors contributing to the success of the campaign.  Customer Lifetime Value (CLV) Not every customer will respond in a similar pattern. In this case, Customer Lifetime Value will help to calculate a relative amount of Risk-Adjusted Revenue and Risk-Adjusted Loss, which helps to assess the risk-return link. This gives an examined level of possibility of making money or suffering a loss on an investment. This includes adding some Net Present Value (difference between the present value of cash inflow and cash outflows for a certain period of time) and deducting the customer’s services. Customer Potential Presently, the customers who are not profitable for you may have a possibility of being profitable in the future. By this, we can say that it is very important to recognize the customer who has a high capability and adjust the marketing strategy accordingly. Customer Loyalty Analysis To retain an existing customer is more economical and effective rather than getting a new one. It’s important to get the best plan for the retention of customers by evaluating the reason they are leaving. Here, big data analytics helps you to understand customer retention considering the various factors that influence customers to drill down any transaction which might result in the change of the loyalty of customers. Cross-Selling Retailers rely on the data of the existing customers while cross-selling other products at the time of purchasing. Cross-selling can be done through product portfolio analysis as the portfolio represents all the products or services offered by a company. This way, it is possible for the retailers to sell the products that are missing from the portfolio.  Price Optimization Data analytics uses algorithms that perform several important functions for price optimization. It tracks the demand for the products in the market and observes the activities of the competitors. These factors are considered while optimizing the price of the product.  Future Performance Prediction Big data analytics helps collect and observe the customer’s interaction behavior about the product and services, such as customer queries via call, email, or social media. This further helps the companies compare the test to take preventive measures.   To Select the Highest ROI Opportunities Most retailers benefit from big data analytics in understanding the possibility of the customers base. With all this information they can estimate the market strategies with the highest Return on Investment (ROI). Demand Forecasting Demand forecasting is another benefit big data analytics contributes to the retail sector. Here, the factors to be considered are sales figures, environmental conditions, market conditions as additional help to determine the demand for the production services.  Out-Of-Stock Analysis Big data analytics in the retail

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Big Data Analytics in Business: Why Do Businesses Need It?

The term ‘big data’ refers to the complex process of analyzing a huge amount of data to find information such as market trends or customer preferences. This information can aid businesses in making quick and well-informed decisions. In other words, data analytics techniques and technologies allow organizations to analyze data sets and collect new data. Thereby, incorporating exterior elements to develop predictive models and statistical algorithms. A recent survey states that 97.2% of organizations are now investing in artificial intelligence and big data. The reason is that advanced analytics involves complex applications with various elements such as statistical algorithms, predictive models and other powerful analytics systems that help improve business operations and performance. This complex process of examining big data helps identify hidden patterns, market trends, correlations and customer preferences. This can support companies to make analytical and informed business decisions.  We can clearly see that there is a major role of big data in business analytics. This is because the data analytics technologies and techniques provide businesses with a method to collect new data & analyze them. Today, we will learn more about big data analytics and its various applications and benefits in business. Let’s begin! How is Big Data Used in Business? Companies can use big data analytics to make data-driven decisions that improve results and return on investment. It also improves prediction and business-related outcomes. This, in turn, leads to effective marketing, better opportunities, improved customer acquisition, and higher efficiency in operations. About 95% of U.S. companies state that their inability to manage unstructured data is holding them back. This implies that the applications of big data & analytics can give organizations an edge over their competitors.  In this digital age, statisticians, data scientists, predictive modelers, and data analysts prefer to collect and process structured data. This data could be in the form of web server logs, cloud applications, mobile applications, social media content, contact information, mobile phone and email address record, internet clickstream data, survey responses, machine data captured by sensors, etc. Data preparation and processing are required to cleanse the data— this is where an analytics tool comes into the frame. Why is this process required? Well, as per a recent survey, poor data quality costs the U.S. economy approximately $3.1 trillion annually. Benefits of Big Data Analytics for Business As per a recent survey, the big data analytics market is predicted to reach about $103 billion by the year 2023. This points towards the various benefits of big data analytics in the business and management sector. Netflix is the biggest example that avails the benefits of this analytical tool— with the use of big data, the OTT platform saves $1 billion per year on customer retention. This is done by providing personalized recommendations, showing similar videos to the ones the user has already watched, along with the ‘continue watching’ suggestion and displaying numerous options for a specific genre.  Well, not just the entertainment or e-commerce industry, every sector is benefitted from big data analytics. As a matter of fact, the big data analytics market in healthcare will be worth $67.82 billion by 2025. And we all are aware of the increase in cyber scams which demands the IoT, banking and tech companies to take extra precautions. There are many other proven use cases of big data analytics in various industries such as Banking and Finance, Accounting, Aviation, Agriculture, Automotive, Government and Law Enforcement and even for small businesses Let us understand how data analytics helps organizations in different ways: Customer Acquisition and Retention Structured data can help provide improved customer experiences and increase customer loyalty. The collection of consumer data can ease the marketing efforts of the organization, thus directing them towards the right path to customer satisfaction. Organizations get a competitive advantage over their rivals by implementing a unique approach. With the use of big data, companies are able to pinpoint consumer demand and present them with exactly what they are looking for. In simpler words, big analytics observes consumer patterns. For instance, Amazon uses the big data analytics technique by providing personalized shopping experiences to their customers. The e-commerce portal suggests items to their customers based on purchase history, browsing behavior and other such factors.  Focused and Targeted Ads With the accumulation of data such as product page viewing history, purchase history and customer queries, ad campaigns can see large-scale success as they can now target the right set of audience and convert them into customers. With sophisticated analysis of customer trends, businesses are able to monitor online purchases and observe point-of-sale transactions.  If you are a business that has been spending a massive amount of marketing with zero return on investment? It is time to include big data analytics in business operations and marketing. The in-depth insights allow companies to launch focused and targeted campaigns. Thus, building brand loyalty and exceeding customer expectations.  Price Model Price optimization can be more convenient using model data, thus maximizing revenues by understanding consumer behavior. The tool also helps organizations to evaluate finances and give clarity on the business front. The data analysis also helps with cost savings that result from new business optimizations and efficiencies. Product Development Improve your product viability, progress measurement and development decisions and take it into the direction of customer satisfaction with the use of big data analytics. Valuable data helps companies update existing products with better products. Thus, implementing innovation and remaining competitive in today’s digital era.  While many businesses have been operating on instincts, gut feeling alone cannot take any company a long way. Big data analytics helps companies collect large amounts of data and understand consumer needs.  Supply Chain and Inventory Management B2B supplier networks and inventory can be managed with predictive analytical models and route optimization as data is sourced from analytics software. With the use of big data, companies offer supplier networks with greater precision and insights.  Data analytics now act as a disruptive technology by including high-level collaboration and applying new knowledge to

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Big Data Analytics in Healthcare: Need of the Hour

The projected value of big data-based financial analytics services in healthcare stands at more than 13 bn USD by 2025. However, with data and technology being the two major drivers of transformation and growth in the healthcare industry, we expect the figure to be actually higher than this.  However, what makes big data analytics of such a big consequence in healthcare? How can it spur what the global technology and business experts are calling the Fourth Industrial Revolution in the healthcare sector? And what are the major applications of big data analytics in the healthcare industry? Let’s explore the answers to all these questions and examine the various big data analytics use cases in the global healthcare industry.  What Is Big Data Analytics? In easy terms, big data analytics is the process of parsing, processing, and making sense of huge chunks of organized or unorganized hybrid data blocks. Big data analytics is a powerful tool to get actionable insights from the huge amount of data that is otherwise dormant and of no use because of its unstructured composition.  Big Data Analytics in Healthcare Presently, the healthcare industry generates around 30% of the world’s data volume and by 2025, this figure will sit at 36%.  While you might argue that is not a significant increase, it is 6% faster than manufacturing, 11% faster than media & entertainment, and 10% faster than financial services.  There are various sources of data in the healthcare industry, such as patient data, medical insurance data, research data, innovation and medicine data, medical institution data, student data, business development data, etc. Further, as healthcare has unbreakable ties with many other industries, such as medical tourism, wellness and counseling services, different schools of medicine, equipment manufacturing, etc, the data can become a huge complex puzzle. And, big data analytics is a powerful and reliable key to not only solving this puzzle but to using the insights generated for improving all the business processes and discovering hidden trends in healthcare.  Below, we are sharing a basic overview of the various business use cases of big data analytics in healthcare. Big Data Analytics Healthcare Use Cases 1. Business Side The business side use cases focus on the business aspects of the healthcare industry and comprise many sectors, as shown in the following image: 2. Operations These use cases focus on the operational side of the healthcare industry and some of the common use cases are shown in the following visual: 3. Applications Next, we present some big data analytics use cases on the application side: Before we discuss these use cases in detail, let us understand why big data analytics is important in healthcare. Importance of Big Data Analytics in Healthcare World’s health and wellness data are invaluable, in fact, it is as indispensable for modern healthcare services, as water is for life! And, all the major brands and stakeholders operating in the sector are in a battle to access and utilize the digital representation of this data.  As the number of people that are using their mobiles and personal devices for healthcare services is increasing massively, the market is estimated to reach a market value of 189 bn USD in 2025. Apart from the widespread concern about health and well-being because of the pandemic, the penetration of the internet and the availability of mobiles and tablets are the key drivers of this market boom. Apart from creating opportunities for new healthcare services and offerings for digital consultation, information sharing, and personalizing the medical healthcare services, these trends are also generating heaps of data.  However, in order to make the most of this data, the industry stakeholders need proper means to process and analyze this data to understand the key pain points and discover actionable insights.  Hence, big data analytics! Big data analytics empowers the medical caregivers and service providers to learn granular details about their patients and facilitate the care, attention, and services accordingly. When healthcare professionals can see the key service points and unfulfilled patient requirements they can optimize their service portfolios and business processes.  They can also add new services, products and create new treatment routines depending on the results of the previous ones. With big data analytics and other advanced technologies, such as blockchain technology medical claim frauds and health insurance frauds can be detected even before they have happened and can be stopped before they become a business risk.  Using big data analytics will also spawn innovation in medicine and established treatment procedures or medicines, to make way for smarter, better, and more targeted healthcare facilitation.  Healthcare professionals can also find ways to deliver more cost-efficient and clinically relevant services to patients.  Next, we discuss some of the most impactful and compelling applications of big data analytics in healthcare. Big Data Applications in Healthcare 1. Predictive Patient Analytics for Improved Staffing Staffing is one of the major concerns and challenges that need robust and reliable resolution in a healthcare institution. Sometimes, there are too many nurses in a department while the patient inflow and tasks at hand are low, and on others, a few nurses are grappling with the hospital duties.  Using big data analytics for predictive analysis of staffing requirements and scenarios based on the historical data and upcoming forecast can resolve these and many other issues with ease.  You can see which type of staff is the most suitable for your business and operational model.  For example: Medical institutions can not only save money via proper staffing but also increase their turnover rates and increase job satisfaction among their staff.  2. Electronic Health Record Management Electronic health records consume very small space (they typically reside on your server) and come with a bucketload of benefits. You can access the data for any patient, any treatment record, and any transaction that was done at any point in time, without having to go through the innumerable files in the data room of your hospital. While the majority of hospitals are using electronic health records,

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Data Visualization in Banking: Why Is It Essential in the Finance Sector?

Data visualization is the graphical representation of insights generated in real-time. The reports are used by banks to effectively streamline daily activities and make faster decisions. Data visualization is the process of graphically visualizing large data sets. The visuals make it easier to understand the relationship between various factors and identify the trends. Data visualization gives you insights in real-time and helps in faster decision-making.  According to the latest survey, data visualization is among the top three BI trends, with data quality management and self-service business intelligence. The banking sector deals with large amounts of data every day. Banks have huge volumes of historical and real-time data that need to be processed quickly.  Spreadsheets can no longer help banks keep up with the increasing pace in today’s world. Banking professionals are too busy to sift through endless spreadsheets and arrive at a conclusion without using the latest technology. Moreover, spreadsheets can’t handle large amounts of data.  Power BI is a well-known business intelligence tool that offers data visualization dashboards in real-time. Many banks from different countries have been integrating Power BI to create data visualizations and help employees work efficiently. Considering effective scenarios, a Power BI consulting firm is responsible for assisting banks in implementing data visualizations for day-to-day activities.  How Is Data Visualization Used in Finance? Data visualization is much more than PowerPoint presentations. It creates detailed graphs and charts to help employees understand large datasets in less time. There are several uses of data visualization in the banking industry.  Easier Interpretation of Data  Data visualization is an easy way to understand and learn from data. Since many of us are visual learners, using data visualization helps convey the message/ insight easily compared to blocks of text or spreadsheets.  Moreover, the traditional methods can only provide static data based on historical information. By the time the latest data is collected, cleaned, and processed, it becomes outdated and may not be as effective when used in decision-making. Banking professionals can take one look at the data visualization report of a customer’s journey with the bank and determine the best financial service.  Quick Identification There’s no need to sort and filter countless rows and columns of data. You can generate graphical representations of any dataset. Create bar charts, line charts, pie charts, heat maps, etc., to instantly identify the macro and micro details of the same situation.  For example, you can look at the graph of monthly transactions in a bank branch to identify which month recorded the highest number and why.  Big Data is Not a Problem  PowerPoint is not enough to create reports for big data. But Power BI and data visualization are perfect for big data. Since the financial industry deals with huge volumes of data, using data visualization tools is the best choice to understand datasets.  Detect Anomalies and Patterns  Financial fraud is one of the biggest concerns in the banking sector. Data visualization reports help detect patterns you might otherwise not notice. The reports give employees the necessary information to minimize the risk of fraudulent transactions and prevent them from occurring. Banks and insurance companies have separate dashboards for fraud detection and risk management.  Data Sharing and Collaboration  The visualization reports are not limited to only a few employees in the bank. The reports can be shared between departments, teams, and employees. This simplifies data sharing and collaboration within the bank. Teams from different branches can work together irrespective of their locations.  One of the most commonly used tools for data visualization is Microsoft Power BI. It is a complete suite of applications that allows banks to collect, process, analyze, and present data in real-time. However, implementing Power BI requires expertise. Hiring a consultant makes it easy to use the business intelligence tool in the bank. Overview of Power BI  Power-BI is a cloud-hosted business intelligence tool by Microsoft. It collects data from multiple sources and processes it to generate visualization reports based on your chosen parameters. Power BI is flexible and deals with a range of data. It is used for data science, data analytics, and data visualizations.  Moreover, Power BI can be accessed through the web, desktop, and mobile phones. It helps in sharing data across the enterprise and empowers teams to collaborate remotely. Power BI is one of the leading tools for data visualization in banking establishments.  Benefits of Using Power BI in Banking  There is no escaping data in the financial industry. It makes sense that banking and financial institutions want to adopt data-driven models to streamline their processes and make better decisions. While banks can implement Power BI through an in-house team, many prefer to hire experts. After all, Power BI consulting services simplify the process and minimize the risks of adopting new technology. Banks have experienced several benefits by using Power BI for analytics and data visualization.  Microsoft details how the Metro Bank in the UK has used Power BI to revamp its systems and strategies to gain a competitive edge in the market.  Internet and Mobile Banking Banks are no longer limited to physical offices. People rely more on the internet and mobile banking for various online transactions. Power BI helps collect and process this data to understand customer behavior.  Customer Service  Power BI helps make customer service better and efficient by analyzing the helpdesk data from tracking complaints to speeding up the resolving time.  Staffing and Work Distribution  When should the banks hire additional staff? How should the manager distribute work? What are the peak times for offline and online transactions? This data allows managers to organize the teams and increase productivity.  Data Storage Power BI allows banks to work with big data using powerful algorithms. There’s no need to delete old data or aggregate datasets. Moreover, you can access historical and real-time data at any given point in time.  Identify Data Trends  Processing large datasets in seconds/ minutes is possible using Power BI. This makes it easy to identify data trends for the factors you want. You can add or remove parameters to understand how

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How to Create Financial Analytics Dashboards for Banks and Accounting Services

A financial dashboard gives banks complete control over their daily operations. The dashboard provides real-time reports about the performance of the establishment against the specified KPIs. Let’s see how the dashboard is created and why it plays a prominent role in the financial industry.  Banks and financial institutions face heavy customer traffic and are under immense pressure to deliver the best to each customer. Managing a multitude of transactions for deposits, withdrawals, loans, repayments, insurance, mutual funds, bonds, etc., gets stressful for the employees. The advancement in technology has made it easier to manage the huge volume of transactions in less time without compromising the quality of services.  One of the latest tools adopted by the accounting industry is the financial analytics dashboard. The dashboard is built using business intelligence tools like Power BI. It provides valuable data to employees and empowers them to make decisions faster. The necessary information is presented in attractive and simplified reports. Banking professionals only need to use the dashboard to access any report in real-time. It takes just a few clicks to get a clearer picture of the issue.  Creating the financial dashboard is the job of experts. Even though financial institutions have in-house IT departments, most of them hire the services of business intelligence consulting companies. The experts set up the dashboard, integrate it with the existing business systems, and streamline data flow.  Power BI is a popular and preferred business intelligence tool used to create financial dashboards. Let’s read more about the software and how analytics can help financial companies manage their business and customers.  How is Analytics Used in Banking? Data analytics has become a part of various industries. The finance sector is no exception. Banks use analytics to identify leads, understand customer behavior, and trace market trends for financial products and services. It helps understand and identify which customer might need a loan, which customer is likely to obtain an insurance policy, and what kind of investment options are gaining popularity. Keeping up with current trends gives the bank an advantage over its competitors. There are more opportunities to grab and become successful. Banks and financial institutions have largely benefitted from using data analytics.  So how can analytics help finance companies? What is a Financial Dashboard?       A financial analytical dashboard is a data management tool used to collect, monitor, and maintain finance-related information of the establishment. A financial dashboard is further classified into different types. Each dashboard has a purpose and provides insights into the related objectives.  Types of Financial Dashboards  Cash Management  Cash management is a vital part of the banking system. The cash dashboard provides the current ratio and quick ratio to determine the bank’s ability to pay off obligations and increase financial health.  Profit & Loss  The P&L dashboard summarizes the expenses, revenue, and costs incurred by the institution for the fiscal year. It provides the necessary information to help banks understand the reasons for the losses incurred or profits gained.  Financial Performance  The financial performance dashboard created using Power BI gives insights into top-level information about the bank’s financial status. Metrics such as return on equity, return on assets, debt-equity ratio, and the working capital ratio is featured in this dashboard.  Accounts Payable & Receivable  The income and expenses of the financial institution are monitored using this dashboard. Employees can add more data columns to include all expenses paid by the bank.  CFO Scorecard  The CFO scorecard dashboard is designed for the Chief Finance Officer to keep track of the bank’s assets, liabilities, cash flow, liquidity, and other aspects. This helps in making the right decisions to achieve the goals.  Based on their objectives and requirements, banks can have all the above dashboards or combine two or more. Employees are given access to the dashboards as per their roles and responsibilities.  How to Create Financial Analytics Dashboards? Creating a financial dashboard for a bank or financial establishment requires planning, implementation, monitoring, and optimization to ensure that the dashboard serves its purpose. The aim is to automate repetitive tasks and generate reports in minutes instead of slogging over them for weeks and months. Following are the steps to create a financial dashboard in Power BI: 1. Start With Your Goals  What are your business goals? What do you want to achieve this fiscal year? Are there any long-term plans? The dashboard is designed based on the metrics you want to measure and track.  2. Finalize the Metrics  It’s not surprising for a bank to have several metrics to monitor its growth and performance. However, adding all those to the dashboard will cause more confusion than providing any help. Shortlist the essential metrics to be directly displayed on the dashboard. The rest can be included in the filters and used whenever necessary.  3. Monitor and Report Performance  The aim of creating a Power BI finance dashboard is to monitor the establishment’s performance. From day-to-day activities to overall health, you need to consider creating visualizations for each of them.  4. Create Data Visualization The dashboard displays data visualizations in real-time. How do you want the reports to show up on the interface? Choose the layout, types of graphs, and their positions to provide you with the necessary information in just one glance. You should find what you want simply by looking at the dashboard.  5. Setup the Frequency for Reports  How often do you want to run analytics in your bank? Monthly, quarterly, or semi-annually? You can set up the frequency to automate the process using Power BI. The dashboard will directly provide you with the reports based on the parameters you defined. The frequency of reports is also decided based on your budget.  6. Emphasize Customer Experience The primary aim of using financial dashboards is to increase customer satisfaction and thereby generate more profit. Your metrics and filters should align with your ultimate goal of enhancing the customer experience.  7. Don’t Clutter the Dashboard  The Power BI dashboard is highly customizable and can be created as per your requirements. However, it’s not recommended to clutter

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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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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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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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