Page 34 – DataToBiz

Deeksha Anand
February 22, 2022
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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

Kavika Roy
February 21, 2022
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Data Science for Risk Management

While 57% of senior-level executives rank “compliance and risk” as the top two risk categories that must be managed, only 36% of organizations have a formal risk management plan, and 69% of executives are not even confident about their current risk management policies. The global enterprise risk landscape is highly volatile and teeming with new and critical challenges now and then, and one such example is the ongoing COVID-19 pandemic. Despite being a significant organizational process, risk management processes and practices across the globe continue to be a challenging business aspect to manage. One of the primary reasons behind this is the consistent technological and market evolution and industry disruptions. While having a 100% sure and fool-proof risk management strategy is a utopia, getting data-driven insights from historical data and predictive analytics, in short, Data Analytics, can give you the key to a lot of many concerns. How to know whether your business is running at maximum efficiency, and what is your exact risk profile? Do you understand your risk profile in its entirety, and is your exposure to business risks, accidents, and disruptions minimum? Here, we explore how data analytics can help you resolve these and many other doubts by taking all the guesswork out of your risk management practices. Let us begin by understanding the term in the modern digital business landscape. What Is Risk Management and What Activities Does It Involve? Gartner defines enterprise risk management (ERM) as the process of identifying, analyzing, and treating the enterprise’s exposure as visualized by the executive management. It includes looking at the various exposures, such as frauds, credit, finance, strategic and operational matters. ERM is a top-down strategy to identify, assess, and prepare for the potential dangers, losses, and hazards that might interfere with the operations and objectives of a firm in multiple ways. Hence, instead of being a siloed analysis of different processes, departments, or operations, it is a holistic and highly consolidated analysis of all the business units and segments. The following visual shows the various activities involved in risk management: 1. Align ERM Processes to Business Goals Senior management and enterprise leaders work together to establish the organizational and business goals and educate the staff about the importance of ERM. 2. Identify Enterprise Risks Compile a list of various risks that can affect the enterprise and impede it from achieving its goals. 3. Risk Assessment Risk examination, keeping the likelihood and impact of risk on the organization. 4. Select Risk Response Selecting an appropriate risk response based on the impact and risk appetite, such as risk acceptance, avoidance, reduction, transfer, sharing, etc. 5. Risk Monitoring Monitor the ways risks are changing and how your responses are faring against them. 6. Communicate or Report Risks You have to communicate the risk events, their results, and steps you took to counter them, to the stakeholders and upper management, via reports. Traditional Risk Management vs. Modern Risk Business: The Paradigm Shift Traditional risk management approaches are highly subjective and are based on individual perceptions. They also tend to be non-optimal in dealing with the emerging risk landscape. Today, enterprises generate limitless data, the technology is getting smarter with every passing day, and business dynamics are becoming highly challenging. Hence, having an effective risk management process is a must-have to steer your business towards growth. And so is having a data-based approach towards risk management! As businesses continue to digitize the operations and automate the processes, the risk profiles continue to assume a more critical, diverse, and complicated stance. Why? – You might ask. Well, this is because of many reasons, such as: Hence, to survive in this new digital era, businesses should identify the early indicators of risk events and have to act proactively to mitigate those risks before they become disruptions. Further, it is important to understand your risk portfolio and requirements for advanced technologies to minimize your risk exposure. Below, we discuss some key considerations that you must consider while applying data analytics for risk management. Applying Data Analytics to Risk Management Process: 5 Key Considerations 1. How to Measure and Quantify Risk? While there is no exact science or mathematical formula to measure risk, using analytics, you can easily create measurement parameters that can, in turn, help you in establishing and examining the likely risk scenarios. Once this is done, understanding the potential impact of any risk becomes easier. Now, you can start planning around it and gradually establish a baseline of data using analytics to measure risk across the entire organization. Hence, your end goal should be to use data analytics for the entire organization in a holistic manner. 2. Understanding “What Is New” in Analytics One of the most common doubts people have about analytics is that they have been using it for years, so what’s all this hype about? While we have been using data analytics in some form for years, there is a world of difference between that tradition and the current version. The modern data analytics platforms are highly sophisticated, and modern risk analytics is more focused on data exploration, statistical clustering, data segmentation, predictive analysis, scenario analysis, event simulation, etc. So, even if you have been using numbers for risk analysis and management for years now, modern risk analytics leverages various advanced technologies and is undoubtedly more effective and reliable. 3. Using ERM Solutions With in-Built Analytics While having an ERM department with an entire team is a huge asset for every business, it comes at a higher cost, and most organizations have a standalone ERM function. Hence, it is essential to opt for professional risk analytics services that can tap into the business-wide structured and unstructured data blocks and understand the potential impacts of a wide range of risks. Further, as risk management gradually becomes more complex, an ERM with in-built risk analytics becomes a huge overhead. 4. Using Analytics for Financial Statements and Reporting Analytics and financial reporting have a lot of natural overlapping and are mutually fulfilling. Analytics can offer data

Yashica Chopra
February 18, 2022
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Is BI a Revolution in the Banking Industry?

Business intelligence is no longer a strange phenomenon in the banking industry. It is a revolution that’s changing how banks perceive data and analytics. We’ll discuss the role of BI in banks in the modern-day scenario. Technological innovations are the key to being relevant in this fast-changing world. The banking industry is not an exception to this rule. From following traditional bookkeeping methods to computerizing the transactions, we’ve noticed many changes in the banking sector. Another such development is the use of analytical tools to streamline internal operations and enhance customer experience. Even the banking industry has competition as more banks are going multinational and opening new branches around the world. Collecting data, creating a centralized database, and processing huge volumes of data requires much more than legacy systems and spreadsheets like MS Excel. Business intelligence has successfully entered the banking, insurance, and financial sector. Banking analytical solutions can help employees, management, and the leadership have more control over day-to-day operations and provide better services to their customers. BI is undoubtedly a revolution in the banking industry, and here’s how. What Is Business Intelligence? Before we talk about how BI is changing banks and financial establishments, let’s first understand what business intelligence is and why it is important. Business intelligence is a set of technologies, processes, architectures that help collect, clean, and convert raw data into meaningful and actionable insights. BI is a suite of software applications and services used for transforming data into intelligence. Business intelligence impacts the operational, strategic, and tactical decisions of an establishment. BI tools are used to collect, clean, format, process, analyze, and present data in real-time. BI uses historical and real-time data to derive insights and generate reports. Employees can run queries by setting parameters to get accurate responses from the tools. Business intelligence is important to measure KPIs, set benchmarks, identify market trends, overcome challenges, and make data-driven decisions. Applications such as Power BI, Tableau, Qlik, etc., are some well-known business intelligence tools used by businesses from different industries. Power BI is the most preferred tool in the banking industry. What Is Business Analytics in Banking? Business analytics help in understanding the bank’s data based on statistical models and quantitative methods. Analytics can be used in various ways in the banking industry. The core reason to use business analytics is to answer questions like: Business analytics is a part of BI. How Is Business Intelligence Used in Banking? Consumer Trend Forecasting Knowing what the customers want is important to achieve the bank’s goals and generate profits. Banks offer a range of financial services for individuals and business entities. From operating a savings account to funds management, loans, credit cards, and insurance services, the banking industry deals with everything related to money management and control. Business intelligence helps in analyzing the huge volumes of data generated every day to identify the trends in customers’ demands/ requirements. Is there a decrease in home loan applications? Are more customers investing in a certain mutual funds scheme? Finding answers to these questions will help the management make the necessary changes to their financial products and services. Better Marketing Effectiveness Understanding your customers is a good way to increase your customer base and ensure brand loyalty. The banking industry has become a competitive field. You need to reach out to the target audience with offers that’ll surely make them consider you as a first choice. But how do you know what the market wants? BI in banking helps use customer data and data analytical tools to derive insights about each target base. It is not essential to spend money on ads that may or may not be effective. You can create ads for each target group with the confidence of knowing what they want. The bank can generate more returns with less investment, identify the weak points in the existing marketing strategies and correct them in time. Data and Cybersecurity According to a report shared by Security Magazine, the banking industry saw a 1318% increase in ransomware attacks in 2021. Banking data of individuals, corporate entities, and non-profit organizations are highly confidential information. While this data can be obtained through several malpractices like hacking and phishing, a cyber attack on a bank’s database gives criminals access to valuable information in bulk. Business intelligence tools play a role in monitoring the network traffic and can alert you in case of irregularities. The data collected and analyzed by BI tools gives security experts the necessary information to identify hot spots and improve the security layers at each level. Retain Customers Customer retention is necessary to maintain a healthy customer base. A bank needs to have customers who’ve been with them for years and will continue to be. Providing personalized services and ensuring quality customer experience promotes customer retention. BI tools can be used to identify customers who are more likely to impact their business with the bank or move to a competitor. You can also understand the reasons behind this trend and find ways to prevent it from happening. CEOs, CFOs, and other chief executives can run analytics on historical and real-time data to understand the patterns and come up with a solution. Calculating Credit Risk Should you approve the loan application of a customer? What is the risk associated with it? Banks already have a system to process credit requests and approve/ reject applications based on it. However, changing times require advanced systems. You may need to consider more risk factors, especially when the customer is a business entity and the amount is millions. Banking business analytics is used to process customer data from multiple sources. It helps analyze spending habits, income stability, and the probability of fraud before a loan application is approved. This lowers the credit risk for a bank and reduces defaulters. Compliance Assurance The banking industry has to adhere to the various regulations set up by the national and global bureaus that help in establishing anti-money laundering norms, etc. Banks have to regularly send reports and updates

Arya Bharti
February 14, 2022
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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,

Deeksha Anand
February 10, 2022
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Power BI for Retail Industry: Dominate the Sector

With an increase in competition in the retail sector, it has become extremely important for every retail company to understand advanced technology stack, people and data. Power BI is one such technological advancement that allows retail businesses to leverage its functionality for data visualizations to improve their profitability and efficiency while acting on real-time insights. In fact, the usage of any business intelligence tool is considered a crucial key to managing a company’s data in this digital age. It is used to generate reports, surface insights and connect to a wide range of information for better understanding. The reports offer visualization of the data that boosts the sales for the business by facilitating better planning and strategizing. Today, we will discuss the applications of Power BI for retail and what makes it one of the most efficient retail reporting tools. Benefits of Power BI in the Retail Industry Since the outbreak of the pandemic, the retail industry is slammed with the challenge of uncertainties. Moreover, the growth of digitalization has enhanced customer expectations. As per a survey, the e-retail industry in the Gulf Cooperation Council (GCC) region was predicted to grow at the rate of 12.2 percent of the offline retail in the year 2025 (before the COVID-19 pandemic). The forecasted penetration rate grew to approximately 16.3 percent after the pandemic. Therefore, customer demands follow a different pattern in today’s digital era. Along with the products or services, the customers are also given equal importance in today’s economy. Every business and retail industry is looking forward to providing their customers with quick satisfaction and a smooth experience. The retail industry is suggested to analyze and collect the data for coping with the changing customer expectations and rising demands. With the applications of Power BI, the industry can expect the transformation of raw data into actionable retailing insights. It can offer every business a 360-degree view of their entire industry at a glance. Moreover, the efficiency of the Power BI retail dashboard helps understand the selling and buying habits of the customer. Thus proving to be the top retail business intelligence tool in the e-retail market. Let us look into the major advantages of using Power BI in retail: Since it is clear how Power BI for retail can prove to be beneficial, let us now understand its technicalities. How Does the Power BI Sales Dashboard Work? As we discussed earlier, the business intelligence tool by Microsoft helps to analyze data present in different sources and build dashboards and reports based on the data. Top-level management effectively uses Power BI’s dashboard for retail operations as it is capable of generating an alert once it notices low stock. It helps to create accurate reports on the profitability of the products. Apart from endless uses of the Power BI retail dashboard, the report generated by the BI tool helps to analyze the cash tied to the existing stocks. The industry can avail the advantage of getting the data source with the help of Power BI. Companies can keep track of the inventory status, changes in customers’ shopping trends and the value that any supplier holds using the BI tool. For example, a Customer Analysis Dashboard will help evaluate profit and sales while generating valuable insights based on product turnover-related, dynamic customer behaviors. As an illustration, retail companies in the Middle East and North Africa (MENA) could analyze a decrease in in-store shopping following the COVID-19 pandemic. Further, noticing an increase in shopping via the PC. A survey conducted in 2020 supported the data analysis by predicting the growth curve of the e-commerce industry in the Middle East region to accelerate to five years in about five months. How Would the Retail Industry Use Business Intelligence? The powerful dynamic of business intelligence can be used in many ways by the retail industry. Some of the most useful ways are as mentioned below: Following up with Changing Trends Keeping up with the latest customer trends has become seamless with the help of Power BI. Now every industry can use Power BI to analyze big data that projects the pattern of change in the customer’s purchasing behavior. Using the retail visuals can help the industry monitor, plan and assess the effectiveness of various promotional activities. It can also draw a report on how a particular group of customers reacts to specific marketing campaigns. Moreover, the Power BI can collect social media data while analyzing it to measure the high engagement level, track the demographics, and assess the influencing behavior while understanding the buying patterns. With the help of the retail analytics dashboards, you can boost the efficiency of the supply chain while meeting your customers’ demands. It can also help meet the seasonal demand while enhancing the forecasting ability. Get the Omnichannel Experience The multi-channel retail would get replaced with the help of a more engaging omnichannel experience. Power BI helps to unify the customer in every action and touchpoint. It helps to treat the single-channel interaction for a more delightful customer experience. Power BI helps to aggregate the data from various offline and online sources with the help of a visualization tool that helps predict user behavior. It also helps get a unified picture of the expectations and demands of the customer. Assessment through the budget is only possible with the help of Power BI. It helps to understand the different areas of intelligent plans, cost-cutting, predictive analysis and floor optimization to balance the demand and supply of the perishable results. Assists All Team Members The data analysts can dig deeper with the help of the advanced features of Power BI, including map visualization. It also helps them unfold the platform’s predictive analysis potential by using R scripts. The team helps the executive keep an eye on the critical parameters while helping them with an all-encompassing and comprehensive report generated via Power BI sales dashboards. It can help them to access a high-level picture of the situation within less time. The top-level management is

Yashica Chopra
February 08, 2022
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Why BI is important for C-Level Executives in BFSI

Business intelligence is used to derive insights from data and helps in decision making. Make the most of big data to understand customers and improve their experience with the financial company. Let’s discuss how C-level executives can use BI tools to achieve their goals. The Banking, Financial Services, and Insurance industry has changed over the years and is now leaning towards the customer-centric model. The increase in competition, the unexpected losses, and the changing trends in the market are some reasons for this development. The C-suite executives have a huge role to play in streamlining the banking operations, increasing revenue, and enhancing customer experience as they are the decision-makers in the establishment. Providing customers with a worthy experience has become essential for a financial company to establish itself and become a popular name. However, achieving customer satisfaction is not possible without knowing what customers want. This knowledge comes from the data collected by the institutions. That said, raw data is of no use unless it is cleaned, processed, and analyzed using data analytics tools. In short, financial institutions need advanced tools to adopt the data-driven model. The tools and the processes come under business intelligence. Financial establishments mostly hire the services of offshore BI consultants to build the data-driven model and implement the necessary tools like Power BI for data analytics and reporting. A financial establishment with noteworthy customer experience is proof that it has everything under control and is at the top of the game. What is Business Intelligence? Business Intelligence is a technology-based process used by business organizations to collect, clean, store, streamline, and analyze large volumes of data in real-time. Business intelligence delivers high-quality and accurate insights used to make business decisions. Data mining (collecting data from multiple sources), predictive analytics, and data visualizations (reporting) are an integral part of the business intelligence process. There are several BI tools in the market. For example, Microsoft Power BI and Tableau are well-known tools for business intelligence processes in various industries. The BFSI industry relies on Power BI for financial, operational, and marketing purposes. This certainly doesn’t mean that BI is only limited to the banking and finance sector. Let’s look at some of the benefits of using business intelligence in banking and financial companies. Benefits of BI Tools Why BI is Important for C-Level Executives in BFSI Business intelligence has a prominent role in customer experience. CEOs, CFOs, COOs, CTOs, etc., can use BI tools like Power BI for the following: Real-Time Reporting The executive leaders don’t need to wait days and weeks to access data reports. The reports are automatically generated in real-time and shared using the dashboard of the BI tool. They can export the reports to other applications and send them to team members, management, etc., whenever required. Why spend hours of your time creating reports when you can set up the process and let the software do the rest? Omnichannel Customer Insights Structured and unstructured data from multiple sources (social media, call recordings, emails, messages, surveys, feedback, etc.) is collected in a centralized database. This data is cleaned and analyzed to derive insights to get a complete picture of a customer (to build customer profiles). This helps create better financial products, plan marketing campaigns, and convert more leads into customers. The chief executives don’t have to rely on outdated reports or past experiences to determine the best way forward. They can use real-time reports to target the markets and capture a larger customer base than the competitors. Collaborations The C-suite executives have to work in tandem with teams from sales, marketing, and financial departments in the establishment. From setting up the budget for customer acquisition to allowing the sales and marketing teams to brainstorm and share information seamlessly, BI tools help create a healthy environment in the organization and promote collaborations between employees and stakeholders. Automate Customer Engagement The metrics and data thresholds of BI tools act as natural triggers for automation. You can establish workflows and campaign timelines and schedule customer meetings based on the analytics. When you contact a prospective customer at the right time in the right way, the results will be favorable for your organization. Business intelligence gives you the necessary information to identify the best course of action and helps automate the processes. Predict Customer Demands It’s no surprise that customer demands change based on geographical location, market trends, and demographics. By predicting the changing conditions and demands, banks and insurance companies can offer financial products useful for customers and thus increase their market share. You can conduct surveys and use beta groups to study potential customers in new/ unknown markets. Root Cause Analytics Root cause analytics is where you identify the actual cause for a problem at hand. This is done by analyzing relevant information (historical and real-time) for the adverse condition you need to overcome. Business intelligence tools like Power BI can quickly process big data and generate data visualization (graphical) reports about the reasons for the problem/ adverse condition. Convince the Team One of the toughest tasks the chief executives face is to convince the topmost leadership and other chief executives about a campaign, an investment, or a decision. The reports generated by BI tools help present data clearly for the leaders to understand the potential of a project/ campaign and the expected ROI. Since the reports are generated using data analytics, it becomes easier to make decisions that impact the organization. Risk Mitigation Chief executives need to stay one step ahead and filter the leads to minimize the risk of attracting fraudulent customers. Identifying who/ which group is likely to commit financial fraud or misuse services will help banks cut down losses. Be it unpaid loans or vanishing customers, the financial establishments have to handle various types of frauds and find ways to overcome the losses. Integrating BI tools with data sources and existing apps will help in running automated analytics and sending alerts to the respective employees when there’s a possibility of a fraudulent transaction Better Customer

Kavika Roy
January 31, 2022
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Mixed Reality and AI for Safer Surgeries

Mixed reality and AI can help make surgeries safer by assisting surgeons during the process. From providing 3D imaging to handling instruments, AI is a vital part of the operating room. Here, we discuss what mixed reality means and how AI is taking surgeries to the next level. Artificial intelligence, machine learning, and computer vision are becoming an essential part of the healthcare industry. AI is helping doctors, nurses, and the hospital administration streamline patients’ records, accurately diagnose the medical condition, and provide better treatment. The COVID-19 pandemic has increased the focus and adaptation of remote patient care and telemedicine to ensure that patients don’t suffer due to lockdowns. Doctors increasingly rely on AI-based technology and tools to provide patients with quality healthcare. Technology like augmented reality (AR), virtual reality (VR), and mixed reality (MR) are being used in hospitals to assist doctors and train medical students. These are designed to assist doctors during surgeries to make the process safer and increase the success rate. The market size value of AI in healthcare is $10.4 billion in 2021 and is estimated to reach $120.2 billion by 2028 at a CAGR (Compound Annual Growth Rate) of 48 percent. North America has shown the highest adoption rate of AI in healthcare at 58.9 percent in 2020. The same report suggests that the use of AI in the healthcare industry will grow rapidly in the Asia Pacific during the forecast period. What is Mixed Reality? Mixed reality is a combination of virtual reality and human vision. The AR filters used in mobile apps such as Instagram, Snapchat, etc., are a simple example of mixed reality solutions. Surgeries require a lot of planning, practice, and precision. Doctors cannot afford to get distracted or lose focus when operating on a person. The use of AI in surgery aims to support doctors and supply them with the necessary information and surgical tools without disturbing them at any point. Mixed reality makes it possible to use technology to assist doctors during surgeries and minimize risks. Paul Milgram and Fumio Kishino first introduced the term mixed reality in 1994 in their paper titled A Taxonomy of Mixed Reality Visual Displays. MR combines computer vision, cloud computing, graphical processing, etc., to blend the physical and virtual worlds. Many companies have been developing MR applications that can be used in various industries. MR and Healthcare MR in healthcare can be explained using a head-mounted device that gives doctors a clear and better picture of the patient’s data chart and related images. The images of the affected area where the surgery has to be performed can be displayed directly on the patient. This increases accuracy and makes it easy for surgeons to be as precise as possible. MR devices are also changing the interaction method between doctors and patients. Data is more easily accessible and is used to understand the patient’s condition in-depth. Microsoft’s HoloLens is a prime example of MR Headgear that creates realistic images and holograms to help medical students understand human anatomy. Medical schools have begun investing in MR devices to empower their students. Mohammad Abdul Mukit, an MS student in electrical and computer engineering, a graduate fellow and research assistant at the University of Oklahoma, and Dr. Christian El Amm, a plastic and reconstructive surgeon, have collaborated to create an AI-based device (that accepts voice commands) to increase safety and efficiency during surgical reconstructions. SentiAr, a digital health company, has developed a holographic AR platform to provide surgeons with 3D images of a specific anatomy and data visualizations of the patient’s medical chart. The adoption of MR in the healthcare industry is still in its early stages. Technology can bring many more positive changes to improve healthcare facilities and surgical procedures. Use of Mixed Reality in the Healthcare Industry Mixed reality can help doctors, nurses, and healthcare professionals in several ways. From providing real-time information to developing realistic 3D images, here’s what MR can do. Resource Optimization Mixed reality reduces the dependence on nurses and medical assistants. Data is automatically collected, processed, and projected as images and charts whenever the doctor requires it. This saves resources and reduces expenditure without compromising the quality of healthcare services. Real-Time Data Access The mixed reality headsets can provide data in real-time to surgeons. For example, if a surgeon wants crucial data about the patient during an emergency, the MR device can immediately pull it up from the database it is connected to and display it for the surgeon. AI works in the backend to make this possible. Live Streaming Surgeries The MR devices can record and live stream surgeries without interrupting the process. These videos are used to train medical students to become proficient at surgeries. It also helps doctors rewind and replay complex surgical steps and explain the processes clearly to the students. Remote Patient Care Patients can continue to be under home care and still get excellent treatment from their doctors. The latest data is updated to the systems to help doctors understand the patients’ current condition and prescribe medication accordingly. AI has made it possible to deliver quality patient care remotely during the pandemic. Natural 3D Imaging Mixed reality is known for creating realistic 3D images with depth and layers of detail. This feature is very useful when surgeons want to know how deep the infection is or when to stop the incision. Natural 3D imaging increases accuracy and precision in surgeries. Virtual Surgery Intelligence This is used to educate patients about what happens during the surgery. Being aware of the procedure will give patients more confidence and prevent them from being stressed or worried about the results. Remote Assistance in Surgeries MR headsets like Hololens come with microphones and sensors. Surgeons can directly interact with other experts and take their advice during a complicated, high-risk surgery. Experts can provide remote assistance to surgeons in the operation theater. Simplified Data Sharing Critical medical information is stored in a unified format and can be shared with the physicians without any delay. Doctors

Yashica Chopra
January 17, 2022
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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

Yashica Chopra
January 12, 2022
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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

Arya Bharti
January 06, 2022
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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