Page 32 – DataToBiz

Deeksha Anand
February 25, 2022
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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

Yashica Chopra
February 22, 2022
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Power BI HR Analytics Dashboard: Simplify HR Processes

Data analytics for HR helps streamline the human resource processes within the organization. The Power BI dashboard is commonly used to derive actionable insights to improve employee performance and retention rates. Let’s read more about the importance of HR analytics in an enterprise. The HR (Human Resource) department is the heart of every organization. From recruitment to compensation to performance appraisal and employee wellbeing, the HR department plays several roles in the enterprise. The HR acts as a mediator or a bridge between the employees and the management or enterprise. It’s no surprise that the HR department is already burdened with work. Providing them access to the latest technology and means to derive insights in real-time will help reduce the workload and create a healthy environment throughout the organization. HR analytics or people analytics is the key to helping the HR department provide a better working experience for the employees. Unlike other analytics, HR analytics doesn’t collect data about employee performance. It gathers data about the HR processes and the work environment so that the processes can be made more efficient to keep employees happy and productive. Issues such as high employee turnover rate, decreased productivity, employee grievances, etc., are the focus of HR analytics. What is HR Analytics? Human Resource or HR analytics is where analytical processes are applied to human capital in the business to improve outcomes and enhance critical talent. The analytics aims to provide in-depth insights into the HR processes to increase employee retention, performance, and productivity. The Covid-19 pandemic has affected the Middle East market along with the rest of the world. A report published in Jan 2021 showed that around half of the employees wanted to change jobs. While some responders said that flexible working and job security were their primary factors, 45% admitted that a higher salary was their aim. The employees in Gulf and UAE regions saw an increase and decrease in salary. While the increase was to retain top-level professional employees, the pay cuts were due to the pandemic and resorted after the market conditions improved. Around 64% of the employers in the Gulf said they would be hiring additional staff in 2021 and 2022. So how does the HR department make these decisions in volatile market conditions? They rely on HR analytics to understand the existing situation and come up with a new one. A survey report by CIPD showed that 82% of the respondents were already using HR analytics in the Middle East. Almost 72% of them responded that they will continue to invest the same amount or increase the investment in HR analytics. Middle Eastern organizations are actively upgrading the legacy systems and adopting the latest technology to provide employees with more growth opportunities. HR analytics will continue to play a vital role in helping organizations design robust and agile end-to-end HR processes. How to Get Started with HR Analytics? Data analytics in HR helps build the data-driven model to make decisions based on actionable insights in real-time. Implementing HR analytics requires proper planning and continuous monitoring to ensure that the analysis reports are accurate. Have a Data Management Plan How do you plan to manage the data within the organization? Start by creating a data governance program and define policies to collect, clean, process, and store data. Then you need to align the data definitions with analytical processes and focus on the four main key areas of human resources- recruitment, absenteeism, turnover, and retention. Choose the Reporting Software Which reporting and visualization software do you want to use to derive analytics? For example, Power BI is popular software for HR analytics and is used by many businesses from around the world. Build the Dashboard You also need to determine whether you have the necessary talent to run analytics or if you need the assistance of a Power BI consulting company. Many organizations in the UAE hire offshore solution providers to set up the HR analytical dashboard. The metrics you choose for the dashboard should align with the KPIs of your business and achieve short-term, midterm, and long-term goals. Run a Pilot Test You don’t need to invest heavily in HR analysis from the very first day. Start small by focusing on minimum metrics and run a pilot test to see how the insights are helping in making faster and better decisions. Gradually, you can expand the dashboard and hire HR analysts to manage the processes. 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. Work on Capacity Building HR analysis reports are used to improve the processes and capacity of your business. Focus on one target group first before implementing the changes throughout the enterprise. Starting with the capacity building of HR Business Partners is recommended as they are the ones who are in constant contact with your customers. Consider Data Privacy and Security Dealing with employee data means you have to be extra careful about data privacy. Your employees need to know that their data privacy rights won’t be compromised to run HR analytics. Set up policies to find a balance between both. What are the Benefits of HR Analytics? The HR analytics dashboards help the business adopt a strategic approach to growth and development. How Power BI Consultants Can Help You Overcome the Biggest HR Analytics Challenges? Though the HR analytics dashboard offers numerous benefits to the organization, the process is not without challenges and complications. How you overcome the roadblocks determines the success of your enterprise. Power BI consultants can help the HR team find a comprehensive solution for the challenges and create HR analysis dashboards to derive real-time insights. A Gap in Talent Pool As we previously discussed, the lack of data analysts within the enterprise can make it hard to use technology and derive insights for the HR team. This problem can be solved by hiring offshore consultants. Bad Data The accuracy of the reports depends

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