Data Engineering Archives - Page 7 of 11

How Can Data Analytics Enhance Investment Decisions to Maximize Private Equity Returns?

As the data coming from company portfolios becomes vast and complex, a standard approach to private equity (PE) business analysis crumbles. It is no longer sufficient to get precise and data-rich insights to measure, track, and grow the performance of your company portfolios. As technology keeps evolving and businesses and their underlying operational models become more complex, making profitable and smart private equity investment decisions becomes even more daunting. Some key questions arise: The other key pain points that need a data-driven remedy include operational efficiency, increasing market valuation, performing due diligence, etc. Furthermore, the ongoing pandemic has also led to a paradigm shift in the PE investment sector and investors are more positive about potential portfolio companies that can adapt to the market changes as well as stay on the growth path. All this compiles into a highly relevant and massive use case umbrella for data analytics or private equity analytics. Combine that with automation and digital transformation and we have a very thought-provoking discussion topic: the rise, challenges, and applications of analytics and automation in the PE sector. Let’s explore the various ways analytics can help both companies as well as investors to make well-informed investment decisions. Data Analytics For Private Equity Investment Decisions: An Introduction A recent study published by Deloitte identified the huge potential and different applications data analytics offers in private equity investment decision-making. The study outlined seven key points that make data analytics extremely relevant and crucial for investment decision-making. Data and analytics are the key drivers of success when it comes to tech startups and organizations having their roots or branches in IT. These companies are spurring worldwide disruptions and giving birth to innovative and digital business models. Some common examples include Rocket Loans and Quicken Loans, where approvals for mortgages and loans are given based on simple and automated processes. These companies are more relevant for data analytics-based investment decisions, as data is readily available in the ready-to-process form. However, even brick-and-mortar establishments can respond to technology-enabled disruptors. Such is the power of data analytics and automation! Key Benefits Offered In Decision-Making For Private Equity Investments Automation and data analytics can offer three major benefits for the digital transformation of private equity investments (PEIs), which are discussed in detail in this section of the article. 1. Tracking Project Profitability Investors can establish a preferred vendor approach by adopting a hybrid model for investment decision-making. This hybrid model can help investors develop better governance policies and performance expectations and calculate better scale efficiencies. Hence, the investors can see the projects running behind schedule, or the companies running over budget, or identify the other key problems that are leading to unprofitable businesses. Thus, the investors can change, manage, or increase investments in their portfolio companies in a better and data-driven manner. 2. Ensuring Transparency Of Cash Flows Volatile cash flows can not only amount to outstanding loans or debts, but they can also drag massive investments down as they crash or become a steady drain of resources. Using predictive analytics in association with quantitative and qualitative studies done specifically on portfolio companies can lead to highly consequential results or data insights. Investors can see how well their investments are going to be used or are going to spur results in the long run. They can conduct these studies again after some time and compare the results with the previous analysis to identify any hidden patterns or discrepancies in the reports. Such comparative studies will also help them in understanding the various points of concern that arise over time and whether they can escalate into business risks or not. 3. Comparative Analysis Almost all PE investors are prone to the common occurrence, where the same brand is yielding different returns in different locations. While some branches tend to bring higher revenues, others fail to even stay afloat. In such cases, data analytics can be of immense importance as it can help in collecting, processing, and evaluating multiple types of data, such as: Thus, data analytics is of extreme importance and can have many amazing and crucial applications in PEI decision-making for better returns. Up next, we discuss the three major trends in PEIs that are all set to transform the sector and reinforce the hold and relevance of data analytics in the sector. Private Equity Investment Decision-Making: Why Data Analytics? Below, we discuss the various reasons PE stakeholders, right from PE firms to investors, are looking for data analytics-based solutions for key decision-making and strategizing. 1. The Role Of PE CFOs Is Evolving Given the massive change spurred by the COVID-induced meltdown, the roles and responsibilities of PE CFOs have seen a major revision and evolution. CFOs now need to be more tactical, technical, strategic, and empowered with insights stemming from data and analytics rather than hunches or business acumen. Recent studies done by E&Y suggest that 78% of CFOs are looking for raising larger investments that have led to a serious race for assets among investors. So, PE firms are looking at ways to make more strategic decisions, in terms of capturing investor allocations for alternative investments, such as: All this requires a heavily data-driven mindset and resourcefulness that screams for data analytics-based offerings such as software tools, analytics apps, or custom SaaS offerings. 2. The Talent Profile Of PE Firms Is Also Evolving Amid the increasing skill gap and the Great Resignation, and COVID-wave scares, companies are also focusing on increasing workplace engagement and employee motivation to retain and enhance their talent profiles. Studies reveal that 73% of PE managers focus on employee productivity and engagement for talent management. Talent management activities and attitude require an immersive understanding of the various factors that influence the entry and exit of an employee, such as: Such specific decision-making further creates the grounds for embracing data analytics to gain visibility into the granular level of organizational operations and processes. 3. Process Improvement And Innovation Within a short span of just four or five years, the digital transformation and innovation vision of

A Step-by-Step Roadmap to Big Data Implementation (Infographic)

Big data projects start by defining business needs. A step-by-step roadmap gives a clear picture of what results to expect from the project. The success of the establishment depends on how well the big data analytics model is integrated with the existing applications to provide seamless and real-time insights. Consolidate Data Sources Finalize and build a big data solution for the business. Choose between Data Warehouse or Data Lake to collect data from multiple sources and build a data flow within the enterprise. Data Storage The master data storage sends historical and real-time data for analytics. Choose technologies to build the data architecture and leverage big data solutions. Prepare and Train Data The quality of data determines the accuracy of the analytics. Clean, format, prepare, and train data to deliver actionable insights for better decisions. Data Governance Manage big data flow in the business and set up employee access to master data storage. Ensure consistency in data quality while optimizing cost and resources spent on the project. Data Visualization Establish a data-driven model and build self-service analytics at different verticals in the organization. Invest in data visualization tools to generate in-depth graphical reports at any time.

Self-Service Analytics Framework (Infographic)

More than 60% of the collected data is not used for analytics. This is due to the excessive load on the IT department to handle all data requests while troubleshooting and providing maintenance support. Self-serving analytics can solve the problem and help employees make the most of data by running analytics at each vertical and department in the enterprise. The self-serving framework is a part of the big data implementation project. Business Needs Convert the business needs into use cases to define the analytical framework in the enterprise. It helps create a proper data flow for uninterrupted data analytics and insights. Data Architecture The big data architecture should align with the business needs and long-term goals. It should be flexible, scalable, and secure. System Integration Which existing applications are important for the business? How do the applications use the insights derived from the big data model? Integrate the systems to streamline the workflow. Data Quality Get rid of poor quality and duplicate data by establishing data governance regulations. Derive better and more accurate insights. Coding It’s time to turn the design into code and build the big data pipeline in the enterprise (either on-premises or cloud servers). Training Employees Finally, train and empower employees to use data analytics and data visualization tools to derive insights without relying on the IT department.

Data Analytics in FMCG: Its Meaning, Importance & Use Cases

Data analytics help the FMCG industry to reduce costs, streamline production and supply chain, and take the products to the right target market at the right time. It provides companies with the necessary insights to adopt the data-driven model for decision-making. Let’s understand the effective role of data analytics in the FMCG sector. Data analytics is the practice of analyzing datasets to derive meaningful insights. It answers questions, gives patterns, and helps in making decisions based on proper evidence/ context. During the last few years, many establishments from various industries have adopted new technology to process data and derive insights. The FMCG (Fast Moving Consumer Goods) industry too followed suit to streamline its manufacturing, supplying, and customer care services. The FMCG industry is also known as Consumer Packed Goods (CPG) industry and happens to be the largest market internationally. The MENA (Middle East and North Africa) region saw high demand for consumer goods before the Covid-19 pandemic pushed things into turmoil. Statistics show that Saudi Arabia has a total retail consumer spending of $271919.41 million. Though there has been a 64% decrease in customers who went to supermarkets, the demand for goods hasn’t gone down. The market shifted from brick-and-mortar stores to online retailing. Moreover, the consumer trends in GCC (Gulf Corporation Council) have begun to resemble the trends in the US.There has been a polarization of consumers based on price, branding, quality, and convenience. In fact, the changes started after 2015, when major developments took place in the GCC, be it the introduction of VAT or an increase in working hours. A research report shows that 69% of UAE and 63% of Saudi Arabia consumers prefer to pay more if a brand guarantees quality products. Furthermore, modern trade is continuing to become famous in the region. In such instances, an FMCG manufacturer/seller needs to understand the market/ consumer requirements to become successful and gain a competitive edge over other players. This is possible when the establishment makes use of big data analytics to gather accurate and meaningful insights for decision-making. The Significance of Big Data Analytics in FMCG Data analytics in FMCG companies helps improve the scalability, flexibility, and value creation of the business. It is a dynamic industry that requires an equally dynamic approach to stay ahead of the trends and provide consumers with valuable services. For example, the omnichannel shopping experience can be best delivered when the enterprise leverages big data analytics. Running data analytics requires making changes to the existing business systems. Outdated technologies and legacy systems cannot work efficiently with advanced analytical tools. Businesses should first work on digital transformation and invest in data management solutions to build a streamlined data flow within the establishment. Knowing which tools and technology to use is vital to successfully implementing the data-driven model in the business. That’s where offshore solution providers step into the picture and offer end-to-end services to set up, integrate, implement, and manage big data analytical tools. The main purpose of using data analytics in the FMCG industry is to increase sales and profits by providing consumers with what they want. This requires flexibility, adaptability, and agility to align and realign the goals with the market demands. Understanding customers and working on real-time customer engagement will lead to an increase in customer loyalty. Why Data Analytics Is Important in FMCG Be it inventory, supply chain, or consumer experience, data analytics can help the FMCG industry use predictive intelligence in real-time to make the right decisions. Here, we’ll focus solely on how big data analytics companies assist businesses in dealing with consumer preferences and understanding the target market. Analyzing Target Customers There’s no denying the pressure of a competitive market. It’s hard to have loyal customers in the FMCG industry with new brands, products, and companies entering the market every day. Data analytics help you to understand your target audience. Who are your target customers? What do you know about them? What are the demographics? Such questions can be answered using data analytics. It helps identify potential customers for a long-term relationship with the business. Better Targeting Marketing and promotions play a significant role in today’s world. Businesses need to grab customers’ attention with marketing and wow them with their products. For that, the business should know which type of marketing and advertising strategies will deliver results. For example, having a business website and optimizing it for search engines has become compulsory to reach internet users. TV ads work for brands whose target customers spend more time watching the telly. Data analytics will give the necessary insights to plan marketing campaigns. Reduce the Risk of Fraud If attracting customers is one aspect, identifying fraudsters is another crucial issue. How does a business know that the person is a genuine customer and not a fraudster? The advent of online retailing and eCommerce has increased the rate of fraudulent transactions. Many businesses face losses due to a lack of awareness and control over such incidents. Data analytics can help define the weak spots and analyze a customer to determine the chances of a fraudulent transaction. Such transactions can be blocked, rejected, or prevented to avoid losses. Anticipate Customer Needs One way to inspire consumer loyalty is by anticipating their needs and delivering the products/ services. If a customer wants to go digital and make online purchases, an eCommerce website will help keep the customer instead of losing them to a competitor who offers this service. Data analytics in FMGC uses data from multiple sources and processes this data in real-time to anticipate what customer wants through predictive analytics. Plan Innovations What kind of products will the customers buy? What changes does a brand need to make to the existing products to drive sales? How can you redesign the store to make customers spend more time because of the attractive atmosphere and not because they can’t find what they want? Data analytics will help the business read the market trends accurately and be ready to grab opportunities before the competitors. Moreover,

How to Assure Data Security in Internal Business Communications

Digital security has become of utmost importance to most companies, especially those who operate online or have large transactions. Cyber attackers and hackers lurk behind every unsafe click and untrusted network or website. This is crucial for employee internal communication as they often send and receive confidential information. So, if the hackers are so powerful, how can you make sure your data is safe? Impact of Insecure Communications With each email or text message you receive from a risky sender, you’re putting your entire organization at risk. Some of the most common and prominent risks include identity and account theft, exploitability, fraud, or any similar means of data abuse. You can’t predict when the next cyberattack is going to happen. However, you can implement certain safety procedures that will keep your employees, data, and transactions safe. Since, communication is usually the least protected, unlike banks and codes, you need to strengthen security in that field. 1. Write Clear Policies Many may think that cyber security policies are unnecessary since most people know how to properly use a computer. However, we don’t live in the 2000s anymore, as cyber threats have advanced. So, if you run a business with employees, create a policy that will help your workers understand the importance of data and information preservation. So, what should one policy include? Firstly, you need to set password requirements and help employees create stronger passwords that are harder to breakthrough. If you have IT experts, you need to ensure each employee has a unique password for their email. Since email is the channel most employees use to communicate, outline email security protocols, and help them detect spam, unsafe, and junk mail. On top of that, don’t forget to include assessment in the policy. IT experts should evaluate security levels daily to keep the organization safe. 2. Train Your Employees The importance of safe digital interactions can’t be fitted into a single manual. To highlight the true dangers of cyber attacks, you need to organize training where employees will see the consequences in person. It will help them understand the severity of the issue and make them aware of their role in it. How can training help in communication? It will help employees utilize technology in communication safely. Teach them how to handle sensitive data and when it’s appropriate to share information with others. Also, show them the uses of machine learning in communication which will, with proper training, make things easier for them. Lastly, educate them on what to do during an incident. Even though you can do everything in your willpower to prevent it, you can’t predict it! 3. Implement Encryption In business communication with little to no security, information and sensitive data can leak quite easily. Therefore, encryption is necessary. Even though some may think that encrypting every single communication channel is excessive, it can save you a lot of struggles later on. So, how does encryption work in business communication? Encryption is locking data from anyone who doesn’t have the key to access it. It ensures that two entities can communicate freely, without having a third party intervening or listening in. This sounds great and safe for businesses, but it has its downsides. True and effective encryption requires a lot of resources many companies don’t have, which makes them sustainable to cyber attacks. 4. Multi-Factor Authentication In digital communication, you can’t be sure who’s sitting on the other end of the line, especially if you’ve never met this individual in person. This is especially common in remote teams. Therefore, employers usually limit who access sensitive data by using two or multi-factor authentication. What does that mean and how does that improve business communication? With these types of authentications users can’t access sensitive data and conversations without presenting two or more pieces of evidence it’s truly them. For instance, the password can be one, while fingerprint, face, and code sent directly to the phone can be other types of verification. It’s a complex method companies use in only extreme cases. 5. Use Safe Tools Instead of wasting resources on expensive and complicated tools and solutions, you can simply use safer tools for business communication. As you’ve probably noticed, not all apps and platforms offer the same quality of data security. That’s why you need to be careful when choosing technology for your internal communication strategy. Depending on the strategy you’ve tailored for your business, a wide variety of apps are suitable for your business. Check out internal communication examples that will help you create the best strategy and choose the best solution for your business and employees. Prioritize safety, usability, and effectiveness over price. 6. Create Backups In times when cyberattacks are becoming more frequent and you never know when they might happen, having clouds and backups are vital for business communication. It’s one of the last lines of defense and data preservation. How do they work? During conversations, data is automatically backed up on an online cloud. Only the chosen people have access to it. This prevents hackers and malware from stealing your data as it adds another layer of protection. Even though clouds aren’t unhackable, breaking through all the security systems is sometimes not worth it. Final Thoughts Cyber security has become one of the top priorities in every company. With skillful hackers, negligent employees, and unsafe networks and systems, you’re making it too easy for attackers to get what they want. So, implement these tips to ensure safety in your organization and communication.

What is Big Data Analytics in Finance?

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

Customer Analytics in Banking: Understand Your Customers

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

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

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

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,