What is Big Data Analytics in Finance?

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

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AI in Gaming: Smarter and Intelligent Gaming Experience

With an estimate to hit the CAGR of 12% by 2025, the gaming industry is growing significantly. According to the reports, Artificial Intelligence (AI) and Virtual Reality (VR) are an integral part of the global gaming industry. Moreover, it is assumed that almost $4.5 billion were spent by immersive gaming (a game with a real-like world) players in 2020 alone. Though gaming is one such industry where AI set its footsteps a long time ago, let’s understand what is the role of AI in gaming. What is AI in Gaming? Since AI inculcates human intelligence into machines, making them function like humans and carry out actions like humans have changed the gaming industry drastically. With the application of artificial intelligence in gaming, now players encounter a realistic gaming experience on a virtual platform and showcase their skills to their full potential against an AI-based worthy opponent. For example, Mortal Kombat is a duo fighting game where the AI-based opponent learns your fighting skills and defends itself by foreseeing them beforehand, giving you a much more challenging environment to play in, underlining why AI is used in games. However, AI in the gaming industry had existed even before the gaming industry gained its recognition. It was in the 1950s when the MiniMax algorithm was used to develop the chess game; it was the first example of how artificial intelligence is used in gaming. This algorithm worked on choosing the best possible move based on the contemporary board setting. ‘Donkey Kong’ was another example that used AI to enhance players’ difficulty levels. Nevertheless, the use of AI in the gaming industry was still underdeveloped back then since the opponents repeated the same mistakes and were incapable of self-learning the gamer’s tactics. But things changed when AI gaming examples like GTA, Need for speed, etc., gave a new dimension to the gaming world. Today, with more advanced features and attributes, artificial intelligence in gaming has completely revolutionized the gaming experience. Advantages of AI in Gaming Here are a few advantages of AI in gaming: Stimulates interest Gaming is a pervasive recreational activity and a getaway from stressful and hectic life. After all, playing games helps in creating a sense of satisfaction and achievement. However, now the AI-based opponents are trained with a variety of styles and techniques, giving you a tough fight.  As a result, you start getting involved in strategy building and brainstorming new tactics to defeat the opponent, who is way too powerful for predictive and straightforward gameplay styles. Moreover, it gives a realistic approach to the game. Hence, artificial intelligence in the gaming industry is giving an open and challenging platform for enthusiastic gamers to play at their full potential, therefore, stimulating emotions. Developing smarter games A new range of smarter games across a variety of genres is giving rise with the help of Artificial Intelligence. AI-based Voice Intelligence is one of the significant uses of artificial intelligence in game development. Also, AI can assist in planning and building a strong framework of gameplay.  An excellent example of this would be, using the reinforcement learning technique of AI to make the game characters self-learn and function as per the designed surroundings. For instance, in ‘Assassin’s Creed’, the enemies self-learn the gamers attacking and defending strategies and attack back strategically when you as a player would be vulnerable or prone to damage. More realistic games The advancement of artificial intelligence and gaming has led to gameplays having interactive 3D visualizations and merging games with Augmented and Virtual Realities.  It gives a realistic feeling, especially when games follow the laws of physics. Moreover, such gameplay is not only visually appealing, but they also give the gamers a feeling of living in a parallel world where they have the freedom to do what they want. For example, ‘Grand Theft Auto’ is a widely popular game where the user experiences living in a whole new virtual city where you can roam, drive luxurious cars, go shopping, etc. Ways to Implement AI in Games Decision Trees Decision Trees are the primary AI methods for designing games. These supervised learning models are used to predict and classify outcomes. They predict the outcome of the desired action done by the user by following simple decision-making rules based on the data that it gets, unlike the Narrative-based games that use DTs to proceed with one among their various alternative narratives depending on the actions of the gamer.  With AI, it is easy to interpret results from Decision Trees (DTs) and display them with interactive visualizations. In AI-based gaming, for example, the game ‘Star Wars Jedi: Fallen Order’ uses DTs to display hints of the past and future outcomes of the protagonist, given the specific circumstances you are in. So, you can now make a reasonable choice among the available options and direct the gameplay’s narrative in the way you want. Neural Networks Neural Networks can model a reality-based world and create complex gaming scenarios. Moreover, they cover up the limitations of basic AI techniques, self-learn, and adapt to the constantly changing gaming environments.  The Neural Network algorithm interprets data in two ways. They are either manually trained through vast data and then launched in the game or learned in real-time during the gameplay through the live generated data. The latter helps in improving the gaming agents even while the game is played. They also help in controlling Non-Playable Characters (NPCs) and gaming environments. Reinforcement Learning This Machine Learning method functions based on trial and error. The model is trained by letting itself act on certain scenarios and self-learning based on good or bad outcomes from those actions. Then, the algorithm remembers the bad results and finds a way to avoid them in further steps. This technique is quite beneficial in designing Non-Playable Characters (NPCs) and enhancing their decision-making skills when they are put in new environments. Games are the primary targets for testing reinforcement training because this technique has been in practice for a long time. Many computer

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Management Information System Vs. Business Intelligence: How is BI Better Than Traditional MIS

MIS is a traditional information system used to collect and process data. BI is an umbrella term covering various processes and technologies that empower an enterprise to use data effectively and make the right decisions. Before business intelligence took over, many enterprises had another information system called the MIS. Management Information Systems were the heart of every business and played a vital role in data collecting, storage, processing, and reporting. However, MIS had its disadvantages and became a burden for many businesses.  With business intelligence entering the market, enterprises adopted BI, not to replace MIS but to revamp their entire internal system. MIS is a small part of the business intelligence framework and is no longer enough in the competitive world to help establishments make the right decisions. Let’s read more about MIS and BI in detail and understand how BI is a better and more comprehensive alternative to traditional MIS.  What is Management Information System or MIS? MIS or Management Information System is based on an old concept where data is collected from various sources, compiled, and presented in a readable format. It is used by mid-level and top-level managers to make the necessary business decisions. MIS is a collection of systems and processes that facilitate data collection, compilation, and reporting within the enterprise.  The definition sounds similar to what a BI tool does. However, the difference lies in the technology used, the approaches to data collection, storage methods, and reporting formats. Before we read more about the differences, let’s take a brief look at the different functions of MIS.  Data Collection: Different types of data can be collected by MIS. Financial information, employee details, sales figures, etc., can be sent to MIS, which is integrated with other software used by employees.  Decision Making: MIS is used to help with decision-making. It presents aggregate data reports that help the top management make day-to-day decisions.  Collaboration: Employees can be provided access to edit and share the data stored in MIS. The MIS department handles the everyday issues related to the system.  Business Reporting: Managers make decisions based on the business reports generated by MIS. Even non-managers can get reports to compare data or compile a data file.  What is Business Intelligence or BI?  Business intelligence or BI is a wider concept that combines MIS, business analytics, data mining, data visualization, and much more. It is a modern framework that helps enterprises adopt the data-driven model to make better decisions based on historical and real-time data. BI gives businesses a comprehensive view of the enterprise data and makes use of this information to understand market trends, improve customer experience, evaluate existing policies, and make changes to build a better enterprise.  Today’s BI tools and solutions offer self-service analysis to employees from different levels in the organization. Business intelligence tools are flexible, scalable, and user-friendly. Here’s what the BI framework handles in an enterprise:  Data Mining: It is the process of analyzing large volumes of data to gather more information. Datasets, statistics, and machine learning are used for data mining.  Data Preparation: Data from internal and external sources is collected, cleaned, and formatted to keep it ready for data analytics.  Types of Analytics: Business intelligence is capable of providing different types of analytics based on the organization’s requirements. For example, preliminary data is used for descriptive analytics; descriptive analytics data is used for statistical analytics; predictive analytics is a combination of statistical analytics, data mining, and machine learning.   Querying: An employee asking the BI tool for specification information is called querying. The tool uses computing power to collect data from datasets and processes it to provide an answer to the query.  Data Visualization: Visualization is the technique of presenting data in a graphic form in real-time. The reports generated by the BI tool are presented in charts, graphs, holograms, heat maps, etc., for easy understanding. What is the Difference Between Management Information System and Business Intelligence?  MIS and BI are interrelated. They have common elements, though BI is an umbrella term that encompasses a range of processes and tools used to implement the data-driven model in an enterprise.  Scope and Focus  Management Information System or MIS is a part of the business intelligence framework. Traditionally, enterprises used MIS mainly for revenue and expense reporting. However, they had to face several issues with inaccuracies, inconsistent data, and other system complications. While business intelligence deals with the analysis of products and customers, MIS is used to analyze the business in terms of finances.  Difference in Reporting MIS delivers aggregate data reports which are not enough to get a complete picture of the business situation. It doesn’t offer in-depth insights that help the managers identify the problem. For example, the MIS report gives aggregate data about income and expenses. This doesn’t include transactional data that are key to identifying the root cause of the issue. The managers have to run multiple queries to gather the relevant information.  Business intelligence tools provide in-depth analytics for the given data. Moreover, the reports are generated in real-time. The dashboard of a BI tool allows employees to get a macro and micro view of the same report just with a few clicks. Business intelligence not only works faster but also requires lesser computing power as it has a higher processing capacity.  Self-Servicing  Business intelligence tools allow self-servicing within organizations by empowering employees to access data and run analytics as and when necessary. The dashboards can be accessed from different platforms such as web browsers and mobile apps. While MIS is complicated and confusing, BI is elaborate yet efficient and user-friendly. Automation is possible with business intelligence.  MIS and Business Intelligence: What’s the Connection? Management Information System has a narrow range of services, and business intelligence deals with a wider and broader scope where different aspects of the business are taken into consideration. Furthermore, BI is an advanced and modern framework that uses the latest technology to deliver accurate insights and reports. The disadvantages of MIS are solved using the BI tools.   Accurate Insights: Unlike MIS, the reports generated

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Power BI Supply Chain Dashboard: What Can It Do for Your Business?

Supply chain analytics helps businesses understand their datasets to make informed decisions about their logistics and transportation processes. Power BI is an incredibly useful business intelligence tool to create supply chain dashboards and use the insights in real-time. Let’s discuss the role of supply chain analytics and Power BI in various industries.  The supply chain is a network of people, businesses, and processes that ensure that a company continues to produce and sell products/ services in the market. From sourcing raw materials to moving finished goods to the warehouse and delivering them to the end customers, the supply chain network is intricate, complex, and systematic.  The implementation of the supply chain strategy has an impact on the success of a business. After all, how can a company deliver the products/services to the end-users if it cannot source the materials or transport the finished goods as per the schedule?  Covid-19 has disrupted the global supply chain, and the Middle Eastern markets, too, faced the same trouble. Challenges such as shortage of labor due to lockdowns, increase in demand, and reduction in supply and adhering to the safety protocol makes it difficult for businesses to manage their supply chain. However, the companies also know a solution to the problem.  Many Middle Eastern businesses are heavily investing in technology, especially artificial intelligence, to make their supply chains more resilient even in adverse conditions. For example, Hypermotion Dubai is an ME edition of the German-initiated convex that gives the transportation and logistics industries a fresh start. The aim is to be sure that the emirate continues to lead in supply chain management and speed up development.  The Dubai-based global logistics company, Aramex, has built a data lake and streamlined its supply chain process by relying on machine learning. This led to a whopping 74% increase in shipping accuracy. It also resulted in a 40% reduction of calls to customer support. Middle Eastern companies know that digitalization is the way to the future. Business intelligence for the supply chain is becoming an integral part of various companies.  But what is supply chain business intelligence? How does it help the companies, and which BI tools can be used to build a supply chain dashboard?  What is Supply Chain Analytics?  Supply chain analytics is the analysis of the individual elements of the supply chain that work together to help the business achieve its goals. Procurement, inventory, warehouse, order management, shipping, and deliveries are the elements of the supply chain.  Understanding how every element contributes to keeping the supply chain running will help businesses overcome roadblocks and prevent delays. While there are various software applications to manage individual elements, companies need to bring it all together on a single platform/dashboard. This makes it easier to identify the correlations and come up with the necessary solutions.  Types of Supply Chain Analytics The following are the four major types of supply chain analytics that help enterprises improve the efficiency of their business operations, streamline logistics, and save time and money without compromising quality.  Key challenges that Supply chain analytics solution solves: 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. How Do Companies Use Supply Chain Analytics?  Companies use analytics to process and analyze the data generated at different points in the supply chain. The logistics dashboard empowers companies to stay up to date and get real-time information about their supply chain processes. It gives them the information to make last-minute changes and make decisions based on historical and real-time information. Managing local, national, and global supply chains is simpler when using supply chain analytics.  How Can Supply Chain Analytics Transform a Business?  Businesses need to invest in building supply chain dashboards to make the most of data analytics. Since companies don’t often have in-house teams to manage business intelligence, they hire offshore solution providers to create the BI dashboards. Many AI service providers recommend Power BI for supply chain analytics. Power BI is a Microsoft product that helps build business intelligence dashboards for various business requirements.  Power BI experts help business enterprises use supply chain analytics to transform their business in the following ways:  Streamline Inventory and Warehouse Management  Accurate forecasting can reduce the uncertainties in inventory and warehouse management. Analyzing customer preferences, purchase history, market trends, and the inventory at different distribution centers will help businesses plan the stock in their inventory to keep up with the customers’ requirements. It is one of the most vital supply chain management techniques.  Increase Sales Knowing which products have a higher demand and anticipating the changes in the market conditions will increase sales for the business. Supply chain analytics gives the management the necessary insights about the demand and supply ratio for their products. Releasing products into the market at the right time will give the business a better chance to capture the customer base. Improve Quality Standards  The quality standards here pertain to the logistics and supply chain processes. For example, the Power BI logistics dashboard will make it easy to identify the gaps in the supply chain that are causing disruptions in procurement or deliveries. By rectifying the gaps, the business increases the quality standards of the process and gets higher returns.  Data Utilization  How a business uses the supply chain dataset determines whether it can achieve its goals or not. Big data solutions facilitate businesses to better understand the changing demands in the market. Not only does supply chain analytics use historical data, but it also processes real-time information from multiple sources to increase the accuracy of the forecasts/predictive insights. Supply chain analytics makes businesses proactive instead of reactive.  How is Power BI used in Supply Chain Management? Power BI is a popular business intelligence tool used by enterprises of all sizes and volumes. Multiple dashboards can be created for each department to provide employees access to the centralized data sets and the derived insights. Supply chain analytics using Power BI improves functional efficiency and aligns the processes

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The Ultimate Guide to AIOps: The Future of IT Operations

The modern world is gearing up for the Fourth Industrial Revolution, with smart devices in its hands and rocket-fast data processing capabilities empowering business operations. Amidst the trends, automation and digitization emerge as the two major agendas that exist on the majority of business portfolios.  More than 80% of business organizations are going to increase their spending on automation. And recent market studies predict that the digital process automation market will hit the 16.12 bn USD mark by 2026, demonstrating a CAGR of 12.98%.  As the automation needs in modern workplaces are reaching new heights and IT operations are looking for smarter, more intuitive, and reliable ways to automate and digitize routine tasks, people are turning to AI. And promising to fulfill such a diverse set of process automation requirements in IT is AIOps.  Coined by Gartner in 2016, AIOps stands for Artificial Intelligence for IT Operations and combines ML and big data for process automation in IT operations, such as causality determination, anomaly detection, event correlation, etc. In upcoming sections, you can learn about AIOps in detail, why and how you should consider them for your business, and major ways AIOps will change the face of IT operations in 2022.  What is AIOps? AIOps is an industry category that uses AI and ML analytics for automating, streamlining, and enhancing IT operations analytics. AIOps provides a real-time understanding of any type of underlying issues in the IT organizations and real-time insights into various processes. It also offers predictive business intelligence in various forms and across a wide range of business processes because of its smart analytics capabilities.  Increasing complexities of IT systems, organizational data growing at an extreme pace, and frequent industry disruptions, such as remote work, new trends in technology, rapidly changing markets, etc., have fuelled the demand for AIOps.  Why AIOps: Exploring the Need for Futuristic IT Ops Tech AIOps software not only empowers the organizations to manage the heaps of data they generate, but it will also help in harnessing the true power of this data via analytics. You can identify any hidden patterns for growth, cost efficiency, redundancy, productivity, etc., and gather actionable insights from them. Apart from the data insights derived from historical organizational data, you can also use predictive analytics to have futuristic insights into various segments that are reliable and data-driven. But then again, mere insights cannot solve the massive automation and digital transformation needs; right? Hence, AIOps also caters to the team collaboration needs and provides inferences drawn from the data insights, such as: Finally, AIOps aids in automating the response and remediation in various instances, to facilitate precise and quick process automation across your business model.  The following image shows some of the core functionalities of AIOps, as discussed above: Now that we have had an overview, let us move on to discuss some key use cases of AIOps. AIOps: Key Use Cases While there can be n number of business use cases for AIOps, we are discussing the top 5 core use cases that target the key problem areas when it comes to operations, processes and automation.  1.  Big Data Management While big data is a powerful technology to manage, process and make sense of the tons of organizational data that is generated, managing the volume, variety, velocity and efficiency of big data usage is also a challenge. As more and more firms are using big data analytics, getting the best value out of these efforts is also a must.  Hence, proper management of big data is one of the most crucial use cases of AIOps.  2.  Performance Analysis Organizational performance analysis is an extremely daunting task as it requires micro-monitoring apart from micro-management. Now, you might think that using time tracking and employee tracking software can do the job to a greater extent. However, when it comes to getting granular insights about the results, driven by individual employee performance, this software crumbles down.  Having impeccably smart capabilities stemming from AI, ML, NLP, etc., AIOps is your eye to the nitty-gritty details related to performance analysis in any department and for any number of employees.  It can rapidly gather and process the vast amount of event logs and data to drill down to the root cause of factors that are obstructing the overall productivity of your organization. 3.  IT Service Management IT Service Management or ITSM refers to designing, building, supporting, delivering, and managing IT services in an organization. All the processes, policies, and procedures that facilitate the delivery of IT services to the end-users in an organization also come under ITSM.  AIOps uses artificial intelligence to discover any underlying issues in ITSM and helps fix them quickly, thereby improving the efficiency across all the departments. AIOPs also find usage in data monitoring and infrastructure process management. It can make more accurate predictions for planning capacity, allowing you to maximize your resource and inventory space utilization. You can also identify, predict, analyze and prevent any underlying IT service issues. 4.  Event Correlation and Analysis When it comes to event storms, most of the traditional analytics software fails because they lack the ability to provide insights into these storms.  For example, suppose your event correlation report suggests a delay in project delivery for 2 of your crucial clients, as a designer has given a notice period and you still have not hired a new designer. Now, even if you “know” what is going to happen, there is no suggestion or insight to rely upon.  AIOps, on the other hand, can help you make the best decision for remedying the situation by grouping similar events. So, you can either get an insight about a solution that came to your rescue in such a situation sometime earlier, say a few months, or you can get to know which one is the best option – outsourcing, a quick hire, or assigning the work to other designers in the company.  So, AIOps reduces the burden of the IT teams when it comes to managing organizational events and offers

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Leveraging Business Intelligence in Retail Industry: Benefits and Getting Started

The business intelligence market is expected to grow with a CAGR of 8.7% and hit the 43.03 bn USD mark by 2028. Further, a recent survey suggests that 58% of companies consider business intelligence crucial for retail and wholesale sectors.  Technological innovation is happening faster than ever and digital transformation is sending ripples across all the industries. As data analytics and business intelligence overtake the hunches and guesswork, the disparate and fragmented industries, such as retail, are looking at a major makeover. A recent feature in Forbes talks about customers becoming increasingly conscious in terms of making purchases and how important it has become to deliver exceptional services alongside great products.  This has led to the widespread adoption of smart analytics platforms that offer business value across the most crucial 4 verticals, as shown below: The smart analytics tools allow retail business owners to dig deep into their customer data and uncover hidden insights about service, support, customer journeys, and brand expectations. The software solutions allow them to streamline their business offerings in a customer-centric manner and monitor the results of all such efforts.  We discuss how business intelligence, such as interactive data visualization software, can offer retailing insights to steer your business towards growth. How would the retail industry use business intelligence and what is the best way to leverage it for boosting your business? Below, we explore these problem statements at length. Why Business Intelligence for Retail and eCommerce? Business intelligence allows you to make the most critical business decisions in a data-driven manner. You can dig deep into the customer data and find out what issues or concerns they have regarding your products.  This way, you can improve your products and services to deliver delightful customer experiences. The best way to leverage business intelligence in retail is via software tools. Power BI is an excellent data visualization software that can dig deep into the customer, market, organizational, and business data to discover the most compelling retail visuals.  The data generated from them are interactive in nature and highly secure, so you don’t have to worry about the safety and security of your data resources. You can connect these software tools to multiple data sources and tap into all your customer segments with its interactive and intuitive retail dashboards.  What makes the entire endeavor so amazing is the fact that you can generate data-intensive reports from a combination of dashboards with different kinds of visualizations that are relevant to particular business areas.  So, business intelligence has all the arms in its arsenal to drive a critical transformation in a retail business scenario via data visualization and business intelligence.  Shown below, is how the entire process comes out to be: While the infographic is highly engaging and granular, it can be a bit overwhelming, especially for beginners who are just taking the first steps towards digital transformation and business intelligence. Hence, we explore the various benefits of using business intelligence for retail in the next section.  Business Intelligence in Retail Industry: Exploring Benefits and Functionalities  Business intelligence finds many applications in retail, ranging from learning about your customers to employees. Using BI tools, you can identify the major issues that are ailing your supply chain efficiency and resolve them. Further, BI tools offer smart data visualization to help you make the most of your organizational data. 1. Knowing Your Customers: Inside Out Digital shoppers are smart, more perceptive, and look beyond the reward points and discounts when it comes to customer service and experiences. Retail analytics dashboards help you gain visibility into customer insights and purchase patterns.  You can leverage the predictive analytics and identify their shopping predictions to scale effectively alongside your customer demands. You can identify, adapt, and deliver across the ever-evolving needs of your customers, and that too across all channels. As you have a 360-degree view of your customers’ expectations and requirements, you can deliver across all of them, thereby ensuring that you streamline the entire customer journey instead of standalone touchpoints.  You can personalize your customer interactions on every channel and deliver proactive customer service and support in an omnichannel manner.  Business intelligence tools allow you to use hundreds of data connectors for combining Petabytes of data from external and internal data sources. Hence, you can have data-rich and cohesive customer insights that are reliable as well as intuitive. 2. Empowering Your Employees: The Right Mix of Human and Automation Your employees are your core strength no matter what level of automation you have in your organization, or what size your retail setup is. Hence, it is important to empower your employees with the right tools for collaboration, data analytics, customer intelligence, and business operations.  All these retail insights are data-rich and actionable. So, your management or analysts don’t have to spend hours making sense of the huge blocks of data they have. They can easily figure out the next course of action with highly intuitive retail KPIs dashboards.  They can easily export and share this data in various formats for real-time collaboration and presentation to take business decisions in an agile manner. What makes the entire process so awesome is the fact that the insights can be implemented without any doubt, as they are stemming from exhaustive data processing and not from any third-party market research data.  Further, your employees have 360-degree customer profiles that have all the contextual, behavioral, and interaction information they might need to form a wholesome opinion about a new strategy or marketing decision.  3. Smart Supply Chains: Resilient and Efficient The retail industry is inseparable from supply chains that are inherently inefficient with data silos and fragmented components. Using business intelligence in retail, you can also steer your supply chains towards transformation.  You can get the right product at the right price and deliver the same to the right customer at the right time without succumbing to the inefficiencies of the supply chains. Data analytics empowers the supply chains by incorporating different elements, such as IoT, automation,

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Quantum Computing Concepts and Implementation in Python

Quantum computing is a fascinating concept in the science and technology industry. There’s a huge scope to use quantum computing in daily business processes in the future. We’ll discuss quantum computing concepts and see how it’s implemented using Python.  Quantum physics, as such, is a highly complex and extensive subject. The theories and concepts of quantum physics can confuse most of us, including the experts. However, researchers are making progress in utilizing the concepts of quantum physics in computing and building systems. Quantum computation might sound like something from the future, but we are very much proceeding in that direction, albeit with tiny steps. IBM, Microsoft, and D-Wave Systems (partnering with NASA) have developed quantum computers in the cloud for public use. Yes, you can actually use a quantum computer from the cloud for free.  Of course, it’s easier said than done. Quantum computing technology is not a substitute for classic computing. It’s an extension or a diversification, where classic computing and quantum computing go hand in hand. Given how building a single quantum computer can cost thousands of dollars, using the cloud version is the best choice for us. But where does Python come into the picture? And what exactly is quantum computing?  Let’s explore this topic further and understand how to implement quantum computing concepts in Python.  An Introduction to Quantum Computing  The term ‘Quantum’ comes from Quantum Mechanics, which is the study of the physical properties of the nature of electrons and photons in physics. It is a framework to describe and understand the complexities of nature. Quantum computing is the process of using quantum mechanics to solve highly complicated problems.  We use classic computing to solve problems that are difficult for humans to solve. Now, we use quantum computing to solve problems that classic computing cannot solve. Quantum computing works on a huge volume of complex data in quick time.  The easiest way to describe quantum computing would be by calling it complicated computation. It is a branch of Quantum Information Science and works on the phenomena of superposition and entanglement.  Superposition and Entanglement The smallest particles in nature are considered quantum. Electrons, photons, and neutrons are quantum particles.  Superposition is when the quantum system is present in more than one state at the same time. It’s an inherent ability of the quantum system. We can consider the time machine as an example to explain superposition. The person in the time machine is present in more than one place at the same time. Similarly, when a particle is present in multiple states at once, it is called superposition.  Entanglement is the correlation between the quantum particles. The particles are connected in a way that even if they were present at the opposite ends of the world, they’ll still be in sync and ‘dance’ simultaneously. The distance between the particles doesn’t matter as the entanglement between them is very strong. Einstein had described this phenomenon as ‘spooky action at a distance’. Quantum Computer  A quantum computer is a device/ system that performs quantum calculations. It stores and processes data in the form of Qubits (Quantum Bits). A quantum computer can speed up the processes of classic computing and solve problems that are beyond the scope of a classical computer.  If the classical computer takes five seconds to solve a complex mathematical problem like (689*12547836)/4587, the quantum computer will take only 0.005 seconds to give you the answer.  Quantum Bits (Qbits Concept) A quantum bit is a measure of data storage unit in quantum computers. The quantum bit is a subatomic particle that can be made of electrons or photons. Every quantum bit or Qbit adheres to the principles of superposition and entanglement. This makes things hard for scientists to generate Qbits and manage them. That’s because Qbits can show multiple combinations of zeros and ones (0 & 1) at the same time (superposition). Scientists use laser beams or microwaves to manipulate Qbits. Though the final result collapses to the quantum state of 0 or 1, the concept of entanglement is in force. When the two bits of the pair are placed at a distance, they are still connected to each other. A change in the state of one Qbit will automatically result in the change of state for the related Qbit.  Such connected groups of Qbits are powerful compared to single binary digits used in classical computing.  Classical Computing vs. Quantum Computing Since you have a basic idea of quantum computing, it’s time to delve into the differences between classical computing and quantum computing. These differences can be categorized based on the physical structure and working processes.  Architecture Level (Physical Structure) Differences  Phenomenon and Behavior  In classical/ conventional computing, the electric circuits can be only in a single state at any given point in time. The circuits follow the laws of classical physics. In quantum computing, the particles follow the rules of superposition and entanglement and adhere to the laws of quantum mechanics.  Information Storage  The information in classical computing is stored as bits (0 and 1), based on voltage/ charge. The binary codes represent information in conventional computing. The same is stored as Qubits or Qbit in quantum computing polarization of a photon or the spin of an electron. The Qbits include the binary code (0 & 1) and their superposition states to represent information.   Building Blocks  Conventional computers use CMOS transistors as basic building blocks. Data is processed in the CPU (Central Processing Unit), which contains an ALU (Arithmetic and Logic Unit), Control Unit, and Processor Registers.  Quantum computers use SQUID (Superconducting Quantum Interference Device) or quantum transistors as basic building blocks. Data is processed in QPU (Quantum Processing Unit) with interconnected Qbits.  Working Process Differences  The way data is represented is the major difference between a classical computer and a quantum computer.  The bits in classical computing can take the value of either 0 or 1. The Qbits in quantum computing can take the value of 0 or 1 or both simultaneously in a superposition

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Artificial Intelligence in Oil and Gas Industry: Remarkable Transformation

Using AI or artificial intelligence in oil and gas industry helps analyze historical and real-time data, run analytics, plan and schedule production, optimize the supply chain, and use resources to increase returns. Let’s learn more about how artificial intelligence impacts the oil and gas industry operations.  The oil and gas industry falls among the prominent industries in the global market. As enterprises and companies from other sectors adopt new technology, it’s noteworthy to mention that the oil and gas industry is not lagging behind. Artificial intelligence is playing an important role in streamlining the on-field and off-field activities in the oil and gas industry.  Whether it is using AI tools for oil production, transportation, or improving the safety standards at work, artificial intelligence and machine learning are helping companies solve challenges in various areas.  The Middle East produces one-third of the world’s oil and is among the biggest suppliers of fossil fuels. This is because the largest oil reserves are found in the Middle East, thanks to the geographic changes that occurred over the centuries. Most of the oil-producing states in the Middle East come under the Organization of the Petroleum Exporting Countries (OPEC).  Statistics show that around 39% of the crude oil produced by OPEC countries comes only from the Middle East region. In fact, Saudi Arabia is the largest producer, with more than 12.2 barrels of production per day. Moreover, the Middle East region accounts for 48% of proved oil reserves, 40% of proved gas reserves, and 18% of the global gas production.  What role do AI and ML have in the oil and gas industry, especially in the Middle East? How can the global oil and gas companies use artificial intelligence applications to revamp their systems and increase returns? Let’s read to know the answer.  How is Artificial Intelligence Used in the Oil And Gas Industry? Managing capital and investments, attracting shareholders, and increasing operational efficiency are some crucial aspects to consider. The role of artificial intelligence is to help oil and gas companies overcome their problems with exploration, production, processing, logistics, and to streamline backend (or office end) services. In short, AI is used in just about every stage in the energy and utilities industry.  Exploring and Producing  AI is used to forecast the reserve volumes that can be recovered, analyze the reservoir data, help create well-developed plans on the field, choose production models based on advanced analytics, and make decisions based on market trends and changes.  Drilling and Refining  From designing to execution of drilling, artificial intelligence helps oil and gas companies to find ways that optimize the returns. Risk prediction, daily, monthly, and lifetime production forecasting, detecting defects and anomalies, etc., are done using AI and ML systems.  On-Field Equipment & Services  Artificial intelligence assists the on-field managers and workers in scheduling drilling activities and optimizing fleet management and supply chain. Equipment maintenance, improving workplace safety, and forecasting consumer demands are done through the latest AI systems and tools.  Office Software  AI plays a role in many areas beyond the ones where major action occurs. The backend and corporate offices also use AI and ML to empower employees with automated software to increase productivity. The top management makes decisions based on the reports generated through AI applications.  How Will Artificial Intelligence Impact Upstream Oil and Gas? Globally, the oil and gas AI market is valued at $2 billion in 2019. Statistics show that it is estimated to touch $3.81 by 2025 at a CAGR (Compound Annual Growth Rate) of 10.96%. Machine Learning and artificial intelligence in the oil and gas industry are being used for data interpretation and analytics to help forecast future trends, discover new reserves, and increase production in existing reserves.  While AI, IoT (Internet of Things), and cloud-based technology are gaining acceptance in the oil and gas sector, the lack of professional expertise is slowing down the adoption rate. The oil and gas companies have to hire AI professionals or train their senior employees to use AI and ML tools as a part of their day-to-day work.  Generally speaking, the focus of global oil and gas companies is on simplifying and streamlining the exploration and production processes using AI. Digitalization, automation, and data analytics can help companies improve their operations while aligning their goals to the market trends.  The Growing Role of Artificial Intelligence in Oil and Gas Industry The Oil and Gas Authority (OGA) has begun using artificial intelligence in 2019, thanks to the National Data Repository (NDR) in the UK. The pandemic has caused a slump in the industry in 2020 (oil prices decreased by about 50% during the lockdown). However, many companies have been actively investing in artificial intelligence to:  It’s no surprise that early adopters have the advantage of protecting their assets and attracting more investors. The early adoption of artificial intelligence in the oil and gas industry is delivering similar results as with other industries, i.e., empowering the companies to retain their market positions and expand their share by gaining a competitive edge over others.  Applications of Artificial Intelligence in Oil and Gas Development Geological Assessment  Geological assessment is also known as surface analysis, where AI robots can detect oil seeps and reserves deep inside the earth or the ocean. The Wadia Institute of Himalayan Geology (WIHG) came out with a new AI technique in 2020. This technique helps analyze data from semantic waves and helps discover the presence of hydrocarbons (like oil and gas) with minimum effort and in quick time. AI tools analyze and assess geophysical data to increase accuracy in mapping the natural oil deposits.  Tracking Assets  Asset tracking and management include project planning, managing the product lifecycle, and aligning the process to match the market demands. Digital twin technology is being used by the oil and gas companies to deal with the uncertainties in the market (especially due to the pandemic) and to add more flexibility to their system and approach to work.  Automating Equipment Maintenance A vital role of artificial intelligence applications in the oil and gas industry is to reduce

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Customer Analytics in Banking: Understand Your Customers

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

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Impact of Big Data Analytics in Retail Industry (Simplified)

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

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