Introduction to Business Analytics

Introduction to Business Analytics F U N D A M E N TA L S O F B U S I N E S S A N A LY T I C S A U T H O R: D R. B O N I FA C I O P. B A I RA N P R E PA R E D BY: P R O F. J O A H N N A M A R Q U E Z WEEK 1...

Introduction to Business Analytics F U N D A M E N TA L S O F B U S I N E S S A N A LY T I C S A U T H O R: D R. B O N I FA C I O P. B A I RA N P R E PA R E D BY: P R O F. J O A H N N A M A R Q U E Z WEEK 1  Chapter 1 Learning Objectives as Microsoft Excel, statistical software, and data visualization tools to perform basic data analysis 1. Understand the Core Concepts. After and generate meaningful insights from data sets. completing this course, students should be able to grasp the fundamental concepts of 4. Statistical Analysis: Students develop the ability to perform basic statistical analysis, including business analytics, including data types, descriptive statistics, hypothesis testing, and data sources, and the role of analytics in correlation analysis, to draw meaningful decision-making. conclusions from data. 2. Data Collection and Preparation: Students 5. Applications: This course should enable students to identify and solve real-worlds should be proficient in collecting and business problems using analytics. They should preparing data for analysis, which includes be able to apply their analytical skills to make data cleaning, data transformation, and data data-driven decisions and recommendations and integration techniques. They should understand the practical applications of analytics in various industries understand the importance of data quality and how to address common data issues. 3. Basic Analytical Tools: By the end of this course, students should be able to use basic Preliminary Questions 1. How can an understanding of business analytics contribute to making more informed and data-driven decisions in various industries? 2. What are some real-world examples where businesses have successfully used analytics to identify new opportunities for growth and innovation? 3. Can you discuss the role of predictive and prescriptive analytics in anticipating future market rends and optimizing business strategies? 4. How might an understanding of business analytics help in addressing challenges related to customer satisfaction, retention, and overall brand loyalty? 5. What ethical considerations should business keep in mind when collecting, analyzing, and interpreting data for decision-making purposes, and how can they ensure responsible and transparent practices in this process?  Introduction to Business Analytics Introduction to business analytics refers to the process of collecting, analyzing, and interpreting data to make informed business decisions. In today’s data-driven world, companies are increasingly turning to analytics to gain insights and create a competitive advantage. Business analytics involves various techniques, tools, and methodologies to analyze both historical and real-time data. By examining trends, patterns and relationships in the data, organizations can identify opportunities, mitigate risks, and optimize their operations. One key aspect of business analytics is data collection. This involves gathering relevant data from various sources, such as transaction records, customer feedback, social media, and sensors. Data can be structured (e.g stored in databases) or unstructured (e.g text documents, audio files). The ability to collect and store vast amounts of data has been facilitated by advancements in technology, such as cloud computing and big data infrastructure.  Analytical Techniques Descriptive analytics- aims to summarize and understand past data, such as sales trends or customer demographics. Predictive analytics- on the other hand, uses statistical models to forecast future events or behaviors. For example, a retail company can use predictive analytics to predict customer churn or demand for certain products. Prescriptive analytics- takes it a step further by suggesting actions or decision based on analysis. Also, Business analytics relies heavily on statistical analysis, mathematical modeling, and machine learning techniques. Statistical analysis- helps identify patterns and correlations in the data. Mathematical modeling enables businesses to simulate different scenarios and optimize their decision-making Machine learning algorithms- can automatically extract insights and patterns from data, improving over time as more data becomes available. By utilizing business analytics, companies can make: 1.Data-driven decisions that lead to increased efficiency 2.Profitability 3.Competitiveness. It can also help identify: 1.cost-saving opportunities 2.Target the right customers with personalized marketing campaigns 3.Optimize supply chain management 4. Improve customer service  History of Business Analytics BACK TO THE EARLY 20TH CENTURY WHEN COMPANIES STARTED COLLECTING DATA TO GAIN INSIGHTS INTO THEIR OPERATIONS AND CUSTOMER BEHAVIOR. HOWEVER, THE FIELD HAS UNDERGONE SIGNIFICANT TRANSFORMATIONS OVER THE YEARS, INFLUENCED BY TECHNOLOGICAL ADVANCEMENTS, CHANGING BUSINESS LANDSCAPE AND EVOLVING METHODOLOGIES. 1. Emergence of Statistical Analysis (1920- 1950s) During this period, statistical methods were applied to collect and analyze data in various industries. Techniques like trend analysis, regression analysis, and hypothesis testing were used to understand business factors affecting performance. These early statistical methods laid the groundwork for the development of more sophisticated analytical techniques and the integration of advanced REGRESSION ANALYSIS TESTING HYPOTHESIS TESTING 2. Management Information Systems (1960-1980s) With the advent of computers, companies began to store and process large volumes of data. This era saw the development of Management Information Systems (MIS) that focused on generating reports and dashboards for executives, providing them with key performance indicators (KPIs) and financial metrics. 3.Decision Support Systems (1980-1990s) Decisions Support System (DSS) emerged during this period , providing tools to assist managers in decision- making. These systems incorporated analytics techniques like optimization, simulation, and forecasting to help solve complex problems and support strategic planning. 4.Data Warehousing (1990- 2000s) As data volumes continued to grow exponentially, data warehousing became crucial to handle and store vast amounts of information. Organizations started consolidating data from various sources into a central repository, enabling more comprehensive analysis. 5.Business Intelligence (2000s) The rise of the internet and digitalization led to concept to the concept of Business Intelligence (BI). BI platforms allowed companies to access and analyze data in real-time, making it easier to monitor performance, detect trends, and identify areas for improvement. 6.Big Data and Advanced Analytics (2010s-present) With the proliferation of internet-connected devices and social media, the volume, variety and velocity of data exploded, giving birth to the era of big data analytics. Advanced analytics techniques, such as machine learning, natural language processing and predictive modeling, gained prominence for gaining insights from large and 7.Predictive and Prescriptive Analytics (2010s-present) The focus shifted from understanding historical data to predicting future outcomes. Predictive analytics leveraged statistical models and algorithms to forecast trends, customer behavior, and market shifts. Prescriptive analytics took it a step further by recommending actions to optimize outcomes based on predictions. 8.Cloud Computing and Sel- service Analytics (2010s-present) The cloud revolutionized data storage, processing and analytics. Cloud-based platforms and tools made analytics more accessible to businesses of all sizes. Self- service analytics empowered business users to perform their own analyses and generate insights without relying on IT departments. 9.Artificial Intelligence and Automation (Present and Future) The integration of AI and machine learning algorithms into business analytics is transforming the field. Automated analytics allows organizations to derive insights faster, make data-driven decisions, and personalize customer experiences based on real-time data. II. Definition of Business Analytics Business analytics is a multidimensional process that involves the iterative and methodical exploration of an organization’s data using various statistical techniques and tools. By examining and analyzing different datasets, businesses can gain valuable insights into various factors such as customer behavior, market trends, operational efficiency, and financial performance. These insights can then be used to identify patterns, correlations, and trends, which in turn enable businesses to make data-driven decisions. Business analytics is based on 3 Key Topics: 1. Understanding Business Analytics and its Significance 2. Types of Business Analytics and their Applications 3. Tools and Techniques in Business Analytics 1.Understanding Business Analytics and its Significance By systematically exploring data, statistical analysis and predictive modeling can be used to uncover valuable insights that can be applied to business strategies and operations. By embracing data-driven insights, organizations can make informed decisions that are grounded in empirical evidence, leading to improved outcomes and sustainable growth in an increasingly competitive and dynamic business landscape. Example: Amazon, the multinational technology company, relies heavily on business analytics to understand consumer behavior, predict market trends, and optimize its e-commerce platform. By analyzing customer data, purchase history and browsing patterns, Amazon employs business analytics to personalize product recommendations, enhance user experience, and target specific customer segments with tailored marketing strategies. Amazon’s success in leveraging business analytics underscores its commitment to data- driven decision-making, customer-centric strategies, and continuous innovation in the highly competitive online retail industry. 2. Types of Business Analytics and their Applications Business analytics encompasses several distinct types, each serving a specific purpose in deriving insights and informing decision-making processes within organizations. Descriptive analytics involves the examination of historical data to understand past trends and performance providing valuable context for assessing the present state of the business. Example: Netflix, a popular streaming service, employs descriptive analytics to analyze the viewing habits and preferences of its subscribers, examining historical data related to their past viewing behavior. By studying the types of content watched, the duration of viewing sessions, and the frequency on interactions with specific genres, Netflix gains valuable insights into user preferences and trends. This analysis helps Netflix understand which shows or movies are popular among its subscribers, enabling the company to curate its content library and develop original programming tailored to the interests of its diverse audience base. By leveraging descriptive analytics, Netflix can Diagnostic analytics , on the other hand, focuses on identifying the root causes of specific outcomes or events, allowing organizations to gain a deeper understanding of why certain trends or results occurred. By utilizing these insights, businesses can address underlying issues and make necessary improvements to their operations. Example: Starbucks, a renowned coffeehouse chain, utilizes diagnostic analytics to understand the performance of its various products and identify the factors influencing consumer preferences and purchasing behavior. By analyzing sales data, customer feedback, and market trends, Starbucks can identify the root causes of fluctuations in sales or shifts in customer preferences for specific products. This analysis helps the company gain a deeper understanding of the impact of factors such as seasonal variations, product promotions and changes in consumer tastes on its sales performance. By leveraging diagnostic analytics, Starbucks can make informed decisions about product offerings, pricing strategies and marketing campaigns, enabling the company to address any underlying issues and refine its operations to better meet the evolving demands and preferences of its customer base. Predictive analytics employs statistical models and forecasting techniques to analyze historical data and predict future trends and outcomes. It enables organizations to anticipate market shifts, customer behavior and potential risks, thereby allowing for proactive decision-making and strategic planning. This type of analytics is particularly valuable in optimizing inventory management , sales forecasting and customer relationship management among other areas. Examples: Amazon, global e-commerce giant, utilizes predictive analytics to forecast customer demand, optimize inventory management and enhance its supply chain operations. By analyzing historical sales data, browsing behavior and market trends, Amazon employs sophisticated predictive models to anticipate customer preferences and forecast future demand for various products. This allows the company to optimize its inventory levels, minimize stockouts, and ensure timely product availability to meet customer needs efficiently. Additionally, Amazon’s use of predictive analytics enables it to streamline its logistics and delivery operations, ensuring timely order fulfillment and enhancing overall customer experience. By leveraging predictive analytics, Amazon continues to maintain its position as a market Prescriptive analytics goes beyond predictive analytics by recommending optimal courses of action to achieve desired outcomes. It leverages advanced algorithms and simulation techniques to provide actionable recommendations and strategies for decision-makers. By considering various constraints and objectives, prescriptive analytics helps organizations make well-informed decisions that align with their business goals, leading to improved operational efficiency and enhanced overall performance. Example: McDonald’s global fast-food, employs prescriptive analytics to optimize its operational efficiency and enhance the customer experience across its numerous locations worldwide. By analyzing various factors such as customer footfall, sales data, and inventory levels, Mcdonald’s utilizes prescriptive analytics to recommend optimal staffing levels, menu offerings and production schedules at its restaurants. This allows the company to ensure efficient service delivery, minimize wait times and maintain consistent product quality, thereby enhancing customer satisfaction and loyalty. Additionally, McDonald’s leverages prescriptive analytics to identify opportunities for menu optimization, pricing strategies and promotional campaigns, enabling the company to align its business decisions with its 3. Tools and Techniques in Business Analytics Tools and techniques in business analytics play a critical role in processing and interpreting large volumes of data, enabling organizations to derive actionable insights and make informed decisions. Data mining techniques are instrumental in extracting valuable patterns and relationships from complex data sets, aiding in the identification of key trends and correlations that can inform strategic business decisions. Statistical analysis techniques, including regression analysis, hypothesis testing and correlation analysis, help businesses to quantify and interpret the relationships within datasets, providing a solid foundation for making data- driven decisions. Example: Google, a global technology company is a prime example of how business analytics can be effectively used. The company uses data mining techniques to extract patterns and insights from user data, such as search queries and browsing habits. This information is then used to improve search algorithms and personalize user experiences across its platforms. Google also uses advanced data visualization tools to present complex data sets in a user-friendly format, enabling stakeholders to make informed decisions. Statistical analysis techniques like regression and correlation analysis are used to quantify relationships within data sets, supporting data-driven strategies for product development and market expansion. The integration of artificial intelligence and machine learning technologies further enhances Google’s analytics capabilities, delivering more accurate search results, improving advertising targeting, and enhancing engagement. End of Part 1 THANK YOU! III. Importance of Business Analytics Business analytics is of utmost importance for businesses in today’s data-driven age. It serves as a cornerstone for strategic decision-making, competitive advantage, and sustainable growth, enabling businesses to harness the power of data to drive innovation, optimize operations, and adapt to dynamic market demands effectively. Here, we will discuss its significance in depth. 1.Data-driven decision making By analyzing historical performance, market trends, and customer behavior, organizations can make informed decisions that are more likely to lead to success. This helps reduce risks and improves the overall effectiveness of decision-making processes. Example: Microsoft leverages data analytics to inform key business decisions and drive innovation. By analyzing user data, customer feedback and market trends, Microsoft gains valuable insights into consumer preferences, enabling the company to refine its product offerings and develop tailored solutions that cater to the evolving needs of its diverse customer base. Furthermore, Microsoft’s focus on data-driven decision-making extends to its internal operations, with the company leveraging data analytics to optimize resource allocation, enhance operational efficiencies and drive continues improvements in its business processes. By prioritizing data-driven insights and leveraging advanced analytics tools, Microsoft continues to solidify its position as a global leader in the technology industry, demonstrating the transformative impact of data-driven decision-making on business success and innovation. Through its commitment to data-driven decision-making, Microsoft has been able to foster a culture of innovation and agility, driving continuous advancements in the technology sector and delivering cutting-edge solutions to its global customer base. Thus, Microsoft uses data analytics to innovate and engage dynamically. 2. Optimizing operations Business analytics provides organizations with the tools and techniques to optimize various aspects of their operations. It allows businesses to identify bottlenecks, inefficiencies, or areas of improvements within their processes. For example, by analyzing supply chain data, businesses can identify cost- saving opportunities, reduce inventory levels and improve logistics. This optimization leads to increased efficiency, reduced costs, and improved customer satisfaction. 3. Gaining a competitive edge Business analytics helps organizations to identify market trends, customer preferences, and emerging opportunities before their competitors. With accurate and timely insights, companies can make strategic decisions, develop innovative products or services, and target specific market segments that their competitors might overlook. 4. Improving customer satisfaction Understanding customer behavior and preferences is cruicial for businesses to deliver personalized experiences and anticipate future demands. 5. Enabling predictive and prescriptive analysis Business analytics goes beyond merely analyzing historical data. It also empowers businesses to predict future outcomes and prescribe actions to optimize results. IV. Types of Business Analytics 1. Descriptive Analytics it involves analyzing historical data to understand past trends and patterns. It focuses on answering questions such as “What happened in the past?”, “How many products were sold last month?”, or “ What was the revenue for a specific quarter?”. Descriptive analytics uses tools like data aggregation, data mining, and visualization techniques to summarize and present the data in a meaningful way. Example: Salesforce, a leading customer relationshipp management (CRM) platform, utilizes descriptive analytics to provide businesses with a comprehensive understanding of theri sales performance and cutomer interactions. By leveraging data aggregation and visualization techniques, Salesforce enables organizatins to gain insights into their sales pipelines, customer interactions, and overall business operations. Through the use of customizable dashboards and detailed reports, Salesforce empowers businesses to track key performance indicators, monitor sales trends, and identify opportunities for enhancing customer engagement and driving revenue growth. This comprehensive approcah to descriptive analytics equips businesses with the tools necessary to make data-driven decisions, improve sales strategies, and optimize customer relationship management, thereby fostering long-term business success and customer satisfaction. 2. Diagnostic Analytics it goes beyond descriptive analytics and aims to identify the reasons behind specific outcomes. it focuses on answering questions like “Why did sales decline in a particular region?” or “What factors caused customer churn?”. It uses techniques such as data drill-down, data discovery, and root-cause analysis to uncover the undeerlying organizations can make necessary adjustments and avoid repeating mistakes in the future. Example: Oracle’s diagnostic analytics solution provide businesses with real-time visibility into their operations, enabling informed, data-driven decisions. They offer a hoolistic view of performance matrics and opertaional processes, helping businesses identify areas for improvement and implement targeted strategies to enhance efficiency, product quality and customer experience. Oracle’s robust suite of diagnostic analytics tools and services helps unlock valuable insights and optimize operations, strengthening its positions as a trusted partner for data- driven decision-making and business success. 3. Predictive Analytics it uses statistical models and machine learning algorithms to forecast future outcomes based on historical data. It aims to anser questions like “What is the probability of a customer making a purchase?”, What will be the demand for a product next month?”, or “Which customers are most likely to churn?” Predictive analytics helps organizations anticipate trends, identify opportunities, and mitigate risks. Example: SAP, a global leader in enterprise software solutions, leverages predictive analytics to enable businesses to forecast future trends, anticipate market shifts, and optimize decision-making processes. By utilizing advanced statistical models and machine learning algorithms, SAP empowers organizations to analyze historical data and identify patterns, correlations and potential future outcomes. 4. Prescriptive Analytics It takes predictive analytics further by suggesting the best course of action to achieve a desired outcome. It focuses on answering questions like “What should we do to increase market share?”, “Which marketing channels should be prioritized?”, or “What is the optimal inventory level?”. Prescriptive analytics uses optimization techniques, simulation models, and decision algorithms to evaluate multiple scenarios and recommend the most effective actions. Example: Adobe, renowned for its digital experience and creative software solutions, employs prescriptive analytics to provide businesses with actionable recommendations and strategies to optimize their marketing efforts and enhance customer experiences. By leveraging advanced algorithms and simulation techniques, Adobe enables organizations to make data-driven decisions that align with their business objectives and drive positive outcomes. V. Key Components of Business Analytics 1. Data Collection and Integration are essential for businesses to make informed decisions, understand consumer behavior and discern market trends. Techniques like Extract, Transform, Load (ETL), data virtualization, and APT integration facilitate the seamless amalgamation of data from various sources. With the rapid expansion of data volumes, managing big data and unstructured data has become a critical competence. Distributed computing frameworks like Hadoop and Spark enable efficient processing and analysis of large datasets. Segmentation and prioritazation of data help manage and analyze large volumes of information efficiently. Advanced analytics techniques like machine learning algorithms extract meaningful insights from big data. Example: Cisco, a leading provider of nertworking and IT solutions, emphasizes the importance of data collection and integration to enable businesses to harness the power of data for informed decision-making and operational efficiency. Through its robust networking infrastracture and data management solutions, Cisco facilities the seamless collection of data from various sources, including IoT devices, network endpoints, and cloud-based applications. By integrating diverse data sets into a centralized platform, Cisco enables organizations to gain a comprehensive view of their operations and customer interactions, facilitating more accurate analysis and insights. 2. Statistical Analysis and Data Mining These are essential in business analytics, enabling businesses to extract valuable insights from complex datasets. These methods include exploratory data analysis, descriptive statistics, predictive modeling, regression analysis, time series analysis, and clustering and segmentation. Example: JP Morgan Chase, a prominent global financial services firm, relies on statistical analysis and data mining to drive informed decision-making and risk management across its operations. By leveraging advanced statistical models and data mining techniques, JP Morgan Chase analyzes financial market trends, customer behaviors, and transaction patterns to identify potential risks and opportunities within the market. By incorporating statisitcal analysis and data mining into its operations, JP Morgan Chase continues to solidify its position as a leader in the financial services industry, providing innovative solutions that support its clients in achieving their financial objectives and navigating the dynamic landscape of global markets. 3. Data Visualization and Reporting Transforming complex data sets into actionable insights. Effective visualization techniques such as charts, graphs, and infographics, help businesses understand and interpret large volumes of data efficiently. Example: Wells Fargo, a prominent financial services company, places a strong emphasis o data visualization and reporting to enhance its data-driven decision-making processes and provide valuable insights to its stakeholders. Through the use of sophisticated data visualization tools and reporting mechanisms, Wells Fargo effectively communicates complex financial data and performance metrics in a visually intuitive format. By creating interactive dashboards and comprehensive reports, Wells Fargo enables its stakeholders to gain a comprehensive understanding of key performance indicators, customer trends and market dynamics. 4.Business Intelligence and Decision Support Sytems are crucial tools in modern business operations, providing critical insights and tools for informed decision-making. BI tools help organizations identify market trends, consumer behaviors and emerging patterns enhancing competitive advantage and market positioning. Example: Citigroup, a global banking and financial services corporation, relies on business intelligence (BI) and decision support systems to streamline its operations and enhance its decision-making processes. By utilizing sophisticated BI tools, Citigroup aggregates and analyzes financial data, market trends and customer insights to gain a comprehensive view of its performance and market dynamics. The decision support systems at Citigroup enable the organizations to assess risks, identify opportunities and optimize its business strategies. These systems provide real-time data analysis and reporting capabilities, empowering Citigroup’s executives and decision-makers to make informed and timely decisions that align with the organization’s objectives and regulatory requirements. 5.Performance Measurement and Optimization These are essential for organizations to improve efficiency and productivity. KPIs provide quantifiable measures that reflect business activities, allowing organizations to track sales growth, customer acquisition costs and conversion rates. Example: Bank of America, a leading financial institution, places significant emphasis on performance measurement and optimization to drive operational efficiency and enhance customer satisfaction. By employing robust performance measurement techniques, Bank of America evaluates KPIs such as customer retention, transaction processing times and service quality to assess the effectiveness of its operations. Through a comprehensive analysis of these metrics, Bank of America identifies areas for improvement and implements strategic initiatives to optimize its service delivery and customer experience. The bank’s commitment to performance optimization allows it to refine its operational processes, streamline its service offerings and deliver innovatice financial solutions that cater to the evolving needs of its diverse customer base. VI.Application of Business Analytics in Various Industries 1. Retail and E-commerce (ex.Alibaba) 2. Healthcare and Life Sciences (ex.United Health Group) 3. Finance and Banking (ex.American Express) 4. Manufacturing and Supply Chain (ex.General Electric (GE)) 5. Telecommunication and Media (ex. AT&T) VII. Tools and Technologies for Business Analytics 1. Advanced Data Visualization Tools These tools have embraced interactive dashboards that facilitate real-time data exploration and manipulation, enabling users to uncover intricate insights effortlessly. The seamless incorporation of AI and machine learning within these visualization tools has further amplified their impact, allowing business to generate predictive models, conduct sentiment analysis, and anticipate market trends. (ex. Warner Bros.) 2. Predictive Analytics Platforms The landscape of predictive analytics platforms has undergone a substantial evolution, revolutionizing the way businesses harness data for forecasting and decision-making. These platforms are equipped with diverse capabilities ranging from trend prediction to risk identification, empowering businesses to anticipate market shifts and proactively respond to emerging challenges. (ex.Microsoft Azure Machine Learning) 3. Big Data and Cloud Technologies The intersection og big data and cloud technologies has transformed the landscape of business analytics, offering unparalleled opportunities for organizations to harness the power of extensive data sets. Leveraging cloud- based solutions for storing and processing large volumes of data enables businesses to access scalable and cost-effective storag and computing resources, facilitating more efficient data analysis and decision-making processes. (ex.Google Cloud Platform (GCP)) VIII. Challenges and Opportunities in Business Analytics Business analytics presents both challenges and opportunities for organizations striving to leverage data-driven insights to gain a competitive edge. One of the foremost challenges lies in data security concerns, as a business grapple with protecting sensitive information from unauthorized access and cyber threats. Data quality issues, such as incomplete or inaccuaret datasets, further impede the efficacy of analytics strategies, robust data governance and quality assurance measures. By addressing the challenges through comprehensive data security measures, enhanced data quality protocols and talent development initiatives, businesses can fully harness the potential of business analytics and position themselves for sustained success and innovation. 1. Data Governance and Privacy Challenges in Business Analytics Data governance and privacy present critical challenges in the realm of business analytics, demanding meticulous attention to regulatory compliance and ethical data handling practices. In the context of data governance, businesses must navigate complex regulatory constraints such as GDPR(General Data Protection Regulation (EU Law)) and other data protection laws, to ensure the responsible collection, storage, and use of data. (ex.Instagram) 2. Integration of Machine Learning and AI in Business Analytics It presents a wealth of opportunities along with specific challenges that demand careful consideration. By leveraging machine learning algorithms, businesses can unlock actionable insights from complex datasets, facilitating more informed decision-making and strategic planning. (ex.HBO) 3. Talent Management and Skill Developement in Business Analytics In the ield of business analytics, it has become a paramount in the era of data-driven decision-making. The rising demand for skilled data analysts, data scientists and business intelligence professionals has created a competitive landscape for recruiting and retaining top analytics talent. (ex.Yahoo) IX. Future Trends in Business Analytics 1. Artificial Intelligence and Machine Learning (ex.Tesla) 2.Predictive and Prescriptive Analytics (ex.Visa) 3. Real-time Analytics (ex.PayPal) 4.Data Visualization and Storytelling (ex.Walmart) 5.Ethical Analytics (ex.Home Depot) 6. Augmented Analytics (ex.eBay) 7.Cloud-based Analytics (ex.Spotify) X.Business Analytics across Key Industries 1. Financial Services 2. Retail 3.Healthcare 4.Manufacturing 5.E-commerce 6.Telecommunications 7.Energy and Utilities 8.Transportation and Logistics 9.Marketing and Advertising 10.Human resources