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• Business analytics involves collecting, analyzing, and interpreting data for informed business decisions.
• It enables companies to gain insights and competitive advantages through data analysis.
• Techniques, tools, and methodologies are used to analyze both historical and real-time data in business analytics.
• Examining patterns, trends, and relationships in data can help organizations identify opportunities, mitigate risks, and optimize operations.
• Data collection involves gathering data from transaction records, customer feedback, and other sources.
• Data can be structured (e.g., in databases) or unstructured (e.g., text documents, audio files).
• Advances like cloud computing and big data infrastructure have facilitated the collection and storage of vast amounts of data.
• Descriptive analytics aims to summarize and understand past data like sales trends and customer demographics.
• Predictive analytics uses statistical models to forecast future events as exemplified by a retail company predicting customer churn.
• Prescriptive analytics suggests actions or decisions based on data analysis.
• Business analytics relies heavily on several techniques, including statistical analysis, mathematical modeling, and machine learning.
• Statistical analysis helps identify patterns and correlations in data.
• Mathematical modeling enables businesses to simulate scenarios and optimize decision-making through machine learning algorithms which extract data insights and improve over time with new data.
• Business analytics leads to data-driven decisions, increased efficiency, profitability, and competitiveness.
• Cost-saving opportunities, targeted marketing campaigns, optimized supply chains, and improved customer service can be identified.

History of Business Analytics

• Companies began collecting data in the early 20th century to gain insights into operations and customer behavior.
• The field has greatly changed due to technological advances, evolving methodologies and shifts in business landscape
• Statistical methods like trend analysis and regression were applied to analyze data across industries from 1920-1950.
• Management Information Systems (MIS) emerged from 1960-1980 as computers allowed for the storage and processing of large data.
• MIS focused on generating reports and dashboards with KPIs and financial metrics for executives.
• Decision Support Systems (DSS) emerged from 1980-1990, providing tools for managers to assist in decision-making.
• DSS incorporated optimization, simulation, and forecasting to solve complex problems, promoting strategic planning.
• Data warehousing became crucial as data volumes continued to grow between 1990-2000.
• Organizations consolidated data into central repositories for comprehensive analysis.
• Business Intelligence (BI) rose in the 2000s with the rise of the Internet and digitalization.
• BI platforms allowed companies to analyze data in real-time to monitor performance, detect trends, and identify areas of improvement.
• Big Data and Advanced Analytics emerged from 2010-Present.
• The proliferation of internet-connected devices and social media led to explosions of data volume, variety, and velocity.
• Advanced analytics includes natural language processing, machine learning and predictive modelling
• Predictive analytics focuses on predicting future outcomes, while prescriptive analytics recommends actions to optimize outcomes based on these predictions.
• Cloud computing revolutionized data storage, processing, and analytics.
• Cloud-based platforms made analytics accessible to businesses of all sizes.
• AI and machine learning algorithms into business analytics are transforming the field.
• Automated analytics derive insights faster, enable data-driven decisions, and personalize experiences using real-time data.

Definition of Business Analytics

• Business analytics is a multidimensional process using statistical techniques and tools for organization data exploration.
• Businesses that examine and analyze different data sets can gain valuable insights into customer behavior, market trends, and financial performance to assist with patterns and correlations which in turn enable data-driven decisions.
• Three key topics of business analytics are understanding its significance, types and applications, and tools and techniques.

Understanding Business Analytics

• Systematically exploring data through statistical analysis and predictive modeling uncovers valuable insights applicable to business strategies and operations.
• Embracing data-driven insights empowers organizations to make informed decisions, improve outcomes and promote sustainable growth.
• Amazon uses of business analytics includes analyzing customer data, purchase history, browsing patterns to personalize product recommendations, and improve their customer experience.

Types of Business Analytics

• Business analytics includes distinct types, each with specific purposes, informing decision-making processes.
• Descriptive analytics involves examining historical data to understand past trends and performance.
• Netflix uses descriptive analytics to analyze subscribers' viewing habits, understand user preferences, and curate content for their audience.
• Diagnostic analytics focuses on identifying the root causes of specific outcomes or events, increasing understanding of certain trends. Issues can then be addressed and operations improved
• Starbucks utilizes diagnostic analytics to understand product performance and consumer behavior. Fluctuations and shifts can be traced to underlying factors like seasonal variations.
• Predictive analytics uses statistical models and forecasting techniques to analyze historical data and predict future trends. Predictive analytics helps in streamlining delivery operations, forecasting demand and more.
• Amazon uses predictive analytics to forecast customer demand, optimize management and enhance its supply chain.
• Prescriptive analytics recommends courses of action to achieve desired outcomes, aligning them with business goals for improved operational efficiency. McDonald's analyses customer flow, sales, staffing and stock levels utilizing prescriptive analytics help identify menu optimization and promotional campaigns

Tools and Techniques in Business Analytics

• Tools enable organizations to derive actionable insights and informed decisions.
• Data mining techniques extract valuable patterns and relationships from complex data sets.
• Key trends and correlations are present that inform strategic business decisions.
• Statistical analysis techniques quantify relationships within datasets, which provides a foundation for data-driven decisions.
• Google uses data mining techniques to improve its algorithms and personalize the user experience on different platforms supporting data development, market expansion and insights

Importance of Business Analytics

• Business Analytics is of importance for strategic decision-making, competitive advantage, and sustainable growth.
• Data analysis drives innovation, optimizes operations, and helps to adapt to dynamic market demands.
• Informed decisions can be made by analyzing historical performance, market trends, and customer behavior. This reduces overall risk
• Microsoft leverages data analytics to inform decisions and drive innovation and product development.
• Optimizing operations involves identifying inefficiencies or areas of improvements within organizational processes. It can also reduce inventory and improve logistics
• Business Analytics helps businesses identify market customer preferences, and emerging opportunities prior to their competitors which creates a competitive edge.
• Understanding behaviors and preferences is crucial for businesses to deliver and anticipate needs
• Business analytics goes beyond historical data, and empowers businesses to foresee outcomes and prescribe actions to optimize results.

Types of Business Analytics

• Descriptive analytics focuses on answering questions about the past, like what was revenue for a specific quarter? These techniques use tools like aggregation, data mining and visuals to summarize
• Salesforce employs data aggregation and visualizations to gain insights into sales pipelines, customer interactions, and operations for greater customer engagement
• Diagnostic analytics aims to identify the reasons behind outcomes seeking to know why sales in a particular region declined which can be achieved by way of data drill-down.
• Oracle's diagnostic analytics solutions provide businesses with real-time visibility into their operations, improving targeted strategies to improve product quality
• Predictive analytics predicts outcomes by way of historical data. It allows businesses to anticipate trends and opportunities and can answer questions like: what is demand for a product next month? SAP uses their techniques to enable businesses to forecast market trends and optimize decision-making processes
• Prescriptive analytics suggests the best course of action. It helps businesses evaluate multiple scenarios and guides business objectives and drives positive outcomes. Adobe uses advanced algorithms to provide businesses with actionable recommendations to optimize and improve user experiences

Key Components of Business Analytics

• Collection and Integration are essential for businesses to decide, understand consumer behavior, and discern market trends. ETL data virtualization, AI and PT. manages structured an unstructured data, provides data analysis to support insights on big data. Cisco facilities seamless collection of data from IoT platforms, including cloud based and various sources to enable gain of comprehensive view of operations

Statistical Analysis and Data Mining

• Enable businesses to extract value from complex data sets using methods to assist regression. time series analysis can lead to exploratory data to gain useful insights. JP Morgan Chase relies on statistical analysis. As a global group, their insights on financial markets are key to staying successful.
• Data Visualization and transforming complex data sets into actionable insights through different mediums like infographics allows businesses to retain and receive efficient data. Wells Fargo offers data through multiple mediums to deliver data for customer trends and market dynamics to gain a performance understanding.
• Business intelligence tools enhance customer behavior and competitive advantages. As part of competitive advantage for global banking CitiGroup relies on BI to assess for potential business opportunities Performance measurement and optimization is a process utilized, and offers organizations growth through metrics and customer acquisition costs. The bank of america finds ways to operate and streamlines business
• Application of Business Analytics in industries: Retail (Alibaba ex), HealthCare (UnitedHealth Group ex), Finance (American Express ex), Manufacturing (General Electric ex), Telecommunications (AT&T ex)

Tools and Technologies for Business Analytics

• Advanced Data Visualization Tools offer dashboards and manipulation and Al tools to generate predictive models and sentiment analysis
• Predictive Analytics platforms revolutionizes the ways to gather information and forecasting, risk identification and anticipating new changes (ex. Microsoft Azure Machine Learning)
• Tech og big data and cloud technologies allows organizations to process data sets through storing (Google Cloud Platform (GCP))

Challenges and Opportunites

• Business analytics requires many tools from various sectors that offer key business opportunities
• Security and Data Security requires companies to leverage from business to leverage analytics

Challenges

• Quality issues and measure for quality assurance

Data Governance

• Data governance presents critical challenges in law and what is being handled with privacy (ex. Instagram)
• ML and AI poses challenging considerations that need to be accounted for
• Talent is a paramount component in the field for new data
• Trends for data: (AI with tesla brand ex), prescripticvies (Visa ex), real time Paypal ex, data visualization, (walmart ex, ethical (home depot example