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Business analytics and it's tools and competitive advantage

  2. Definition Of Business Analytics Business analytics refers to the practice of analyzing and interpreting data to gain insights and make informed business decisions. It involves using statistical and quantitative analysis techniques to extract meaning from large and complex data sets, as well as tools and technologies to visualize and communicate the results.
  3. Competitive Business Analysis : • ´Competitive business analysis is done to identify both offensive and defensive opportunities within your industry so you can develop a sound strategy and take advantage of possible opportunities, while dealing with threats in any sized market space.
  4. Steps to Conduct Competitive Business Analytics Analyze Analyze data and plan strategies Perform Perform SWOT analysis Analyze Analyze competition and find out market share Identify Identify current and future competitors Define Define your market and customers
  5. Analyze measures: Forward and Backward Integration Economies of Scale Strength of Buyer/Consumer Competitor Intelligence SWOT Analysis Price Skimming PESTEL Analysis Benchmarking
  6. Business Intelligence • Business intelligence (BI) refers to the process of collecting, analyzing, and transforming data into actionable insights to support business decision-making. It involves using data mining, statistical analysis, and visualization techniques to identify patterns, trends, and relationships in data.
  7. Uses of Business Analytics Supermarkets – BI helps in gathering comprehensive data about customers and shoppers to determine who may be candidates for promotional offers. For example, pregnant moms can be targeted for discounts on diapers or baby items. Service Providers – Utility companies use BI to predict when a customer will likely transfer to another service provider by collating billing information, website visits, customer inquiries, and other such metrics to give each customer a probability score, then offer incentives to customers perceived to have a higher risk of transferring. E-commerce – You have probably come across Amazon’s line “People who viewed that product also like this…” which is a BI-driven, data mining method to promote cross- sells and up-sells. This is a popular technique in online selling sites
  8. Tools of Business Analytics • SAS • Python • Power BI • Apache Spark • Tableau
  9. SAS • SAS is a collection of modules that are used to process and analyze data. • It began in the late ’60s and early ’70s as a statistical package (Statistical Analysis System). • SAS is also an extremely powerful, general-purpose programming language. • In recent years, it has been enhanced to provide state-of-the-art data mining tools and programs for Web development and analysis.
  10. Why SAS? • Able to process large data set(s) • Easy to cope with multiple variables • Able to track all the operations on the data set(s) • Generate systematic output • Summary statistics • Graphs • Regression results • Most government agencies and private sectors use SAS
  11. Python Python is object-oriented Indentation Open source It’s Powerful It’s Portable 6. It’s easy to use and learn 7. Interpreted Language 8. Interactive Programming Language 9. Straight forward syntax
  12. • Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. • Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library. • Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python 0.9.0. Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.
  13. Power BI • Microsoft makes Power BI among the many business analytics tools. It offers dynamic visualizations with self-service business intelligence features, allowing end users to create dashboards and reports independently without assistance.
  14. Apache Spark • Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interfacefor programming clusters with implicit data parallelism and fault tolerance. Originally developed at the University of California, Berkeley’s AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since.
  15. Tableau • Tableau is a visual analytics platform transforming the way we use data to solve problems—empowering people and organizations to make the most of their data.