Introduction to Business Analytics PDF

Chapter 1 Introduction to Business Analytics Introductory Case: Vacation in Belize After graduating college, Emily is planning a vacation in Belize. She is worried about staying within her budget, and researches her options for flights and hotels by looking for deals and packages. Flights: wh...

Chapter 1 Introduction to Business Analytics Introductory Case: Vacation in Belize After graduating college, Emily is planning a vacation in Belize. She is worried about staying within her budget, and researches her options for flights and hotels by looking for deals and packages. Flights: where does she depart from and arrive at? Hotels: location, amenities, activities, reviews 1. Find a flight that is convenient as well as affordable. 2. Choose a reputable hotel that is priced under $200 per night. 1.1: Overview of Business Analytics Data and analytics capabilities have made a leap forward. Growing availability of vast amounts of data Improved computational power Development of sophisticated algorithms Colleges/universities have curriculum emphasizing business analytics. Data and analytics capabilities have changed the way businesses make decisions. Companies need data-savvy professionals Turn data into insights and action 1.1: Overview of Business Analytics Business analytics (data analytics) involves extracting information and knowledge from data. Improve the bottom line Enhance the customer experience Develop better marketing strategies Deepen customer engagement Enhance efficiency and reduce expenses Identify emerging markets Mitigate risk and fraud Business analytics is widely applied. Marketing Human resource management Economics Finance Health, sports, and politics 1.1: Overview of Business Analytics Business analytics is a broad topic. Statistics Computer Science Information Systems Business analytics differs from data science. Data science: develop applications for end users Business analytics: data analyses for business applications Business analytics combines qualitative reasoning with quantitative tools. Identify key business problems Translate data analysis into decisions Improve business performance 1.1: Overview of Business Analytics Business analytics begins with understating the business context. Ask the right questions Identify the appropriate analysis Communicate information Numerical results are not very useful unless they are accompanied with clearly stated actionable business insights. 1.1: Overview of Business Analytics There are three different types of analytics techniques. Descriptive analytics: what has happened? Predictive analytics: what could happen in the future? Prescriptive analytics: what should we do? Turning data-driven recommendations into action also requires thoughtful consideration and organizational commitment beyond developing descriptive and predictive analytical models. 1.1: Overview of Business Analytics Descriptive Analytics: what has happened? Gather Organize Tabulate Visualize Summarize Descriptive information can be presented in a number of formats. Written reports Tables Graphs Maps 1.1: Overview of Business Analytics Descriptive analytics is referred to as business intelligence (BI). Access and manipulate data through reports, dashboards, application and visualization tools Uses past data integrated from multiple sources Inform decision-making and identify problems and solutions Examples A firm’s marketing expenses and sales Financial reports Crime rates across regions and time 1.1: Overview of Business Analytics Predictive Analytics: what could happen in the future? Use historical data to make predictions Analytical models help identify associations Associations used to estimate the likelihood of a favorable outcome Commonly considered advanced predictions Build models that help an organization understand what might happen in the future Use statistics and data mining Examples Identifying customers who are most likely to respond to specific marketing campaigns Transactions that are likely to be fraudulent Incidence of crime at certain regions and times 1.1: Overview of Business Analytics Prescriptive Analytics: what should we do? Optimization and simulation algorithms to provide advice Explore several possible actions Suggest course of action Commonly considered advanced predictions Build models that help an organization understand what might happen in the future Use statistics and data mining Examples Scheduling employees’ works hours Select a mix of products to manufacture Choose an investment portfolio 1.2: Types of Data An important first step for making decisions is to find the right data and prepare it. Compilation of facts, figures, or other content Numerical and non-numerical All types and formats are generated from multiple sources Often we have a large amount of data Even small data can give insights Data that have been organized, analyzed, and processed in a meaningful and purposeful way become information. Use a blend of data, contextual information, experience, and intuition to derive knowledge. 1.2: Types of Data It is not feasible to collect data that comprise a population of all elements of interest. Too expensive It is impossible A sample is a subset of the population and is used for analyses. Traditional statistical techniques use sample information to draw conclusions about the population. 1.2: Types of Data Cross-sectional data Collected by recording a characteristic of many subjects at the same point in time Recording a characteristic of many subjects at the same point in time Time series data Collected over several time periods focusing on certain groups of people, specific events, or objects Hourly, daily, weekly, monthly, quarterly, or annual observations 1.2: Types of Data 1.2: Types of Data 1.2: Types of Data Structured data Reside in a pre-defined, row-column format Spreadsheet or database applications Enter, store, query, and analyze Numerical information that is objective and not open to interpretation Historically, companies relied mostly on structured data. High cost to store and process Performance limitations 1.2: Types of Data Unstructured data Do not conform to a pre-defined, row-column format Textual Multimedia content Do not conform to database structures Human- or machine-generated Structured human: price, income, retail sales Structured machine: sensors, speed cameras, web server logs Unstructured human: email, text, social media, presentations Unstructured machine: satellite images, video data, camera images 1.2: Types of Data Businesses generate and gather more and more data at an increasing pace: Big Data. A massive volume of structured and unstructured data Extremely difficult to manage, process, and analyze using traditional data processing tools Present great opportunities to gain knowledge and game-changing intelligence Does not imply complete (population) data Big data may not be used when available Inconvenient and computationally burdensome Benefits may not justify costs 1.2: Types of Data gartner.com has a widely accepted definition of big data. “[H]igh-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” 1.2: Types of Data There are three characteristics of big data. Volume: immense amount of data compiled for a single or multiple sources Velocity: generated at a rapid speed, management is a critical issue Variety: all types, forms, granularity, structure or unstructured Additional characteristics Veracity: credibility and quality of the data, reliability Values: methodological plan for formulating questions, curating the right data, and unlocking hidden potential Having a plethora of data does not guarantee that useful insights or measurable improvements will be generated. 1.3: Variables and Scales of Measurement A variable is a characteristic of interest that differs in kind or degree among various observations (records). There are two types of variables: categorical and numerical Categorical Also called qualitative Represent categories Labels or names to identify distinguishing characteristics Arithmetic operations on the labels/values are not meaningful Coded into numbers for data processing Example: marital status 1.3: Variables and Scales of Measurement Numerical Also called quantitative Represent meaningful numbers Arithmetic operations are meaningful Discrete: assumes a countable number of values Example: number of children in a family Continuous: assumes an uncountable number of values within an interval Example: investment returns 1.3: Variables and Scales of Measurement Analysis techniques depend on the type of data. There are four major scales: nominal, ordinal, interval, ratio Nominal Categorical Least sophisticated Values differ by label or name Example: marital status Ordinal Categorical Reflect labels or name, but can be ranked Cannot interpret the difference between the ranked values Example: reviews from 1 star (poor) to 5 stars (outstanding) 1.3: Variables and Scales of Measurement Interval Numerical Categorize and rank, differences are meaningful Zero value is arbitrary and does not reflect absence of characteristic Ratios are not meaningful Example: temperature Ratio Numerical Most sophisticated A true zero point, reflects absence of characteristic Ratios are meaningful Example: profits 1.3: Variables and Scales of Measurement Example: The owner of a ski resort gathers data on tweens. Music: nominal Food quality: ordinal Closing time: interval Own money spent: ratio 1.4: Data Sources and File Formats 90% of the data in the world today was created in the last two years. Data sources for this book mostly come from Google. Bureau of Economic Analysis Bureau of Labor Statistics Federal Research Economic Data U.S. Census Bureau National Climatic Data Center Yahoo Finance Zillow 1.4: Data Sources and File Formats There are standard file formats. Fixed-width format: each column starts and ends in the same place in every row Delimited format: a delimiter separates fields, typically a comma (csv file) There are three widely used markup languages. Extensible Markup Language (XML): structured data, each piece enclosed in a pair of tags, gives information on what the data are HyperText Markup Language (HTML): structured data with tags, gives information on how to display the data JavaScript Object Notation (JSON): alternative to XML, transmit human- readable data in compact files, not as verbose as XML, supports wide range of data types, parsing is faster R Notebooks Markdown In R Notebooks, the text is written using a formatting language called “R Markdown.” Markdown is designed to be simple and to be human readable before formatting. R Notebooks consist of three types of sections: A configuration header written in YAML Text sections (chunks) written in R Markdown Code sections (chunks). For us, these are written in R R Markdown cheatsheet