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Practical Business Intelligence

Book Description

Learn to get the most out of your business data to optimize your business

About This Book

  • This book will enable and empower you to break free of the shackles of spreadsheets
  • Learn to make informed decisions using the data at hand with this highly practical, comprehensive guide
  • This book includes real-world use cases that teach you how analytics can be put to work to optimize your business
  • Using a fictional transactional dataset in raw form, you’ll work your way up to ultimately creating a fully-functional warehouse and a fleshed-out BI platform

Who This Book Is For

This book is for anyone who has wrangled with data to try to perform automated data analysis through visualizations for themselves or their customers. This highly-customized guide is for developers who know a bit about analytics but don't know how to make use of it in the field of business intelligence.

What You Will Learn

  • Create a BI environment that enables self-service reporting
  • Understand SQL and the aggregation of data
  • Develop a data model suitable for analytical reporting
  • Connect a data warehouse to the analytic reporting tools
  • Understand the specific benefits behind visualizations with D3.js, R, Tableau, QlikView, and Python
  • Get to know the best practices to develop various reports and applications when using BI tools
  • Explore the field of data analysis with all the data we will use for reporting

In Detail

Business Intelligence (BI) is at the crux of revolutionizing enterprise. Everyone wants to minimize losses and maximize profits. Thanks to Big Data and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Just knowing how to analyze data is not enough, you need to start thinking how to use data as a business asset and then perform the right analysis to build an insightful BI solution. Efficient BI strives to achieve the automation of data for ease of reporting and analysis.

Through this book, you will develop the ability to think along the right lines and use more than one tool to perform analysis depending on the needs of your business. We start off by preparing you for data analytics. We then move on to teach you a range of techniques to fetch important information from various databases, which can be used to optimize your business.

The book aims to provide a full end-to-end solution for an environment setup that can help you make informed business decisions and deliver efficient and automated BI solutions to any company.

It is a complete guide for implementing Business intelligence with the help of the most powerful tools like D3.js, R, Tableau, Qlikview and Python that are available on the market.

Style and approach

Packed with real-world examples, this pragmatic guide helps you polish your data and make informed decisions for your business. We cover both business and data analysis perspectives, blending theory and practical hands-on work so that you perceive data as a business asset.

Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.

Table of Contents

  1. Practical Business Intelligence
    1. Practical Business Intelligence
    2. Credits
    3. About the Author
    4. About the Reviewer
    5. www.PacktPub.com
      1. Why subscribe?
    6. Customer Feedback
    7. Preface
      1. What this book covers
      2. What you need for this book
      3. Who this book is for
      4. Conventions
      5. Reader feedback
      6. Customer support
        1. Downloading the example code
        2. Downloading the color images of this book
        3. Errata
        4. Piracy
        5. Questions
    8. 1. Introduction to Practical Business Intelligence
      1. Understanding the Kimball method
      2. Understanding business intelligence architecture
      3. Who will benefit from this book?
        1. Manager
        2. Data scientist
        3. Data analyst
        4. Visualization developer
      4. Working with data and SQL
      5. Working with business intelligence tools
        1. Power BI and Excel
        2. D3.js
        3. R
        4. Python
        5. Qlik
        6. Tableau
        7. Microsoft SQL Server
      6. Downloading and installing MS SQL Server 2014
      7. Downloading and installing AdventureWorks
      8. Summary
    9. 2. Web Scraping
      1. Getting started with R
        1. Downloading and installing R
        2. Downloading and installing RStudio
      2. Web scraping with R
      3. Getting started with Python
        1. Downloading and installing Python
        2. Downloading and installing PyCharm
      4. Web scraping with Python
      5. Uploading data frames to Microsoft SQL Server
        1. Importing DiscountCodebyWeek
        2. Importing CountryRegionBikes
      6. Summary
    10. 3. Analysis with Excel and Creating Interactive Maps and Charts with Power BI
      1. Getting to know your data in SQL Server
      2. Connecting Excel to a SQL Server Table
        1. Exploring PivotTables in Excel
      3. Connecting Excel to SQL Statements
        1. Exploring PivotCharts in Excel
      4. Getting started with Microsoft Power BI
        1. Downloading and installing Microsoft Power BI
      5. Creating visualizations with Power BI
        1. Publishing and sharing Microsoft BI
      6. Summary
    11. 4. Creating Bar Charts with D3.js
      1. Some background about the D3 architecture
        1. Exploring HTML
        2. Understanding CSS
        3. Learning JavaScript
        4. Diving into SVG
        5. Working with a source code editor
      2. Loading D3 templates for development
        1. Understanding JS Bin
        2. Downloading from D3js.org
      3. Setting up traditional HTML components
        1. Adding a new paragraph the traditional way
        2. Adding a new paragraph the D3 way
        3. Adding SVG shapes the traditional way
        4. Adding SVG shapes the D3 way
      4. Blending D3 and data
        1. Visualizing hardcoded data
        2. D3 and JavaScript functions
        3. Reversing the y axis
        4. Adding some color
        5. Labeling
      5. Fusing D3 and CSV
        1. Preparing the CSV file
        2. Setting up a web server
        3. Testing the web server
        4. Developing a bar chart with CSV data
      6. Summary
    12. 5. Forecasting with R
      1. Configuring an ODBC connection
      2. Connecting R to a SQL query
      3. Profiling dataframes in R
      4. Creating graphs in R
        1. Creating simple charts with plot() in R
        2. Creating advanced charts with ggplot() in R
        3. Creating interactive charts with plot_ly()
      5. Time series forecasting in R
        1. Forecasting 101
        2. Smoothing 101
        3. Forecasting with Holt-Winters
      6. Formatting and publishing code using R Markdown
        1. Getting started with R Markdown
        2. R Markdown features and components
        3. Executing R code inside of R Markdown
        4. Exporting tips for R Markdown
        5. The final output
      7. Exporting R to Microsoft Power BI
        1. Merging new columns to dataframes in R
        2. Integrating R with Microsoft Power BI
      8. Summary
    13. 6. Creating Histograms and Normal Distribution Plots with Python
      1. Preparing a SQL Server query for human resources data
      2. Connecting Python to Microsoft SQL Server
        1. Starting a new project in PyCharm
        2. Installing Python libraries manually
        3. Establishing a connection with the PyPyODBC library
        4. Building a SQL query inside Python
        5. Building a dataframe with Python
      3. Visualizing histograms in Python
      4. Visualizing normal distribution plots in Python
      5. Combining a histogram with a normal distribution plot
        1. Annotating in Python
        2. Analyzing the results
      6. Alternative plotting libraries with Python
      7. Publishing Jupyter Notebook
      8. Summary
    14. 7. Creating a Sales Dashboard with Tableau
      1. Building a sales query in MS SQL Server
      2. Downloading Tableau
      3. Installing Tableau
      4. Importing data into Tableau
        1. Exporting to a text file
      5. Building a sales dashboard in Tableau
        1. Building a Crosstab
        2. Building custom calculation fields
        3. Creating bullet graphs
        4. Creating a KPI indicator selector
      6. Building a sales dashboard in Tableau
        1. Beautifying the dashboard
        2. Connecting worksheets to dashboards
      7. Publishing dashboard to Tableau Public
      8. Summary
    15. 8. Creating an Inventory Dashboard with QlikSense
      1. Getting started with QlikSense Desktop
        1. Downloading QlikSense
        2. Installing QlikSense
      2. Developing an inventory dataset with SQL Server
      3. Connecting SQL Server query to QlikSense Desktop
      4. Developing interactive visual components with QlikSense Desktop
        1. Building a sheet
          1. Creating a filter pane component
          2. Creating a custom calculation and KPI
          3. Creating a bar chart with multiple measures
          4. Creating a scatter plot with two measures
      5. Publishing the inventory dashboard
        1. Exporting to a PDF
        2. Exporting to Qlik Cloud
      6. Summary
    16. 9. Data Analysis with Microsoft SQL Server
      1. Comparing tools head-to-head
        1. Comparing the data discovery desktop applications
          1. Data connectivity
          2. BI maturity
        2. Comparing the traditional programming languages
          1. Data connectivity
          2. Delivery
      2. Developing views in SQL Server
      3. Performing window functions in SQL Server
        1. Rank functions in SQL Server
        2. Sum functions in SQL Server
        3. Average functions in SQL Server
        4. Building crosstabs with case logic
        5. Building crosstabs with pivot in SQL Server
      4. Performing stored procedures in SQL Server
      5. Summary