As you are reading this book, you may be asking yourself, "How will this book benefit me if I'm too technical or if I'm not technical at all? Is the book geared towards managers rather than developers?" The answer to your questions is "Yes." Not every chapter of this book will be for everyone, but having spent the last 10 years in the Business Intelligence industry, I believe there is something for everyone in this book. Certain tools such as Tableau, Qlik, and Power BI allow for quick and flashy visualizations out of the box without much customization. Other tools such as R, Python, and D3.js require more of a programming background, which can lead to massive customization of a visualization but also more of a learning curve when it comes to producing something out of the box.
If you are a business intelligence manager looking to establish a department with a variety of tools to help flesh out your requirements, this book will serve as a good source of interview questions to weed out unqualified candidates. Additionally, the book will highlight specific tools more geared towards data scientists as opposed to data analysts, dashboard developers, and computer programmers. A manager could use this book to distinguish some of the nuances between these different skillsets and prioritize hiring based on immediate needs. In addition to hiring resources, managers are also tasked with licensing decisions based on new and existing software used by their department. At the last count, the Gartner BI Magic Quadrant listed 24 different BI platforms in the current market (https://www.gartner.com/doc/reprints?id=1-2XXET8P&ct=160204). That does not even take into account that some companies, such as SAP, offer multiple sub BI platforms within their main BI platform. This can be a daunting task for a BI manager when it comes to evaluating which platform tool is best suited to meet their organization's needs. With the emphasis on a different BI tool in each chapter, a manager can compare the similarities and differences for each one and evaluate which is more appropriate for them.
Data science is a relatively new position to fill within organizations and in 2012 was deemed the sexiest job of the 21st century by the Harvard Business Review (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century).
The term data scientist has been more often misused in the BI industry than any other position. It has been lumped in with data analyst as well as BI developer. Unfortunately, these three positions have separate skill sets and you will do yourself a disservice by assuming that one person can do multiple positions successfully. A data scientist will be able to apply statistical algorithms behind the data that is being extracted from the BI tools and make predictions about what will happen in the future with that same dataset. Due to this skill set, a data scientist may find the chapters focusing on R and Python to be of particular importance because of their abilities to leverage predictive capabilities within their BI delivery mechanisms. Very often data scientists find themselves doing the job of a BI developer to prepare the data that they need in a way that allows for statistical analysis. Ideally this task should be left to the BI developer with strong querying skills and allow the data scientist to focus on the hidden story behind the data.
Data analyst is probably the second most misused position behind a data scientist. Typically, a data analyst should be analyzing the data that is coming out of the BI tools that are connected to the data warehouse. Most data analysts are comfortable working with Microsoft Excel. Additionally, may have some working knowledge of how to build or alter existing SQL scripts. Often, they are asked to take on additional roles in developing dashboards that require programming skills outside their comfort level. This is where they would find some comfort using a tool such as Power BI, Tableau, or Qlik. These tools allow a data analyst to quickly develop a storyboard or visualization that allows a quick analysis with minimal programming skills.
A dataviz developer is someone who can create complex visualizations out of data and showcase interesting interactions between different measures inside a dataset that cannot necessarily be seen with a traditional chart or graph. More often than not, these developers possess some programming background such as JavaScript, HTML, or CSS. These developers are also used to developing applications directly for the Web and therefore would find D3.js a comfortable environment to program in.
