• Volume is based on the quantity of data that is stored in any format such as image files, movies, and database transactions, which are measured in gigabytes, terabytes, or even zettabytes. To give context, you can store hundreds of thousands of songs or pictures on one terabyte of storage space. Even more amazing than the figures is how much it costs you. Google Drive, for example, offers up to 5 TB (terabytes) of storage for free according to their support site.
• Velocity is the speed at which data is generated. This process covers how data is both produced and consumed. For example, batch processing is how data feeds are sent between systems where blocks of records or bundles of files are sent and received. Modern velocity approaches are real time, streams of data where the data flow is in a constant state of movement.
• Variety is all of the different formats that data can be stored in, including text, image, database tables, and files. This variety has created both challenges and opportunities for analysis because of the different technologies and techniques required to work with the data.

Understanding the 3Vs is important for data analysis because you must become good at being both a consumer and producer of data. The simple questions of how your data is stored, when this file was produced, where the database table is located, and in what format I shouldstore the output of my analysis of the data can all be addressed by understanding the 3Vs.

This leads us to a formal definition of data analysis which is defined as a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusion, and supporting decision-making, as stated in Review of business intelligence through data analysis.

What I like about this definition is the focus on solving problems using data without the focus on which technologies are used. To make this possible there have been some significant technological milestones, the introduction of new concepts, and people who have broken down the barriers.

To showcase the evolution of data analysis, I compiled a few tables of key events from the years of 1945 until 2018 that I feel are the most influential. The following table is comprised of innovators such as Dr. E.F. Codd, who created the concept of a database to the launch of the iPhone device that spawned the mobile analytics industry.

The following diagram was collected from multiple sources and centralized in one place as a table of columns and rows and then visualized using this dendrogram chart. I posted the CSV file in the GitHub repository for reference: https://github.com/PacktPublishing/python-data-analysis-beginners-guide. Organizing the information and conforming the data in one place made the data visualization easier to produce and enables further analysis:

That process of collecting, formatting, and storing data in this readable format demonstrates the first step of becoming a producer of data. To make this information easier to consume, I summarize these events by decades in the following table:

From the preceding summary table, you can see that the majority of these milestone events occurred in the 1990s and 2000s. What is insightful about this analysis is that recent innovations have removed the barriers of entry for individuals to work with data. Before the 1990s, the high purchasing costs of hardware and software restricted the field of data analysis to a relatively limited number of careers. Also, the costs associated with access to the underlying data for analysis were great. It typically required higher education and specialized careers in software programming or an actuary.

A visual way to look at this same data would be a trend bar chart, as shown in the following diagram. In this example, the height of the bars represents the same information as in the preceding table and the Count of Milestone events is on the left or the y axis. What is nice about this visual representation of the data is that it is a faster way for the consumer to see the upward pattern of where most events occur without scanning through the results found in the preceding diagram or table:

The evolution of data analysis is important to understand because now you know some of the pioneers who opened doors for opportunities and careers working with data, along with key technology breakthroughs, significantly reducing the time to make decisions regarding data both as consumers and producers.

I will now break down the contributing factors that make up a good data analyst. From my experience, a good data analyst must be eager to learn and continue to ask questions throughout the process of working with data. The focus of those questions will vary based on the audience who are consuming the results. To be an expert in the field of data analysis, excellent communication skills are required so you can understand how to translate raw data into insights that can impact change in a positive way. To make it easier to remember, use the following acronyms to help to improve your data analyst skills.

Know Your Data (KYD)

Knowing your data is all about understanding the source technology that was used to create the data along with the business requirements and rules used to store it. Do research ahead of time to understand what the business is all about and how the data is used. For example, if you are working with a sales team, learn what drives their team's success. Do they have daily, monthly, or quarterly sales quotas? Do they do reporting for month-end/quarter-end that goes to senior management and has to be accurate because it has financial impacts on the company? Learning more about the source data by asking questions about how it will be consumed will help focus your analysis when you have to deliver results.

Voice of the Customer (VOC)

The concept of VOC is nothing new and has been taught at universities for years as a well-known concept applied in sales, marketing, and many other business operations. VOC is the concept of understanding customer needs by learning from or listening to their needs before, during, and after they use a company's product or service. The relevance of this concept remains important today and should be applied to every data project that you participate in. This process is where you should interview the consumers of the data analysis results before even looking at the data. If you are working with business users, listen to what their needs are by writing down the specific points on what business questions are they trying to answer.

Schedule a working session with them where you can engage in a dialog. Make sure you focus on their current pain points such as the time to curate all of the data used to make decisions. Does it take three days to complete the process every month? If you can deliver an automated data product or a dashboard that can reduce that time down to a few mouse clicks, your data analysis skills will make you look like a hero to your business users.

Always Be Agile (ABA)

The agile methodology has become commonplace in the industry for application, web, and mobile development Software Development Life Cycle (SDLC). One of the reasons that makes the agile project management process successful is that it creates an interactive communication line between the business and technical teams to iteratively deliver business value through the use of data and usable features.

The agile process involves creating stories with a common theme where a development team completes tasks in 2-3 week sprints. In that process, it is important to understand the what and the why for each story including the business value/the problem you are trying to solve.

The agile approach has ceremonies where the developers and business sponsors come together to capture requirements and then deliver incremental value. That improvement in value could be anything from a new dataset available for access to a new feature added to an app.

See the following diagram for a nice visual representation of these concepts. Notice how these concepts are not linear and should require multiple iterations, which help to improve the communication between all people involved in the data analysis before, during, and after delivery of results:

Finally, I believe the most important trait of a good data analyst is a passion for working with data. If your passion can be fueled by continuously learning about all things data, it becomes a lifelong and fulfilling journey.