Welcome to the second installment of the blog that brings you all the data. Well, probably not all the data, but we are going to talk a lot about data in this article. Terms like 'Big Data', 'Data Science', and 'Data Analytics' are thrown around a lot, but what do they mean? How do they affect our lives? Let's dive in. In this post -- Part 2, we'll look at Data Science and Data Analytics. If you missed it, check out Part 1 - Big Data.
Data Analytics
Previously, we talked about Big Data as the huge ocean of data collected by companies and organizations every day from their websites, sensors, and machines throughout the world. This data, though in raw form, holds the keys to finding solutions to challenges faced by these companies in reaching consumers in meaningful ways. Mining this data and gaining insights into consumer patterns and needs is essential to helping companies launch new products and services and meet our needs in more accurate ways.
Data analytics is, in a word, the field or process of making sense of Big Data with the goal of solving problems we know we have. By problems we already know we have, I mean issues in reaching consumers or providing services that companies have identified and now seek to solve through data analytics of data collected from their services.
For example, consider Amazon's Dash buttons. Amazon Dash buttons are physical thumb-sized clickers that you can keep in your home, connected to WiFi. Each dash button is connected to a specific product on Amazon. Simply clicking the dash button will instantly place an order for its associated item on your Amazon account. This is incredibly useful for items that we go through regularly, like toilet paper, paper towels, protein powder, granola bars, deodorant, etc -- items that we never shop around for and frequently order the exact same brand, size, etc. The Dash button saves customers the time required to physically go on Amazon.com or the app and place an order for one of the above items.
In my view, the Dash buttons are one of the coolest innovations in the last year. So -- how did Amazon figure out we needed Dash buttons? I would argue by data analytics. One of Amazon's top thinkers probably identified a problem -- people tend to have some items that they order frequently from the exact same brand and in the same size. Could we make this repeat ordering process faster and more convenient, further incentivising shopping on Amazon instead of brick and mortars like Wal-Mart or Target? Also important -- I guarantee you that the consumer world knew they had this problem also. I am confident that a considerable number of people frequently placing repeat orders wished there was a way to streamline the process. From there, data analytics most likely kicked in. I assume Amazon's data analysts used a combination of algorithms and hand-sorting to gather meaningful figures on how many minutes on average consumers waste in repetitive shopping carts on the site, and how many customers fit in a category to enjoy something like a Dash button... once that data validates the perceived need, Amazon then most likely started brainstorming a product that would solve the problem, and the answer was the Dash button.
That's data analytics.
Data Science
Data science is very similar to data analytics in that it also centers around processing and gaining insights into Big Data. The difference, however, is that data science seeks to give us answers to problems we do not even know we have yet. For example, in the realm of consumer data collected by companies like Amazon, Tesla, or Netflix as we have been talking about, data science seeks to solve consumer problems and meet needs we do not even know we have.
In my view, Uber is a phenomenal example of a data science success. Before Uber existed, no one even knew they had the problem of "needing to hail peer-to-peer transportation from their smartphone". This was so true that Uber was rejected on the popular venture capital television show, Shark Tank. The idea for Uber was so new and innovative that most couldn't even conceptualize using the product even when it was laid out for them in an investment pitch. All this to say, the founders of Uber found a solution to a problem the world did not know it had. In contrast to the data analytics that lead to the Dash button, consumers did not know about their need yet, and the founders of Uber barely knew the depth of it until they used data science to consider the market. Now, I do not claim to know exactly how Uber's founders did early market testing, but at least for the sake of this conversation, I am going to guess they employed data science to look at big data statistics on hours logged by taxis, customer satisfaction with taxis, and money spent on taxi rides to lead to the idea for Uber.
This is data science -- mining through Big Data to find insights that lead us to solutions for problems we did not even know existed.
A brief disclaimer to conclude, I do not know exactly how Amazon or Uber developed their products. I am sure there was some overlap of data science and data analytics in both innovative processes, I was merely trying to illustrate the difference between the two. At the end of the day, they are pretty similar disciplines, departing at the point where the problem being solved is known or unknown to the world and those seeking insights from the data.
