Insights

Data Analytics – How ready are you to become a data scientist?

Data Analytics – How ready are you to become a data scientist?

Apr 19, 2017

The world is changing, and data analytics is a frequent topic when we are making business decisions. Mark Cuban, owner of the NBA’s Dallas Mavericks, said in an article that the world’s first trillionaire will be a person who invests in artificial intelligence (AI). So what is AI, and how do data analytics fit in? 

Introduction

Let’s start with the basics:  What is AI, and what is data analytics? Artificial intelligence can be summed up by saying it is any “intelligent” system that doesn’t require human interaction. Data analytics is one piece of the large puzzle of AI. Profitable companies have been utilizing data analytics in their business decisions for many years. However, it has been only recently that they have begun to understand the importance of the data, how it is organized, and how their business can leverage it to help achieve success. 

Three examples of industries using data analytics

A brewery’s beer sales.  Let’s take a look at sales of beer by a brewery. When the brewery first started, it might have stored data on its sales only by ZIP code, date, and seller name. This would give the brewery the data it wants:  “What regions like our beer the most, and what companies are the best at selling it?” However, after seeing the ups and downs of its sales, the company may want to see whether there is a correlation between the weather outside and the sales inside a store. In this scenario, the brewery was not tracking weather in its data set. So now the options are to start collecting that data today or to try going back and filling in that data point to help paint somewhat of a historical picture. What we are seeing today is that once a business decides to see one metric, it often wants to see a second one and then a third, thus taking the company into a data analytics mindset.

A manufacturer’s machines. Manufacturing companies are attaching computers to machines to collect real-time data regarding how each machine is operating. Information like downtime, transition time, and operating time is then linked directly to dashboards that can be accessed through iPads or smartphones. In addition, companies like Microsoft have invested in products to help visualize all of your data. With its Office 365 product, you can use PowerBI to easily connect to data sources and supply your users with easy-to-configure dashboards. The name of the game is reacting quickly, and these data analytics are allowing companies to more efficiently focus their resources in the most effective ways. There is so much information out there that companies could be capturing to help them make timely business decisions. They no longer have to rely on “gut” instinct, since they can leverage data to make smart, strategic, quantifiable decisions. 

Baseball.  If we look at the world of sports—and specifically baseball—we will see an analytical trend that has been going on for decades. What began as “Moneyball,” coined by Billy Bean and the Oakland As, is now commonplace among MLB teams. The graph below shows the total number of analysts (also known as “data scientists”) now employed by MLB teams1, a 330% increase in seven years. Teams have seen that there is more than “gut” instinct when it comes to making personnel decisions and that there are better ways to make both pre-game and in-game decisions, and they are quickly investing in building out their data architecture and interface to the data. These new systems are allowing teams to find the edge in the numbers and answer questions like “What type of pitch does this pitcher throw on a 0-2 count?” or “Where should we place our outfielders with the current batter?” Many of these questions can be answered by analyzing the numbers, which can help teams build a better game plan. This thought process has expanded into many industries, and now companies are trying to “look at the numbers.” What are they telling you about your business? 

Foundations for your data warehouse

As a business continues to expand its data warehouse and make acquisitions, it is critical to ensure it starts cleaning, managing, and removing legacy systems to help keep its data in a good state. Trying to run reports and analytics across multiple data systems can be very tedious and in some cases very costly. With a clean data set on all the data you are collecting, you can ask yourself, “How can the data help us achieve success?” The current trend is to hire a data scientist who can build understanding into the terabytes of data the business collects. If you collect enough of the right data, you also can start to see trends and oddities. This analysis of the data can help you increase your number of customers and serve them more effectively.

The millennials trend

As the trend of hiring data scientists continues, more and more millennials are taking college courses to graduate as a data scientist. Why do millennials make good data scientists? They have grown up with data literally at their fingertips. Gone are the days when we wait for a response to an inquiry. Millennials work in a manner similar to how political campaigns fine-tune their message and platform—by analyzing social data to help react to the competition, better understand current issues, and take their campaign in the public direction. 

Millennials can easily navigate their platter of social media and gain information, various viewpoints, and a stance on topics based on public perception and other data points they find pertinent. So applying it to the business world can sometimes be a natural transition for their skill set. Even when high school students apply to colleges, their first action is conducting some do-it-yourself data analytics:  compiling trends, likes, and perceptions of certain colleges in relationship to the field they want to enter.  What picture does this paint for them? Once they see the picture, it is easy for them to pick their direction. In the end, these do-it-yourself data scientists probably find the colleges they want to visit through mining data found on their social media apps.

Conclusion

As your business continually refines its technology plan, where does data analytics fit into your picture? Are you taking steps to increase your focus on your data landscape? There are many questions to ask and decisions to make as you direct your company to be more analytically focused. From taking steps to ensure more quality data to centralizing certain components to help streamline the data creation for data visualization and reporting capabilities, as businesses continue to take on more and more data, deriving meaningful insights from the data is easier said than done. If you are looking to convert knowledge into action, maybe a data scientist is right for you.

1Graph from Jed Hoyer (Chicago Cubs General Manager) from a Chicago Cubs event

Author(s)

Richard McClure
Richard McClure
Manager
View Profile