How To Build A Data Science Dream Team
Mark Twain once said, “Data is like garbage. You’d better know what you are going to do with it before you collect it.” This gives data science teams food for thought.
The go-to method in data collection for many teams has been to “collect it all” and sort it out later, though this data strategy brings up several issues for managing, qualifying, and processing data later on.
The solution? Strong collaboration between data scientists and subject-matter experts (SMEs) on the data is essential for building an infrastructure of capturing data for rapid, accurate decisions. But where to start? First, it’s important to understand the role of a data scientist in order to determine the best way to build your data science “dream team.”
What does a data scientist do?
There is much discussion, and often confusion, around the term “data scientist.” In short, the definition of data science is the process of asking questions and getting answers from data. By defining the different roles of data scientists and breaking them into four distinct categories, it may better clarify the different uses of the term data scientist, each with its own focus.
The first category of data scientist; which in this article will be referred to as Data Scientist 1 (DS1), is going to have the responsibility to create the data strategy and overall technological requirements surrounding how the data will be collected, stored, formatted, and accessed throughout the life cycle of rapid insight gathering. Additionally, this type of data scientist will be leaned upon to develop AI and other coding mechanisms that enable the other groups to gain the ability to ask, and have answered, their questions from the data. Another key element is making sure that the users have ‘good data’. Good data is data that is clean of errors and difficult formatting.
The DS1 has a critical, and often difficult, role engaging SMEs that have vital knowledge about how processes function and are measured. Being able to understand the needs of SMEs may be daunting when being asked to assist with creating meaningful algorithms. Getting the right data in the right format to the right people is the basis for creating a top-notch organization. Additionally, the DS1 plays a significant role in the technical aspects of making the data rapidly available since the volumes and velocity can make analysis of the data an overwhelming task for the data scientists to be described later.
The second category; i.e., Data Scientist 2 (DS2) delves into the types of data with SMEs and their needs to perform advanced analytics. They are supported by statisticians and a new breed of individuals who graduate with a master’s degree in analytics. The latter is focused on analyzing data and less on the underlying mathematical theory. Both disciplines provide significant support to SMEs to perform advanced analytics. The DS2 must also recognize that a major consideration for integrating analytics relates to how it plays out with the 4th generation of the Industrial Revolution. Terms like Industry 4.0, Manufacturing 4.0, Smart Manufacturing, and Quality 4.0 are being used, and sometimes misused, more often. One must consider how data creation, collection and formatting changed in this environment.
Defining the most relevant and useful data to be created and collected is valuable. Taking time to brainstorm what key questions need to be answered with data can also add value to the discussion around data sources and what data to collect.
The next exercise would be to measure the volume of data that is desired and the velocity at which it is being created. Taking time to work through measuring the impact of the volume and velocity of data available will oftentimes educate both the DS1 and DS2 in ways that optimize the tasks of each.
