Much about becoming a data analyst can be like learning a new language. As in any industry, there is a slew of jargon, slogan and lingo that are often used amongst data professionals. From KPIs to linear models, Ad Hoc analyses to queries, understanding the meaning and weight of these terms can be the difference between data novice and data fluency. This process of recognizing and contextualizing key phrases comes with a learning curve that must be conquered so consequential work can make progress.
New graduates exiting university and entering the workforce have seen this process in full motion. More than anything, what a structured learning environment accomplishes is learning the how in data analytics. How to pull data from databases. How to create statistical models. How to effectively visualize and report findings. These hows are like putting a new language in practice.
Knowing how to perform these key procedures are certainly significant but are made purposeless without the what and why. Data and business analytics are unique in the sense that they are industries comprised of many industries. Just as a chameleon that changes colors and blends in with its environment, data is blended within every industry. There are contexts and circumstances that become important to understand when performing analytics within certain industries.
Where learning the language of data analytics is a process that should lead to mastery, learning the language and contexts of a specific industry should lead to mere sufficiency. Simply put, a data analyst must learn enough about an industry to get the job done. There are subject matter experts and industry leaders who are the masters of their domain. The expectation for an analyst is not to be a master but merely a student of a domain while mastering the tools and techniques of data.
Through different internship and project experiences I’ve had the opportunity to work in the industry of organic foods, energy consumption and now telecommunications. In these experiences I’ve identified three specific principles that can aid a data analyst in learning the what and why for specific domains. These are:
Ask the right questions
Develop a base knowledge
It’s said that the optimal way to learn a new language is to immerse oneself in the environment and cultures of the people who speak said language. This principle applies fully to learning the language of an industry.
Empathy is a powerful concept, not just within analytics but in the general sense. The ability to understand a person’s motives, desires and actions is an innate skill that can be developed. When exploring a foreign domain, a data analyst must adapt quickly and use empathy.
In rubbing shoulders with subject matter experts and industry specialists, it is important to recognize what they care about and why it is they care. Key performance indicators can be made more powerful if there’s an understanding as to why it is, they are considered “key”.
Occasionally, there may be a rush to assume that the data alone is sufficient to understanding a domain. There will be a temptation to do an immediate deep dive into the numbers. This can be necessary at times for initial exploration and patterns can be found off the bat. While data can certainly tell stories, without developing empathy and understanding the context the data is in, an analyst runs the risk of telling the wrong story.
By utilizing empathy, a data analyst is able to grasp a fuller view of the big picture. They are able to see the story told by the data, married with the context and flavor brought in by the industry experts.
Ask the right questions
In the journey of developing empathy, questions will lead to answers and answers will lead to more questions. Asking the right questions can lead an analyst closer to finding the right solutions. At the same time, asking the wrong questions can lead an analyst down a rabbit hole that may result in losing precious time.
A recent experience of mine highlights this principle. Within the past month I’ve started a position with a prominent telecommunications company and have had to develop a working knowledge of the industry. In this process, there was a specific acronym that repeatedly came up in my studies and I came to believe that this was a significant metric.
No matter where I turned to whether it was a Google search or internal resources, I could not discover what that acronym meant. My fixation on this became dire and I believed that I would be unable to move on in my work without its meaning. The opportunity came to ask a peer the meaning of this acronym. Much to my chagrin, the acronym turned out to be the name of the company and my fixation turned to light embarrassment and laughter.
While this example was small, there are times when these deviations can be much more impactful. Asking the right questions can prevent these from occurring and leave an analyst on the right track.
Develop a base knowledge
Through the process of utilizing empathy and asking the right questions, an analyst must do their own homework as well. Developing a base knowledge of your specific project industry is important, especially when communicating with subject matter experts. There will be base concepts that will appear foreign for a data analyst but second nature for an expert.
In developing a base knowledge, an analyst should use their ability to learn on their own and arrive at the point where they can ask the right questions. In communicating with the industry leaders, it is important to spend your questions on ones that count, rather than information that can be discovered through a simple Google search.
By developing a base knowledge, a data analyst is able to speak the same language as an industry expert.
Becoming a data analyst can be like learning a new language with much jargon, slogan and lingo to learn. Learning to learn is an essential skill for a data analyst as well. In the process of integrating to specific industries, an analyst will have much success when they can immerse themselves quickly.