Meet the person behind the role
I began my analytics journey in my first role at Suntrust. Over the next 20 years, I lent my skills to prominent enterprises such as Verizon and Geico before venturing into the dynamic realm of Hyperscale Data Centers. Driven by an insatiable passion for learning, problem-solving, and imparting knowledge, my heart truly lies in the world of data analytics.
Finding my way to becoming a Data Analyst didn’t just happen; it was over a decade before I understood what I didn’t understand. I didn’t just want to solve a problem, I also wanted to learn as much as possible surrounding the problem. I received my Associate's degree in my 30s and was feeling behind the curve compared to younger students, despite my professional experience. It was when a professor was teaching about data mining, R code, and his role as a current data analyst that finally turned me towards one of the more focused, and unfocused roles, in this digital age. It was also the first time I had heard the title Data Analyst, and I never forgot it.
Diving into data
Data keeps changing, but Data Analysts understand how to change and adapt with it. The truth behind a Data Analyst is that we understand as much as we can, which makes us versatile employees.
This also makes it easy to put us in the wrong position, limiting the skills we can showcase. Used properly a Data Analyst can break down your data into all the possibilities and not just the 'wants'.
Data Analytics, and the Data Analyst, can be broken down into a simple and complex Venn diagram. Welcome to the “Understand What?” diagram of data analytics.
Beginning in data analytics
Present-day Data Analysts find themselves entangled in the endeavor of showcasing the essence of their work. To us, data simply exists; content and row count adhere to the Law of Large Numbers.
Perhaps because of this, there's no definitive 'beginning' to data analytics.
Sometimes I will be handed a report and told that they need it to be updated, more accurate, show different information, etc. In this case, I am shown the results my client wants and I understand that first. Other times I will be handed the information and dive into understanding what I have and what I need.
However, the easiest way to start is to understand the whole so I know exactly what I am working with. The ‘whole’ can be a bit amorphous depending on the project or business model, but that is the point of a DA. We understand that ‘whole’ and show you best practices.
A good DA (Data Analyst) will:
- Understand the entirety of the work
- Grasp the intricacies of the information
- Comprehend the implications of the results
Within these, a DA will be able to answer the questions:
- What action is required?
- What data is necessary?
- What reporting is essential?
Data Analysts may perform many duties akin to Data Scientists, but they remain distinct roles, each with their own strengths when utilized effectively. I collaborate with Data Scientists more frequently to address issues, since they possess specific best practices to execute the work.
Now, let's delve into the breakdown of Data Analytics, starting with Understanding.
Understand the Whole:
This step can be as simple as grasping the database structure and data flow to identify any issues. It can also entail comprehending the entire company and its processes to pinpoint areas of revenue loss.
Starting here helps me gain a fundamental understanding of the project or business I'm working with. However, as a Data Analyst, it's impractical to be the master of the entire domain. It's too complex for one individual to have mastery over everything, but we aim to understand it sufficiently to comprehend the information.
Once we have a grasp of the whole, we can then proceed to understand what actions are needed. This may encompass data clean-up, data mining, report building, and numerous other aspects.
Understand the Information:
As Data Analysts, we recognize that data is a subset of information, not the other way around.
Consider Excel, one of the most widely-used platforms for data consolidation. Although Excel facilitates data transfer, not everything within an Excel document qualifies as data.
Data possesses specific requirements:
- It must be easily transmissible.
- It must be easily queried.
- It must be quantifiable.
Excel can house multiple spreadsheets, each with its own set of rules for the information it contains. Consequently, Excel serves for data clean-up and data transfer, but it is not a standalone database.
Once you grasp the distinction between data and information, you can proceed to understand the reporting needed.
Understand the Results:
As Data Analysts, we are fortunate to encounter a diverse range of challenges in our work. Sometimes, our company provides us with precise instructions on the specific problem they want us to investigate and solve. On other occasions, we are encouraged to explore the entire dataset from fresh perspectives.
Our role as Data Analysts goes beyond merely delivering expected results; it involves gaining a comprehensive understanding of the dataset. This understanding empowers us to present additional insights and reporting that may not have been initially considered, serving as a crucial bridge between data and end-users.
Among the numerous statistical and reporting standards we employ, the Empirical Rule holds significant prominence. Despite its widespread use, it often remains overlooked. Why is that the case?
The answer lies in the nature of data analytics - the Empirical Rule has the potential to reveal truths that the stakeholders may be hesitant to confront. Data reports the reality, even if it presents challenging truths. As Data Analysts, it is our responsibility to present the truth to enable the best possible results and recommendations. We see ourselves as messengers, conveying the insights that can steer organizations toward informed decisions.
In conclusion
The world of Data Analytics welcomes individuals who can rightfully be called the "Jacks and Jills of all Trades" in the digital age. As Data Analysts, we possess a versatile skill set that is adept in various domains. However, it is essential to recognize that our true mastery lies in handling and interpreting data. Embracing the role of both a versatile problem-solver and a data expert empowers us to unlock valuable insights and drive impactful outcomes in today's data-driven landscape. Adept at many skills but always masters of data – welcome to the world of Data Analytics.
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