Today I learned an interesting fact about women in data professions. The number of female data analysts is approximately equal to that of men, and has remained that way for the past decade. Over the same 10 years, the proportion of female data scientists has slowly crept up to ~20%, lagging severely behind that of men. Both data professionals share responsibilities for interpreting, understanding, and presenting data, and they have shared organizational goals to derive value from the data. Both use data visualization to discover, understand, and illustrate data patterns, and they use data storytelling – blending data visualizations with narrative to explain, explore, and engage in ways that audiences find intriguing and compelling.
One reason so few women work as data scientists may be as obvious as the need for more STEM education as early and foundational learning for girls, as well as advocacy and mentoring to sustain enthusiasm and engagement throughout their education. It seems a natural fit for women with the right education, experience, and interests to make the transition from being data analysts to becoming data scientists.
Less obvious are the reasons that women have found a level playing field in data analysis and how they have been able to sustain that position over the last 10 years. What skills or talents do they have that make them a good choice for data analyst? Let’s look back a decade to begin to find the answer.
Self-service reporting and analytics liberated business departments from total reliance on IT departments to meet their data needs. Tools, such as Power BI, Tableau, and Qlik brought a flood of analytic capabilities to the business user. The ability to get data, perform analysis, and gain insights quickly became a priority for many business units. Self-service capabilities increased the demand for data analysts and created new opportunities for women to advance their careers.
I am sure that for some organizations it was a bumpy road to gain value from self-service data. I remember the adage years ago about automating processes. If you automate bad processes, you get bad results much faster. Now, however, we are 10 years out. Self-service data analysis is mature and has become the norm for the majority of business organizations. Data analysis is no longer centralized. It is common for departments to have data analysts on staff, creating new opportunities for data-savvy business professionals to advance into data analyst positions.
What skills and characteristics make a good data analyst?
The best data analysts are naturally curious about the world around them and driven to understand how things work. They enjoy the investigative aspect of being an analyst and excel at root cause analysis and problem solving. Their innate curiosity keeps them yearning to learn more, fine-tune analysis methods, discover new tools and technologies, share ideas, and learn from others.
Data analysts combine analytical thinking and critical thinking skills to understand business dynamics—what happens and why it happens. They perform causal analysis to identify problems, then blend lateral thinking and creativity to seek solutions and make recommendations. A skilled data analyst blends multiple thinking styles—analytical thinking, critical thinking, lateral thinking, systems thinking, etc.—with data exploration, statistical analysis, data visualization, and hypothesis testing to arrive at well-reasoned conclusions. They employ enough methodology to achieve repeatable and teachable analysis processes while remaining open to new concepts and techniques.
Understanding data is a core skill for data analysts, but what exactly does this mean? It begins with basic data skills such as knowing how to work with files and databases. Next comes numeracy—being good with numbers and having robust knowledge of statistical concepts. Ultimately, however, you need more than just strong data and statistical skills to truly understand your data. You need business domain knowledge to understand the data in business context. That context is essential to see patterns, find meaning, draw inferences, and discover insights through data analysis.
What is the primary responsibility of a data analyst?
Analyzing data is only part of the analysis process. Good data analysis begins with problem framing and finding the right data for the problem. Next comes data exploration – understanding data contents – followed by data cleansing and preparation. All of these steps are necessary before undertaking analysis to find meaning and produce data visualizations. But the end-game of data analysis isn’t in producing analysis results; it is in communicating those results to those who will act upon information and insights.
Consider as an analogy the scenario of a person in a master’s or doctoral program. They put their blood, sweat, and tears into collecting data, conducting research, performing analysis, and preparing their findings. Once their master thesis or doctorate dissertation is complete, they see a light at the end of the tunnel. They then prepare for their presentation to defend their research.
But without strong communication and presentation skills they can’t make a compelling argument. This final stage often means the difference between a post-graduate degree and a program that is incomplete. Analysts face similar circumstances, so highly skilled data analysts are also skilled communicators and presenters.
The goal of a data analyst is to share insights and knowledge with those who will get value from and act upon the knowledge. Communicating with stakeholders in their language is a fundamental requirement that uses communication skills and the domain knowledge discussed earlier. This kind of communication is the basis for the art of data storytelling.
What role does data storytelling play in communicating analysis results?
Stories are powerful and we’ve used them throughout history to capture attention, convey ideas, fire the imagination, and spur people to action. Business leaders use stories to bind workforces together in common values and goals. Advertisers use stories to make us feel cool for using their product or pathetic for using something else. Lawyers and prosecutors use stories to demonstrate or deny motivation or to demonstrate guilt or innocence.
I remember attending a data storytelling class 15 years ago, at a time when the concept of combining data and narrative was just emerging as a way to create compelling and persuasive communications. Over time, just about every data visualization vendor recognized the value of data storytelling as a complement to their products. In a recent Google search, I found dozens of data storytelling and data visualization courses and certifications from notable organizations and tools from vendors, such as Tableau, Google Data Studio, Datawrapper, and Infogram.
It is interesting to note that both the data storytelling and the self-service trends began to mature at the same time, and they were powerful tools for data analysts. Women are able to gain ready access to data and blend their data knowledge, communication and presentation skills, data visualization skills, and their natural talents in storytelling to excel in data-driven environments.
A Call to Action
Business departments now expect that sophisticated analytic capabilities will be at their fingertips. Women can meet that need. They can create a compelling data story that connects with cause and effect, elicits personal responses, and drives conversation and interaction. They are good storytellers. They can use their soft skills to bring people together and create great relationships. They can make human connections and use their communication skills, both spoken and listening, to connect with their audiences and present their findings.
Many of the skills needed to excel as a data scientist are built on the foundations of the data analyst skills described here. We have leveled the playing field for women in data analysis. Let’s build on that success to create new opportunities for women in data science. If you’re a skilled data analyst, it is time to take the next steps. Feature your data analyst skills in an attention-getting resume. Then expand your skills with knowledge of statistics and analytic modeling to enter the exciting world of predictive and prescriptive analytics, artificial intelligence, and machine learning.
Here is an example of a data analyst resume which demonstrates how to highlight some of the soft skills that I’ve mentioned above.
Other resume examples, including those for a Data Engineer and a Data Scientist are available at: https://www.itresumetips.com/it-resume-examples/
About Jennifer Hay
I’ve been writing technical resumes and advising on career transitions for almost 15 years.
Throughout that time, I’ve read numerous articles about best practices for IT resume writing. What I found in those articles is a lot of bad information because it’s the same advice they give for non-technical professionals. This is important because IT resumes are different.
I built this website to share what I’ve learned in my career. I think you’ll find information on this website that will help make your IT resume a success.