A few weeks ago I was asked to speak to students at my graduate school about my career in data analytics – shout out to Professor Sera Linardi and the University of Pittsburgh’s Graduate School of Public and International Affairs for their invitation! – I quickly realized I had never intentionally entered the data analytics field. How was I going to talk to a room full of new graduate students? Because I believe in the importance of data – and I wish someone had told me during my studies why I should take as many “hard skills” courses as possible.
Data Analyst. Data Scientist. Descriptive Analytics. Prescriptive Analytics. Predictive Analytics. What do all these terms mean – and why are they important in the field of data analytics? All of these are important terms with their own definitions. For the purposes of this blog post – and for simplicity – I’m only going to define data analytics. Data analytics is the process (automated or manual) of analyzing raw information in order to draw conclusions and make decisions.
Data analytics is not the same as data reporting. Presenting facts and figures does not make for great analysis. However, great data – and knowing how to tell good data from bad – are a necessary part of great analytics.
For me, this was the hard part. I had a math tutor throughout all of highschool and I barely passed my college economics courses. However, by the time I entered graduate school and my brain finished developing (experts say this happens around age 25), I could focus my attention enough to succeed in advanced coursework in statistics (quant I and II), economics (micro, macro, behavioral, development, and more), and mathematical theory/logic (game theory).
Math skills aren’t enough. If you want to get into data analytics you need to know how to apply these skills to problem solve. Think about this example: your company is losing money, your boss wants to know why. You could answer “because sales are down.” Or, you could dig into the data, analyze it, determine why sales are down, and recommend a solution. Which approach adds more value? Rhetorical question.
I never took a coding or programming course. It would have made me so much more marketable, and it would have made repetitive tasks so much easier. Take Python, R, SQL – anything. I name those three specific languages because they are used a lot in data analytics. But the language itself doesn’t matter so much as learning how to do it and how to think like a programmer. From writing a few simple lines of code to clean a dataset, to building a custom algorithm programming is an essential skill for anyone in the field of data analytics.
If coding isn’t a skill you can learn, make it your mission to learn business intelligence software platforms like Tableau or PowerBI, become a power-user on a CRM platform like Salesforce, or learn how to incorporate geographic information system (GIS) skills into your analysis. This will help differentiate you from every other data analyst applying for a job.
Data visualization is an essential skill in data analytics. Learning how to pick the right chart for the right audience is just as important as learning how to implement user-centered design principles. Taking a key performance indicator (KPI) from a metric that is tracked in a database to a visual on a dashboard makes it more useful and more actionable.
By the way, one of my favorite data visualization blogs is Ann K. Emery’s Depict Data Studio blog – they have an amazing interactive chart chooser that can help any beginner (or seasoned data analytics professional looking for something new) select the right chart for their project.
Network. Go to the events hosted by your graduate school. Talk to the speakers and ask questions. Send follow-up emails. Ask to meet for coffee. You never know when a job opportunity will become available.
Tell a story. Don’t just report facts, explain what matters and why. Whether your writing a paper for a professor, preparing an analysis for your manager, or submitting a proposal to win a client contract, you’ll do better by learning how to tell an impactful story.
A data analytics career isn’t for everyone. At its core, data analytics is about knowing how to ask the right questions, and trusting your training and instincts to make the right decisions. Before any analysis, I ask myself one question: “how can I use the data I have to make my client succeed?”
According to Jeff Barrett in his 2018 Inc.com article, up to 73% of company data goes unused for analytics. Data collected without analysis doesn’t add any value. Anyone entering the data analytics sector should know that their number one job is to add value – to their team, their company, or their client. For an example of how VISIMO used data to improve a client’s financial position, read our Case Study on Huckestein Mechanical Services.
VISIMO adds value to their clients by exposing hidden information in company data, enhancing it using the unique skills of our employees – including predictive analytics, programming, and machine learning applications – and elevating our customer’s business outcomes. See how VISIMO can help you by taking our five minute growth quiz!