Featuring Insights From Iun Chen & Vish Srivastava
Read: 4 Minutes
Data analytics and business analytics are often confused, understandably, because both data analysts and business analysts work with data. What matters — and differentiates these two roles — is what the data is intended to do.
When comparing the roles of business analyst and data analyst, one must consider the audience. Who will be taking action based on the analyses?
Business analysts use data to improve business metrics.
Business analysts work directly with stakeholders to steer company objectives and keep the business on a successful path. They set and maintain key performance indicators for the organization. A business analyst may recommend strategies or business plans to executives, sometimes when a company is at a critical juncture, say quarterly or during a turnaround. Stakes can be high, but so can the rewards. (Think McKinsey analysts or other coveted consultancy jobs.) Business analysts are more likely to use presentation skills as they’ll need to present findings to executives and give recommendations in high-level meetings.
Data analysts collect, extract, and analyze data.
Data analysts are more technically focused. They are responsible for getting the data and analyzing it, working with datasets and tables. For example, a data analyst at an eCommerce company may analyze customer information, aggregate email marketing lists, or use data to identify demographics for new customer acquisition plans. Data analysts are more likely to work in teams alongside marketing partners or with other technology roles such as programmers or product managers, depending on the size of the company. They also work with business partners across entire organizations, including business analysts, as needed for tasks and projects.
Different roles mean different salaries.
Both business analysts and data analysts solve business problems. As such, they are in high demand. According to Glassdoor, the average salary for a data analyst in the U.S. is $72K. Compensation for business analysts is a bit more, averaging $79K. Of course, exact amounts depend on location and will vary from country to country. While a business analyst can command a higher salary, there is wider latitude for data analysts to carve out their niche in practically any industry. Since the function of data is increasingly integral to every enterprise, there is more flexibility for data analysts to dig into areas of the business where they can make the most difference, with more potential for creativity.
In GA’s Intro to Data Analytics course, Iun Chen teaches SQL, Tableau, and Excel, business intelligence tools she uses in her professional role as a data analyst at LinkedIn.
“My formal job function is to build data tools for internal colleagues so they can successfully grow our business,” she says. “I create dashboards, reports, and anything else to ensure revenue keeps going up and anticipated risks go down for the company. In my experience, the skill set and mindset of the individual can define the role of a data analyst in any organization, large or small. Everyone uses data in their day to day so being able to clean, prep, analyze, and report data — regardless of what your actual job title is — is critical to not only the company’s success but your personal success as well.”
Both business analysts and data analysts are storytellers.
Whether a business analyst’s more strategic and decision-making role is for you, or the technical, numbers-crunching, team-playing data analyst sounds more your speed, know that the two roles share one crucial skill: They use data to tell stories. Those stories lend insights that factor into decisions that affect the bottom line. Translating raw data into digestible and human narratives can be one of the most challenging skills for analysts to master, according to Vish Srivastava, who’s led multidisciplinary teams across tech sectors. So how does an analyst develop this multifaceted skill and set their career on the path for success?
“My recommendation is twofold,” he says. “One, always start your analysis with a hypothesis that you’re testing. You need to know right out of the gate why your analysis is going to matter. Two, after you’ve spent some time with your data, step away and write down your presentation storyline in three to five bullets. The final bullet should be your recommended next step. Of course, make sure you have the analysis and charts to back up your storyline and fill in the gaps as needed.”
When it comes to storytelling with data, the difference between a boring story and a compelling one can come down to data visualization. The tools at your disposal and your proficiency with them can make or break a presentation. Communicating the insights for business intelligence hinges on clear and impactful data viz, whether we’re talking business analytics or data analytics.
One classic example of data visualization’s power is the cholera map by John Snow, an early pioneer of disease mapping. “This is a beautiful example of how collecting data and visually presenting it can generate amazing insight,” says Srivastava. “In this case, the insight was that the sewer systems were spreading disease. This informed public policy and saved so many lives.”
The future of business intelligence will be determined by the democratization of data.
The prevalence of data and its part in tech careers is changing. To hear Srivastava tell it, future conversations on business intelligence will center less on the specificities of data analysis vs. business analysis and more on how data is creeping into even more roles.
“We’ve come a long way, but there is still far to go for data analysis skills to be deeply embedded in all functions across a company. In the future, I think we will see fewer dedicated teams for business analysis and data analysis; instead, all professionals will have these skills and utilize them daily. This democratization of data analysis will be incredibly powerful. It will create even more emphasis on making high-quality data available across every enterprise.”
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