BY Peter Olsen-PhillipsMarch 09, 2022, 3:24 PM
A commuter walks up a staircase at the Oculus transportation hub in New York, U.S., as seen in March 2020. (Photographer: Demetrius Freeman—Bloomberg/Getty Images)
Professionals in the field of business analytics occupy a unique space in the workplace. Requiring both business acumen and data-driven analytical skills, their role demands a mix of qualitative and quantitative insights. Ultimately, the goals of the position are to increase efficiency and help the larger business make difficult decisions.
More and more companies are finding a need for a business analyst. The U.S. Bureau of Labor Statistics projects that by 2030, the number of business analyst-type jobs will grow about 25%, more than three times as fast as the average for all roles.
But what does this work really look like? What data do they use and how are business analytics professionals interacting with it to create value? Fortune spoke with three business analytics pros to learn more about their work and how they operate within their larger business contexts.
From data silos to data dashboards
These days, you can find business analysts in almost any workplace department, including some that aren’t traditionally considered to be quantitatively-focused. The goals are the same: To predict trends and find answers that help the overarching business. In human resources analytics, for example, the questions asked are usually about the workforce: What does it look like and what will it look like?
“Our job is to help our leaders and key stakeholders better understand our workforce and make better decisions using data,” explains Marcello Cabrera, an HR analytics manager at the University of Maryland Medical System.
For Cabrera, part of his work has been to integrate previously siloed data sets, turning isolated sources of information into comprehensive “data warehouses.” These more expansive sets of data can serve as the basis for powerful data dashboards, which provide real-time snapshots of the organization’s personnel in a user-friendly format.
These dashboards increase efficiency by “giving stakeholders a place where they can go to one application and get a sense of the workforce,” says Cabrera. “They’ll be able to see who makes up the workforce, what are the trends on things like turnover—so, the rate that people are leaving—what does vacancy look like, in terms of ‘what areas do we need to staff?’ And diversity and inclusion.”
Just as the analysis in the role is wide-reaching, so too are the skills it requires.
“Part of the job is developing those tools and then part of the job is being a resource when there’s a specific business problem the organization needs to understand,” Cabrera explains. More targeted business questions can require running regression analyses to find how different variables correlate and using programs like Excel’s Solver to optimize outputs given certain constraints.
The results of these analyses can help to predict key metrics like staffing levels. “We’ll look at a specific group and try to understand what our hiring goals are and pull some data to try to see ‘OK, here’s what we’ve done historically, so what do we project will be needed over the next 12 months?’” says Cabrera. “As an example, for nursing, we need to know how many [people] we need to target for hiring to be able to staff appropriately.”
Analyzing data to tailor recommendations
Business analytics techniques can also unearth important patterns in the world of corporate social responsibility, helping organizations to find more effective ways of giving back.
Deed, a workplace giving platform, helps companies track and manage data on employees’ charitable donations, fundraising drives, volunteering opportunities, and more. The metrics it collects hold valuable insights both for the client companies seeking to spur employee engagement and Deed’s internal team, which is looking for giving patterns that can inform corporate philanthropy campaigns in the wider field.
Christine Tringale, the head of data strategy at Deed, tells Fortune that her first priority is to ensure the quality, accessibility and usability of the company’s data. “My key goal is for our data to be accessible, so we’re transparent in our data. So that people, either internally, or our clients using the platform, can easily access the data and the data is accurate.”
A robust and reliable data set can then be used to divine patterns behind particularly successful campaigns. “As you think about donations, you can break that down by the number of people donating, the amount they donate, the number of times they donate, etc.,” says Tringale. “So, then you take it a step lower, and you say, ‘Well, what are the levers that pull each of those things and how can the app support those behaviors?’”
Tringale’s goal is to be able to tailor recommendations to clients and their employees based on the best practices her team identifies. “Are there certain common threads between really successful fundraisers? Is it something to do with the cause area? Is it something to do with the outreach approach? Is there a tipping point, in terms of the number of employees that get involved, and then it kind of goes viral in the company?”
She believes that with the right combination of an engaging platform and data-driven recommendations, companies will see significant increases in workplace giving. “Our hypothesis is that if we can create a really engaging platform for giving and volunteering, that people are going to give and volunteer a lot more,” says Tringale.
Weighing tradeoffs and communicating results
Often, the business questions that analytics pros are working on don’t have clear-cut answers. For Ryan Howard, who was an analytics manager in a previous role at Uber, successful business analytics requires both the ability to find answers to discrete questions and the wherewithal to go a step further and map out some of the potential costs and benefits of different courses of action.
Howard uses a hypothetical to illustrate the complexities of the role. “If we launch a new feature for drivers, or for delivery partners, sometimes we’ll monitor: Is this change well received? Are people engaging with the application more? Are they driving more? Is their satisfaction higher?” explains Howard, who now works on Uber’s operations team. “We’ll weigh that against, you know, what if people are using this more, but this actually causes some people to leave the platform? How do we weigh those decisions? Is that overall good? Is that overall bad?”
His analytics team often used technologies like SQL to query and join data, while programming languages including Python and R were used for in-depth analysis. Visualization software such as Google Data Studio or Tableau came in handy for presentations.
When it comes to communicating findings to stakeholders, however, it’s best not to rely solely on charts and variance reports, Howard says. Rather, business analytics professionals need to be able to effectively condense and communicate their findings in plain language.
“I think having a high-level overview, sometimes even without numbers, is a very effective way to get the message across and then have some of the numbers and graphs to help back up the story,” Howard says. “Being able to simplify that in very plain language, I think, is one of the most important, yet kind of undervalued, skills.”
Developing this skill is something Howard encourages among his colleagues. “I would say one of my core tenets, and one of the things I tell my team, is simplify to amplify.”
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