BY Peter Olsen Phillips09 March 2022, 15:24
A commuter walks up a flight of stairs at the Oculus Transportation Hub in New York, US, as seen in March 2020. (Photographer: Demetrius Freeman—Bloomberg/Getty Images)
Business analytics professionals occupy a unique space in the workplace. Requiring both strong 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 large companies make tough decisions.
More and more companies need a business analyst. The U.S. Bureau of Labor Statistics predicts that by 2030, the number of business analyst type jobs will increase about 25%more than three times faster than average for all roles.
But what does this job really look like? What data do they use and how do business analytics professionals interact with it to create value? Fortune spoke to three business analytics professionals to learn more about their work and how they operate in their broader business contexts.
From data silos to data dashboards
These days, you can find business analysts in nearly every department in the workplace, including some that aren’t traditionally considered quantity-oriented. The goals are the same: to predict trends and find answers that help the business as a whole. In human resources analysis, 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,” says Marcello Cabrera, head of human resources analytics at the University. of the Maryland Medical System.
For Cabrera, part of his job has been to integrate previously siled datasets, turning isolated sources of information into comprehensive “data warehouses.” These larger datasets 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 access an application and get a sense of the workforce,” says Cabrera. “They’ll be able to see who’s making up the workforce, what the trends are on things like turnover – so the rate of people leaving – what the vacancy looks like, in terms of ‘what areas do we need to fill in staff?’ And diversity and inclusion.
Just as the role analysis is broad, so are the skills required.
“Part of the job is to develop those tools, and then part of the job is to be a resource when there’s a specific business issue that the organization needs to understand,” says Cabrera. More focused business questions may require running regression analyzes to determine the correlation between different variables and using programs such as Excel’s Solver to optimize results given certain constraints.
The results of these analyzes can help predict key metrics such as staffing levels. “We’re going to look at a specific group and try to understand what our hiring goals are and pull data to try to see ‘OK, here’s what we’ve done historically, so what do we think it’s going to take over the 12 next few months? ?’” Cabrera said. “For example, for nursing, we need to know how much [people] we need to target hiring to be able to staff appropriately. »
Analyze data to adapt recommendations
Business analytics techniques can also reveal important patterns in the world of corporate social responsibility, helping organizations find more effective ways to give back..
Act, a workplace giving platform, helps companies track and manage data on employee charitable giving, fundraising, volunteering opportunities, and more. The metrics it collects contain valuable insights both for corporate clients looking to drive employee engagement and for Deed’s internal team looking for giving patterns that can inform corporate philanthropy campaigns. in a wider area.
Christine Tringale, Head of Data Strategy at Deed, says Fortune that its first priority is to ensure the quality, accessibility and usability of the company’s data. “My main goal is that our data is accessible, so that we are transparent in our data. So that people, whether internally or our customers using the platform, can easily access the data and that the data is accurate. »
A robust and reliable dataset can then be used to guess the patterns behind particularly successful campaigns. “When you think about donations, you can break it down into how many people donate, how much they donate, how many times they donate, etc.” Tringale said. “So you go a little lower and say, ‘Well, what are the levers that are pulling each of these and how can the app support these behaviors? “”
Tringale’s goal is to be able to adapt recommendations to clients and their employees based on the best practices identified by its team. “Are there some commonalities between truly successful fundraisers? Is it something to do with the cause area? Is it something to do with the sensitization approach? Is there a tipping point, in terms of how many employees get involved, and then it goes viral in the business? »
She believes that with the right combination of an engaging platform and data-driven recommendations, companies will see a significant increase in workplace giving. “Our hypothesis is that if we can create a truly engaging platform for giving and volunteering, people will give and volunteer a lot more,” Tringale says.
Weigh trade-offs and communicate results
Often the business questions that analytics professionals work on don’t have clear answers. For Ryan Howard, who was head of analytics in a previous role at Ubersuccessful business analysis requires both the ability to find answers to discrete questions and the means to go deeper and map out some of the potential costs and benefits of different courses of action.
Howard uses a hypothesis to illustrate the complexities of the role. “If we launch a new feature for drivers, or for delivery partners, we will sometimes monitor: is this change well received? Are people interacting more with the app? Are they driving more? Is their satisfaction higher? explains Howard, who now works in Uber’s operations team. “We’re going to weigh that against, you know, what if people used it more, but that actually drives some people off the platform? How do we weigh those decisions? Is that good overall? Is overall bad?
Its analytics team often used technologies such as SQL to query and join data, while programming languages including Python and R were used for further analysis. Viewer software such as Google Data Studio Where Picture has proven useful for presentations.
However, when it comes to communicating results to stakeholders, it’s best not to rely solely on charts and variance reports, says Howard. On the contrary, business analytics professionals must be able to effectively condense and communicate their findings in simple language.
“I think having a high-level overview, sometimes even without numbers, is a very effective way to get the point across, and then having numbers and charts to help back up the story,” says Howard. “Being able to simplify this into very simple language, I think, is one of the most important, yet undervalued skills.”
Developing this skill is something Howard encourages among his colleagues. “I would say one of my core principles, and one of the things I tell my team, is to simplify to amplify.”
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