3 Steps To Take Before Telling Your Brand’s Data Story

You log into your analytics dashboard on Monday morning to check traffic and engagement from the past week. All metrics are up by some miracle, so your day is off to a good start.

But when’s the last time you stopped to think about the method to the madness—the story behind the data? Without understanding the context, how much are the charts and graphs really worth?

I recently moderated a panel at the 2018 National Association of Black Journalists convention called “Predictive Data and Its Virtual Seat at the Table.”

The panelists—data pros from Dataminr, Spotify, and New York University—dug into how data decides what we believe matters and influences the content we create.

L to R: Sade Muhammad (moderator), Roland Carter, Nekpen Osuan & Brian Clarke taking a question from the crowd at the NABJ data panel in Detroit.

Data is so powerful because it has “direct implications on [so much]—from what we think is healthy, to what we think is meaningful, to what we believe is true,” says Nekpen Osuan, adjunct professor of Data & Analytics at New York University and founder of WomenWerk.

Here are three ways to gut-check the data in your dashboard and make sure your team is equipped to evaluate progress and drive success.

1. Uncover any biases in your data.

“There’s a saying, ‘garbage in, garbage out,’” says Roland Carter, data scientist at Spotify. “The same can be said for data—bias in, bias out.” Data teams at Spotify have access to an algorithmic bias checklist that helps them assess how biases can influence data.

The Spotify voice speaker is one example of where bias can come in. An algorithm might misinterpret artist names with special characters like A$AP Ferg or Ke$ha. If Spotify had assumed certain fans didn’t use the voice command feature, an entire audience that wanted to engage could have been overlooked. But because the data scientists account for how bias impacts product data, they’re able to catch and address such flaws.

2. Adapt your strategy over time.

“We use a lot of data to determine how can we reach a user with the right content at the right time,” says Carter. A lot of that success, he explains, is about having a robust data set to train models on past listener activity. With each new Spotify playlist, Carter and his team aim to drive engagement while also staying on-brand. Through sophisticated algorithms and personalized playlists, casual visitors turn into full-fledged fans.

How do you work toward building that level of customer loyalty? Create a roadmap and be open to evolving your KPIs and strategy over time. You can then deliver a more accurate view of what success for your campaign means now, and what it could mean in a year’s time. “As important as the algorithm is, the metric you’re optimizing is equally as important,” says Carter.

3. Know what the numbers don’t show.

In addition to her work as an educator at NYU, Nekpen Osuan has worked as a political organizer on presidential campaigns. She recalls a campaign where a consulting firm advised her team about how a set of voter data should inform campaign strategy. The data might have been technically accurate based on the polled users, but Osuan knew from her work on the ground that it wasn’t a true depiction of the region. She didn’t speak up—and later wished she had.

Professionals must bring their own expertise into the room to help gut-check technology and data, Osuan says. “Don’t check your conscience at the door when you have a technical meeting. Even if it looks intimidating, get closer to the data sets that actually drive outcomes.”

Dataminr monitors budding trends, breaking news and big moments to help clients keep up with the fast pace of social media. Brian Clarke, senior manager of event detection and operations, may be an expert in trend-spotting and making decisions based on data, but the data scientists who collect data are just as important to his team’s success.

Clarke says he consults with his technical colleagues to discover how data sets become the insights he uses to make strategic decisions. He recommends stepping outside of your comfort zone to deepen your technical knowledge and get a fundamental understanding of what data is really saying.

“It’s important to ask yourself: How do my users want to interact with my product?” he says. “How do I make this product as accessible as possible? How do we make sure it’s user-friendly, efficient, and [delivered] in the way [the user] likes it?” Ultimately, he explains, a shrewd understanding of your data story is the first step to meeting—and exceeding—audience expectation.

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