“Data in itself is not a value add.. It’s whether or not you’re turning it into meaningful insights” — Taylor Wells, News Corp

These days, data is often viewed as a silver bullet. We can collect, store, and analyze more data than ever before and use it to learn so much about consumer demographics, behavior, and preferences. But how you collect and analyze data makes a huge difference. Going about it in the right way can lead to actionable insights that improve business outcomes — whereas the wrong approach can be messy, costly, and unhelpful (or worse, get you into legal trouble).

To learn how to properly plan and implement a data strategy, we turned to Taylor Wells, the director of data products at News Corp. With over 15 years of experience in data analysis, Taylor is an expert on how to collect and analyze data responsibly, legally, and effectively to drive real business outcomes. We asked Taylor to share his top tips for organizations looking to implement or improve their data strategy.

Don’t collect everything

It might be tempting to collect as much data as possible and sort out what’s useful and what’s not later. But, as Taylor points out, there are multiple problems with this approach. First, it creates a ton of noise for your data team to deal with, which can slow down your data analysis and time to value. Second, data storage is costly — why pay to collect and store data that you’re not going to use anyway? Third, and perhaps most importantly, collecting too much data can get you in trouble with data regulation and user privacy laws like GDPR.

Instead of collecting as much data as possible, first decide what data points are important for informing your business decisions. Establish a well-structured framework using an off-the-shelf data collection solution, and focus on 10-20 actions that you’re most interested in tracking. You can always expand as you scale, but this is a good place to start.

Ditch the dashboard

Dashboards are commonly used to chart the results of data analyses as trends and percentages. But when it comes to sharing key insights across teams and with executives, dashboards aren’t the most effective — to the non-expert viewer, they can be overwhelming and difficult to interpret.

When Taylor was at Disney+, he found a better way of communicating the results of his analyses: he sent a daily email with a focused summary of the latest data insights for upper management to be aware of. As much as Taylor personally loves dashboards, he recognizes that they don’t provide a lot of value in the business decision-making process. “I don’t actually want to look at dashboards anymore. I don’t want to look at reports, and I don’t care about month-over-month if it’s not actionable,” he said. “I’d much rather you tell me that we’re seeing a weird trend where people that log in after 10 p.m. on weekends wind up being our longest tenured customers over time. Or that people who sign up for the bundle annually wind up using it 40% more than other consumers.” Deriving and communicating helpful information from raw data is an important part of an effective data analytics strategy.

Follow the data

One of the most important lessons Taylor has learned is not to let assumptions and personal biases prevent you from gleaning meaningful insights from your data. To illustrate this point, Taylor talked about his experience on the team building the Disney+ streaming app. Originally, the team expected that Disney+ would be popular with kids, but after analyzing the data, he and his team were surprised to learn that over 50% of their audience was single adults. This knowledge fundamentally changed how the team organized and marketed content within the app.

As Taylor points out, this highlights how important it is to listen to the data. “If you’re going in with these presumptions instead of being curious and allowing [the data team] the leeway to explore, you can miss out,” he said.