5 Things You're Probably Doing Wrong with Data Analysis and How to Fix It

Every organization wants to make data-driven decisions, but if you’re anything like 76% of businesses today, you’re probably not doing a good job at it.  Here’s why you may be struggling to build a data-driven organization and tips on how to fix it.

#1 Not Aligning Your Data Strategy With Your Mission

You’re never going to create a culture that lives and breathes data if you don’t tie the importance of data analysis to your company mission. Deciding you’re going to use data to fuel your mission is step one in driving organizational change. Apple's mission is “to bring the best user experience to its customers.” Do you think they’d be able to do this without in-depth understanding and rigorous ongoing analysis of their users? I don’t work at Apple or never have, but can say quite confidently every single product metric is evaluated with a lens of “best user experience”.

#2 Allowing individual teams to remain data illiterate

If your team is data illiterate, they’re never going to use data to make business decisions. Period, end of story. And robust data analysis, don’t even think about it. Even though 90% of business leaders say data literacy is the key to success, only 25% of workers feel confident in their data literacy skills. There are lots of reasons why employees struggle with adopting data, from an outdated data stack to lack of adequate training and support to centralized and siloed data teams. On top of this, if you’re not intentionally building a truly data-driven culture (more on that below), there’s no real incentive for employees to change their ways.

#3 Not investing in the right easy-to-use tools

You’ll never get widespread adoption of data analysis if you don’t ensure your data stack includes easy-to-use data visualization and analysis tools for non-analysts. You heard me… non-analysts need to be using your data tools. While tool setup and deep investigative work can be left to the analysts, everyone on every team at your organization should be equipped with tools that allow them to effectively monitor and investigate changes in their metrics and ask questions without having to build dashboards, know SQL or run queries. There are some amazing tools out there that remove all of the manual work of data analysis, allowing even the most non-technical people to easily understand and investigate data. Invest in them. You’ll see the ROI immediately.

# 4 Underestimating the importance of analysis in day-to-day monitoring

Speaking of tools, make sure your tools actually enable your teams to stay on top of metrics every day with ease. Sure, dashboards are pretty, but they can only get you so far. Updating them usually requires an expert and if metrics don’t change meaningfully, most people don’t do much more than quickly glance at them.

The problem is, if you’re not doing some level of data analysis every single day, even when it seems like there’s nothing major going on, you’re probably missing important information. But data teams (if you even have a team) can’t spend time monitoring small fluctuations in metrics because they’re focused on bigger investigations or higher priority items. And even if they weren’t, that shouldn’t be their job. Organizations with top data-driven cultures don’t centralize data analysis. Individual teams need to be responsible for monitoring and understanding their KPIs and metrics every day.

# 5 Not making data a language everyone at your organization understands

To be truly data driven, data has to be a common language - something everyone understands, tied to the mission, and the why behind all decisions. Metrics should be reviewed daily and data tools should be vehicles for collaboration. Rather than decks and PDFs to share information, have conversations take place in your data tools and make decisions in real time. Gone should be decisions made on experience and intuition. Make data the lifeblood of your organization and results will follow.

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