You have more data than you know what to do with. Having an abundance of data seems like a good thing, right? But without the proper infrastructure for storing and analyzing it, you lack that essential “single source of truth” that underpins effective decision making. Creating the most informed strategy, asking the right questions, and backing up your ideas depends on having the right tools and teams in place to ensure data accuracy. But that’s not all. Unless you have a data-driven culture, it won’t matter what infrastructure or tools you have. Getting your organization to use data correctly requires a shift in attitudes and workflows. It isn’t easy, but without a data-driven culture, you’ll end up saddled by unnecessary costs and missed potential.

Person using a laptop looking at data.

The costs of misusing your data are high

Making decisions based on instincts — or worse, unreliable data — makes it impossible to achieve optimal business results. It’s like trying to navigate unfamiliar roads in the complete darkness. There’s a chance you’ll reach your destination, but it’s highly unlikely you’ll find the easiest, fastest route. There are two major problems with misusing your data (or not using it at all).

Problem #1: You never even know the problem exists

Unless data analysis is a routine activity, it is unlikely that you’ll be able to identify costly problems or high-potential opportunities. For example, only after analyzing our customer engagement data was I able to identify an opportunity to reduce customer churn. For all SaaS companies, churn is inevitable. But by analyzing our data, I could see an important correlation: The more a customer engages with WalkMe (i.e. creating content via the editor), the less likely they are to churn. In other words, the customers who make the most of the solution’s capabilities also realize the greatest value and are more likely to renew their contracts. Based on this finding, our customer success managers changed their strategy. They began focusing more time on encouraging customers to increase engagement and better utilize the editor. This is a win-win — better value for our customers and lower churn.

Problem #2: Your “truth” isn’t reliable

When you lack standards and protocol for analyzing data, you’ll be left with unreliable and even inaccurate insights. A common example is cherry-picking, which means presenting data that backs up your claim or agenda while disregarding data that doesn’t. For example, say a content marketer needs to present the performance of his latest blog post to his team. Instead of explaining the full range of data metrics he analyzed, the writer cherry-picks the data that makes his post appear most successful. He notes that there were 3,000 visitors in the last week, but doesn’t mention that the bounce rate was 95% and the conversion rate is virtually nonexistent. By cherry-picking the data that seems to signify positive performance, the content marketer gives a false impression that his blog post performed better than it truly did. Not only does his presentation lack credibility, but he’s also ignoring opportunities to optimize the post.
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