Your data follows a similar pattern to Moore’s law. It doubles every few months. You might not even see the doubling, but your team is experiencing the effects of this exponential growth. 

Drowning in data isn’t the goal of any company. Every executive wants to use data to make better decisions and drive business outcomes. So how do teams end up in the deep end of the pool without a floating device? 

There are four reasons why teams fail to get insights out of their data. Let’s examine each one. 

Reason #1: No Practical Data Strategy 

Deciding to collect every data point around customer interaction isn’t a strategy. It’s a tactical plan, but it won’t quite get you to insights.  

The technical details of data are becoming easier with every passing day. Yet, the psychology for using data remains the same and grows more difficult as the volume of noise increases. 

I worked with a client that had fantastic data, but no one was using it. After asking some questions, I learned that the team didn’t trust the data. This then required an audit of the company’s major reports and working with several individuals to answer questions and build trust back into the data.  

There wasn’t anything technically wrong with the data, but the team needed assurances and training. You can tackle these kinds of challenges in a data strategy. Think about the people that will be using the data, the process to gather insights, and the providers (or technology) that you will need. 

Reason #2: No Training on How to Use Data 

Don’t assume that data is self-explanatory. In my experience, teams need basic training to understand how the data works and how to use it in their specific role. You can design your training in group, individual, and ad hoc formats to get people moving in the right direction. 

For some people, working with data is a throwback to statistics in college. In reality, they don’t need to know advanced statistics to find relevant insights — they need to know how to build relevant reports and look for patterns in the data. 

Reason #3: No Support from Data-Specific Roles 

Companies understand that they need to hire data-specific staff, such as data analysts, but these roles can quickly become bottlenecks. They have way too many requests and not enough time. They also have to deal with any changes in the data quality and ensure no technical issues. 

Here are a few ways to get the most out of your data analysts or data scientist: 

• Provide self-exploratory data tools 
• Limit requests to only the most important, and ask individuals to justify their requests 
• Automate as many reports as possible and make them easy to find 

Reason #4: No Tangible Motivation 

Your people are busy. They already have too many things on their plates, and asking them to dig into the data is just another to-do item. There needs to be serious motivation for them to commit to spending more time with their data.  

The motivation will come from seeing tangible cases in which the data helped other people in the company. The marketing team might have discovered hidden levers in their campaigns, or the product team increased customer satisfaction by understanding user behavior. 

Motivation, coupled with ease of use, reasons #2 and #3, will help team members believe they can do faster or better work by leveraging data. 

You don’t need to reinvent your entire approach to data. Instead, look for slight shifts in improvement that can get you closer to the ultimate goal: insights.