This article originally appeared on the MultiBrief blog: https://exclusive.multibriefs.com/content/why-your-team-is-drowning-in-data-instead-of-actionable-insights/association-management
Your data follows a similar pattern to Moore’s law. It doubles every few months, even when if you’re collecting millions and billions of data points. 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 dive deeper into each one in this post.
Reason #1: No Practical Data Strategy
Deciding to collect every data point and 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. However, the psychology of using data is still the same and getting more difficult as noise increases.
I worked with a client that had fantastic data, but no one was using it. After going through some interviews, I learned that their team didn’t trust the data. So I audit their major reports and then work with each person to answer their questions and build trust back into the data.
There wasn’t anything technically wrong with the data, but their team needed assurances and training. You can tackle these kinds of challenges in a data strategy. Think about the people who will be using the data, the process to gather insights and the providers (or technology) 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 just need to know how to build relevant reports and look for patterns in the data.
Reason #3: No Support From Data-Specific Roles
Companies need to hire data-specific roles 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 ideas to get the most out of your data analysts or data scientist:
- Provide self-exploratory data tools
- Limit requests to 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. There are already too many things on their plate, 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 case studies on how the data helps other people in the company. For example, the marketing team might discover hidden levers in their campaigns, or the product team increases customer satisfaction by understanding user behavior.
Motivation is then coupled with ease of use, reason #2 and reason #3, respectively. So if someone believes that they can do something faster or better by leveraging data, and if that process isn’t like pulling teeth, they will consistently do it.
I’ll finish with one more story. A few years ago, I was on a flight to Dallas on a tiny plane. The plane was so small that the carry-on luggage could barely fit in the overhead compartment. I kept seeing people struggle to fit them in.
In particular, one couple was having a hard time, and they eventually just started hitting the luggage to make it fit. Then, suddenly, the luggage shifted slightly, and it slid in perfectly.
You don’t need to reinvent your entire approach to data. Instead, you’re looking for slight shifts in improvement that can get you closer to the ultimate goal: insights.