The size of the global Big Data and Insights market is over $170 billion and growing at 14,8% (2018). The more we generate data and rely on tools, the better we need to get at working with data science and analytics experts.
Most organisations are still taking their first steps with data-analytical thinking. And as with anything new, there are still many myths and misconceptions around how to become data-driven.
One such myth is that every effort at data analysis must produce an insight. I’ve seen this to be a source of much frustration and love lost between marketing managers and their analysts.
It doesn’t have to be that way and here’s why. 👇
Reporting vs. analysis
The first step towards becoming data-driven is data gathering. The next step is reporting, where you organise meaningful metrics. Reporting brings meaningful data together for you to understand how things are.
The step after reporting is analysis. This is where you look at the metrics presented in the report and ask questions in order to gain insights.
Enabling your analysts to deliver better insights
If you have a dedicated analyst on your team, count yourself lucky. Otherwise, you’re working with an internal or external analytics partner.
Having a dedicated analyst means they’re just as familiar with your business or function as you are. That’s awesome!
Getting your external analyst to deliver better insights means helping them understand the business context better. This means asking them questions that are meaningful to you, as opposed to just waiting for them to come up with the right questions on their own.
How do you know what to ask?
What questions you ask from an analyst, and when you ask them, depends on the types of information you’re dealing with.
In academic terms (🤓 nerd alert!), there are three types of information. Knowing which types of information you’re dealing with can help you ask better questions.
When you’re dealing with data directly from the source, e.g. web analytics, you’re dealing with primary information. It’s often raw and unprocessed. You should ask questions about the methods through which this data was gathered but don’t try to forcibly extract insights at this stage.
When you’re reading a report or looking at a dashboard based on your web analytics data, you’re dealing with secondary information. A dashboard ideally contains fewer but more meaningful metrics chosen to answer specific questions.
Now’s the time to ask questions about the metrics and probe the cause-effect relationship between them.
If you’re given an analysis based on the report that’s tertiary information. It’s a further refined version and intended to deliver insights. This is where you can push and prod to verify the quality and relevance of the insight.
When given an analysis, keep your intuition handy
This is the time to validate the insights and update your intuition based on new evidence delivered through analysis.
Applying the right amount of intuition and data analytical thinking is the only way you’re going to discover insights.
Insights – that stuff of magic data nerds keep going on and on about – are what will allow you to make an informed decision. A decision that has the potential to be effective as well as timely.
Otherwise, you’re out trying to sell Christmas trees in July. 🙉
As your analysts, in-house or not, get more familiar with your business context, the quality of their insights will improve, but until then, be prepared to bring the context to make sense of the data you’re gathering.