- Give data a job. This is the foundation of data analysis. Every piece of data you gather should help you answer questions and make smart decisions.
- Use hypothesis testing to convert questions into strategies. This is what makes data meaningful. It’s the process of transforming raw data into business decisions.
- Apply context to account for the unmeasurable. Some things are hard to measure. For those situation, you need to contextualize the data.
Analytics and data shouldn’t be stressful. But it’s easy to feel that way when there are so many sources to draw from, each formatting the data differently, sometimes even giving different numbers for the same metric.
Principle #1: Give your Data a Job
One of the easiest ways to understand data is to think of the marketing funnel.
The marketing funnel
This is a foundational concept that makes it easy to visualize customer acquisition in marketing. Your brand marketing creates awareness and attracts new visitors to your website. Some of these new leads will be interested enough to evaluate your business and products, and a percentage of them will go on to become customers.
For sales, that’s a good model, but we need to tweak the funnel to work as well for analytics and data.
This model is a funnel metrics flowchart that not only maps the stages of a customer’s journey, it also lists the metrics that should be measured at each stage.
Principle #2: Using Metrics to Solve Problems
Data is collected on a dashboard, right? But on the dashboard, it’s raw data. Your job, as data analyst, is to turn raw data into active data.
For that, you use the analytic decision-making process.
This process works a lot like the scientific method, except it’s based on metrics.
In the scientific method, you start with questions and hypotheses, and then you make predictions about what might happen if you test different hypotheses.
It’s the same with data and analytics. You review your data and start asking questions about it. You make hypotheses about what might happen if you could impact any of those numbers. And then you devise a test to see if you’re right.
Simply by reviewing the results, you can clearly see what needs to be done to improve your business. Making decisions is no longer about your gut instincts, but about what the data is telling you.
That’s the theory, anyway.
But in practice, we often don’t know enough to know what questions we should be asking. In those situations, it often helps to take another data dive.
Principle #3: Contextualizing Data to Account for the Unmeasurable
It doesn’t matter how good your data is, sometimes it doesn’t tell you everything you need to know.
For instance, let’s say you’re reviewing your data and you see a trend. Why is that trend taking shape? Maybe you ran a campaign during that period. Maybe your competitors did something unique. Or maybe you had a technology problem that skewed the data.
If you don’t consider these factors when evaluating your data, you’re likely to make an assumption based on a false set of data. Your conclusion won’t be valid.
