By Audrey Gerred

Have you ever delved into the world of Power BI and found yourself standing at the crossroads of a crucial decision: explicit measures vs. implicit measures? If you’re scratching your head thinking, “What’s the difference, and why does it even matter?” – well, you’re not alone. But today, I’m here to break it down for you.

Explicit Measures: The Clarity Champions

Imagine explicit measures as the clear and detailed storytellers of your data. They’re like friends who explain everything step by step. When you create an explicit measure, you explicitly define the formula it uses. You get to name it, specify how it should behave, format it, add descriptions to make it understandable for everyone, and utilize them in Analyze in Excel and or other viz tools when you connect to a Power BI semantic model with XMLA endpoints.

The beauty of explicit measures is that they offer precision. If you need a calculated field that counts something specific or performs a unique calculation, you can create it as an explicit measure. This helps you ensure consistency and reliability across your reports. It’s like having a well-annotated map for your data journey.

Implicit Measures: The Quick & Dirty Hack

Now, let’s chat about implicit measures. These are more like your speedy friends who can give you a quick answer but might not explain how they got there. In Power BI, implicit measures are created automatically when you drag and drop fields onto visuals. They save you time by providing basic aggregation (like sum or count) without you writing a single line of code.

Implicit measures are fantastic for speedy report creation or quick exploratory data analysis. However, they come with a trade-off – they might not always give you the exact results you desire, especially for complex calculations and you can’t use them in Analyze in Excel or when visualizing in another tool through XMLA endpoints. They can also become confusing when dealing with multiple visuals and changing requirements.

So, What’s the Best Choice?

Here’s the deal: there’s no one-size-fits-all answer. Both explicit and implicit measures have their place in the Power BI toolkit.

• Use explicit measures when:
  • You need precise and custom calculations
  • Clarity and documentation are vital for your reports
  • You want to create KPIs or unique metrics
  • Ease of use for users with Build Access
  • Utilizing in Analyze in Excel and or other viz tools through XMLA endpoints
• Use implicit measures when:
  • You’re in the early stages of report development
  • You need a quick answer for basic analysis
  • You’re exploring data on the fly

In an ideal world, a balanced mix of both can work wonders. You can start with implicit measures for initial data exploration and report sketching. Then, as your report matures and specific requirements arise, shift to explicit measures for those custom calculations and crystal-clear explanations.

Think of it like a conversation – sometimes you need a quick answer, and sometimes you need a detailed discussion. In Power BI, explicit and implicit measures give you the flexibility to do just that. So, go ahead, use both wisely, and tell your data story with confidence!

What’s your take on the explicit vs. implicit measures debate in Power BI? Share your thoughts in the comments below!