Power BI

Original by Reinie, 2025Hamster_gagarin_linkedin
hamster writter This summary note was posted on 19 May 2025, by in Excel & PowerBI #

Design

  • Reduce the cognitive overload by:
    • removing distractions
    • give timely relevant information
  • Tailor to the audience
  • Focus on one story per page
  • Strike a balance between charts and complexity
  • Doe sit contribute to the story?
  • Is this the right visual element?
  • Is this visual element necessary?
  • Highlight “pre-attentive” attribute with colour
  • Think of your audience
  • Use progressive disclosure (see interactions-design.org)
    • Start with abstract to the specific only when requested
    • Hide information when you don’t need it
  • Use bookmarks -> with buttons for visuals
  • Avoid click, drag or scroll for user journey
  • Hiding visuals resets filters
  • Custom themes with Json files
  • Don’t use too many colours. Good practice is 8 colours max.
  • Remove bi-directional measures use filtering instead (use DAX)
  • Distinguish dashboard designs from report designs

Data

  • EDA (Explanatory DAta)
    1. Understanding data structure
    2. Identifying missing data
    3. Describe the data with descriptive statistics and distributions
    4. Identify outliers: When medium is less than average distribution is right skewed. Lower outliers -3xSD or 25%-(1.5xIQR) for upper 3xSD or 75%+(1.5xIQR) do it using Percentile.Inc(data, 0.25) IQR is 75-25 percentiles
    5. Examine and quantify relationships between variables
    6. Form hypothesis
  • Clean data
    • Free from NULL
    • Typos
    • duplicates
    • Irrelevant data
    • Outliers
    • correct data types
    • Use short and descriptive data names
    • always trim and clean text columns
  • Consider the different types data models (KimBall, Snow Flakes)

Coding

  • Prefer explicit measures like “totalSales”
  • Data Analytics Expressions (DAX) is at runtime
  • Power query is faster for complex calculations
  • M code is used to extract, transform and load data while DAX is used to analyse it
  • Data modelling should be done at 80% in PowerQuery and 20% in PowerBI
  • Rounding in PowerQuery actually changes the data, so prefer rounding in PowerBI
  • Iterative functions end with X and run on rows
    • SUMX(table, filter, expression)
    • RANKX(All, variableq, variable2)
  • Better to add columns after summarizing
  • For time intelligent functions it is better to create a data table
  • Keep measures in a separate table
  • SHIFT enter to start a new line
  • TAB to indent
  • // to comment
  • Store results in a name variable: VAR <name>=<expression> … RETURN
  • CALCULATE is used to computer when using filters