This article covers best practices for choosing the right chart type in Lens and interpreting what your dashboards show you.
Choosing the Right Chart Type
KPI cards work best for single headline numbers you want to track over time. Use them for total revenue, total units, or any metric where the period-over-period comparison is what matters most.
Horizontal bar charts are ideal for rankings and comparisons where you need to see relative sizes clearly. Use them for your top artists, top retailers, or any "Top N" question. They are easier to read than vertical bars when you have long labels like artist or retailer names.
Donut charts work well when you have a small number of categories (under 10) and the proportions are relatively balanced. They are a good fit for territory share or sales type distribution. Avoid them when one category dominates heavily (e.g., streaming at 85%+), because the chart becomes a single large segment that is hard to read. In that case, a bar chart will give you a clearer picture.
Stacked bar charts are useful for comparing totals while also showing how those totals break down. They work when you need to answer two questions at once: "how much?" and "made up of what?" Use them for period-by-period breakdowns where you want to see both the trend and the composition.
Line charts are best for tracking trends over time, especially when comparing two or more values on the same timeline. Use them to compare revenue across periods, labels, or any dimension where the shape of the curve matters more than the absolute numbers.
Heatmaps are powerful for spotting patterns across two dimensions at once. The "Artist x Retailer Performance" template, for example, reveals which retailers matter most for which artists. The color intensity shows concentration, making it easy to spot outliers without scanning rows of numbers.
Pivot tables and data tables are your go-to for detailed exploration. When you need exact figures, sorting, and the ability to scan across many rows, tables give you the most flexibility.
Interpreting Your Data
Always check your date range. The date range selector controls which data flows into every component. If numbers look unexpectedly low or high, confirm that your date range covers the period you intended.
Use filters to isolate variables. When a chart shows something unexpected, apply a filter to narrow the view. Filtering by a single retailer or territory often reveals whether a trend is broad or driven by a single source.
Compare periods, not just totals. KPI cards with period comparison are more useful than raw totals. A number only becomes meaningful when you see how it has changed. Use the "Previous Period" option to understand whether your revenue is growing, declining, or stable.
Watch for data that hasn't landed yet. Revenue data depends on your sales imports. If you are looking at a recent period and the numbers seem incomplete, check whether all expected sales reports have been imported for that period before drawing conclusions. Our system load sales near real time, at worst you can expect a 10-min delay between your sales ingestion and insights being updated.