Dashboards

You have narrowed the table to the dangerous few; now step back and see the whole field. A dashboard is for seeing the shape of the whole database where a table is for reading it row by row. It is a page of charts, and each chart counts your records and groups them by a dimension. Nothing here is frozen: the charts redraw themselves as patents, products and mappings arrive.

A dashboard at a glance#

Say you have spent a week pulling together every patent you can find in your field: hundreds of them, filed by dozens of companies over the past decade. Read as a table, it is hundreds of rows you will never finish. Read as a dashboard, it is four questions answered at once.

In a single look: filings have risen steeply since 2018, most of the portfolio is still in force, one applicant leads the field, and a large block of recently filed applications is still working through examination. That is what a dashboard is for. It turns a pile of records into a picture you can reason about.

The anatomy of a chart#

Every chart is built from a handful of pieces: a data source to count, optional filters that narrow what it counts, the axes that shape how it is drawn, and, on some chart types, limits that trim and order the result.

Data source#

The source is the records a chart counts. Patent charts run aqua; point the same chart at your products and it turns orange.

• Patents: count and group the patents in your research database.
• Products: chart your products instead, by category, manufacturer or territory.
• Mappings: chart the links between patents and the product features they match.

Filters#

Filters narrow which records a chart counts, separately from how it groups them. They are the same filters you use on a table or in search, and they show as chips on the chart.

Axes#

The axes decide how a chart is drawn. The x axis is the dimension you plot: a field like applicants, status or territory, or a date. When it is a date, the Group by control rolls the timeline up by year, quarter or month (the bucket it counts into). The y axis is always a count of records, so every value on the x axis shows how many. To compare two dimensions at once, Subdivide by a second field and the bars or bands split into stacked or grouped series. Pies and donuts trade the x axis for slices, and a tree breaks the total into branches, but the choice is always the same: pick a dimension, count the records.

Limits#

Some shapes can show more values than fit on screen. Bar, pie, donut and tree charts take a limit that keeps the leaders and folds the rest away: Top 5, Top 10, Top 20 or All. A sort then orders them, by count (Highest first or Lowest first) or by name (A to Z). The single number, line and area charts have none: a stat is one figure, and a timeline already runs the whole date range.

Chart types#

Seven shapes, each suited to a different question. Here is what each one looks like, drawn from the same research database.

Single number#

A stat answers the simplest question of all: how many? It is the headline count of whatever the source holds, with no grouping. Reach for it when one number is the whole point.

Bar charts#

A bar chart lines categories up so you can compare them by length. It is the natural answer to "who files the most" or "which countries appear most often"; with long labels it turns horizontal, and a limit trims it to the leaders.

Line charts#

A line traces a count across time, so momentum is the whole story. Here filings climb year after year.

Area charts#

An area chart traces the same count over time but fills the space underneath, and when you split it by a second dimension the bands stack into a running total. This one breaks each year down by status, so you read the growth and the mix together.

Pie charts#

A pie shows composition: the share each slice takes of the total. Keep it to a handful of slices and it answers "how is this split" at a glance.

Donut charts#

A donut is a pie with its centre hollowed out: a calmer take on the same composition.

Tree charts#

A tree breaks a total into nested parts you can expand. Start from the top applicants, open one, and see who it most often files alongside. The same shape walks a classification down from section to class to subclass.