Published on 2025-06-28T06:36:26Z

What is Slice-and-Dice in Analytics? Examples with Plainsignal and GA4

Slice-and-dice is a core analytics operation that lets you break down large datasets into smaller, more meaningful pieces by selecting specific dimensions and metrics. Originating from OLAP (Online Analytical Processing) cubes, slicing fixes a single dimension (e.g., page = “/home”), while dicing selects multiple dimensions at once (e.g., page + browser) to create a multi-dimensional subcube. This technique empowers analysts to explore user behavior, traffic patterns, and conversion funnels from different perspectives. By interactively filtering and pivoting, you uncover hidden insights, compare segments side-by-side, and drive data-informed decisions. Both Plainsignal and Google Analytics 4 provide interfaces and APIs to perform slice-and-dice operations seamlessly.

Illustration of Slice-and-dice
Illustration of Slice-and-dice

Slice-and-dice

Breaking analytics data into subsets by dimensions and metrics for deeper insights.

Understanding Slice-and-Dice

Slice-and-dice is one of the two fundamental OLAP operations (the other being drill-down). It allows analysts to extract specific cross-sections of a multi-dimensional dataset by choosing one or more dimensions and one or more metrics. This gives a granular view of performance across different categories, time periods, or user groups.

  • Slicing vs. dicing

    Slicing narrows data by fixing a single dimension to one value, while dicing selects two or more dimensions to form a smaller, multi-dimensional subcube.

    • Slice:

      A slice fixes one dimension (e.g., all pageviews for the “/pricing” page) and returns metrics for that subset.

    • Dice:

      A dice picks multiple dimensions (e.g., page + browser) to return a grid of metrics showing every combination.

  • Origins in olap

    Slice-and-dice emerged in OLAP systems to enable rapid, ad-hoc querying of data cubes for business intelligence and reporting.

    • Olap cube:

      A multi-dimensional array of data organized by dimensions (e.g., time, product, region) and measures (e.g., sales, views).

Implementing Slice-and-Dice in Plainsignal

PlainSignal offers a lightweight, cookie-free analytics solution that supports dimensional filtering and multi-dimensional queries via its dashboard and REST API. After embedding the tracking snippet, you can slice your traffic by page, referrer, country, device, and more.

  • Embedding the plainsignal snippet

    Install PlainSignal on every page to start collecting data for slicing and dicing.

    • Tracking code: 
      <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
<script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>

  • Filtering data by dimension

    Use the PlainSignal dashboard or API parameters to apply filters on dimensions like page, country, or referrer to slice your dataset.

  • Constructing multi-dimensional queries

    Leverage the PlainSignal REST API to request multiple dimensions at once and dice your data for detailed cross-analysis.

    • Sample api call: 
      GET https://eu.plainsignal.com/api/data?dimensions=page,browser&metrics=views

Leveraging Slice-and-Dice in Google Analytics 4

Google Analytics 4 (GA4) provides the Explorations workspace and custom reports for advanced slice-and-dice analysis. You can drag and drop dimensions and metrics, define segments, and compare slices side by side.

  • Explorations canvas

    In GA4 Explorations, add ‘Rows’ (dimensions) and ‘Values’ (metrics), then apply filters or segments to slice-and-dice your data interactively.

    • Dimension breakdowns:

      Add dimensions like ‘city’ or ‘device category’ under ‘Rows’ to see how metrics like users or events distribute across them.

  • Custom segments

    Define user segments (e.g., mobile purchasers, first-time visitors) and apply them as filters in Explorations to compare different slices.

    • Creating a segment:

      In Explorations, click ‘SEGMENTS’, then ‘New segment’, choose criteria, save, and apply.

  • Custom reports

    Use the Report Library to build tables and charts combining multiple dimensions and metrics for a pre-built slice-and-dice view.

Best Practices for Effective Slice-and-Dice

While slice-and-dice unlocks powerful insights, overuse or poor planning can lead to confusion and performance slowdowns. Follow these guidelines to keep your analysis clear and efficient.

  • Limit the number of dimensions

    Too many dimensions can overwhelm your dashboard and slow query performance. Start with 2–3 core dimensions.

  • Use meaningful dimensions

    Select dimensions that align with business goals (e.g., campaign source, user type, region) to drive actionable insights.

  • Combine slicing with drill-down

    After slicing, use drill-down to explore a subset hierarchically (e.g., Country → State → City) for deeper context.

  • Maintain consistent naming conventions

    Standardize dimension and metric names across reports to avoid confusion and ensure comparable slices.

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