How to analyze your data

Introduction to using Daasity for reporting and ad-hoc data analysis.

Analyze Data in Daasity: Explores, Dashboards, and Collections

This guide shows how to do ad‑hoc analysis with Looker Explores, monitor KPIs with Dashboards, and organize your work in Collections. It’s written for business users and analysts who want a fast, reliable workflow.

How Daasity Organizes Your Data

Daasity provides a curated semantic layer in Looker so you can analyze accurate, unified metrics without wrangling joins.

Unified models (recommended for most reporting):
- UOS – Unified Order Schema: Direct‑to‑consumer orders, revenue, discounts, refunds.
- URS – Unified Retail Schema: Retail/wholesale performance across retailers and channels.
Source‑specific models: When a vendor/source doesn’t cleanly join to others or you need raw, channel‑level detail.
Dimensions vs. Measures:
- Dimensions are “fields you group by” (e.g., Product Name, Order Date, Traffic Source).
- Measures are calculations (e.g., Net Sales, Orders, AOV).

Why isn’t everything in one Explore? Different data sets have different grains and join rules. Combining them can create duplicates or incorrect totals. Separate Explores keep queries fast and results trustworthy.

When to Use Explores vs. Dashboards

If you want to…

Use

Ask a one‑off question or iterate quickly

Explore

Start from a best‑practice template of fields/filters

Quick Start (Explore)

Monitor a set of KPIs and trends over time

Dashboard

Make a small change to a dashboard tile and dig deeper

Explore from here

Pick the Right Explore

Explores are grouped by subject area. Start with your business question, then choose the matching subject.

Common subject areas

Orders, Customers, Marketing, Traffic
Inventory, Returns, Shipping, Subscribers

Examples

DTC revenue trend → UOS (Orders)
Retail sell‑through across accounts → URS (Retail)
Channel‑specific deep dive (e.g., ad platform raw data) → Source‑specific Explore

Start in an Explore: Blank or Quick Start

You can begin from a blank slate or use a pre‑curated Quick Start with common fields, filters, and visual defaults.

Blank Explore (full control)

Open Explore and select the model (e.g., UOS or URS).
Add Dimensions (e.g., Order Date) and Measures (e.g., Net Sales).
Apply filters, click Run.
Choose a visualization and tweak formatting.

Quick Start (faster)

Open Explore and pick the model.
Select a Quick Start (e.g., “Orders by Day (Net)”).
Review the pre‑loaded fields/filters; adjust if needed.
Click Run and customize the visualization.

Best practice: Start with a Quick Start for common questions; switch to a blank Explore for bespoke analyses.

Dashboards = Collections of Tiles (Each Tile Is a Saved Explore View)

A dashboard is a page of tiles. Each tile is backed by an individual saved Explore view (a specific query + visualization).

Use dashboards for monitoring and sharing KPIs.
Each tile can be opened for deeper analysis with Explore from here (see below).
You can clone dashboards or add your own tiles from saved Explore views.

“Explore from Here” (Adjust Any Dashboard Tile Safely)

When a dashboard tile raises a new question:

On the tile, open the ⋯ menu and choose Explore from here.
You’ll land on the underlying Explore with the tile’s fields/filters pre‑applied.
Adjust fields, filters, pivots, or visualization.
Save your adjusted analysis (see Collections below). You won’t change the original dashboard tile unless you explicitly update it.

Tip: Use “Explore from here” to keep the dashboard stable while you iterate in your own workspace.

Work Faster: Pivots, Filters, and Visualizations

Pivoting

Hover over a Dimension and click Pivot to turn its values into columns.
Keep pivoted columns under ~50 for snappy browser performance. (Up to 200 pivoted values supported; visibility often limited for performance.)
Sort columns by clicking headers; Shift + click for multi‑column sort.
To unpivot, use the gear menu (Unpivot) or click the Pivot icon again.
Include at least one unpivoted Dimension and one Measure.

Filtering

Basic filters: Use the filter icon next to any field in the picker.
Advanced: matches (advanced) supports pattern matching for complex expressions.
Custom Filter: Build compound logic using Looker expressions.
Behavior:
- Dimension filters limit the raw rows considered.
- Measure filters apply after aggregation (e.g., only show products where Net Sales > X).
Limits: Set row/column limits to keep queries responsive (typical defaults: up to 5,000 rows and 200 columns).

Visualization

Quick‑switch among Table, Column, Bar, Line, Pie, etc.
Click Edit to configure axes, series, labels, number formatting, and totals.
For tables, add row/column Totals and drag headers to reorder columns.

Performance tip: Prefer fewer, more targeted fields; use filters and modest pivots to keep results fast and readable.

Quick Decision Guide: Choosing the Correct Explore

Define the question: Revenue trend? Cohort behavior? Traffic source? Inventory position?
Match the subject area: Orders, Customers, Marketing, Traffic, Inventory, Returns, Shipping, Subscribers.
Pick unified vs. source‑specific:
- Use UOS/URS for standardized, cross‑source reporting.
- Use source‑specific when you need vendor‑level details that don’t belong in a unified model.

FAQs

Can I customize a default dashboard tile? Yes. Use Explore from here, make changes, and Save to your Private or Shared Collection. You can also clone the dashboard and replace tiles.

Where should I save finished analyses? Place team‑relevant, stable content in a Shared subcollection. Keep drafts in Private.

What if I don’t see an Explore I need? Some models are permissioned or require enablement. Contact your admin or Daasity Support.