What is Hub?

Hub is the central data management and orchestration layer of Canvas 2.0. While other modules like Studio are for building surveys and Analyse is for generating insights, Hub sits in the middle as the engine that cleans, organizes, and connects your information.

The "Big Picture" Purpose

Hub exists to solve three main challenges in research:

1. Data Hygiene: Ensuring questions and variables are organised clearly so anyone can understand them during analysis.

2. Efficiency: Allowing you to use the same data points across multiple projects without starting from scratch.

3. Connectivity: Breaking down "data silos" by letting you create customised variables from different independent studies and merging them into a new custom datasets.

Key Capabilities

1. Data Processing & Cleaning

Sharpen and optimise your dataset through Hub by:

• Rename & Refine: Change internal question codes and/or its order into human-readable labels, organised to your needs

• Curate Visibility: Hide irrelevant metadata or sensitive fields so the Analyse view remains clean and focused.

2. Custom Dataset Creation (Cross-Study Analysis)

Hub allows you to create Custom Datasets by dragging and dropping variables from various independent studies into a single table. Leveraging on this ability enables deeper data exploration.

Example: You can compare "Brand Awareness" from a study conducted in January with "Purchase Intent" from a separate study in June, all on one screen.

3. The Data Library (Question Reuse)

With the Data Library capability in Hub, build up your own repository of questions to use across your surveys, to ensure consistency and connectivity for tracking and analysis.

4. Automated Refreshing

Keep your data up-to-date through the Refresh Frequency feature in Hub. With it, you can customise the frequency in which the variables in surveys are pushed to your community/ audience to get the most updated data that makes sense to your insights.

Who could Hub be for?

• Data Managers: To configure permissions and ensure data quality.

• Analysts: To build custom views and connect multiple data sources for deeper insights.

• Project Leads: To standardize questions across the organization using the Data Library.