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Logical Data Model

Our Concept

Map your physical data to our Logical Data Model and your team can start getting value and insights immediately.

You've already been working with data and may have done a lot of data modeling. We just need to reflect the structure of your data in our Logical Data Model with Metadata. We allow you to tweak things so they make more sense in an analytic environment.... just simple. not rocket science... you don't need to be an engineer for this.

Domains and Workspaces

We let you organize and control access to your data at various levels of sharing.

Think of your data in different context and scope. In SaaS firms it's common to have pooled-multi-tenant models for ease of managemnt and efficiency of processing. This means your structure may remain consistent across all of your clients. In Quick Intelligence this is considered a Domain Level dataset. You define it at the Domain level, yet all workspaces can use it. Data is secure per workspace.

Another scope of data may be your clients data from their own systems. In Quick Intelligence, this can be modelled as a workspace level dataset that is only visible and usable within a specific workspace.

Finally there may be your own data stores. You likely have your own financial and sales data that you would like to see in the same space as your client data. This is your own workspace level data only visible and usable by you!


We allow you to mix data from multiple sources to extract true organization-wide insights. Our dashboard allows you to mashup analytics from many sources, you can easily add datasets from multiple sources to have a full view or your business. All in the same platform and tool that is embedded in your product, that your clients are also using.


Define what users are allowed to see which attributes with our powerful RBAC data-level security. Define security at all levels. Each workspace can have thier own security, complex or simple. It's all possible in the Quick Intelligence platform.

Metadata Driven

With a few simple and straightforward steps, you can start getting value from your data.

  • Map your attributes
  • Add meaningful labels
  • Define calculated fields
  • Configure time-based attributes
  • Geo location options for maps

Dataset Management

  • Manage logical table relationships
  • Set Time Dimension
  • Dataset Labels for end users