Data360 DQ+ is a next generation self-service business intelligence platform designed to handle every step of your data processing workflow.
Data360 DQ+ receives definitions and data from external sources and allows you to create, share and analyze interactive data visualizations. Organizations that have chosen Data360 DQ+ have chosen to leverage the power of visual analytics on-demand and to apply that power to untamed sources of information that already exist within their systems.
Data360 DQ+ was created to enhance business analysis, by allowing you to explore massive amounts of information, on-demand. With Data360 DQ+, data exploration can take many different forms, allowing you to produce many different types of Data Stages.
Acquiring and preparing data
In Data360 DQ+, everything starts with incoming data.
You can use Data360 DQ+ to prepare data in many ways. The following diagram shows an example of how you could use Data Stages to manage your organization's incoming data:
If you are responsible for acquiring and preparing your organization's incoming data, see:
Alternatively, if you are not responsible for acquiring, preparing and operationalizing data, see Dashboards and Using the Visualizer.
Exploring and analyzing data
Once you have acquired and prepared incoming data, you can begin to explore and analyze, creating ad-hoc data visualizations, dashboards, or analytics using the Analysis Designer. See:
Sharing and collaborating
After you, or someone else, has created and saved a dashboard, it will be available for collaboration and continuous use. See:
See also:
Environments
An Environment is the "space" where a set of Data Stages exists. Typically, your Data360 DQ+ Administrator will create multiple Environments for multiple use cases, depending on the needs of your organization. A typical setup might include Environments for Development, Testing, and Production. Each Environment can then be configured to contain only what its users need.
If you are granted access to multiple Environments, an Environments icon will appear in top right corner of your screen. Use this icon to switch from one Environment to another.
If you are an Administrator and want information on configuring Environments, see Environments.
Pipelines
Within environments, data stages are organized in pipelines. A pipeline may contain any combination of data stages, and the contents and structure of any given pipeline will be determined by your Data360 DQ+ administrator, or by users who have certain privileges.
Pipelines typically contain the "curated content" of your organization, in the form of dashboards that serve as official reports, along with the multiple types of data stages underlying these dashboards.
Your user role will determine which help topics will be most relevant for you:
- If you are involved with creating pipelines, you should be familiar with data management, transformation, and exploration as you will need to be able to create every type of data stage. To get started, see the User guide section of the help. If you need a deeper understanding of pipeline structure and promotion across environments, see the Administration section of the help.
- If you are only required to use Data360 DQ+ to view and interact with data stages created by other users, see Dashboards and Using the Visualizer.For example, you may only need to view a pipeline's dashboards or use its data views to explore data. You may not need to be involved in the creation and maintenance of the data stages underlying reports, you may just want to perform self-service BI.
Data stages
Each step of your data processing workflow is known as a "Data Stage". Data stages are stored within the pipelines that exist in your organization's environments.
Data store
|
Data pushed to Data360 DQ+ resides in data stores. Data stores are containers for tabular information coming from internal or external data sources, and are the starting point for all data exploration performed within Data360 DQ+. See Data Stores. |
Analysis
|
An analysis is a drawing board for data manipulation. You can use an analysis to prepare, transform, and analyze data in preexisting data stores by applying functions to selected fields. These manipulations can then be output to other data stores. See Analysis. |
Analytic model
|
Analytic models enable machine learning, by allowing you to train, score, and evaluate data sets, via the analytics nodes present in analyses. |
Data view
|
|
Data views are used to define which fields within a chosen set of data stores can be used when creating dashboards and exploring data in the Data360 DQ+ visualizer. See Data Views . |
Process model
|
|
A process model is a drawing board, used to orchestrate the flow of data loads and executions between interdependent data stages. The "output" of a process model is an automated workflow that can perform a number of tasks and ultimately keep your data stages in sync. See Process Models. |
Dashboard
|
Dashboards are visual representations of data views, and they can display multiple pieces of information at once in many different ways. Upon completion, dashboards can also be saved and shared for analysis and collaboration. See Dashboards. |
Case store
|
A case store allows you to associate records from other Data360 DQ+ data stages in a common repository. These records can then be managed by users of the case store via screens that you define for the case store, to display specific information about cases. See Case Stores. |
Rule library
|
A rule library allows you to create reusable rules that you can use within analyses. See Rule Library. |
Navigating to a selected stage
To quickly move from one Data Stage to another, click Go to Selected Stage.
This option is available in places where one Data Stage uses or references another, when:
- Viewing or editing an Analysis and highlighting a node that represents another Data Stage, for example, when highlighting a Data Store Input node.
- Viewing or editing a Process Model and highlighting a node that represents another Data Stage, for example, when highlighting an Execute Stage Task.
- Viewing the executions history of an Analysis or a Process Model, and highlighting a node that represents another Data Stage, within the bottom panel.
- Using the Show References or Find Usages functionality.
Clicking Go to Selected Stage will take you directly to the View mode of the Data Stage that you have selected.
Identifying related stage data
To identify which data stages use a selected data stage:
- Select the Pipelines menu at the top of the page.
- Click the menu button to the right of the data stage and select Find Usages.
The Usages dialog lists all data stages and all fields within those data stages that use the selected data stage.
For example, the Usages dialog for Data Store A shows that the data store is used by Data View 1, and lists the data store fields that are used by Data View 1.
Conversely, to identify which data stages are used by a selected data stage:
- Select the Pipelines menu at the top of the page.
- Click the menu button to the right of the data stage and select Find Usages.
The Usages dialog lists all data stages and all fields within those data stages that use the selected data stage.
For example, the Usages dialog for Data Store A shows that the data store is used by Data View 1, and lists the data store fields that are used by Data View 1.
Conversely, to identify which data stages are used by a selected data stage:
- Select the Pipelines menu at the top of the page.
- Click the menu button to the right of the data stage and select Show References.
Following from the previous example, the References dialog for Data View 1 would show that the data view references Data Store A, and lists every data store field that the data view references.
Working across environments
As you create and work with different data stages, you may move through multiple environments and pipelines.
To become skilled at Data360 DQ+ data exploration, it can be useful to begin with a high level consideration of how you will typically interact with the application. The following diagram provides a general process flow for a multi-environment implementation of Data360 DQ+, illustrating where incoming data comes from and the relationship between data stages.
