Databanks and Datasets
In Onestop, your organizational data is stored in datasets that are arranged into databanks.
Data is intentionally separated from your workflows by design. This allows for a single source of truth when it comes to your organizational data, allowing your workflows to access organization-wide datasets, thus creating a truly integrated environment.
This ensures consistency and accuracy in data usage, and also makes it easier to manage and update the data. Additionally, by separating data from workflows, it allows for more flexibility and scalability in how the data is used and allows for the data to be accessible to different users or systems in your organization.
What is a Databank?
A databank is a collection of multiple datasets that store data used by your process workflows. It is synonymous to a database.
What are Datasets
A dataset is a collection of data organized for you to use in and drive the workflows powering your organizational processes. You create organizational datasets by defining the structure of your data, similarly to how you would create a database table.
When creating a dataset, you need to define the structure of the data, which includes specifying the data types and format of the various fields or attributes, as well as any constraints or rules that must be followed when entering data into the dataset. This allows the data to be stored in a consistent and predictable format, making it easier to retrieve, update, analyze and manage.
Datasets are created with different fields and columns, and then new rows of data are added as needed. Fields or columns typically represent different attributes or characteristics of the data, such as name, address, date, or product price. Each field is given a specific data type, such as text, number, date, or Boolean, which determines the kind of data that can be stored in that field.
Datasets are used in different ways:
Data Entry: You can use datasets to enter and store data in a structured format.
Form Selection fields: Datasets can be used as the source of data for form selection fields, such as dropdown lists, to allow users to select from a predefined list of options. This can be useful for fields such as "state" or "department" for example.
Data Validation: You can create field validators that use data stored in your datasets to validate user input. For example, you could use a dataset of valid email addresses to ensure that only valid emails are entered into a form.
Updated by workflow tasks: Datasets can be updated by automated tasks depending on various configuration parameters. Automating dataset updates can help to reduce the workload in making sure that data stays up-to-date and accurate. For example, a workflow task can be configured to automatically update a dataset containing customer information, by adding new records or removing old ones when a new customer is added or when a customer unsubscribes. Another example can be a workflow task that automatically updates an inventory dataset by decreasing the stock quantity when a product is sold.
Design layouts: Dataset records can be referenced and placed in to design layouts for documents, notifications etc.
Last updated