Switchboards workflow actions and what they do

Harry Brown
February 15, 2023

Switchboard offers 8 unique action tools to help cover a wide variety of integration challenges we face on a day-to-day basis. It's important for us at Versori to inform our users the purpose of each action tool. After all, knowing the full capabilities of an action tool could help extend your companies workflow even further. Saving money and time.

Filter

A "Filter" action in integrations refers to a mechanism that allows you to specify certain criteria or conditions that data must meet in order to pass through the integration. Essentially, a filter acts as a gatekeeper, allowing only the data that meets your specified criteria to flow through the integration, while blocking or ignoring data that doesn't match.

For example, if you have an integration set up to collect data from a website form, you may want to apply a filter to only capture submissions that contain certain keywords or meet certain criteria, such as those submitted by users from a specific geographic location. In this way, you can prevent irrelevant or undesirable data from entering your system, while ensuring that the data you do collect is high-quality and useful.

Filters can be applied to a variety of integration actions, such as when retrieving data from a source system, when transforming data before sending it to a destination system, or when triggering specific actions based on specific data conditions. They can be implemented using a variety of tools and techniques, such as scripting languages, regular expressions, or visual configuration interfaces, depending on the specific integration platform you're using.

Transform

A "Transform" action in integrations refers to a process of converting, manipulating or restructuring data as it passes through the integration. Essentially, a transform action allows you to modify the format, content or structure of data, in order to prepare it for use in a different system, application or workflow.

Transformations can be applied to different types of data, such as text, numeric, or binary data, and can involve a range of operations, such as formatting, parsing, mapping, filtering, aggregating or calculating. Some common examples of transform actions include:

  • Converting data from one format to another, such as from CSV to JSON or XML
  • Extracting specific fields or values from a data stream
  • Combining or splitting data from multiple sources into a single stream
  • Normalising or standardising data to a consistent format or structure
  • Applying mathematical or statistical calculations to data, such as summing, averaging, or filtering

Transform actions can be implemented using a variety of tools and techniques, depending on the specific integration platform you're using. Some integration platforms provide visual drag-and-drop interfaces, scripting languages, or pre-built libraries of functions and transformations that you can use to create custom data processing logic. The goal of transformation is to ensure that data is properly prepared, cleaned and structured before it is used in downstream applications or systems.

Condition

A "Condition" action in integrations refers to a process of evaluating data against one or more criteria or rules, and making a decision based on the outcome of that evaluation. Essentially, a condition action allows you to check if a certain condition or set of conditions is met before proceeding with an integration action.

Conditions can be applied to different types of data, such as text, numeric, or boolean data, and can involve a range of operations, such as comparing, matching, or validating data against predefined rules or patterns. Some common examples of condition actions include:

  • Checking if a data value is greater than or less than a certain threshold
  • Validating if a data value matches a specific format or pattern
  • Testing if a data value contains or does not contain a certain string
  • Evaluating if a data value is true or false

Condition actions can be implemented using a variety of tools and techniques, depending on the specific integration platform you're using. Some integration platforms provide visual drag-and-drop interfaces or scripting languages that allow you to create custom conditional logic. The goal of a condition action is to ensure that data meets specific requirements or rules before allowing it to proceed to the next step in the integration process.

Merge

A "Merge" action in integrations refers to the process of combining data from multiple sources into a single output stream. Essentially, a merge action allows you to merge data that may be spread across different systems, applications or workflows, into a single data stream that can be used in downstream processes.

Merging data typically involves identifying common fields or keys that can be used to match and combine data from multiple sources. For example, if you have customer data in a CRM system and purchase data in an e-commerce platform, you might use a customer ID field to match customer records and combine the data into a single record.

Some common examples of merge actions include:

  • Combining customer data from multiple sources to create a unified customer profile
  • Merging sales data from different stores or locations to create a consolidated sales report
  • Combining inventory data from different warehouses to create a real-time inventory status report

Merge actions can be implemented using a variety of tools and techniques, depending on the specific integration platform you're using. Some integration platforms provide visual drag-and-drop interfaces, scripting languages, or pre-built libraries of functions and transformations that you can use to create custom merge logic. The goal of a merge action is to ensure that data from multiple sources can be combined and used together in downstream processes, such as reporting, analysis, or decision-making.

Process

A "Process" action in integrations refers to a wide range of operations that are performed on data as it passes through an integration. Essentially, a process action allows you to perform custom data processing logic, such as business rules, calculations, validations, or other data manipulations.

Process actions can be used for a variety of purposes, such as to transform, filter, enrich, validate, or aggregate data, or to trigger specific actions or events based on specific data conditions. Some common examples of process actions include:

  • Applying custom business logic to data, such as calculating discounts or fees based on specific rules
  • Enriching data with additional information from external sources, such as geolocation data or weather forecasts
  • Validating data against specific rules or requirements, such as ensuring that email addresses are valid or that data is within acceptable ranges
  • Triggering specific actions or events based on specific data conditions, such as sending notifications or alerts when certain data thresholds are exceeded

Process actions can be implemented using a variety of tools and techniques, depending on the specific integration platform you're using. Some integration platforms provide visual drag-and-drop interfaces, scripting languages, or pre-built libraries of functions and transformations that you can use to create custom data processing logic. The goal of a process action is to ensure that data is properly prepared, cleaned and structured before it is used in downstream applications or systems, and to enable custom data processing logic to be executed as needed.

Passthrough

A "Passthrough" action in integrations refers to a process of allowing data to pass through an integration without any modification or processing. Essentially, a passthrough action allows you to send data from one system or application to another, without any transformation, filtering, or enrichment.

Passthrough actions are typically used when data does not require any modification or when it is already in a format that is compatible with downstream systems or applications. For example, if you have a webhook that is receiving data in a format that is already compatible with a downstream system, you may choose to simply pass the data through without any modification.

Some common examples of passthrough actions include:

  • Passing data from one system to another without modification, such as forwarding data from a webhook to a third-party system
  • Bypassing certain steps in an integration workflow that do not require data processing or transformation
  • Providing a quick and easy way to test or troubleshoot an integration by passing data through without making any changes

Passthrough actions can be implemented using a variety of tools and techniques, depending on the specific integration platform you're using. Some integration platforms may provide a dedicated passthrough action or step in the integration workflow, while others may allow you to simply skip certain steps or actions in the workflow. The goal of a passthrough action is to allow data to flow through an integration without any unnecessary processing or modification, in order to improve efficiency and reduce the risk of errors or data corruption.

Data Builder

A "Data Builder" action in integrations refers to a process of creating or generating new data objects or structures based on the input data. Essentially, a data builder action allows you to construct complex data objects or structures that may not exist in the source data, or to modify existing data objects to suit the needs of downstream applications or systems.

Data builder actions are typically used to construct data objects that do not exist in the source data, or to transform and restructure existing data objects into a new format. For example, if you are integrating data from multiple sources into a data warehouse, you may need to build a new data object that combines data from multiple sources and aggregates it into a single record.

Some common examples of data builder actions include:

  • Creating new data objects based on a set of predefined rules or templates, such as generating invoices, reports or alerts based on specific data conditions
  • Aggregating data from multiple sources into a single record or data object, such as building a customer profile that combines data from a CRM system and a billing system
  • Transforming existing data objects into a new format that is compatible with downstream applications or systems, such as converting data into a specific file format or structure

Data builder actions can be implemented using a variety of tools and techniques, depending on the specific integration platform you're using. Some integration platforms provide visual drag-and-drop interfaces or scripting languages that allow you to create custom data objects and structures. The goal of a data builder action is to ensure that data is properly structured and formatted for downstream applications or systems, and to enable the creation of custom data objects and structures as needed.

If reading this article has sparked a potential use case in whichever sector your business is in. Please don’t hesitate to contact our sales team and begin the steps towards a more efficient & accurate future!

sales@versori.io

Harry Brown
February 15, 2023
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