What is data mapping?<\/strong><\/h2>\n\n\n\nIt\u2019s the process of taking the data model of one system or application and mapping it to the data model of another system or application.<\/strong> This typically occurs at the field level, and not the object level.<\/p>\n\n\n\nFor example, let\u2019s say you track cases for clients in a CRM<\/a> and in an ITSM tool. The data fields might differ between the two apps in the following ways:<\/p>\n\n\n\n\n- The CRM labels the client as a \u201cCustomer,\u201d while the ITSM tool labels them as a \u201cUser.\u201d<\/li>\n\n\n\n
- The CRM denotes the issue\u2019s description as \u201cIssue summary,\u201d while the ITSM tool simply marks it as \u201cDescription.\u201d<\/li>\n\n\n\n
- The CRM shows the case number through its \u201cCase number\u201d field, while the ITSM only uses \u201cNumber.\u201d<\/li>\n<\/ol>\n\n\n\n
Integrating the two applications and keeping their cases in sync requires mapping the corresponding fields.<\/p>\n\n\n
\n![\"An](\"https:\/\/blog2workato.wpengine.com\/wp-content\/uploads\/2026\/09\/AKSdH5aIuK0Kl2lG8HjHlCZZNTDNxI2BZCovjijygQu9KhYDMtP0g1C-WBzwE-ENcxcxFXbmusx6lwcr6bq1rknegRuBjG_rQamRiPeRmje3NDLZmZuHfJu_1aJhXRR00VEHpPIO.png\")
<\/figure>\n<\/div>\n\n\nData mapping can also be applied to the more complex case of adopting a common data model (also known as a canonical data model). <\/p>\n\n\n\n
With this approach, you\u2019d have a source system that maps fields to corresponding ones in a data warehouse. In the latter, the object\u2019s fields will undergo changes based on the standard data model you\u2019ve defined. These fields will then map to those in the destination apps and systems. <\/p>\n\n\n
\n![\"An](\"https:\/\/blog2workato.wpengine.com\/wp-content\/uploads\/2026\/09\/kBSXPS44k-4-fXfH6HY526lcT5A4ChzsRpWY99DpmXrN0JpU8ZunCW9P7-wri_oTOewdeuIYtH4T4p49c9X3zEdV3VNlnnFHbY1h-g8SS3nRlBuVSBDkC4OgGqiF9yN5aLk8JW05.png\")
An example of a common data model where the CRM is the source system and an ITSM tool, marketing automation platform, and messaging platform serve as the destinations.<\/em><\/figcaption><\/figure>\n<\/div>\n\n\nThis can be a great approach when you have several destination systems or apps. Reason being, the only maintenance involved is keeping the fields between the data warehouse and the data source mapped properly.<\/p>\n\n\n\n
To keep things simple, we\u2019ll be referencing the first example throughout the following section.<\/p>\n\n\n\n
Related: <\/em>Common task automation use cases<\/em><\/a><\/p>\n\n\n\nWhat you need from your data mapping tool <\/strong><\/h2>\n\n\n\nIt requires a platform that provides the following:<\/p>\n\n\n\n
1. An intuitive, code-free UX<\/strong><\/h3>\n\n\n\nThe ability to map data across systems should be as simple as the following: seeing all of the fields in your source system alongside all of the fields in the destination system that need to be mapped and then moving fields from the former to the latter by dragging and dropping them.<\/p>\n\n\n\n
Say we\u2019re acquiring attendees for a webinar. If we\u2019re using Marketo<\/a> to acquire attendees, and we need to add those who register to the Google Calendar event, we’ll need to map the attendees’ emails between the two apps in order to keep them in sync. <\/p>\n\n\n\nThis simply involves dragging the Marketo email field to the \u201cAttendee email\u201d section highlighted below (the field Google Calendar uses to store attendees\u2019 emails).\u00a0<\/p>\n\n\n
\n![\"Drag-and-drop](\"https:\/\/blog2workato.wpengine.com\/wp-content\/uploads\/2026\/09\/jvg-SvUvIOtTE4LX2-Vjyies8XIeUIZqPNN_WIu2Bo9zHdtd3uKy48yE3HPTO_zirkELqqwvKMCeU7FmWnqMtyvnQOQAsUqBUWr8s9VBSMLPjCvp-6g93SoUWb0GxvuOqDtnAWtK.png\")
<\/figure>\n<\/div>\n\n\nThis drag-and-drop data mapping process offers a variety of benefits: It allows more of your employees to get involved in implementing integrations and data flows (as a technical background isn\u2019t necessary). <\/p>\n\n\n\n
It helps prevent mistakes and data quality issues, as your team can avoid entering information manually. Also, it empowers your team to move quickly in setting up their integrations, and even when the fields can\u2019t be mapped directly, simple formulas can power the data transformations needed to map the fields.\u00a0<\/p>\n\n\n\n
2. Custom fields can also be mapped with ease<\/strong><\/h3>\n\n\n\nIt\u2019s more than likely that your organization will customize various fields across apps to make them more relevant and understandable.<\/p>\n\n\n\n
Your data mapping solution should be able to account for these custom fields automatically, in addition to those that come out of the box. Otherwise, you\u2019ll need to rely on your developers to map them via code that follows rigid specifications set forth in the apps\u2019 API documentation.\u00a0\u00a0<\/p>\n\n\n
\n![\"Your](\"https:\/\/blog2workato.wpengine.com\/wp-content\/uploads\/2026\/09\/Workato-48.png\")
<\/a><\/figure>\n<\/div>\n\n\n3. Machine learning helps you map fields at scale<\/strong><\/h3>\n\n\n\nYou\u2019ll often need to map a high volume of fields between apps. <\/p>\n\n\n\n
In this case, even the drag-and-drop experience may not be ideal. Instead, you\u2019ll need to leverage machine learning technology that identifies the fields that should be mapped. <\/p>\n\n\n\n
Obviously, you should be able to make modifications afterwards in case any mappings are incorrect, but given the similarities between fields across apps, machine learning should be able to perform this with a high degree of accuracy.<\/p>\n\n\n\n
For example, let\u2019s say you track cases for clients in a CRM<\/a> and in an ITSM tool. The data fields might differ between the two apps in the following ways:<\/p>\n\n\n\n Integrating the two applications and keeping their cases in sync requires mapping the corresponding fields.<\/p>\n\n\n Data mapping can also be applied to the more complex case of adopting a common data model (also known as a canonical data model). <\/p>\n\n\n\n With this approach, you\u2019d have a source system that maps fields to corresponding ones in a data warehouse. In the latter, the object\u2019s fields will undergo changes based on the standard data model you\u2019ve defined. These fields will then map to those in the destination apps and systems. <\/p>\n\n\n This can be a great approach when you have several destination systems or apps. Reason being, the only maintenance involved is keeping the fields between the data warehouse and the data source mapped properly.<\/p>\n\n\n\n To keep things simple, we\u2019ll be referencing the first example throughout the following section.<\/p>\n\n\n\n Related: <\/em>Common task automation use cases<\/em><\/a><\/p>\n\n\n\n It requires a platform that provides the following:<\/p>\n\n\n\n The ability to map data across systems should be as simple as the following: seeing all of the fields in your source system alongside all of the fields in the destination system that need to be mapped and then moving fields from the former to the latter by dragging and dropping them.<\/p>\n\n\n\n Say we\u2019re acquiring attendees for a webinar. If we\u2019re using Marketo<\/a> to acquire attendees, and we need to add those who register to the Google Calendar event, we’ll need to map the attendees’ emails between the two apps in order to keep them in sync. <\/p>\n\n\n\n This simply involves dragging the Marketo email field to the \u201cAttendee email\u201d section highlighted below (the field Google Calendar uses to store attendees\u2019 emails).\u00a0<\/p>\n\n\n This drag-and-drop data mapping process offers a variety of benefits: It allows more of your employees to get involved in implementing integrations and data flows (as a technical background isn\u2019t necessary). <\/p>\n\n\n\n It helps prevent mistakes and data quality issues, as your team can avoid entering information manually. Also, it empowers your team to move quickly in setting up their integrations, and even when the fields can\u2019t be mapped directly, simple formulas can power the data transformations needed to map the fields.\u00a0<\/p>\n\n\n\n It\u2019s more than likely that your organization will customize various fields across apps to make them more relevant and understandable.<\/p>\n\n\n\n Your data mapping solution should be able to account for these custom fields automatically, in addition to those that come out of the box. Otherwise, you\u2019ll need to rely on your developers to map them via code that follows rigid specifications set forth in the apps\u2019 API documentation.\u00a0\u00a0<\/p>\n\n\n You\u2019ll often need to map a high volume of fields between apps. <\/p>\n\n\n\n In this case, even the drag-and-drop experience may not be ideal. Instead, you\u2019ll need to leverage machine learning technology that identifies the fields that should be mapped. <\/p>\n\n\n\n Obviously, you should be able to make modifications afterwards in case any mappings are incorrect, but given the similarities between fields across apps, machine learning should be able to perform this with a high degree of accuracy.<\/p>\n\n\n\n\n
<\/figure>\n<\/div>\n\n\nWhat you need from your data mapping tool <\/strong><\/h2>\n\n\n\n
1. An intuitive, code-free UX<\/strong><\/h3>\n\n\n\n
<\/figure>\n<\/div>\n\n\n2. Custom fields can also be mapped with ease<\/strong><\/h3>\n\n\n\n
<\/a><\/figure>\n<\/div>\n\n\n3. Machine learning helps you map fields at scale<\/strong><\/h3>\n\n\n\n