The impact of artificial intelligence can be felt everywhere, and the world of Salesforce is certainly no exception. This makes the concept of data transformation one that everyone needs to be familiar with to reap the benefits.
In this article, we will delve into data transformation, uncovering how it can help you have a better AI performance. We’ll also explore how data transformation in Salesforce can be realized using native Salesforce tools, and how you can take it even further using third-party solutions. Plus, we’ll see how artificial intelligence is beginning to change data transformation by checking out Plauti Data Action Platform.
What is Data Transformation?
Data transformation may sound like a mouthful, but it’s simple when you break it down into its key components. You’re probably already performing some data transformation operations already, to some degree.
1. Field Mapping
When data needs to be transferred from one system to another, field mapping ensures that data is correctly matched and transferred. For example, if one system uses “First Name” and “Last Name” fields while another uses “Given Name” and “Surname”, field mapping establishes the correspondence between these fields so that data can be accurately transferred.
2. Data Cleansing
Data cleansing, also referred to as data cleaning or scrubbing, is the process of identifying and correcting errors, inconsistencies, duplicates, and inaccuracies within a dataset. This process aims to improve data quality by ensuring that the data is accurate, reliable, and consistent.
Interested in duplicate management? Check out our article on The 4 Best Duplicate Management Apps for Salesforce in 2024.
3. Data Enrichment
Data enrichment involves enhancing existing datasets with additional information from external sources to provide more context, depth, or value. This process typically involves integrating external data sources such as demographic data, geographic data, social media data, or other third-party data.
For example: Plauti Record Validation can enhance records with Geocode information – great for logistics operations!
4. Data Formatting
Data formatting involves standardizing the format of data elements within a dataset to ensure consistency and compatibility across different systems or applications. A common example of data formatting is standardizing date formats (e.g. converting “MM/DD/YYYY” to “YYYY-MM-DD”).
3 Ways Data Transformation Drives Opportunity
Data quality isn’t perused for bragging rights; data transformation is a process that can have positive rippling effects through an organization. Think about it like this, you might start running occasionally during the week to gain some additional fitness. Then, you realize many other benefits, such as better sleep, improved heart rate, and more. Better data quality is the same – when your organization’s data is in better health, a myriad of benefits sprout.
1. Enhanced AI Performance
- Clean data = AI fuel: It’s no secret that artificial intelligence relies heavily on data, and therefore, better data quality will result in a more accurate and reliable outcome from AI initiatives.
- Duplicate Removal: Data transformation processes involve removing duplicate records and merging them, ensuring that there’s a single, accurate representation of each record within the Salesforce system.
2. Tailored Customer Journey & Customer Journey Analytics
- Personalization: By leveraging transformed and enriched data, businesses can tailor the customer journey to individual preferences, behaviors, and needs. This personalized approach enhances customer satisfaction and loyalty, leading to better retention and higher lifetime value.
- Detailed Analysis: Data transformation enables organizations to analyze the customer journey in detail, identifying touchpoints, preferences, and pain points. This analysis provides valuable insights for optimizing marketing campaigns, improving product offerings, and enhancing customer service.
3. Cost Savings
- Error Reduction: Automating data transformation processes reduces the likelihood of errors and inconsistencies in data, minimizing the costs associated with manual data cleanup and correction.
- Improved Decision-Making: By ensuring data accuracy and reliability, businesses can make more informed decisions, avoiding costly mistakes resulting from inaccurate or incomplete data.
Salesforce Native Capabilities and Limitations
Salesforce comes packed with some handy features to help you manage your data. However, some of these do have limitations when it comes to data transformation…
- Mass Delete: To perform mass deletion, you need the rights to “modify all data” and can only remove up to 250 entries in one go. If you’re not designated as an admin, you might lack the capability to delete records linked to associated cases or partner accounts.
- Mass Merge: Merging more than three records simultaneously is not possible with standard Salesforce features. If you have over three duplicates, you must merge them in smaller groups.
- Data Import Wizard: The Data Import Wizard in Salesforce is a tool designed to help users quickly and easily import data into their Salesforce organization. Note that it is capped to importing a maximum of 50,000 records per batch.
- Data Loader: The Salesforce Data Loader is another tool used for importing, exporting, and deleting data in Salesforce. However, it is typically limited to processing up to 5 million records in a single operation. Additionally, there is a limit on the size of the files that can be processed, typically capped at 5GB.
- Batch Apex: The maximum number of batch Apex jobs in the Apex flex queue is 100. Each execution of a batch Apex job is limited to processing a maximum of 50 million records. Also, the total heap size limit for a batch Apex job’s execution context is 12MB.
- Inline Editing: Through “Mass Quick Actions”, users can create or modify up to 100 records from a list view or related list. They can perform updates on a single record or select multiple records for a quick “bulk” update ( limited to 100 records).
- Custom Mass Actions: There is no ability to create your own custom actions in Salesforce using the standard available features.
Data Action Platform for Salesforce
Plauti Data Action Platform (DAP) is designed specifically to overcome the out-of-the-box limits found in Salesforce. What’s more, DAP’s integration with ChatGPT has unleashed some serious AI potential into Salesforce workflows.
Salesforce Meets Artificial Intelligence Through DAP
Salesforce unites with artificial intelligence via Plauti Data Action Platform. The possibilities are endless, but let’s highlight some handy use cases we’ve already found:
- Easily automate lead qualification and prioritization.
- Generate an “Account Health Score” by analyzing data of the accounts.
- Automatically assign cases to relevant departments based on case descriptions.
- Automatically sort contacts into the right department based on job titles.
- Calculate “Opportunity Win Probability” using various data points.
Ready to get your hands on DAP and see how AI can enhance your data operations? Don’t delay, try out DAP today – completely free! No credit cards, no hassle. What are you waiting for?