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Wednesday, September 7, 2022

Salesforce: Dynamic Related List

We discussed dynamic form here and here, dynamic actions here, and this blog will continue with Dynamic Related List. 

So, what is the dynamic related list? The dynamic related lists allow you to choose the list’s fields and sort order, apply filters, specify the component label, and configure actions for the related list, without dependency on page layout. 

To set up a dynamic related list, add Related List - Single component, then upgrade it to Dynamic Related List - Single. 



Click the "Upgrade Now" button to upgrade it to Dynamic Related List - Single



Limitation/issue as of Summer '22 release:

  • If you have the Related List Quick Links component, the Related List - Single / Dynamic Related List - Single component will not be available in the Related List Quick Links component
  • Dynamic Related List - Single component will show only up to 10 records, and does not have "View All", vote for this idea.
  • To use Related List - Single, the related list must be added to the current page layout.

Notes:
  • Because it is an independent component, you can have many Dynamic Related List - Single components, such as one component showing Open Cases and another one showing Case closed in the last 30 days. 


Reference:


Sunday, August 28, 2022

Salesforce: Dynamic Forms (2)

After 18 months from my first dynamic form blog, we finally see a few standard objects covered by the dynamic form. As per the Winter '23 roadmap, we can enable Account (incl. person Account), Contact, and Opportunity with dynamic form.


Overall, the dynamic form functionality is still the same as per the initial release, where we can show/hide each field based on Record Field or Advanced, field with filters defined will have the eye orange icon next to it.



Record Field
  1. Field: another field in the same object
  2. Operator: depends on the selected Field (1) type, e.g. Contains, Equal, Not Equal
  3. Value: also depends on the selected Field (1) type
Advanced
  1. User, which is user info/detail
  2. Permissions, if the user has specific permission 
  3. Record, this can be a value from a related field, such as Created By Profile Name
Tips
you can't select more than 1 value in each filter, but you can add the same field in filters, then use "any filters are true" or filter logic.

Samples of use cases to use the dynamic form

  1. To hide/show fields from a specific profile in the layout, even the profile has visibility on the field-level security
  2. To hide/show fields from users without permission, such as View and Edit Converted Leads
  3. To hide/show field when the user selects a value, such as, when user Type = Customer, show Customer No, otherwise, hide Customer No field

But, do we still need the classic page layout when using the dynamic form? 

1. Record buttons are still determined by page layout, you can handle this by enabling dynamic action, check out here.

2. Related lists are also determined by page layout, you can handle this by removing the "Related Lists" component and adding the Related List - Single component for each related object. However, if you use the "Related List Quick Links" component (which is a great feature since Classic), adding "Related List - Single" will not add the object to "Related List Quick Links", so come and vote for this idea




Wednesday, August 17, 2022

CRM Analytics: Trend dataset with Dataflow or Recipe

Background: The dataset produced is only for the last 30 days of data, but we need to keep the data for much longer.

Solution: use dataflow/recipe to store the trend in a new dataset

1. Download the dataset in a CSV file and load it as a "trend" dataset

2. Use dataflow/recipe to read from the original and trend dataset

3. Append both datasets

4. Use computeRelative node (dataflow) or "Multiple row formula" transformation (recipe), Partition By the key field, this is to mark the 1st record

5. Filter out rows not marked as 1st record

6. Done


Recipe


Multiple row formula




Sunday, July 10, 2022

CRM Analytics: Only in Recipe and not in Dataflow



Here are a few things you can do in  Recipe, but not in Dataflow.

Rename and Save as
Yes, you can download JSON in dataflow and upload it to a newly created dataflow with a new name.

Add Description in each node
Unless you have the EADashboardHelper extension, you can't add descriptions in Dataflow

Join (left, right, outer, inner, cross)
This has been the power of recipes from the beginning

Preview data
Some users would like to see the data after transformation without the need to run it

Column profile
Analyze data at a glance (with a sampling of data)

Direct Input data
If only the objects are not synced to the CRM Analytics platform, this useful to get real-time data from Salesforce

Extract data
Able to write data to Output Connection and CSV

Easier to aggregate, bucket, trim, substring, replace, split, concatenate
You still can do those transformations in dataflow, but just easier in the recipe

Rename field label and API name
You can create a new field using computeExpression in dataflow, but cannot rename the existing field

New machine learning transformations not in dataflow
- Detect Sentiment
- Time Series Forecasting
- Predict Missing Values
- Cluster

A few other things worth mentioning
- Prefix in Dataflow = 02K, recipe: 05v
- Unable to run recipe when there is terminating node without write dataset
- Edit recipe in JSON is complicated compared to dataflow


Is anything else not listed above or incorrect? Comment below so I can add them, thank you!


Reference:

Thursday, June 30, 2022

Salesforce: Query Org ID and Sandbox

You can get your salesforce Org ID and other info from Company Information in the Setup menu. But, if you need to get the data from SOQL, here is the sample query

SELECT Id, Name, InstanceName, DefaultLocaleSidKey, TimeZoneSidKey FROM Organization


While for Sandbox info, you can go to the Sandboxes in the setup menu in Prod. The same, you can use SOQL to get the information, but you need to use Tooling API. 
SELECT Id, SandboxName, Description, LicenseType, HistoryDays FROM SandboxInfo

The Id returned here is SandboxInfo ID, not the Org ID and unfortunately, we can't get the Org ID.



You will get the same result if perform query with Rest Explorer from Workbench
GET /services/data/v53.0/tooling/query/?q=+Select+Id,SandboxName,LicenseType,Description+from+SandboxInfo



Reference:

Wednesday, June 8, 2022

Tips for Salesforce Data Cleaning

Data is arguably one of the most important aspects of a business. Good data helps drive decisions that improve products, services, and customer experiences. If your organization has bad Salesforce data, you will start to notice inconsistencies down the line that can challenge marketing campaigns, sales initiatives, reporting, and analysis.

Data naturally decays over time, so without a proper cleanse that fixes recurring errors, bad Salesforce data will continue to worsen and hinder your business. In this blog, I’m going to identify common types of bad data, why you should complete Salesforce data cleaning, and give you tips for improving these processes.

What is bad Salesforce data?

If you use Salesforce, you may already notice data-related problems due to input error, poor data collection methods, or natural data decay. Bad data, like the types listed below, has been known to cost organizations an average of $13 million in revenue each year. 

These are the most common types of bad Salesforce data that you may encounter:
  • Duplicates: Multiple different records that have similar or the exact same data.  
  • Inaccuracy: Typos, data entry errors, or fields that are filled in incorrectly.
  • Irrelevancy: Records or contact information that is outdated or no longer relevant.
  • Inconsistency: No standardization with formatting fields within records.
  • Incomplete: Missing data in key fields, such as company size, industry, or title.


The reasons for Salesforce data cleaning

Good data improves productivity and insights, which helps organizations reach business goals and stay ahead of the competition. Day to day, good data can help your team make smarter decisions that drive growth and revenue as well as increase overall efficiency. It can also help your team with audience targeting that increases the effectiveness of campaigns. 

Clean data is good for your customers as well. It can help build better customer relationships when you know exactly what they need, so your team can offer more appealing services or products.

And finally, good quality data open your organization to greater and more targeted opportunities that increase your competitive advantage and profitability. 


Five tips for a Salesforce data cleanse

Salesforce data cleaning is an ongoing process that helps your business improve data quality over time. Follow these four tips to increase the effectiveness of your Salesforce data cleanse.

Identify root causes

The first step in cleaning Salesforce data is to identify where the majority of errors occur. Is your team inputting data manually and making mistakes? Or are you relying on a data collection system that doesn’t have the right integrations with your business systems? 

These inconsistencies or errors within your current data collection methods may be the cause of bad data. Catching bad data at the source will help you determine what needs to be changed or improved. This initial data audit provides a clear path toward quality data, whether it be through more team training, better software, improved data governance, or something else. 

Choose the right tools

Whether your organization deals with hundreds or thousands (or even millions) of datasets, having the right tools to effectively collect and manage this data is critical to maintaining it. Manual data cleaning for datasets this large is also generally too big of a task for a single data scientist or administrator. 

Having the right data cleaning tools in your systems accelerates cleaning processes and minimizes manual tasks. These tools can automate cleaning tasks, with specific functions that target and correct data quality issues, such as duplicates or missing values.

In addition to adding data cleaning software, implementing an integrated web form and data collection platform like FormAssembly helps ensure that data entering your Salesforce Org is clean and high-quality from the start. 

Standardize processes

The phrase “garbage in, garbage out” is just as relevant to data collection as any other process. Manual processes that begin with a customer filling out a paper form and end with an employee inputting data by hand into Salesforce are prone to numerous errors. 

These processes make it virtually impossible to have consistent validation or standardization of data collection methods. Adopting online forms for streamlined collection, training employees on validation standards, and using data cleaning software help eliminate many of these data errors. 

Setting standards for data ensures that all data entered into Salesforce through a web form follows the same formatting (email, industry type, address, etc.). Standardization minimizes the discrepancies that lead to duplicate records and helps maintain data quality over time.

Enforce rules

Without rules that are set up correctly, data will never be cleaned or completed. Users are joining and leaving the company, which also means the new users may not be aware of existing rules. Manual intervention wastes a lot of time for small teams and is nearly impossible for larger teams.

Setting mandatory fields, dependency fields, and validation rules are easy and can be implemented by Salesforce admin within minutes.

Complete routine audits

Once you have completed a comprehensive Salesforce data cleanse, the process does not stop there. Without proper maintenance, data errors and data decay will inevitably continue to occur. Conducting routine audits means that you will discover and resolve issues faster, while providing your organization with an in-depth look at how data is being processed and stored.

A data quality audit should always include error identification, error resolution, and improved standardization protocols. These audits, when completed regularly, keep your data clean and give your team the information they need to improve processes, analyze accurately, and maintain productivity. 


Clean data starts at the source

Getting quality data into your Salesforce Org efficiently and accurately is the next step in keeping data clean long-term. A data collection platform with advanced Salesforce integration like FormAssembly helps ensure clean, high-quality data from the source—online forms. 

Using FormAssembly’s web form builder, you can easily create Salesforce-connected forms that create or update data for standard and custom objects, prefill forms, and more. In addition, FormAssembly’s newest solution, Workflow, makes it possible to build entire data collection workflows to further reduce errors, accelerate processes, and increase productivity for your team. 


Learn more

With quality data available to leverage, your team has more time, knowledge, and resources to spend driving initiatives that help increase revenue. To learn more about how FormAssembly helps with clean Salesforce data collection, visit their website or find them on AppExchange.





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