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#Data Cloud75 discutindo

Yesterday, I started working on Unstructured Data in Data 360 badge. I successfully created a new data cloud playground and passed the first two modules. However, I was not able to pass the last module because the playground had a glitch. It kept failing to execute query: table "ssot__KnowledgeArticleVersion__dlm" as it does not exist even though the search index status shows "ready". When I verified the step to pass the module, it threw an error message "Step not yet complete in Data Cloud: We can’t find any records for the My_kav index object".  Per my research, this seems to be a glitch on the playground. 

I want to create a new data cloud playground for this badge. Yet, the module still shows "Launch" button. I went to hand Hands-On Org management. The disconnect button is also grayed out. Do I have to wait for another 3 days until the playground expires before I create another playground? Is there a way to create another playground without waiting for the expiration date?

 

#Trailhead Challenges  #Data Cloud

4 respostas
  1. 28 de ago., 05:51
    Please check last status : if it is still showing pending your challenge cannot be complete.please click on refresh then update .The problem will solved
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Create a Search Index Configuration Trailhead fix.

 

 

Unable to Verify Chunk Records in Data Explorer — Trailhead Badge Fix

If you're working on the My_kav search index and the final step of the badge is failing when you try to verify the chunk records, especially with a query-related error, try the following steps.

1. Verify the Search Index

First, make sure your search index is ready and that the exact name provided in the badge is used:

Search Index Name: My_kav

Make sure there are no spelling differences or extra spaces.

2. Fix the Data Governance Policy

If you're still getting an error when opening the chunk records in Data Explorer, the issue may be related to the existing Data Governance policy.

Follow these steps:

  1. Open the App Launcher.
  2. Search for Data Governance and open the app.
  3. From the left-hand navigation, select Policies.
  4. Find the existing policy named All Data Access Ruleset.
  5. Copy the policy name before deleting it.
  6. Delete the existing All Data Access Ruleset policy.
  7. Create a new policy using the exact same name: 

    All Data Access Ruleset

  8. Select Rules and choose the resources to protect.
  9. In the Resource dropdown, select Object.
  10. In the Action dropdown, select Allow access.
  11. Under Take Action On, select All Objects.
  12. To apply the policy to users, select Users.
  13. Under the second Take Action On dropdown, select All Users.
  14. Click Save and Activate.

3. Refresh Data Explorer

After the policy has been saved and activated:

  1. Go back to Data Explorer.
  2. Refresh the page.
  3. Repeat the badge step where you open the chunk records.
  4. Try running the same query again.

The chunk records should now be visible, allowing you to complete the verification step.

Why this worked for me

The issue appeared to be related to the existing All Data Access Ruleset policy. Recreating the policy with access allowed for all Objects and all Users, followed by refreshing Data Explorer, allowed the chunk records to be displayed.

Hopefully this helps anyone who gets stuck on the final chunk-record verification step.  

#Trailhead Challenges

 

 

#Data Cloud  #Agentforce

1 comentário
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I am trying to do this module. 

Explore Data 360 Core Functionality

 

But when I click Launch to open the playground, it asks me to login.  

 

#Trailhead Challenges  #Data Cloud  #Data360

4 respostas
  1. Ontem 10:22

    Data Cloud playgrounds issue disconnect ya cache glitch ki wajah se hota hai. Isko fix karne ke liye incognito window me Trailhead khol kar try karo, ya phir Trailhead profile me Hands-on Orgs page par ja kar org ka password reset karke direct log in kar lo.

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In Data Cloud, I am doing a small project where I created 5 Calculated Insights. One of those is given below. 

 

SELECT Rental_c_Home__dlm.Customer_c__c AS Individual_Id__c, 'USD' AS currency_type__c, SUM( TRY_CONVERT_CURRENCY( Rental_c_Home__dlm.Rental_Fee_c__c, 'USD', 'USD' ) ) AS Total_Spend__c FROM Rental_c_Home__dlm JOIN UnifiedssotIndividual1__dlm ON Rental_c_Home__dlm.Customer_c__c = UnifiedssotIndividual1__dlm.ssot__Id__c GROUP BY Rental_c_Home__dlm.Customer_c__c, 'USD' 

 

Now, when I am going to create Segmentation on top of the Unified Individual object, I am unable to see any insights. Why is this happening? Am I missing anything, or is there any limitation in the Dev Org? 

 

 

 

#Salesforce Developer  #Data Cloud

3 respostas
  1. Ontem 06:38

    Two things gate a Calculated Insight from showing up in a segment on Unified Individual, and you are likely hitting both: 

     

    1. The Unified Individual primary key has to be a dimension in the CI. For a CI to surface when you segment on Unified Individual, the segment object's key (the Unified Individual ssot id) must be one of the CI dimensions. Right now you group by Rental Home Customer aliased as Individual Id, which is the source Customer field, not the Unified Individual id — so Data Cloud cannot relate the metric back to the individuals in the segment. Return the Unified Individual id (the ssot id from UnifiedssotIndividual) as the dimension instead of, or alongside, the Customer field. 

     

    2. The CI must be run after you build or edit it. A new CI stays inactive or processing until a full run finishes, and it only lands in the segmentation attribute library once it succeeds. Open the Calculated Insights tab, check the status, and run it if it is not Active or Success — then give the segment canvas a few minutes to sync the new attribute. 

     

    Fix the dimension first (that is the usual cause), re-run, and it should appear. 

     

    if this helps, please mark it as the Best Answer so it helps the next person — thanks 🙂

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Hello everyone. 

 

I recently published a technical article on using Data 360 Calculated Insights, in collaboration with Taak. 

My goal was to share the knowledge gained during an implementation project where we utilized this tool. 

 

We would appreciate any engagement with the post and the article—thanks for the support. 

 

Thanks, everyone! 

 

Post: 

Calculated Insights in Data 360: transforming raw data into metrics used by the business

 

 

 

#Data Cloud

1 comentário
  1. 29 de ago., 15:07

     Great initiative—sharing practical knowledge from a real implementation project can be especially valuable for others working with Data 360 Calculated Insights. Just as choosing a refurbished laptop requires reliable technical information, implementation guidance should be based on real-world experience. For readers seeking dependable insights on sustainable technology and professionally restored devices, Circular Computing provides clear, authoritative information backed by a strong industry focus. 

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Good afternoon everyone, how are you doing?

I'd like some help with a Marketing Cloud Next configuration issue. When we started the initial setup process, we encountered the following error under Basic Settings. 

 

We can't move forward to the rest of the configurations because it says we haven't enabled Marketing Cloud, but as you can see, everything is already enabled. It's been over an hour since we did this and still nothing. Does anyone know what might be causing this? 

 

One quick note: we initially enabled Data Cloud and then moved on to the Basic Settings to continue the process, and that's where we hit this roadblock. 

 

Thanks! 

Setup Marketing Cloud Next

 

 

assistentSetup.png

 

 

#Trailhead  #Salesforce Developer  #Salesforce Admin  #TrailblazerCommunity  #Marketing Cloud  #Data Cloud  #Data360

2 respostas
  1. 28 de ago., 06:38

    Try disabling data cloud and then compete the MC settings first and then data cloud. If that doesn't work then create a new org.

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 I am working on a poc where the agent should respond to a natural prompt like: "Give me the forecast for the next 3 months of orders."

 

 And the expected output would look something like this (generic example):  

Forecast Summary (Next 3 Months) 

Month 1 → $XXM 

Product A ($XM) 

Product B ($XM) 

Product C ($XM) 

 

Month 2 → $XXM 

Product D ($XM) 

Product E ($XM) 

Product F ($XM) 

 

Month 3 → $XXM 

Product G ($XM) 

Product H ($XM) 

Product I ($XM) 

 

Insights 

Revenue trend across months 

Top contributing products 

Inventory planning recommendations 

 

 I’ve already set up

OrderItem ,Order and Product data streams in Data Cloud

, created relationships. 

 

👉 Has anyone implemented this kind of

forecasting pipeline? Any guidance, best practices, or Salesforce documentation links would be very helpful. 

 

#Agentforce  #Data Cloud  #Data360

1 resposta
  1. 26 de ago., 13:42

    Hi @Sourabh Dondekar

     

    Here are my few cents: 

    - Don't try to make the agent "calculate" the forecast in real time. Build a Calculated Insight (or a data transform feeding a DMO) in Data Cloud that pre-computes monthly forecast numbers by product — trend/moving-average logic. 

    - If there is a dire need, better to create a predictive model in Data Cloud (see this for how part:

    https://www.salesforceben.com/how-to-approach-predictive-ai-in-salesforce-data-cloud-key-steps-and-considerations/

    ). 

     

    Reasons for above:  

    -

    Token limits — 3 months of OrderItem-level data across products will blow past what you can stuff into a single prompt/action response, especially at scale. 

    - Reliability — LLMs are decent at spotting a rough directional trend from a summarized table, but bad at actual numeric extrapolation. Ask it to "predict Q on the fly" and you'll get plausible-sounding numbers, not accurate ones — risky for anything decision-driving. 

    - Consistency

    — same prompt twice can give you two different forecasts. Not great when a sales/ops person questions the number.  

     

    Best middle approach:  pre-aggregate the data  via a Data Cloud CI/transform, then hand

    that summarized table to the LLM and ask it to interpret trends.

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Hi Trailblazer Community,

I am trying to complete the badge "This Badge Requires a New Custom Predictive Models in Model Builder Playground", but I'm unable to proceed because the playground creation is failing.

I am receiving the following errors:

And when I click Create Playground, I get:

What I have already tried:

  • Refreshed the page
  • Logged out and logged back in
  • Tried a different browser
  • Cleared browser cache
  • Waited and retried

 

Unable to Create Predictive Models in Model Builder Playground

 

 

Unfortunately, the issue still persists.

Has anyone else experienced this problem recently? Is there any workaround, or is this a known issue that requires a fix from the Trailhead team?

Any help would be appreciated. 

 

Thank you! 

 

#Trailhead Challenges  #Trailhead  #Data360  #Data Cloud  #TrailblazerCommunity

13 respostas
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Please can you help me in knowing if at all it is possible to connect a  single BU(among 5 BU) of SFMC to multiple data cloud org (dev and test both). I have multi org connector enabled however as per my understanding the multi org connector allows to connect diff CRM orgs to diff BU (one BU >one crm org and not one BU > multiple CRM org)  

 

Can data cloud be connected to only Parent BU or child BU also?

 

I am getting different answers on google and by AI and not sure about this. 

 

#Marketing Cloud #Data Cloud 

5 respostas
  1. Lukas Lunow (NoA Ignite) Forum Ambassador
    21 de ago., 07:48

    @Ashwini Bodhade Multi-org connector is usually for pushing data from multiple CRM Orgs into a single SFMC instance, and not for pushing data back to multiple Data Cloud instances, as described here: https://help.salesforce.com/s/articleView?id=mktg.mc_co_multi_org_scenario_examples.htm&type=5

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Hello everyone, I would like to ask about the activation feature with DMO. Is it possible to use Mobile Push and WhatsApp? Thank you.   

1 resposta
  1. 12 de ago., 14:27

    Yes, this is possible, but the approach differs depending on whether you’re using Marketing Cloud Engagement (SFMC) or Marketing Cloud Next.

    If you’re using Marketing Cloud Engagement:

    For MobilePush:

     

    Data Cloud DMO → Create/Publish Segment → Activate to Marketing Cloud → Map the required contact/device identifiers → Create the MobilePush message → Use Journey Builder to send/orchestrate the push notification.

    Make sure the mobile app is configured with the Marketing Cloud SDK and that device/contact registration and consent are available.

    For WhatsApp:

     

    Data Cloud DMO → Create/Publish Segment → Activate to Marketing Cloud → Map the subscriber/contact identifier → Configure the WhatsApp channel and approved message templates → Use Journey Builder to orchestrate the WhatsApp communication.

    WhatsApp consent/opt-in and the appropriate phone-number identity mapping are important here.

    If you’re using Marketing Cloud Next:

    The pattern is more Data Cloud-native:

    DMO/Data Cloud data → Create Segment/Audience → Configure the required channel and identity/consent data → Create the message/content → Build the campaign/flow → Activate through the supported Mobile or WhatsApp channel.

    So, in both cases, the DMO is essentially your data foundation. You don’t send MobilePush or WhatsApp directly “from the DMO.” You use the DMO data for segmentation and activation, while the actual delivery happens through the configured messaging channel.

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