What Can Go Wrong in a Salesforce Data Cloud Project (and How to Avoid It)
Find out how we ensure our clients have trouble-free adoption of Salesforce Data Cloud
As Salesforce consultants at CUBE84, we are in the process of helping some of our customers leverage Salesforce Data Cloud to unlock valuable insights and deliver smarter customer experiences. However, like any complex implementation, Data Cloud projects come with their fair share of challenges. This post offers a candid view of common pitfalls we've seen and actionable advice on how to avoid them.
1. Unclear Business Objectives
The Problem:
Many Data Cloud projects start with excitement but lack a clear, measurable outcome. This results in scope creep, delays, and frustration for stakeholders when they don't see immediate value.
How we avoid it:
- We start with a discovery phase to align the project's objectives with your business goals.
- We identify quick wins to demonstrate early success (e.g., real-time segmentation or churn reduction).
- Use Key Performance Indicators (KPIs) to track progress and ensure accountability.
2. Endup using more credits than needed
The Problem:
Credit consumption can exceed what is planned initially quite easily. This can lead to cost overruns and dissatisfaction for everyone involved.
How we avoid it:
- We have found that Data Cloud projects are 80% planning and 20% execution, so we conduct an extensive planning phase before execution.
- We perform a thorough data audit to better understand the data and plan the transformation process before loading it, reducing credit consumption from trial and error.
- We carefully plan the data load volume, refresh frequencies of incremental data loads, calculated insights, and segments based on business requirements.
- We strongly recommend constantly monitoring the need for ongoing projects within the Data Cloud to avoid unnecessary credit consumption.
3. Underestimating Data Complexity
The Problem:
Salesforce Data Cloud unified data from multiple sources—CRM, ERP, marketing tools, and more. This means data which is in different formats and structures are pulled into Data Cloud. As a result mismatched data structures, duplicated entries, or missing fields can arise and lead to incomplete or inaccurate customer profiles.
How we avoid it:
- We conduct a data audit to identify gaps and inconsistencies.
- We help orgs set up data governance frameworks to maintain quality over time.
- Leverage Salesforce-native tools like Data Cloud's harmonisation layer to map and transform data efficiently.
4. Integration Challenges
The Problem:
Data Cloud might require integration with legacy systems that aren't always easy to connect. Poor integration planning can cause delays or even failures in syncing data sources.
How we avoid it:
- We build a comprehensive integration plan with backup options in case certain systems are hard to connect.
- We recommend batch integrations unless streaming integration is absolutely needed thus saving tons of credits.
- Use pre-built connectors, Mulesoft or other preferred middlewares, or APIs to smoothen the process
5. Overlooking User Adoption
The Problem:
Even if the tech works perfectly, the project can still fall flat if employees don't adopt the new tools or processes. Many organisations fail to involve end users early on, leading to resistance and underutilisation.
How we avoid it:
- We engage users early and often to ensure their needs are accounted for.
- We provide role-based training tailored to how different teams will use Data Cloud.
- Incentivise adoption with small wins, like showcasing how AI-based predictions improve sales performance.
6. Misaligned Security and Compliance Policies
The Problem:
Data privacy regulations (like GDPR or CCPA) can complicate Data Cloud implementations, especially if your organisation operates across multiple regions. Inconsistent data permissions or non-compliance could result in fines or project delays.
How we avoid it:
- We perform a Privacy Impact Assessment (PIA) before implementation to ensure compliance.
- We help you set up permission sets and data-sharing rules aligned with your internal policies.
- Use Salesforce's compliance-ready tools like encryption at rest and audit logging for added security.
7. Planning For Contingencies
The Problem:
It's easy to underestimate the time and resources required for a Data Cloud project. Rushed implementations can lead to errors, while budget overruns frustrate leadership and strain internal teams.
How we avoid it:
- Build in buffer time for unexpected challenges—think of it as insurance.
- We use agile project management to tackle tasks in smaller, manageable sprints.
- We set expectations with stakeholders from the start and maintain regular progress reviews.
8. Ignoring Post-Implementation Support
The Problem:
Once the project goes live, some organisations assume the work is over. But, without ongoing monitoring, optimisations, and support, the system can quickly become outdated or inefficient.
How we avoid it:
- We always plan for ongoing maintenance and updates from day one.
- We set up health checks to monitor system performance and data quality regularly.
- Engage a Salesforce Managed Services team to handle routine updates and optimise processes as your business evolves.
Conclusion: Be Proactive, Not Reactive
Salesforce Data Cloud has immense potential, but it requires thoughtful planning and proactive management to deliver meaningful results. At CUBE84, we emphasise a structured, strategic approach to every project—focusing on clear objectives, strong integration, seamless adoption, and continuous improvement.
With the right partner and a solid game plan, these pitfalls are entirely avoidable. If you're considering a Data Cloud implementation, we'd love to help you navigate these challenges and unlock the full potential of your data. Let's connect and make your Data Cloud project a success.
At CUBE84, we don't just deliver solutions—we ensure long-term value through strategic consulting and expert execution.
