Want to see demo?    

← Back to Resources

Why Data Cloud Proof of Concepts Fail

Reading time: 7 min   |  By Mehmet Orun   |  Published in Articles,

Data Cloud Evaluation Guide

And How to Run a Successful Data Cloud Evaluation!

I keep hearing stories of Data Cloud evaluations that led to nothing but frustration. I’ve even begun to hear of top-tier SI partners that have instructed their reps to stop pitching Data Cloud proof of concepts. It is my opinion, that most projects approach Data Cloud proof of concepts completely wrong.

For more than two decades, I’ve evaluated end-to-end data management solutions. I’ve been the solution owner with responsibility for total cost of ownership and ensuring we met the business expectations. I’ve also advised dozens of clients on these solutions as the head of Salesforce’s Data Strategy Practice.

I know what it takes for an organization to purchase complex data management solutions. I seek to share my views on why many Data Cloud proof of concepts are flawed. And how to successfully approach a Data Cloud evaluation.

Three Levels of Evaluation for Technology Purchases aka The Three Proofs

When organizations choose a technology, they scrutinize it at increasingly detailed levels during their evaluation process.

Proof of Technology (PoT)

A proof of technology (PoT) demonstrates that a technology performs specified functions and the effectiveness of it’s performance.

PoTs are useful early in selection processes, especially when considering multiple technology providers to meet a common need. They help you quantify results independent of vendor survey responses.

Most common tests include implementing a simple functionality and then testing it at increased scale levels for performance testing. Another objective is to run security scans to understand vulnerabilities.

Proof of Concept (PoC)

A proof of concept (PoC) determines whether the technology can achieve a specific concept or outcome. PoCs assume technical feasibility has already been established. Now, we want to explore how to apply the technology to solve a specific problem.

PoCs focus on the business need and are impactful in later evaluation stages to differentiate between competing vendors. They help you understand the ease of implementation, quality of results, and usability of shortlisted solutions. They aim to identify which use cases may be easier or faster to implement.

Proof of Value (PoV)

A proof of value (PoV) demonstrates the actual business value or ROI a solution can deliver. PoVs focus on business outcomes, cost-benefit analysis, ROI, and the impact on business processes. They are powerful when building a business case for the solution.

If you aim to show why an organization should invest in an initiative, this is the only relevant proof.

Why Do Data Cloud Proof of Concepts Fail?

Many Data Cloud evaluations run out of steam before they can get to a PoV. Evaluating Data Cloud without understanding how the business will realize value from the technology is a recipe for disappointing results. Instead, I recommend taking the inverted approach.

Given how heavily Salesforce is betting on Data Cloud, assume the tech works—albeit with constraints. However, the core functionality of ingesting and unifying data sources to power AI and customer engagement is solid.

If you have security or compliance concerns, Salesforce should be able to answer or demonstrate these effectively. In most cases, you shouldn’t have to spend time and resources verifying technical capabilities as a prerequisite for proving the technology.

Most Data Cloud evaluations focus on:

  • Can Data Cloud address my business need?
  • What is the likely investment cost (licenses and implementation) to meet a particular business need?
  • What is the cost-benefit?

The only practical path is to conduct a Proof of Value

But Isn’t a Data Cloud Proof of Value Difficult, Time-Consuming, Expensive…?

It could be but it does not have to be.

What I love about Data Cloud is how the needed features are all available under a single umbrella. There are proven techniques to deliver a Data Cloud proof of value. These will help you quantify the business benefits and relative costs and guide your investment decisions.

  1. Define a business use case with a target persona, illustrating how better data can lead to improved results.
  2. Identify a single example of how that persona currently spends time and effort gathering data. Create a hypothesis on the cost of missing data and manual effort. Failure to conduct this step means you cannot link technology investments to business benefit objectives.
  3. Focus on 2-3 data sources, each with 3-5 source objects representing both the customer and a transaction.
  4. Take a deliberate sample of the data, based on probable customer identifiers (B2B) or geography (B2C), to ensure impactful unified customer benefits.
  5. Focus on a minimal number of relevant fields to demonstrate the business benefit. Quickly profile, model, map, and unify insights.
  6. Quantify customer unification statistics (Data Cloud offers a UI for this) and test the benefits of unified insights with the persona identified in Step 2. Correlate improved information and time savings to their daily tasks.
  7. Create a slide illustrating the business benefits relative to implementation costs.

With the correct data scope and data design, you can complete a PoV using 2-3 data sources within 4 weeks without exceeding your free credit limit. Since the work demonstrates real business opportunity, it never goes to waste.

Conclusion: Organizations Will Buy Data Cloud When Business Value is Proven

CFOs evaluate technology purchases for how it will increase revenue, reduce costs, or ensure compliance. A proof of value enables you to build a business case attached to one or more of those outcomes. Anything less will not deliver the results you need to secure a budget for implementation.

By Mehmet Orun

Mehmet has been on a quest to achieve Customer 360 with Salesforce for more than 18 years. As a Salesforce customer, he architected and oversaw the Master Data Management program for Salesforce’s first Life Science customer. During his 10+ year career at Salesforce, Mehmet served as the first Director of Data Quality for the Data.com product team, built and led Salesforce’s Data Strategy Practice, wrote requirement 0 for Salesforce Customer 360, and lead the C360 Data Manager product until its integration with C360 Audiences (aka Marketing CDP) which we now know as Data Cloud. See full leadership profile.


Want to see our platform in action?

By leveraging the Cuneiform Platform, you can obtain and use more accurate, data-driven insights through effective data quality monitoring. Learn more about how we can help you with your important tasks.