Converting to Salesforce Flow

My Data in 2025

Imagine this scenario:

Your children have moved out of the house after college. You’re a happy empty nester, planning your dream European cruise. Then, you receive an email: Here is your coupon for baby diapers.

As a customer, you wonder, “Why did I get this?”

As a marketer, you realize you’ve targeted the wrong person—a result of poor customer data.

The Importance of Data

Data quality has always been critical, but its role has evolved. Over a decade ago, it was about driving business insights for better decision-making. Five years ago, it shaped personalized digital experiences. Today, it’s the foundation for AI-driven strategies.

Take Salesforce’s Agentforce as an example. With good data, Agents can provide seamless customer experiences. They can identify a customer’s order, process a return, and recommend alternatives—all within a single interaction. No need for customers to “press 0” to navigate menus. Instead, the Agent, guided by data and prompts, ensures a streamlined and efficient experience.

How do you prepare your Salesforce org for good data in 2025?

Data Governance

For many customers, their 2025 goal is to define a data governance model to support their AI strategy.  At RadixBay, we see the four pillars of data governance as:

  • Identify Data Quality Requirements
  • Define and Design Data Quality Controls
  • Monitor Data Quality Issues
  • Operationalize the Data Governance Model
Salesforce Flow Builder

Identify Data Quality Requirements

Start by defining what constitutes a valid record. For example:

  • At Acme Corp, a valid Contact record includes First Name, Last Name, Account, and Email.
  • At Widget Co., it might include First Name, Last Name, the last four digits of a Social Security Number, and a Home Mailing Address.

Every organization views their data differently.  You need to define what your data requirements are and document an enterprise data dictionary so that every business unit defines and understands data the same way.

Define and Design Data Quality Controls

Salesforce offers several tools to enforce data quality:

  • Data Standardization: “State and Country/Territory Picklists” provide standardized lists for data entry, preventing errors like typos or alternate spellings.
  • Validation Rules: Guide users by flagging entries that don’t meet your quality standards. For example, rules can ensure no special characters are entered in the First Name field.
  • Duplicate Rules: Minimize duplicate records using up to five rules per object. You can mix standard and custom rules to enforce quality controls.

Monitor Data Quality Issues

Even with controls, data evolves. Monitoring is essential. Salesforce reports and AppExchange tools can help you track trends, uncover gaps, and identify new issues in your data quality.

Operationalize the Data Governance Model

Data governance isn’t just about your Salesforce org; it involves people and processes.

  • Roles and Responsibilities: Identify data stewards who monitor quality and a Data Governance Board to define data ownership and standards.
  • Buy-in: Ensure all levels of the business understand and support data quality efforts.
  • Processes: Define workflows for creating, reviewing, and updating data. Specify who can create data, when it should be reviewed, and how often controls need updating.

 

These four pillars—requirements, controls, monitoring, and operationalization—involve the entire organization. They are essential for maintaining high-quality data and driving AI implementation.

Converting to Salesforce Flow Builder

Investing in data governance today ensures that in 2025, your Salesforce org will not only support AI but also deliver exceptional customer experiences.  Let’s look at how DataGroomr can help with your data governance.

How DataGroomr Drives Data Quality

DataGroomr drives data quality by leveraging machine learning to automate and enhance the process of cleaning, deduplicating, and maintaining high-quality datasets. DataGroomr can help with two pillars of your data governance – design of data quality controls and monitor of data controls. Here are some keyways DataGroomr contributes to data quality:

Data Accuracy and Integrity

  • Automated deduplication removes duplicate records, ensuring that datasets contain only unique and accurate information.
  • When duplicate records are merged, updates to data are consolidated, reducing discrepancies across systems.

Standardized Processes

  • DataGroomr’s machine learning models and customizable rules ensure that deduplication follows standardized guidelines, a foundation for effective data governance.
  • Every deduplication action is tracked, providing a detailed history of changes, which supports transparency and compliance with governance policies.

Enhanced Data Stewardship

  • By automating deduplication, data stewards can focus on strategic tasks rather than manual cleaning. This ensures their efforts align with the business governance goals.
  • As users train the system, it adapts to the organization’s data governance requirements, refining the deduplication process over time.

Reduced Operational Risk

  • Automated deduplication mitigates risks associated with manual processes, such as missed duplicates or human errors.
  • Clean, deduplicated data allows for more reliable analytics and reporting, essential for decision-making.

Sustainable and Scalable

  • DataGroomr’s automation capabilities are scalable, ensuring governance practices are maintained even as data grows.
  • By automating repetitive tasks, businesses can allocate resources more effectively, ensuring governance strategies are sustainable.
Migrating to Salesforce Flow Builder

To wrap things up, data is essential for businesses and continues to increase in importance, maintaining high-quality, reliable data is critical for success. Poor data not only results in missed opportunities but also leads to missteps in customer engagement, as shown in our opening scenario. The four pillars of data governance – defining requirements, implementing controls, monitoring issues, and operationalizing processes – form the backbone of an effective data strategy.

DataGroomr offers an intelligent, automated solution to streamline data governance, ensuring accuracy, consistency, and scalability while freeing up valuable resources for strategic initiatives. By harnessing the power of machine learning, DataGroomr simplifies complex data tasks, reduces operational risk, and empowers businesses to make better decisions, foster stronger customer relationships, and stay compliant with evolving standards. With DataGroomr, your organization can confidently navigate the future of data, driving growth and innovation with trusted, high-quality data at the core.

 

About RadixBay

With headquarters in Charlotte, NC, RadixBay delivers global IT consulting solutions using a new approach that breaks the outdated and stale IT delivery model.

RadixBay’s rural shore delivery centers combine the cost benefits of offshore with the quality, security and simplicity of onshore support.  This approach delivers high-touch, high-speed results and is aligned with our goal of creating jobs in rural North Carolina.

The RadixBay team has a proven track record of developing advanced technologies, process innovations, and business transformation programs across a wide range of Fortune 500 clients, high-growth companies, and government entities.

Tyler - Salesforce team

Thanks for reading!

To learn more about our Salesforce services and how RadixBay can help your organization maximize its CRM software investment, please visit our RadixBay Salesforce Services web page.

Miriam Vidal Meulmeester, PMP
RadixBay Senior Director, Salesforce Practice

Denise Hyland
RadixBay Senior Salesforce Consultant

Rural Shore Delivery Center Manager