7 Best Data Quality Practices

Seven practices to maintain excellent data quality

Microchips are cheap enough now to sit inside almost any device, including a toothbrush that tracks and records its user's habits. The internet is the largest repository of information in human history and is still growing. But the growth of data outpaced most organizations' ability to manage it, and managing a large set of data well remains genuinely difficult.

The analogy holds up reasonably well. Oil doesn't come out of the ground ready to use; it has to be refined and treated before it becomes something a machine can run on. Most data needs the same kind of refining before its valuable parts are usable. Running a business on poor-quality data is like putting poor-quality fuel into an engine: it might run for a while, but it's not a safe bet.

Some of the seven practices below are technical, involving data manipulation and automation tools. Others come down to habits and culture. This chapter goes into more detail than the previous ones on how to put each into practice.

Best practice one: establishing data quality standards

Defining standards for accuracy, completeness, and consistency. Accuracy means the data entered is correct and free from errors. Completeness means every required field is populated, with no gaps. Consistency means uniform formatting across fields, with no duplicates or conflicting entries. Defining all three gives an organization a clear benchmark for measuring and improving its data.

Involving stakeholders in setting policy. Including stakeholders from different departments surfaces the specific data requirements each one has and keeps the resulting standards aligned with actual business objectives, rather than a policy written in isolation. It also builds buy-in, since people are more likely to follow a standard they helped shape.

Documenting standards for reference and consistency. A documented standard, covering definitions, validation rules, entry guidelines, and examples of an acceptable format, gives everyone in the organization, new hires and long-time staff alike, one place to check rather than relying on institutional memory.

Best practice two: regular data quality checks

Conducting regular data audits. Systematically reviewing Salesforce data surfaces inconsistencies, inaccuracies, duplicates, and other issues, letting an organization address them proactively rather than after they've caused a problem.

Automating data quality checks and validation. Automated deduplication and validation processes, scheduled or triggered by predefined rules, reduce manual effort while keeping data accurate, complete, and consistent.

Using data profiling and cleansing tools. Profiling tools analyze data to reveal its quality, structure, and consistency, surfacing missing values, outliers, and inconsistencies an organization can then act on.

Plauti brings these together in one place: cleansing, automated checks and validation, and profiling and deduplication, all built to help an organization maintain high-quality Salesforce data, reduce data-related risk, and make decisions on data it can actually trust.

Best practice three: continuous improvement

Maintaining and improving data quality standards in Salesforce isn't a one-time project. It means monitoring metrics and KPIs, analyzing the root causes of data quality issues, taking corrective action, and iterating on the process itself.

Monitoring metrics and KPIs. Accuracy rates, completeness percentages, duplicate counts, and entry-error rates, tracked regularly, show where data quality is trending and where it needs attention.

Analyzing root causes and taking corrective action. Understanding why an issue happened, whether it's an entry process, a training gap, a system integration, or a data source problem, is what lets an organization fix the cause instead of just the symptom.

Iteratively improving processes and workflows. Data quality is an ongoing process, not a destination. Reviewing and refining entry, validation, and deduplication procedures, along with governance policies and training, keeps a data quality program adapting to changing business needs instead of going stale.

Best practice four: education and training

This practice covers training data stewards and data users, teaching entry best practices, and building data quality awareness across the organization.

Training data stewards and data users. Data stewards carry the responsibility for governance and data quality, so equipping them with the right knowledge, including how to use validation tools, matters directly. Users who interact with Salesforce regularly are the first line of defense against bad data, so they need training too.

Teaching entry best practices. Guidelines on formatting, mandatory fields, and validation rules, reinforced through training sessions, documentation, and periodic reminders, keep entry consistent and accurate.

Building data quality awareness. Workshops and open discussion around data quality topics plant the seeds of a data-driven culture. It works best as an ongoing conversation, not a once-a-year reminder.

Best practice five: collaboration

Encouraging collaboration between owners, stewards, and users. Data owners are responsible for the accuracy and reliability of specific datasets; stewards oversee governance. Open communication and shared problem-solving between the two groups, and the users who enter the data day to day, builds shared responsibility for data quality rather than leaving it to one team.

Establishing cross-functional governance committees. Bringing in representatives from multiple departments avoids the tunnel vision that comes from one team making decisions about data everyone else depends on.

Creating a culture of accountability. Good data habits aren't instinctive; they take deliberate reinforcement. Defining clear roles and responsibilities, and giving employees a real sense of ownership over the data they handle, is what makes accountability stick.

Best practice six: data quality measurement and reporting

This means defining key metrics and performance indicators, reporting and dashboarding them regularly, and using scorecards to track and communicate progress.

Defining metrics and performance indicators. Accuracy rates, completeness percentages, duplicate counts, consistency, and integrity give an organization a concrete benchmark to measure data quality against.

Reporting and dashboarding regularly. Regular reports and dashboards give the organization a real-time, visual snapshot of data quality in Salesforce status and trends over time.

Using scorecards. A scorecard summarizes the current state of data quality against a predefined target, shared with stakeholders to build awareness and accountability.

Best practice seven: data quality tools and technologies

Tools and technologies play a significant role in Salesforce data quality: profiling, cleansing, deduplication, validation, and automation. Plauti is one example of a data quality solution built to support these efforts.

Implementing data quality management systems. A centralized platform to define and enforce standards, automate processes, and monitor metrics, with features like auditing, dashboards, and workflow automation, makes data quality management efficient rather than a manual chore.

Exploring AI and automation. Artificial intelligence and automation can identify and resolve data quality issues, detect patterns, and suggest improvements, streamlining validation, cleansing, and enrichment and reducing manual effort across the board.

Bringing the seven together

Maintaining excellent Salesforce data quality means putting all seven practices into place together, not choosing one:

• Establishing data quality standards for accuracy, completeness, and consistency.
• Regular data quality checks, through audits, automated validation, and profiling.
• Continuous improvement, by monitoring metrics, addressing root causes, and iterating.
• Education and training for data stewards and users.
• Collaboration across stakeholders, with cross-functional committees and shared accountability.
• Measurement and reporting, with defined metrics and scorecards.
• Data quality tools and technologies, for profiling, cleansing, validation, and automation.

Adopting these seven practices, backed by the right tools, keeps Salesforce data quality strong over time. That means data-driven decisions built on accurate, complete, and reliable information, and a stronger data-driven culture across the organization.