5 Ways to Improve Data Quality

The previous chapter covered where bad data comes from: entry errors, incomplete data, duplicates, outdated records, and no shared standards. This chapter covers five ways to fix it, through a combination of technology, process, and habits.

Five key practices to improve data quality

Whether you're managing a CRM or any other system, five practices consistently improve data quality:

1. Data cleansing and deduplication — identify and remove duplicates and errors from a dataset.
2. Data profiling and auditing — analyze data for completeness, accuracy, and consistency.
3. Data governance — put a structured framework and policy in place for managing data.
4. Master data management (MDM) — centralize and govern the organization's critical data assets.
5. Data quality metrics — define and monitor metrics to track and improve data quality over time.

1. Data cleansing and deduplication

Data cleansing identifies and removes duplicate or inaccurate data from a dataset. Removing duplicates improves the reliability and usability of the data underneath it, which matters most for businesses running Salesforce as their CRM.

In Salesforce, duplicates are typically found by comparing fields like name, address, email, and phone for similarities. Salesforce's native tools have real limits here, which is why many organizations bring in Plauti, a Salesforce-native app built specifically to find, merge, and prevent duplicates.

Plauti's matching runs across Leads, Contacts, and Accounts using both exact and fuzzy matching, so it catches duplicates even with misspellings or different formatting. Matching rules are configurable, so an organization can tailor the matching criteria to its own data. Once duplicates are found, merging happens directly inside Salesforce, consolidating records into a single source of truth.

Prevention matters as much as cleanup. Rather than blocking a record outright, Plauti shows a real-time alert at the point of entry when a potential duplicate is detected, so the person entering the data can decide how to handle it immediately.

This approach balances data accuracy with the flexibility to let a person make the final call, without disrupting the entry process itself or slowing anyone down.

2. Data profiling and auditing

Data profiling analyzes data to understand its quality, completeness, and consistency, and it's a critical step in spotting issues before they compound.

Statistical analysis looks at data distributions, frequencies, and patterns to flag anomalies and outliers, comparing data against expected statistical measures.

Pattern recognition analyzes data for recurring patterns or sequences, which helps verify data integrity and surface duplicate or incorrect entries.

Outlier detection identifies data points that deviate significantly from expected values, which can point to entry errors, corruption, or fraud.

Data auditing systematically reviews and validates data against established quality standards, on a regular basis rather than as a one-off. Regular audits catch duplicate records, incomplete data, outdated information, and formatting inconsistencies before they affect reporting or decision-making.

A few well-known tools handle profiling and auditing at scale: Talend Data Quality supports profiling and auditing across various data sources, including Salesforce. IBM's InfoSphere Information Analyzer covers statistical analysis, pattern recognition, and data lineage. Informatica Data Quality analyzes patterns, identifies duplicates, and validates data against predefined business rules. It's worth evaluating more than one option against your own data before choosing.

3. Data governance

Data quality depends on data governance: the structured frameworks, policies, and procedures that govern how data is managed, accessed, and secured. Clear governance keeps data integrity, consistency, and reliability intact across the organization. Our Solutions overview on data governance covers this in more depth.

Governance frameworks give the organization a standard approach to collecting, storing, and sharing data, through classification, naming conventions, ownership, and access controls. Data stewards, the people responsible for maintaining these standards, monitor data quality, resolve discrepancies, and enforce compliance with the policies in place.

Regulatory requirements raise the stakes further. Embedding compliance into a governance framework means data privacy and security requirements, such as GDPR or the CCPA, are handled by the framework rather than left to individual judgment.

4. Master data management (MDM)

Master data management centralizes and governs an organization's critical data assets, its master data: customers, products, suppliers, locations, shared across every system and business unit that touches them.

MDM's goal is a single, authoritative source of truth for master data, removing the redundancies and inconsistencies that come from having the same entity represented differently across disparate systems.

MDM also depends on strong governance: data standards, naming conventions, and quality rules that catch inconsistencies as they're introduced rather than after the fact. Building and maintaining a reliable master-data repository takes profiling, cleansing, and standardization, along with the stewardship to keep resolving issues as they come up.

Integration and synchronization tie it together: updates made to master data in one system need to propagate to every other system that depends on it, in something close to real time, or the "single source of truth" stops being true the moment it's out of sync.

5. Data quality metrics and reporting

Having data isn't the same as having good data, and defined metrics and benchmarks are what tell the difference. Establishing them gives an organization real insight into the health of its data, and a clear target to work toward.

Four metrics show up most often: completeness (how much of the expected data is actually present), accuracy (how well the data matches reality), consistency (whether the data agrees with itself across systems), and timeliness (whether it's current enough to act on). Together they cover most of what "data quality" means in practice.

Defining metrics is only useful once they're actually monitored. That takes tools that give visibility into data quality in something close to real time, with reporting that surfaces trends and flags where to focus next. Measurement isn't a one-time project: revisiting metrics against targets on a regular basis is what catches a regression before it becomes a bigger problem.

Putting it together

This chapter covered five strategies: cleansing and deduplication, profiling and auditing, governance, and master data management, each addressing accuracy, consistency, and reliability from a different angle. None of them work in isolation. Clean data needs governance to stay clean; governance needs metrics to know whether it's working; and MDM needs all three to hold up across every system that shares the same customer or product record.

The next chapter looks at the tools and technologies available to put these practices into action.