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5 Ways to Improve Data Quality

TL;DR: To improve data quality, focus on data cleansing to eliminate duplicates and errors, utilize data profiling and auditing techniques, establish data governance practice and implement Master Data Management for centralized data control. Last but not least, define and monitor your data quality metrics for continuous improvement.

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In the previous article we looked at how data is often disorganized and prone to errors unless there are checks and balances in place. The causes of bad data include data entry errors, incomplete data, duplicate data, outdated data, and the lack of data standards. It’s time to look at the ways we can improve our data through a combination of technology, principles and data handling habits. We are going to examine five ways you can start to improve your data quality today using effective and proven methods.

Key Practices to Improve Data Quality

Enhancing the quality of your data is crucial for optimal performance, whether you're optimizing your CRM or any other system. Here are five key principles to boost data quality:

  1. Data Cleansing & Deduplication: Identify and remove duplicates and errors from datasets.
  2. Data Profiling and Auditing: Analyze data to ensure completeness, accuracy, and consistency.
  3. Data Governance: Establish structured frameworks and policies for data management.
  4. Master Data Management (MDM): Centralize and govern critical data assets.
  5. Data Quality Metrics: Define and monitor metrics to continuously evaluate and improve data quality.
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1. Data Cleansing & Deduplication

Data cleansing involves identifying and eliminating duplicate or inaccurate data within a dataset or database. By removing these duplicates, organizations can improve the reliability, integrity, and usability of their data. This process is critical for businesses that rely on Salesforce as their customer relationship management (CRM) system.

In Salesforce, there are several techniques available to identify and handle duplicate records. One effective approach is using algorithms or matching criteria to compare data and identify potential duplicates. This involves analyzing various data fields such as names, addresses, emails, and phone numbers to find similarities or patterns that indicate potential duplicates.

However, some of the features available in Salesforce might be limited, and therefore many organizations will utilize and 3rd party tool with more extensive features. Plauti Duplicate Check is a unique and powerful tool that helps organizations combat duplicate data in Salesforce. It offers advanced features and functionalities that make the duplicate identification and management process efficient and effective.

One key aspect that sets Plauti Duplicate Check apart is its intelligent matching algorithms. These algorithms analyze data across different Salesforce objects, including Leads, Contacts, and Accounts, and employ both exact and fuzzy matching techniques. This means that even if there are slight variations or discrepancies in the data, such as misspellings or different formats, Plauti Duplicate Check can still identify potential duplicates accurately.

Plauti Duplicate Check also provides customization options for matching criteria. Organizations can define their own matching rules based on specific fields and conditions. This flexibility allows businesses to tailor the duplicate identification process according to their unique requirements and data characteristics.

Once duplicate records are identified, merging them is a crucial step in data consolidation. Duplicate Check streamlines this process by allowing users to merge duplicate records directly within Salesforce. By consolidating duplicate data, organizations can eliminate redundancies, ensure a single source of truth, and gain a holistic view of their customers and prospects.

While identifying, consolidating and merging duplicates is essential, implementing preventive measures is equally important to maintain data quality. Plauti Duplicate Check offers a unique approach to preventing the creation of duplicate records at the point of data entry. Instead of blocking the creation outright, the tool provides real-time alerts to users. These alerts notify users about the potential duplicate and provide them with relevant information to make an informed decision.

By offering real-time alerts, Plauti Duplicate Check promotes data integrity and accuracy from the very beginning of the data entry process. Users are immediately made aware of existing duplicates, allowing them to review and assess the information before proceeding. This proactive approach significantly reduces the chances of creating new duplicates and ensures that only clean and consolidated data enters the Salesforce system.

The benefit of this approach is that it strikes a balance between data accuracy and user flexibility. It allows users to make informed decisions while minimizing disruptions in the data entry process. By empowering users with real-time information, organizations can enforce data quality standards and prevent the proliferation of duplicate records without causing frustration or hindering productivity. Also, by addressing the issue at its source, organizations can reduce the need for subsequent data cleansing and merging activities, saving time and resources.

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2. Data Profiling and Auditing

Another way to improve data quality is through data profiling. Data profiling involves analyzing and assessing data to gain insights into its quality, completeness, and consistency. It plays a critical role in understanding the characteristics and structure of data, enabling organizations to identify potential issues and take appropriate actions for improvement. By employing data profiling techniques, businesses can enhance data accuracy, identify data gaps or errors, and make informed decisions based on reliable information.

There are various data profiling techniques. One example is known as statistical analysis. Statistical techniques examine data distributions, frequencies, and patterns to identify anomalies and outliers. By comparing data against expected statistical measures, organizations can identify data points that deviate significantly from the norm, indicating potential errors or inconsistencies.

Another method is called pattern recognition. Pattern recognition techniques analyse data for recurring patterns or sequences. By identifying consistent patterns, organizations can verify data integrity and uncover irregularities that may indicate duplicate or incorrect entries.

A third type of analysis is called outlier detection. This technique identifies data points that deviate significantly from the expected values or patterns. Outliers can indicate data entry errors, data corruption, or potential fraud. By identifying and investigating outliers, organizations can rectify inaccuracies and ensure data reliability.

Data auditing
is a proactive practice that involves systematically reviewing and validating data to identify anomalies, inconsistencies, or non-compliance with established data quality standards. Regular data audits help organizations maintain data integrity, improve data accuracy, and ensure compliance with regulatory requirements. By conducting data audits, organizations can identify issues such as duplicate records, incomplete data, outdated information, or inconsistencies in data formats. Detecting and resolving these issues promptly enhances data quality, facilitates accurate reporting and analysis, and enables informed decision-making.

There are some tools and software for data profiling and auditing out there. A well-known solution is Talend Data Quality. Talend Data Quality is a data profiling software that supports various data sources, including Salesforce CRM. It offers comprehensive data analysis, profiling, and auditing features, enabling organizations to identify and resolve data quality issues efficiently.

Another tool is IBM InfoSphere Information Analyzer. IBM is one of the most well-known brands in IT technology and their solution helps organizations gain deeper insights into the structure and quality of their data. It comes equipped with a wide range of profiling techniques, including statistical analysis, pattern recognition, and data lineage analysis, to identify data anomalies, inconsistencies, and errors.

A third option is Informatica Data Quality. Informatica Data Quality is a robust data profiling and data quality management tool that enables organizations to analyze data patterns, identify duplicates, validate data against predefined business rules, and perform data standardization and enrichment. There are of course other options out there, and, as always, it’s best to look into several options to find out what suites your organization best.

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3. Data Governance

Data quality can be improved through data governance. In fact, data governance is a foundational concept that exerts a profound influence on the quality of data within an organization. It encompasses the establishment of structured frameworks, policies, and procedures, designed to govern the management, accessibility, and security of data assets. By implementing robust data governance practices, businesses can ensure the integrity, consistency, and reliability of their data, unlocking its full potential and driving data-driven decision-making. By the way, we also have another article on data governance here.

At the heart of data governance lie the frameworks and policies that serve as guiding principles for data management. These frameworks provide a structured approach to data collection, storage, and sharing, promoting standardization and control across the organization. Through meticulous attention to data classification, naming conventions, ownership, and access controls, data governance frameworks create a strong foundation for consistent and high-quality data throughout the enterprise.

Within the realm of data governance, the role of data stewards emerges as instrumental in maintaining data quality. These appointed individuals assume the responsibility of overseeing data assets and ensuring adherence to governance policies. They actively monitor data quality, resolve discrepancies, and enforce compliance with established procedures. By diligently fulfilling their duties, data stewards play a critical role in upholding data integrity, which forms the bedrock of reliable and trustworthy information.

The significance of data governance extends beyond its impact on data quality alone. It aligns data management practices with organizational objectives, enabling businesses to harness the power of data for informed decision-making, innovation, and gaining a competitive edge. Data governance serves as a strategic enabler, allowing organizations to extract maximum value from their data resources, driving operational efficiencies, and fostering growth.

In an era of heightened data regulations and compliance requirements, data governance assumes even greater importance. By embedding compliance measures into data governance frameworks, organizations can navigate the complex landscape of data privacy, security, and ethical use. Adhering to legal and ethical standards becomes second nature, as data governance ensures alignment with evolving regulations such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).

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4. Master Data Management (MDM)

Master Data Management (MDM) is a strategic discipline that plays a pivotal role in ensuring data quality and integrity across an organization. It revolves around the centralized management and governance of critical data assets, commonly referred to as master data. Master data represents the core business entities, such as customers, products, suppliers, or locations, which are shared across multiple systems and business units.

At its core, MDM aims to establish a single, authoritative source of truth for master data, eliminating redundancies, inconsistencies, and inaccuracies that can arise from disparate data sources. By unifying and standardizing master data, organizations can achieve improved data consistency and integrity, enabling more accurate and reliable decision-making processes.

Implementing MDM brings forth a multitude of benefits that directly contribute to enhanced operational efficiency and business performance. One of the primary advantages is the elimination of data silos, where redundant or conflicting versions of master data exist in different systems or departments. By centralizing master data in a reliable repository, organizations can ensure a harmonized view of critical business entities, reducing errors, redundancy, and confusion.

Furthermore, MDM enables organizations to establish robust data governance frameworks and policies, ensuring adherence to data standards, naming conventions, and data quality rules. This, in turn, leads to improved data quality and integrity, as data inconsistencies and inaccuracies are identified and rectified promptly. With accurate and reliable master data, organizations can confidently make informed decisions, drive operational excellence, and enhance customer satisfaction.

Creating and maintaining a reliable master data repository requires a systematic approach and adherence to best practices. It involves data profiling, cleansing, and standardization techniques to ensure data consistency and accuracy. Data stewardship plays a crucial role in overseeing data quality, resolving issues, and ensuring ongoing maintenance of the master data repository. Additionally, implementing data validation rules, data quality checks, and regular data audits contribute to the long-term reliability of the master data.

Data integration and synchronization are also vital components of MDM. Enabling them in your business will result in a seamless data flow between systems and ensure that master data remains consistent across the organization. Integration mechanisms, such as data integration platforms or enterprise service buses, facilitate the exchange of data between different systems, eliminating data disparities and discrepancies. Synchronization processes ensure that updates made to master data in one system are propagated to all relevant systems, maintaining data integrity and consistency in real-time.

Embracing Master Data Management (MDM) is crucial for organizations striving to maintain high-quality data and achieve operational excellence. By centralizing and governing master data, organizations can achieve improved data consistency, accuracy, and reliability. Through the implementation of robust data integration and synchronization mechanisms, master data remains harmonized across the enterprise, enabling seamless data flow and consistent decision-making. By investing in MDM, organizations can unlock the full potential of their data assets, drive innovation, and gain a competitive edge in today's data-driven business landscape.

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5. Data Quality Metrics & Reporting

As we know by now, organizations recognize that data quality is the foundation upon which success is built. Furthermore, it's not enough to merely possess vast amounts of data – the key lies in harnessing its power through the lens of defined data quality metrics and benchmarks.

Why is this important? By establishing data quality metrics, organizations gain invaluable insights into the health and reliability of their data assets. Think of it as a compass that guides decision-making, drives operational efficiency, and ensures regulatory compliance. With a clear understanding of data quality benchmarks, organizations can set their sights on achieving excellence in every facet of their data ecosystem.

Let's delve into the realm of data quality metrics. We encounter various dimensions that shape the overall quality of our data. Completeness, accuracy, consistency, and timeliness are among the core metrics that organizations strive to master. Completeness measures the extent to which data is comprehensive and free from gaps. Accuracy assesses the correctness and precision of the data, ensuring that it aligns with the real-world entities it represents. Consistency examines the harmony and coherence of data across different systems, eliminating discrepancies and contradictions. Finally, timeliness ensures that data is up-to-date and relevant, allowing for informed decision-making in real-time.

But defining data quality metrics is only the first step. To truly harness their power, organizations must establish a robust process for monitoring and reporting these metrics. This involves deploying sophisticated data quality tools and technologies that provide real-time visibility into the quality of your data. With comprehensive reporting capabilities, you can identify trends, pinpoint areas for improvement, and make informed decisions to drive your business forward.

However, the journey towards data excellence does not end with monitoring alone. Ongoing measurement and continuous improvement are essential to maintaining high data quality standards. It's not a one-time project, but a continuous commitment to excellence. By regularly evaluating and benchmarking your data quality metrics against predefined targets, you can identify potential issues before they escalate and proactively take corrective actions. This iterative process ensures that your data remains a strategic asset that adds value to your organization at every turn.

By defining data quality metrics and establishing benchmarks, you empower your organization to unleash the full potential of your data assets. It helps you to drive operational efficiency, enhance decision-making, and gain a competitive edge in the marketplace. Don't settle for guesswork – leverage the power of data quality metrics to transform your business landscape.


Implementing best practices to improve data quality is crucial for organizations aiming to maximize the value of their data assets. Throughout this article, we explored several key strategies, including data cleansing and deduplication, data profiling and auditing, data governance, and master data management. Each of these approaches plays a vital role in ensuring data accuracy, consistency, and reliability.

By prioritizing data quality and implementing these strategies, businesses can gain a competitive edge. High-quality data serves as a foundation for effective decision-making, enabling organizations to make informed choices, identify trends, and seize opportunities. Reliable data enhances customer satisfaction, supports efficient operations, and mitigates risks associated with incorrect or incomplete information.

In the next article, we will delve deeper into the tools and technologies available to improve data quality, streamline data management processes, enable comprehensive data profiling, and provide actionable insights to enhance data quality.

Remember, investing in data quality is an investment in the future success of your organization. By implementing best practices and utilizing appropriate tools, you can unlock the full potential of your data, gain a competitive advantage, and pave the way for data-driven excellence in decision-making and business outcomes.

Ready to embark on the journey towards data excellence? Contact us today to learn more about our state-of-the-art data quality solutions and how they can help you define, monitor, and continuously improve your data quality metrics. Elevate your business to new heights and make data-driven decisions with unwavering confidence. Your path to data excellence starts here.

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