7 Best Data Quality Practices

Seven Best Practices to Maintain Excellent Data Quality

More and more, our world is data driven. It might sound like a cliché at this point, but it is the reality. In fact, it’s hard not to be aware of it. Microchips have become so cheap they can be used in almost any device you can think of. Even a modern toothbrush has the ability to track and record data from its user and connect to the internet. And the internet itself? It has become the largest repository of information in human history and is still growing exponentially. However, the rapid growth of information through digital means came with a caveat whose importance was only later realized -and that caveat is that managing huge sets of data is very complicated and we didn’t scale our data management technologies as fast as the growth of data itself.

You have probably heard it somewhere; ‘data is the new oil’. Perhaps it’s time to make a minor adjustment to that phrase; ‘quality data is the new oil’.

The analogy of data to oil is a rather interesting one and allows for some good analogies. Oil doesn’t just come out of the ground ready to use – it needs to chemically treated, distilled and blended in various ways to create the petroleum products that have changed the world. While in the past some machines could run off crude oil, in today's world all machines rely on processed oil or petrol, because regular crude oil is simply too inefficient. The same idea is true of data – most data needs to be refined and treated to extract the valuable parts of it. Using poor quality data is like putting poor quality fuel into your car. It might run, but who knows for long, and what damage you could do to the engine.

In this article we will discuss the seven best practices known to maintain excellent data quality. Some of the practices involve technical application and the use of data manipulation solutions such as Plauti Data Management. In other areas it comes down to habits, practices and data culture. We’ve discussed some of these already in previous articles, but in this article, we will dive into more details.

Best Practice One: Establishing Data Quality Standards

Establishing Data Quality Standards is a crucial step in maintaining accurate and reliable data within Salesforce. This practice involves three key components: defining data quality standards, involving stakeholders, and documenting guidelines. Let's discuss each component in detail.

Defining Data Quality Standards for Accuracy, Completeness, and Consistency
Defining data quality standards is essential to ensure that the data stored in Salesforce is accurate, complete, and consistent. Defining these terms, we can start off with accuracy, which refers to the correctness and precision of the data. It also involves verifying that the data entered is reliable and free from errors. Secondly, completeness ensures that all required data fields are populated, leaving no gaps or missing information. Thirdly, consistency focuses on maintaining uniformity across different data fields and eliminating duplicates or conflicting entries. By defining these three standards, organizations can establish clear expectations for data quality and set benchmarks for measuring and improving it.

Involving Stakeholders in Establishing Data Quality Policies and Guidelines
By including stakeholders in the process of establishing data quality policies and guidelines, organizations can gather diverse perspectives, identify specific data requirements for each department, and ensure that the standards align with business objectives. Involving more stakeholders also increases buy-in and ownership of the data quality initiatives, fostering a culture of shared responsibility.

Documenting Data Quality Standards for Reference and Consistency
Documenting data quality standards is done to create an easy path to consistency across an organization. This documentation can include definitions of data quality terms, specific data validation rules, data entry guidelines, and examples of acceptable data formats.

Whether someone is new in your organization or a seasoned employee, an up-to-date and easy to access reference for checking these standards makes understanding and following them much easier. Besides plain old PDF manual or walls of text, you could experiment with formats such as video training materials.

Best Practice Two: Regular Data Quality Checks

Regular Data Quality Checks are essential for maintaining high-quality data in Salesforce. It involves conducting regular data audits, implementing automated data quality checks, validation processes, and utilizing data profiling and cleansing tools to identify and correct data issues. Plauti Data Management incorporates these concepts into its offerings. But first, let's delve deeper into each aspect.

Conducting Regular Data Audits to Identify Quality Issues
Regular data audits involve systematically reviewing the data in Salesforce to identify and address any quality issues. This process helps to identify inconsistencies, inaccuracies, duplicates, and other data quality issues. Plauti Data Management solutions provide tools and features to perform comprehensive data audits, allowing organizations to proactively identify and resolve data quality issues on an ongoing basis.

Implementing Automated Data Quality Checks and Validation Processes
Automated data quality checks such as data deduplication and validation processes are crucial for maintaining data integrity and reducing manual efforts. Plauti Data Management solutions incorporate automated checks that can be scheduled or triggered based on predefined rules and criteria. These checks help to validate data against predefined standards, ensuring that data is accurate, complete, and consistent. By automating these processes, organizations can save time and resources while ensuring data quality remains high.

Utilizing Data Profiling and Cleansing Tools to Identify and Correct Data Issues
Data profiling tools analyze data to gain insights into its quality, structure, and consistency. Solutions such as Plauti Data Management leverage data profiling techniques to identify potential data issues, such as missing values, outliers, or inconsistencies. By utilizing these tools, organizations can gain a deeper understanding of their data quality and take appropriate actions to cleanse and correct any identified issues.

Plauti Data Management incorporates these concepts into its solutions by providing a comprehensive data quality management tool. Plauti solutions offer data cleansing capabilities, automated data quality checks and validations, and data profiling and data deduplication tools. Their solutions empower organizations to maintain high-quality data in Salesforce, mitigate data-related risks, and enhance decision-making processes based on accurate and reliable data.

Best Practice Three: Continuous Improvement

Continuous Improvement is crucial for maintaining and improving data quality standards in Salesforce.

This practice involves monitoring data quality metrics and key performance indicators (KPIs), analyzing the root causes of data quality issues, taking corrective actions, and iteratively enhancing data quality processes and workflows. Let's discuss each aspect in detail.

Monitoring Data Quality Metrics and KPIs
Monitoring data quality metrics and KPIs is essential to assess the health and performance of data within Salesforce. Key metrics can include accuracy rates, completeness percentages, duplicate records, and data entry error rates. By regularly tracking these metrics, organizations can identify trends, spot areas of improvement, and benchmark data quality against established standards. Monitoring data quality metrics provides actionable insights into the overall data quality and highlights areas that require attention.

Analyzing Root Causes of Data Quality Issues and Taking Corrective Actions
When data quality issues arise, it is important to identify and understand their root causes. By analyzing the underlying reasons for data quality issues, organizations can develop targeted corrective actions. This may involve investigating data entry processes, user training, system integrations, or underlying data sources. Taking corrective actions requires a proactive approach, addressing the causes to prevent recurring issues. By continuously analyzing and addressing the root causes of data quality issues, organizations can enhance data accuracy, completeness, and consistency.

Iteratively Enhancing Data Quality Processes and Workflows
As we’ve discussed in other articles, data quality is not a destination but an ongoing process that requires continuous improvement. Organizations should regularly review and enhance their data quality processes and workflows. This involves evaluating existing processes, identifying bottlenecks, and implementing improvements to when possible to make data entry, validation, and data deduplication procedures easier and effective. It may also involve refining data governance policies, providing additional training to users, or optimizing system configurations. By iteratively enhancing data quality processes and workflows, organizations can ensure that data quality remains a priority and adapts to evolving business needs.

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Best Practice Four: Education & Training

Education and Training plays a crucial role in ensuring data quality standards in Salesforce.

This practice involves providing data quality training to data stewards and data users, educating users on data entry best practices and standards, and promoting data literacy and data quality awareness across the organization. Let’s see exactly what this involves.

Providing Data Quality Training to Data Stewards and Data Users
Data stewards are responsible for overseeing data quality and governance processes in an organization. It makes sense that since they’re the ones that have the task of data quality resting on their shoulders, providing comprehensive training to them will make sure they are equipped with the knowledge and skills to effectively carry out their duties. This includes understanding data quality standards, utilizing data validation tools such as Plauti Record Validation, and implementing data quality improvement initiatives. Overall, it is important to provide training to data users who interact with Salesforce regularly, as they are at the helm when it comes to combatting poor data.

Educating Users on Data Entry Best Practices and Standards
Data entry sounds boring, but it plays a significant role in maintaining data quality standards. Educating users on data entry best practices and standards is essential to ensure consistent and accurate data input. This can involve providing guidelines on data formatting, mandatory fields, and data validation rules. Training sessions, documentation, and regular reminders can be utilized to reinforce these best practices.

Promoting Data Literacy and Data Quality Awareness Across the Organization
Promoting data literacy and data quality awareness across an organization is like planting the seeds of a data-driven culture. As we’ve mentioned in other articles, the importance of adopting an overall data quality awareness and culture in a company is hard to overstate. You can incentivize this culture by organizing workshops or encouraging discussions and knowledge sharing around data quality topics. At Plauti, a culture of data quality is encouraged through discussions and the sharing of knowledge. It isn’t a boring homework assignment, but an interesting and nuanced topic that makes for engagement.

Best Practice Five: Collaboration

As the adage goes, “there is no ‘I’ in team”, and the same is true when it comes to an organizations’ data quality. At Plauti, we believe collaboration is essential for effective data quality management in Salesforce and we emphasize this aspect of data quality strongly. Encouraging collaboration between data owners, data stewards, and users, as well as establishing cross-functional data governance committees all leads to a stronger team that’s working towards the same goal. Let's explore this aspect further.

Encouraging Collaboration Between Data Owners, Data Stewards, and Users
Data owners are responsible for the accuracy, completeness, and reliability of specific data sets within Salesforce. Data stewards play a vital role in overseeing data quality and governance processes. Encouraging collaboration between data owners, data stewards, and users fosters a shared responsibility for data quality. It enables open communication, knowledge sharing, and collective problem-solving.

Establishing Cross-Functional Data Governance Committees
Tunnel vision can be a problem if an organization doesn’t involve enough representatives from each area of business. Establishing cross-functional data governance committees brings together people from different departments and functions within the organization. By involving stakeholders from various areas, organizations can ensure that discussions about data quality receive feedback and insight from various perspectives.

Creating a Culture of Data Accountability and Ownership
The habits of achieving good data quality have not been encoded into our brain through evolution, and therefore require some training. However, if you create a culture of data accountability and ownership, it will naturally foster data quality. Many people are just simply unaware of their impact or involvement in data quality but illuminating them with awareness in this area turns them into data-conscious beings. This involves instilling a sense of responsibility among employees for the accuracy and integrity of the data they handle. Clear roles and responsibilities should be defined, and employees should be empowered to take ownership of the data they work with, as well as recognize the shared responsibility everyone plays.

Best Practice Six: Data Quality Measurement & Reporting

Data Quality Measurement and reporting involves defining key data quality metrics and performance indicators, implementing regular reporting and dashboarding of data quality metrics, and utilizing data quality scorecards to communicate and track progress. Let's discuss further.

Defining Key Data Quality Metrics and Performance Indicators
Defining key data quality metrics and performance indicators help an organization measure and evaluate the quality of data in Salesforce. These metrics can include accuracy rates, completeness percentages, duplicate records, data consistency, and data integrity. By clearly defining these metrics, organizations establish benchmarks to measure data quality against predefined standards.

Implementing Regular Reporting and Dashboarding of Data Quality Metrics
Implementing regular reporting and dashboarding of data quality metrics allows organizations to monitor and visualize the status of data quality in Salesforce. Reporting mechanisms can be implemented to generate regular reports that highlight key data quality metrics and trends over time. Dashboards provide visual representations of data quality metrics, offering a real-time snapshot of the data quality status

Utilizing Data Quality Scorecards to Communicate and Track Progress
Data quality scorecards are a great way to assist in communicating data quality performance and tracking progress. Scorecards provide a summarized view of key data quality metrics, presenting the current state of data quality and progress towards predefined targets. These scorecards can be shared with stakeholders and data users to create awareness and accountability around data quality.

Best Practice Seven: Data Quality Tools & Technologies

Data quality management tools and technologies play a significant role in enhancing data quality in Salesforce. Organizations use these tools for data profiling, data cleansing, data deduplication, data validation, automation, and more. Implementing data quality management systems and platforms and exploring the use of AI and automation for data quality enhancement are common practices to achieve data quality goals in large organizations. Plauti, specifically its product Plauti Data Management, is an example of a data quality tool that can support these efforts. What else can these tools do?

Leveraging Data Quality Tools for Data Profiling, Cleansing, and Validation
Data quality tools, such as Plauti Duplicate Check, enable organizations to profile, cleanse, and deduplicate their data in Salesforce. Data profiling tools analyze data to identify patterns, anomalies, and inconsistencies in data while data cleansing tools help in standardizing, deduplicating, and enriching data. Data validation tools use predefined rules to enforce validations, such as checking if an email is legitimate, to ensure that data entered meets quality standards.

Implementing Data Quality Management Systems and Platforms
Data quality management systems and platforms, including Plauti Data Management (PDM) offerings, provide comprehensive solutions for managing data quality in Salesforce. These systems offer a centralized platform to define and enforce data quality standards, automate data quality processes, and monitor data quality metrics. They provide features such as data auditing, data quality dashboards, and workflow automation, facilitating efficient data quality management.

Exploring AI and Automation for Data Quality Enhancement
Artificial intelligence (AI) and automation technologies have the potential to significantly enhance data quality. AI-powered algorithms can identify and resolve data quality issues, detect patterns, and suggest data improvements. Another powerful way to streamline data quality management is with the use of automation. Automation can streamline data validation, cleansing, and enrichment processes, reducing manual efforts and ensuring consistent data quality across Salesforce. By exploring AI and automation for data quality enhancement, organizations can leverage advanced technologies to improve efficiency, accuracy, and consistency in managing data quality in their Salesforce org.

In conclusion, maintaining excellent data quality in Salesforce requires the implementation of best practices across various aspects of data management. We have explored seven key best practices in this article.

Establishing Data Quality Standards: Defining standards for accuracy, completeness, and consistency of data.
Regular Data Quality Checks: Conducting audits, implementing automated checks, and utilizing data profiling and cleansing tools.
Continuous Improvement: Monitoring metrics, analyzing root causes, and enhancing data quality processes iteratively.
Education and Training: Providing training to data stewards and users, promoting data entry best practices, and fostering data quality awareness.
Collaboration: Encouraging collaboration between stakeholders, establishing cross-functional committees, and promoting data accountability and ownership.
Data Quality Measurement and Reporting: Defining metrics, implementing reporting mechanisms, and utilizing scorecards for tracking progress.
Data Quality Tools and Technologies: Leveraging data quality tools for profiling, cleansing, and validation, implementing data quality management systems, and exploring AI and automation.

Plauti, with its flagship products like Duplicate Check, serves as an example of a data quality tool that supports these best practices. Plauti solutions offer data validation, automation, and deduplication capabilities, enabling organizations to improve data accuracy and consistency. Overall, Plauti Data Management solutions provide comprehensive data quality management systems and platforms, empowering organizations to enforce data quality standards within their Salesforce org.

By adopting these best practices and leveraging appropriate data quality tools and technologies, organizations can maintain high-quality data in Salesforce. This ensures that data-driven decisions are based on accurate, complete, and reliable information, leading to improved business outcomes and a stronger data-driven culture.