In this blog, we will explore the pivotal role that data governance plays in enhancing data quality, helping you to foster trust, streamline operations, and promote data transparency.

The quality of your data can be synonymous with the health of your organization. From decision-making and operations to customer relations, the ramifications of bad data are expansive and often costly, manifesting in decreased efficiency and compromised reputations.

When you incorporate the right amount of data governance into your data strategy, these data quality concerns diminish, and you can unlock new levels of trust in your data.

In this blog, we’ll cover:

    1. Leveraging Data Governance to Improve Data Quality ↵
    2. Case Study: Improving Data Quality with Data Governance ↵
    3. Best Practices for Implementing Data Governance for Data Quality ↵
    4. Data Quality Tools for Data Quality ↵
    5. How to Avoid Common Missteps in your Data Governance Strategy ↵

Leveraging Data Governance to Improve Data Quality

A graphic illustrating the key strategies to leverage data governance to improve data quality. It features icons and text describing four major strategies: defining and communicating quality standards depicted by a blueprint icon; championing data stewardship represented by a shield icon; undertaking quality improvement initiatives symbolized by a gear icon; and establishing monitoring and reporting frameworks illustrated with a bar chart icon.

The four ways data governance specifically addresses data quality and enables you to foster trust and reliability in your organization’s data assets.

Without knowing the provenance or purpose of a dataset, you risk using poor or inappropriate data in your decision-making processes. There are several elements of data governance that specifically address data quality, including:

  • Setting and Communicating Defined Data Quality Standards and Policies: This central function of data governance lays down detailed standards that provide a benchmark for ongoing data assessments, promoting continuous improvements in data quality.
  • Championing Data Stewardship and Accountability: Leveraging data governance allows for the demarcation of roles and responsibilities to data stewards and custodians responsible for steering vital tasks and proactively pinpointing and mitigating data quality discrepancies.
  • Guiding Quality Improvement Initiatives: Data governance facilitates structured initiatives like data profiling and cleansing, aiding in the identification, remediation, and prevention of data quality dilemmas.
  • Spearheading Monitoring and Reporting Initiatives: This entails the introduction of frameworks for ongoing data quality surveillance through vigilant tracking and reporting, backed by substantial tools for analysis and regular updates to stakeholders, enabling proactive problem-solving to foster data quality.

Data governance is fundamental to maintaining ongoing data quality. It fosters a culture of accountability where individuals have clearly defined roles and are responsible for upholding data quality. This proactive strategy ensures continuous reliability and trust in the organization’s data assets.

Case Study: Improving Data Quality with Data Governance

Metadata Management Revitalizes Data Trust for a Leading Healthcare Provider

In the face of rapid expansion, a Dallas-based healthcare giant faced the pressing issue of data discrepancies due to varied data definitions across different departments. Their goal was to harness a unified data view to facilitate high-quality and personalized care delivery.

To address this, we:

  • Developed a data strategy that focused on iterative data governance, starting with robust metadata management.
  • Implemented standardized data definitions and established clear data lineage using technologies such as dbt, Fivetran, and Snowflake.

As a result, the healthcare provider now enjoys automated insights, unified data, and reliable data access, fostering enhanced trust in their data and empowering them for sustained growth and quality service provision.

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Best Practices for Implementing Data Governance to Improve Data Quality

To set up a robust and quality-focused initiative, follow these best practices:

  1. Understand the current state of your data. Undertaking data profiling exercises can help in recognizing data inaccuracies, duplications, and inconsistencies, serving as a base to benchmark future improvements.
  2. Standardize data quality. Define and communicate specific criteria for data accuracy, completeness, and other vital facets that align with industry benchmarks and regulatory norms. Ensure a thorough organizational understanding of these standards to foster adherence and maintain high quality.
  3. Implement data quality tools and processes. Select and utilize data quality tools to meet organizational needs. Prioritize tools that facilitate data profiling, cleansing, and validation, while also defining workflows to establish regular data cleansing and validation routines.
  4. Define the roles of data stewards and owners. Designate data stewards and owners who will steer the quality management processes. They should be well-acquainted with data quality nuances and empowered to coordinate data cleansing efforts and handle conflicts.
  5. Initiate metrics and monitoring for data quality. Formulate clear KPIs to foster continuous improvement in data quality. Establish automated checks and regular monitoring processes to diligently track progress and maintain high standards.
  6. Foster awareness through training. Implement training initiatives to enhance awareness about the importance of data quality, focusing on the broader impacts of data governance policies and poor data quality on business operations.
  7. Encourage a cycle of continuous improvement. Data governance should be viewed as an ongoing process, where continuous improvement is at the forefront. Regular evaluations of processes and tools and adapting based on feedback should be inherent in your strategy.
  8. Build a culture of collaboration and communication. Fostering collaboration and open communication channels between business and IT teams is critical in achieving shared understanding and a culture of data ownership and responsibility.
A graphic presenting best practices to improve data quality through data governance, featuring a series of icons and brief descriptions of each practice, including understanding the current data state, standardizing data quality, implementing quality tools and processes, defining roles of stewards and owners, initiating metrics and monitoring, fostering awareness through training, encouraging continuous improvement, and building a collaborative culture.

Best practices on implementing data governance to improve data quality in your organization.

Data Governance Tools for Data Quality

There are many tools that can be leveraged for data governance. It is important to note, however, that technology alone cannot solve your data quality problems — you need the right people and processes in place first. That said, here is a list of software solutions and tools that facilitate improved data governance and, consequently, better data quality.

  • Informatica’s Cloud Data Quality facilitates improved data governance with features like in-depth analysis to pinpoint inconsistencies and a user-friendly interface for seamless navigation. Leveraging cloud flexibility, it offers data cleansing and enrichment processes while integrating smoothly with data platforms to nurture uniformity and precision — enhancing the overall data quality.
  • dbt enhances data governance through data quality checks, verifying data accuracy and reliability throughout your data processing pipeline. It integrates well with tools such as Great Expectations, Airflow, and Snowflake, fostering a robust data infrastructure that assures the consistency and reliability of data and enhancing data quality across the organization.
  • For environments that already utilize Tableau Server or Tableau Cloud, it is extremely simple to leverage the built-in data governance features available through their Data Management add-on. The Tableau Catalog feature allows for data exploration, management of data assets, reporting surrounding data quality, analysis of a data element’s impact, and data lineage tracing. It also offers a virtual connection editor that allows users to create a shareable access point to their data or define data policies that can be used to enforce row-level security at the point of connection.
  • Looker helps to provide a single source of truth by allowing users to connect only to data sources that are governed, simplifies change management by ensuring that changes only need to occur in one location and are corrected anywhere they apply, and defines metrics to end-users using tooltips. This increases data quality, transparency, and lessens the burden on data stewards. Additionally, Looker provides access control at the role, role, and column level and offers functionality to help businesses trace data lineage for increased security and easier compliance with data privacy regulations.

Popular Open-Source Data Quality Tools

  • Deequ (developed by AWS) helps to ensure the quality and trustworthiness of data sets in various data analytics and processing pipelines by offering scalability, automated data quality checks, integration with data pipelines and data processing frameworks, data quality reports, and the ability to define custom data quality metrics. It works especially well with large-scale, distributed environments or in collaboration with other AWS services.
  • dbt Core supports many data governance capabilities through use of its key features. You can add description fields to your models, leverage the DAG as a lineage graph to gain additional transparency into the use of data, create groups to control model access, and utilize model contracts and model versions as a part of your change management process.
  • MobyDQ can be used to automatically run data quality checks in a pipeline, record/report on data quality issues, perform data profiling, manage data lineage and metadata and send alerts. It originates from one of Ubisoft’s internal projects that they used to increase data quality.
  • Great Expectations is a great tool for developing human-readable documentation surrounding your data as you work, automatically profiling data, and creating validation checkpoints to ensure data is accurate. You can use “expectations”, assertations that are expressed through easy-to-understand Python in order to easily create data rules to validate against.
  • Soda Core is a CLI tool and addition to the Python library that allows data engineers to streamline data quality checks by directly embedding them into a pipeline in a way that is scalable and versatile across the data lifecycle. It facilitates more reliable data by helping users monitor and understand the quality of their data easily, discover and fix issues, and send alerts to appropriate audiences.
  • Cucumber utilizes behavior-driven development to allow its users to author and automate test scenarios in an easy-to-read language, test and validate data through high-quality and comprehensive testing that is easy to maintain, and reuse created definitions to decrease the time and effort needed to ensure data quality.

Industry-Specific Tools

Tailoring solutions to industry-specific needs, several tools address unique data quality challenges pertinent to different sectors:

  • Healthcare: Platforms like Trillium Health ensure the reliability and precision of patient data, aiding in detecting and rectifying discrepancies and redundancies in medical files, thus enhancing patient service and conformity to regulations.
  • Finance: SAS Data Quality and Talend Data Quality cater to the financial sphere with features that support data validation, fraud surveillance, and compliance with financial rules, assisting entities in robust regulatory conformity, risk oversight, and fiscal reporting.
  • Retail and E-commerce: Tools including Experian Data Quality and Melissa Data are at the service of retail and e-commerce sectors, focusing on data purification, address confirmation, and data deduplication, essential in keeping accurate customer databases and enriching client experiences.
  • Manufacturing: Addressing the sector’s specific hurdles, Winshuttle and SAP Information Steward facilitate critical functionalities like product data verification and master material data governance, crucial in preserving product excellence and efficient supply chain management.
  • Government and Public Sector: Organizations in this sector can rely on IBM InfoSphere Information Governance Catalog and Collibra, which offer avenues for rigorous data governance, policy implementation, and regulatory adherence.

Avoid Common Missteps in Your Data Governance Strategy  

An infographic detailing how to avoid common missteps in developing a data governance strategy to improve data quality. Icons representing each tip accompany concise advice on averting overambition, securing executive sponsorship, fostering stakeholder engagement, ensuring thorough data assessments, maintaining data stewardship, and establishing robust data quality measurement processes.

Navigate your data governance strategy successfully with these crucial tips to avoid common pitfalls and improve data quality.

As you utilize data governance to improve your data quality, steer clear of the following frequently encountered pitfalls:

1.) Overambitious Initial Efforts: Don’t boil the ocean — attempting to address all data quality issues simultaneously can lead to a lack of focus and overwhelming complexity.

Tip: Initiate with a narrow focus, targeting pivotal data domains or business processes based on their operational or regulatory implications. Implement a phased strategy that addresses issues gradually, allowing you to showcase tangible results and build momentum progressively.

2.) Lack of Executive Sponsorship: Without the backing of executive leadership, data governance endeavors may suffer from insufficient visibility and resources.

Tip: Secure the endorsement of senior management by demonstrating the business merits of data governance and its positive ramifications on decision-making and compliance. You don’t need executive buy-in right away — if you start small and show results, gaining buy-in gets easier.

3.) Inadequate Stakeholder Engagement: Disengaged stakeholders can pose a significant barrier to the success of data governance initiatives.

Tip: Involve stakeholders from the inception and actively incorporate their insights into the decision-making process. Facilitate understanding and agreement on the advantages of data governance and work collaboratively to set data quality standards and workflows.

4.) Superficial Data Quality Assessment: An incomplete assessment of the existing data quality issues can impede the right prioritization of improvement endeavors.

Tip: Undertake a detailed data quality evaluation that encompasses data profiling and analysis alongside user feedback to identify and prioritize pressing issues based on business impact.

5.) Forgoing Data Stewardship: Ignoring the essential role of data stewards can be detrimental to the success of data governance and data quality enhancement initiatives.

Tip: Appoint adept data stewards with defined roles and empower them with the appropriate knowledge and authority to manage data quality, fostering a collaborative environment with business and IT teams.

6.) Neglecting Data Quality Measurement: Failure to correctly measure and monitor can hinder the evaluation of governance efforts and the tracking of progress towards data quality goals.

Tip: Establish relevant data quality metrics and KPIs aligned with business aims. Introduce monitoring tools and procedures for regular tracking and reporting on data quality, utilizing these metrics to pinpoint areas needing enhancement and promoting ongoing improvement.

Following these tips will bolster your data governance efforts, consequently improving data quality and analytical insights, and further harmonizing your business strategies with its overarching goals.

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Jenna O'Jea Jenna is an analytics consultant based out of our Raleigh office. She delivers impactful Tableau solutions and works closely with clients in a way that enables them to become savvy developers and end users. She also helps lead the Tableau practice at Analytics8. Outside of work, Jenna enjoys spending time with her son, traveling, and live music.
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