Data Quality

What Businesses Need to Know Before Starting Their Journey to Augmented Data Quality

Explore how businesses can leverage augmented data quality, blending human expertise with ML technologies, to improve data accuracy and boost efficiency

Businesses are naturally curious about tapping into augmented data quality’s ability to deliver faster results on a greater scale. Manual data quality practices can no longer keep pace with modern organizations and their scaling operations. Incorporating augmented data quality into a business can significantly enhance decision-making, improve operational efficiency, and drive better outcomes.

What is Augmented Data Quality?

Augmented data quality is a process that combines human expertise with machine learning (ML) technologies to improve the accuracy and completeness of data. It employs various data quality best practices such as data profiling, data matching, and metadata management to automate traditionally time-consuming or data-intensive processes.

How to Start the Journey to Augmented Data Quality

In reality, organizations can start their journey toward augmented data quality today by implementing simple best practices. Getting data ready to be able to harness augmented data quality Before you attempt to harness the benefits of augmented data quality, get your data ready; establishing a foundation of always-ready data paves the way for opportunities with machine learning and AI.

  1. Assess Current Data Quality: Begin by evaluating your existing data quality. Identify areas where data issues are most prevalent and understand how these issues impact your business processes and outcomes.
  2. Cultivate a Data-First Culture: Fostering a data-driven culture within the organization can empower employees to use best practices when handling data and help maintain quality data long-term. The democratization of data can empower everyday users to gather and analyze data without requiring outside help and without impacting things like accuracy or reputation.
  3. Invest in Data Governance: Establish data governance practices that ensure data quality is monitored, maintained, and improved over time. This includes defining business rules, creating data quality metrics, and enforcing data quality standards.
  4. Leverage Data Quality Tools: Explore available data quality tools and software. These tools can help automate the process of identifying, cleansing, and enriching data. Choose tools that align with your business needs and integrate well with your existing systems.
  5. Implement Data Profiling: Data profiling tools analyze data sources to discover patterns, anomalies, and inconsistencies. This step helps you gain a better understanding of your data quality issues and prioritize areas for improvement.
  6. Data Cleansing and Enrichment: Use data cleansing techniques to correct errors, remove duplicates, and standardize formats. Additionally, consider enriching your data by adding external data sources to enhance its completeness and accuracy.
  7. Employ Machine Learning and AI: Augmented data quality often involves leveraging machine learning and AI algorithms to automate data quality tasks. Solutions like data matching can identify patterns, predict data quality issues, and even suggest corrective actions.
  8. Monitoring and Continuous Improvement: Implement solutions that monitor your data and alert you to potential data quality issues. An up-to-date enterprise data catalog can provide real-time visibility into data terms, rules, policies, and business standards.
  9. Data Privacy and Security: Ensuring compliance with relevant laws stays intact while data is constantly in a state of flux is tricky. Aligning data governance and quality efforts with privacy and security regulations can keep your data compliant based on your organization’s specific requirements.

The Journey to Augmented Data Quality

Maintaining quality data is what keeps the business agile and ready to take advantage of advancements in AI/ML but remember that the journey to using augmented data quality is ongoing. Data quality is not a one-time project but a continuous process. The transition to using augmented data quality in every day business operations should be on every enterprise’s radar, but these digital transformations are not made in leaps and bounds. When it comes to data, incremental change can have a vast impact on the business.

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