Data Quality Assurance

Maintain control over your data quality to ensure the accuracy and validity of analytics, insights, and decision-making delivered by your ML models and AI solutions

Quality data means
accurate ML models,
robust AI solutions

Data is a critical asset for any enterprise looking to take advantage of complex analytics, AI, and machine learning. To tap into the insights stored in data, companies need to ensure that their data is clean, valid, and accurate, which poses a major challenge from a business and technology perspective. Provectus’ Data QA team can help your organization get a clearer understanding of data needed for your AI solutions and ML models, and ensure that your data is ready to power your AI initiatives across your organization.

What Is Data QA?

Data quality assurance is the process of data profiling, to discover inconsistencies and other anomalies within data, as well as data cleansing activities to improve the data quality.

High-Quality
Data Is…

  • Accurate
  • Valid
  • Complete
  • Timely
  • Relevant
  • Consistent

Benefits of Data QA

Based on Data Cleaning: Overview and Emerging Challenges, the most advanced algorithms compete for improved accuracy within a fraction of a percent, while data cleansing contributes 20x more to the quality of the final AI/ML solution.

Business

  • Better informed decision-making due to more accurate ML models
  • Easier implementation of data across different departments for business insights
  • Better data quality means faster discovery of business opportunities and a tighter grasp on the market
  • High-quality data can lead to increased profitability due to more efficient allocation of company resources

Technology

  • The earlier the errors in data are detected, the better the accuracy for ML models
  • Even small unmanaged errors made in the early stages of workflow can lead to significant degradation of a model’s performance in production
  • Metrics computed during model testing measure not only the models and algorithms in isolation, but the entire system, including data and its processing

Stages of QA for Data

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How It Works

By implementing an auditable, AI-ready Data QA system, Provectus enables organizations to ensure robust data governance, strict data management, accurate data collection, and careful design of data control tools.

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Projects for Data QA

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Marketing

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Online Sales

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Financial
Reporting

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AI and

Machine Learning

Ensure the quality of data in your
organization to enjoy accurate, valid
insights driven by analytics, AI & ML!