The Data Integrity Suite's Data Quality service improves the accuracy and completeness of critical data assets. It validates, geocodes, and enriches data to ensure consistency and suitability for business operations and analytics. This service provides a visual design interface for implementing data quality processes, available in a cloud-native environment or on-premise. Key features of Data Quality include:
- Intuitive interface: Monitor dynamic data updates to expedite the creation of data quality protocols.
- Enhanced matching functions: Minimize data redundancy and increase profitability through advanced, machine learning-driven matching and de-duplication methods.
- Intelligent recommendations: Receive automated guidance for enhancing data quality based on the characteristics of data sets to achieve superior data integrity.
- Cloud architecture with global deployment: Formulate rules within a cloud-based framework and implement them across diverse environments for an efficient and scalable solution.
- Data cleaning, standardization, and validation: Ensure precision and uniformity in essential business data such as contact details and addresses.
- Address verification: Strengthen reliability and efficacy in operations that rely on accurate address information.
- Geo-coding of address data: Integrate latitude, longitude, and a distinct PreciselyID with each address.
- Unique identifier attachment: Facilitate data management by assigning a singular unique identifier to every address.
- API driven validation: Augment functions by running quality assessments on pertinent fields through API integrations.
Data Quality configuration guide
The service provides a robust library of features that transform raw business data into actionable insights, uncovering the "who," "where," and "why" behind operations. Users can effortlessly connect to auto-cataloged data sources, create or import sample data to enhance data quality, and design processes in the cloud with an intuitive interface. Experience real-time data dynamics during the design phase and implement rules seamlessly across diverse environments for streamlined execution.
- Select the datasource type: Choose a datasource that will be the target of your data quality services. Select the one where your essential data is stored.
- Establish and catalog a connection: After setting up the datasource, establish a connection and catalog it to guarantee that data quality can access it as required. This step is crucial for maintaining a reliable link to your data assets.
- Create quality pipeline: Design and develop a data quality pipeline that orchestrates the flow of data through various quality checks and transformation processes.
- Configure transformation steps: Implement specific transformation rules and operations that clean, standardize, and transform the incoming data. This might include tasks such as removing duplicates, correcting erroneous entries, converting data formats, and so on.
- Apply run configuration and validate pipeline: Run the data quality pipeline using a run configuration that specifies parameters such as batch size, execution schedule, and error handling strategies. This configuration ensures that the pipeline runs efficiently and effectively, producing reliable and consistent outputs.
- Manage quality jobs: Supervise quality jobs to ensure optimal performance and results. This includes overseeing the job execution, resource allocation, and troubleshooting any issues that arise during the data quality processes.
The Data Quality service helps resolve user queries such as:
- How can I ensure my data is accurate and consistent?
- What processes can I use to validate and enrich my data?
- How can I reduce data duplication and improve data quality?
- What tools are available to visualize and manage data quality processes?