How do you integrate data for better governance?

1 Assess your data sources

Before you can integrate your data, you need to understand what data you have, where it comes from, how it is structured, and how it is used. You can use data profiling tools and techniques to analyze your data sources and identify their characteristics, such as data types, formats, values, relationships, and metadata. You also need to evaluate the quality and accuracy of your data sources and identify any issues, such as missing, duplicate, or inconsistent data.

2 Define your data standards and policies

To ensure that your integrated data is reliable and trustworthy, you need to define and enforce data standards and policies across your organization. Data standards and policies are the rules and guidelines that specify how data should be collected, stored, processed, and shared. They can include data definitions, naming conventions, data models, data quality metrics, data security protocols, and data access rights. You can use data governance frameworks and tools to document and communicate your data standards and policies and monitor their compliance.

3 Choose your data integration approach

Depending on your data sources, requirements, and objectives, you can choose different data integration approaches to combine your data. Extract, transform, and load (ETL) is suitable for batch processing of large volumes of structured or semi-structured data that needs to be cleansed and standardized. On the other hand, extract, load, and transform (ELT) is suitable for real-time or near-real-time processing of large volumes of unstructured or heterogeneous data that needs to be analyzed or queried. Data virtualization also provides a virtual layer that connects to various data sources and provides a unified and consistent view of the data without moving or storing it; this approach is ideal for agile and flexible access to data from different sources and formats that needs to be integrated on the fly.

4 Implement your data integration solution

Once you have chosen your data integration approach, you need to implement your data integration solution using the appropriate tools and technologies. You can use various data integration tools and platforms that offer features and functions to help you integrate your data, such as data connectors, data pipelines, data transformations, data quality checks, data security measures, and data monitoring capabilities. You can also use code-based or code-free methods to design and execute your data integration workflows, depending on your skills and preferences.

5 Validate and verify your integrated data

After you have implemented your data integration solution, you need to validate and verify your integrated data to ensure that it meets your data standards and policies and fulfills your data governance objectives. You can use data validation and verification tools and techniques to check your integrated data for errors, anomalies, inconsistencies, and compliance issues. You can also use data testing and auditing methods to measure and evaluate the performance, quality, and value of your integrated data.

6 Maintain and improve your data integration process

Data integration is not a one-time project, but an ongoing process that requires constant maintenance and improvement. You need to monitor and update your data integration process as your data sources, requirements, and objectives change over time. You also need to collect and analyze feedback and metrics from your data integration process and use them to identify and implement opportunities for improvement. You can use data governance tools and methods to track and report on your data integration process and its outcomes and ensure its alignment with your data governance goals.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?