How do you standardize data from multiple sources for marketing operations?

1 Identify your data sources and requirements

The first step is to identify the data sources you need to standardize and the requirements you have for your data quality and governance. For example, you may need to standardize data from your CRM, website, email, social media, and third-party tools. You also need to define the criteria and rules for your data, such as the format, naming, values, and validation methods. This will help you to establish a common data model and a data dictionary that will guide your standardization process.

2 Map your data fields and relationships

The second step is to map your data fields and relationships across your data sources. This means identifying the common and unique fields in each source, and how they relate to each other. For example, you may have fields like name, email, phone, company, industry, and lead source in your CRM, and fields like page views, sessions, conversions, and channels in your website analytics. You need to map these fields to each other, and to the common data model you defined in the first step. This will help you to ensure consistency and accuracy in your data integration.

3 Transform your data into the standard format

The third step is to transform your data into the standard format and structure you defined in the first step. This means applying the rules and criteria you established for your data quality and governance, such as the format, naming, values, and validation methods. For example, you may need to convert dates, currencies, or units of measurement into a uniform format, or rename or merge fields to match the common data model. You can use tools like ETL (extract, transform, load) software, data cleansing software, or scripts to automate this process.

4 Validate your standardized data and monitor its quality

The fourth and final step is to validate your standardized data and monitor its quality. This means checking that your data transformation was successful and that your data meets your requirements and expectations. For example, you may need to verify that your data fields are correctly mapped, that your data values are accurate and complete, and that your data integration is seamless and error-free. You can use tools like data quality software, dashboards, or reports to automate this process and track your data quality metrics over time.

5 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?