It is the process of adding or updating information to existing data set like as customer profiles, product catalogues etc. Data enrichment can enhance the data quality, accuracy, and completeness of master data. This is the core data that defines your business units and processes. Master data management (MDM) is the discipline of creating, maintaining, and managing a single source of information for master data across an enterprise. In this blog, we are going to discuss best practices for data enrichment in Master Data Management (MDM) and how it can benefit your data integration projects.

Define data enrichment objective:

Before start enriching your master data, you should define your data enrichment goals and scope according to the need of business. What information would you like to add or update to your master data? How will it help you meet your business goals or support your data integration needs? For example, you may want to enrich your customer data with data of your choice. Or you want to enrich your product data with attributes, images, or ratings to improve catalogue management and e-commerce. Or you want to enrich your supplier data with assessment, certification, or compliance data to optimize procurement and risk management. Whatever your goals, they should be aligned with your MDM strategy and data governance policies.

Data Enrichment Source (Identification)

After defining the data enrichment goals, first we need to identify the data enrichment sources. These are external or internal data sources that can provide additional or updated information required for master data. For example, we can enrich our customer or supplier details with industry-standard data using third-party data providers. Or use social media platforms such as Facebook, Twitter, and LinkedIn to enrich our customer and product data with user-generated data. Or using own operating system, such as CRM, ERP, or POS, to enrich our master data with transactional and historical data. Whatever the source, we should evaluate its quality, reliability, and relevance to our data enrichment goals.

Data Enrichment Processes Implementation

Once we have identified our data enrichment sources, we need to implement our data enrichment process. These are the procedures and tools we use to collect, validate, transform, and add data from our sources to our master data. For example, data integration platforms such as Informatica, Talend, and SnapLogic can be used to automate the data extraction, transformation, and loading (ETL) process from sources to the MDM hub. Alternatively, data quality tools such as Trillium, DataFlux, and Ataccama can be used to check, cleanse, standardize, and reconcile the data and master data from the sources. Alternatively, we can enrich our master data with real-time or batch data from our database using data enrichment services such as Clearbit, FullContact. Any process must be efficient, accurate and secure to achieve its data enrichment goals.

Data Enrichment Results (Monitoring and Measurement)

Finally, the results of data enrichment should be monitored and measured. These are the metrics and feedback that we can use to assess the impact and value of our data enrichment efforts on master data quality and business outcomes. For example, use data quality dashboards such as Informatica Data Quality, Talend Data Quality, or SnapLogic Data Quality to track key indicators of master data quality such as completeness, accuracy, consistency, timeliness, and uniqueness. Or use business intelligence tools such as Tableau, Power BI, and Qlik to analyse and visualize the performance and trends of business processes and goals based on master data such as customer retention, product sales, and supplier efficiency. Or use customer feedback tools like SurveyMonkey, Qualtrics to record and measure customer satisfaction and loyalty working with master data such as product reviews, ratings, and recommendations. Whatever results we get should be used to optimize and enhance our data enrichment strategy and execution.