The enrichment process for each company is distinct, shaped by the data they accumulate and their strategic business targets. Nonetheless, there are several best practices that can benefit brands universally, regardless of their specific methodologies.

Articulate unambiguous guidelines

Establishing well-defined criteria is essential. This involves reflecting on the objectives of your data enrichment initiatives and subsequently outlining criteria that facilitate the assessment of these objectives. For instance, if the aim is to enhance the completeness and precision of customer data, a potential goal could be achieving a data accuracy rate of 90% or higher in customer profiles when validated against a third-party verification source. Should these targets not be achieved, it indicates that further efforts are required.

Develop methods that ensure processes can be repeated reliably

The subsequent step involves the establishment of repeatable processes. Continuously designing and executing new processes can lead to unnecessary expenditure of time and resources. By developing consistent and dependable frameworks for data analysis, these can be utilized across multiple enrichment initiatives. For instance, consider the procedure of validating customer profile information through a selection of standardized third-party sources. By implementing a process that automatically verifies these sources for particular data types, the function can be easily reapplied as needed.

Ensure that initiatives possess the capacity for effective expansion

As the size of your data increases, it becomes vital to ensure that your enrichment efforts can grow in parallel. This can be achieved by implementing automation wherever possible, which serves to reduce manual interactions that might complicate processes or lead to unforeseen errors.

Highlight the priority of general applications

One should contemplate the generalizability of processes to various datasets. For example, when designing a method to authenticate customer data entered through desktop website forms, it is advisable to engage partners or services that can guarantee the method’s applicability to mobile users as well.

Data enrichment constitutes a continuous process

Data enrichment should be viewed as an ongoing endeavour rather than a one-off task. Continuous efforts are required to maintain the relevance, accuracy, and timeliness of the data collected.

Applications for Data Enrichment

After establishing the definitions of data enrichment and data cleansing, and considering when to invest in these processes, it is time to discuss the implementation strategies. The idea of manually reviewing extensive data points may be off-putting, especially if your database is large. Such apprehension is completely justified. To alleviate this burden, we have curated a list of data enrichment tools that can assist you in effectively cleaning and managing your data. We invite you to take a closer look.

Data Augmentation Solutions

Developed a database for your data collection and entry and 2) connected all of your internal data sources, it may be prudent to assess the potential of employing a third-party data service. Fortunately, there are numerous data enrichment services that can assist in corroborating and refining your data. Below are a few to take into account.