Quality data is key to success, right? Then a framework to effectively monitor data quality is essential to leverage data and turn it into actionable information. Achieving quality data requires constant monitoring and measurement as well as improvement actions since data are frequently updated.
A very important area of data management operations is to clearly establish the relationship between data failures and business impacts, which requires identifying "good" and "bad" data.
The data auditing and inspection process is complex and involves objective review of the datasets through quantitative measures and analyst review. While an analyst may not necessarily be able to identify all instances of bad data, the ability to report specific situations to the right people within the organization can be very useful to confirm the existences of data problems. This process includes several different steps, beginning with data profiling.
Data profiling
It is a set of algorithms that provides insight into the type and use of your raw data. It is a great tool for exploring relationships between fields and objects (such as contacts and companies) within and across datasets. It can expose embedded value dependencies that represent business rules embedded within the data and other inconsistencies (i.e. missing data) or errors that exist within the datasets.
Data Quality Audit
It examines where specific issues within your data lie. They could be formatting issues, redundancy issues, missing data, duplicate data, and other common errors. According to Insycle, some of the common types of CRM data quality errors are:
- Inconsistent Data. Records with consistency issues, such as countries — "USA” vs. “US” vs. “United States”.
- Poorly Formatted Data. Records with errors such as names in lowercase or uppercase — "CARLOS" or "carlos" vs. "Carlos".
- Low-Quality Data. Records that are useless, such as contacts with hard bounced emails, or records with very little usable data.
- Duplicate Data. Records that share the same (or similar) data with another record, just like same name and email domain or same name and phone.
- Invalid Data. Records with errors that make the data invalid. A clear example would be email addresses with typos, "@gmil.com" vs "gmail.com".
- Incomplete Data. Records thats are missing important data that is critical to your business processes, such as deals without "Amount" or "Close date".
The result of the data auditing and inspection process is a collection of rules, each of which can be categorized within the framework of the data quality dimensions.
If you are using HubSpot or Salesforce, I recommend running the Insycle Data Grader to audit your CRM data in minutes. Then forward the report to us so that we can provide you with our professional advice on how to get all those specific data issues fixed and how to manage your data on an ongoing basis.
