Ensure data quality when copying, migrating, loading, replicating or moving data with data validation rules and reconciling records.
Category: Data Quality
Data Quality impacts the outcome of any Data opportunity. E.g., forecasting customers’ trends or accurate reporting to take timely decisions. It can make the best out of your project, make it useless, or even drive users to take wrong decisions. So it is a crucial element we should consider when designing and implementing a solution.
Data quality refers to the processes that ensure a company’s data is accurate and complete. However, this term has different meanings for different user communities. For example, it is part of the IT community’s comprehensive data warehouse, data management, and governance program. While to the business community, it measures the suitability of data for its intended purpose.
When considering the quality and cleansing processes, we must first determine the needs and requirements that each data set will cover. It doesn’t make sense to apply the same rules to all data. We should use the most rigid processes for the data that support the most precise business needs, such as those meeting regulatory requirements.
In other words, we transform data into information that allows us to make educated decisions and drive outcomes. Our solutions may utilise all types of data. So we need to understand the state and lifecycle of engineering data products very well. Many integration paradigms, service levels, and other utilisation factors should go into the equation of what quality and cleansing levels we assign and manage for each data set.
Here, you can find some best practices and use cases to keep and enhance data quality during the data lifecycle.
Fuzzy Matching Demo: Inconsistent Company Names
Fuzzy matching use case. We improve data quality when loading data into BigQuery using Trifacta software, which simplifies the process.
Fuzzy Matching or Approximate String Matching
Fuzzy matching is a technique used to match text strings that may be less than 100% perfect. We use it in web searches, data quality, etc.