The importance of data quality
Many companies find themselves in possession of a considerable amount of data without knowing exactly what value can be extracted from it or how it can be extracted.
Because they are scattered, have different structures and formats or are incomplete, they become very difficult to use. Difficult, but rarely impossible... With the mechanisms currently in place for conversion and validation, good preparatory work can make analysis of great value to the business possible.
Even in a database, a data quality assessment process can find outliers, duplicate records or inconsistencies. find outliers, duplicate records or inconsistencies. A detailed analysis of these situations may identify opportunities for improvement in the ETL (extrat, transform and load) process or even business-related opportunities. business. The same process could include cross-referencing with external data sources, enriching the sources, enriching the data and generating information of great potential.
A model is only good as the data it learns from. Before investing in application of Machine Learning or Artificial Intelligence, make sure you have full control over the quality of your data, or your results are doomed, no matter how good the model behind it.
The topic of "Data quality" is very broad and can be addressed at different stages of the pipeline or with different pipeline or with different approaches or technologies. Here are some of the steps that we think are important for ensuring data quality:
Rigorous data profiling and data source control
A good Data profiling tool will be important to examine the following aspects of the data: format, patterns, consistency of records, distributions of values and outliers and whether the records are complete. Data profiling will help you identify problems in your data that can be addressed directly at the source or in the ETL process. To do this efficiently there must be a dahsboard, with general KPIs that reflect the results of this profiling and that allow you to monitor data behavior against expectations.
Detailed design of the data pipeline
The of the data entry pipeline includes areas such as the definition of fields, business rules and database connection rules. In large organizations, it is often the case that several different areas interact with the same record, for example a customer. Ensuring that there is a common understanding of the input fields and that there are no duplicate entries are some of the concerns to be taken into account when designing the pipeline. Communication between different areas of the organization must ensure that rules are defined that are as transposable to the systems in order to reduce human error as much as possible. human error.
Precise collection of requirements
Validating the quality must be seen in a well-defined context. The objective of the analysis in order to define the requirements that the data must meet. meet.
Data integrity
Data integrity is an essential aspect of data quality. If your database is relational, you can ensure this by using primary and foreign keys, validating additional conditions to the format (check constraint) and using mechanisms triggered by specific actions (triggers). The problem becomes more complex when the data is spread across different database systems, but its importance should never be ignored or downplayed.
Traceability of data origin
Another of data quality is its traceability. Whenever a problem is detected in a record, it is essential to be able to quickly identify its origin and correct it without jeopardizing the project deadline. For this is possible, traceability must be at the heart of the data pipeline design. data pipeline.
Automatic regression tests
There will always be time when you want to enter a new set of data or change something in the existing fields. existing fields. To ensure that these changes don't impact the services that are running, it's important to have automatic tests in place. running. This will give you the ability to make migrations without postponing without compromising business-critical data.
Because we have been processing our clients' data since the last millennium, experience tells us that experience tells us that this issue of data quality is often underestimated. underestimated. On our side, we are learning and understanding its importance and how it facilitates further processes. Count on us to invest with you in preparatory data quality treatment processes and solid processes so that, together, we can get the most out of your data. data.
Source: https: //towardsdatascience.com/7-steps-to-ensure-and-sustain-data-quality-3c0040591366
