Many companies have large quantities of data but do not know how to extract value from them. When data is dispersed, incomplete or held in different structures and formats, they become difficult to use.
The process of making these data available in a format that can bring value to your company might seem overwhelming, but it is not impossible. With the mechanisms currently available to convert and validate data, investing in its preparing can allow analyses of great value to your business.
Even in a simple database, data quality checks can quickly help identify outliers, find duplicate entries or other incoherent patterns within your business logic. The identification of these issues can help you improve your ETL (Extract, Transform, and Load) process or even provide insights into your business. At the same time, you can cross-reference your internal data with data from external sources, enriching it and generating information with great potential.
A model is only as good as the data it learns from. Before you invest in applying Machine Learning and Artificial Intelligence to your data, you should ensure you have control over the quality of your data, or your results are constrained, no matter how good the model behind it is.
Data quality is a broad topic that can be addressed at different points of your pipeline and with different approaches and technologies. Here we list some important steps in ensuring data quality:
Rigorous data profiling and control over the data sources
A good data profiling tool is important for examining the following aspects of your data: format, patterns, consistency of records, value distribution and outliers, and completeness. Data profiling will help you identify the issues in your data which can then be addressed directly on the source data or within the Extract-Transform-Load (ETL) pipeline. For this to be efficient you should have a dashboard with general KPIs reflecting the results of this profiling and that allow you to monitor how the input data performs in line with expectations.
Detailed design of the data pipeline to avoid data duplication
The detailed design of the data entry pipeline includes areas such as field definition, business rules and database connection rules. In large organisations it is common for several different areas to interact with the same registry, for example a customer. Ensuring that there is a common understanding of input fields and that there are no duplicate inputs are some of the concerns to be considered when designing the pipeline. Communication between the different areas of the organisation must ensure that the rules are defined to be as transposable as possible to automatic processes, in order to reduce human error.
Accurate definition of requirements
Validating your data quality should be carried out in the context of the semantics of that data. You must understand your data analysis goals in order to define the requirements your data must adhere to.
Data integrity is an essential aspect of data quality. If your data is in a single relational database, this is made simpler by the use of primary and foreign keys, check constraints and triggers. The problem becomes more complex when your data is spread across different database systems, but this situation should never be underestimated.
Traceability in data pipelines
Another key aspect of data quality is traceability. Whenever a problem is found in a record, it is essential that its source can be quickly identified and fixed, or your project deadlines might be compromised. To make this possible you should include traceability at the core of your data pipeline.
At some point, you will probably want to introduce new datasets, or change an existing one. To ensure that those changes do not disrupt existing services, it is important to have automated tests in place. This means that migrations will be possible without endless delays (e.g., waiting for approval of different departments) and without compromising mission critical datasets.
As we have been processing our client’s data since the last millennium, experience tells us that the issue of data quality is often underestimated. We are continuously realising how crucial it is and how significant the benefits are in addressing these issues. You can count on us to invest with you in developing data quality processes and solid ETL pipelines so that, together, we can make the most out of your data.