Regardless of the industry, it’s no surprise that within the field of data science, and particularly during the development of new data lake projects, data quality management is critical.
For the renewable energy industry, with distributed assets and harsh environmental conditions, a pragmatic approach is required when it comes to ensuring a sufficient level of data quality is achieved.
Introducing i4SEE DataCheck™, a versatile firewall for the i4SEE suite of analytics applications, designed to maximize the value that can be squeezed out of your data, regardless of its quality.
Effective data quality management involves:
- Establishing realistic data quality standards.
- Performing regular data audits or quality monitoring.
- Involving data stakeholders in defining minimum data quality requirements.
- Utilizing automated tools for addressing specific data quality aspects.
Data labeling is another essential aspect, ensuring consistent data tracing from its source to the output API.
Different data quality strategies may be necessary depending on your goals. For instance, energy production reporting may not require temperature data consistency, while predictive maintenance relies on it. In some cases, on-the-fly data cleaning might suffice, while other situations demand more in-depth corrections.
i4SEE DataCheck™ adapts to your data quality needs, acting as a firewall for your data streams and ensuring only necessary data points enter the analysis process. With i4SEE DataCheck™, you can focus on extracting valuable business intelligence from your data, even if it is of varying quality.
Stop postponing data quality improvements and start adding value with i4SEE DataCheck™, the intelligent solution for data quality management in renewable energy.
