Smart meters tracks the energy consumption on the system in which it is connected to. It tracks the power consumed and sends the real time data which can be used for creating insights on energy optimization. It also helps to control the power consumed by each customer and can be controlled remotely. Data governance around smart meters using Informatica involves implementing processes and policies to ensure the quality, security, and compliance of data generated by smart meters.

By utilizing Informatica tools we can define the business glossaries related to smart meters in Informatica Axon, show its association with the technical metadata (tables, columns) from Informatica Enterprise Data Catalog and see the lineage that shows data movement around smart meters across the different source and target platforms like file systems and databases. Figure 1, shows the Glossary taxonomy created in Informatica Axon.

Figure 1: Smart Meter taxonomy in Informatica Axon.

Figure 2, shows the association of Axon business term hourly consumption as business title to the technical field in Informatica Enterprise Data Catalog. This can be done manually or by enabling the business glossary association. It also shows the description populated from Axon and the value frequency of the data.

Figure 2: Association of technical fields to Axon business Glossary

Figure 3, shows lineage and impact analysis between AWS S3 file system and Snowflake in Informatica Enterprise Data Catalog.

Figure 3: Lineage in Informatica Enterprise Data Catalog.

One of the most important functions of smart meters is the recording of hourly consumption done by the consumer. This data is essential as the supply of electricity can be monitored. By using Informatica Big Data Quality we can profile the dataset and execute technical data quality rules to monitor and identify exceptions within the data sets. The technical data quality rule defined in Informatica Data Quality has an expression defined to validate the data. This rule is related to the local data quality rule created in Informatica Axon where the results will be updated based on the schedule. If there happens to be an outage in the supply the data quality rule will recognize the exception. This is further updated in the local data quality rule of Informatica Axon. Based on the change in result a workflow will be triggered to notify the steward about the exception occurred. Figure 4, shows the exceptions generated in Informatica's Data Quality platform.

Figure 4: Hourly consumption’s exceptions in Big Data Quality.

Figure 5, shows the results updated in Axon local data quality rule.

Figure 5: Data Quality results in Axon Governance.

The smart meter data will have information of the consumer like the meter Id the subscriber’s usage patterns which is highly sensitive. By using Informatica Data Privacy Management we can scan the data and flag the information as sensitive. This helps in understanding the sensitive data and further masking it by using extensions created for Informatica Test Data Management. Figure 6 shows the masking technique and the data before and after masking.

Figure 6: Masking technique and the data before and after masking