Data science methods only provide usable information if the basis is correct. Inaccurate data always means inaccurate findings. Or, to put it another way: if the walls on the first floor are crooked, the building plans for the loft conversion are useless. If you carry on building anyway, you make it all the worse because in the end you can't keep up with the concealing.
Energieversorgung Mittelrhein (evm) wanted to be able to track the customer journey of its customers in a more targeted way. Building on this, an automated and customer-specific approach based on contract and behavioral data was on the agenda. At the end of this value chain of data-driven insights, forecasts and trends were to be analyzed and used as economic decision-making aids.
How do you turn data into a 360° customer view?
Which technologies support data science initiatives?
How can detailed forecasts be made?
In two consecutive workshops, evm and taod analyzed the current situation and identified initial use cases. This resulted in an MVP approach, which is being further developed in an agile sprint model based on the existing data strategy.
Due to insufficient data quality in the billing system, a record linkage model was used. It detects similarities between data records using statistical methods such as Expectation Maximization - supported by a scalable cloud infrastructure.
Azure Synapse enables evm to process data efficiently using ETL pipelines and Spark clusters. Customer identification was automated, results transferred to the Modern Data Warehouse and integrated into operational monitoring.
With taod, evm successfully cleansed its complex database using record linkage. The new Azure-based Modern Data Warehouse provides a scalable foundation for all of the company's future data science initiatives.
Together with taod, Energieversorgung Mittelrhein successfully completed the complex and time-consuming cleansing of its database. Using the record linkage method, the cleansed data was then compared to create a corrected history. The creation of a new cloud-based modern data warehouse in Azure also forms a reliable and high-performance basis for all future data science initiatives.
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How to recognize, promote and accelerate the potential of data initiatives for data science solutions.
