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Data warehouse and BI Development for Rabobank International. Developing ETL tools for various banks worldwide to comply with the new standards for risk management from the ECB.
Staatsbosbeheer was in the process of replacing its vertical application used to register HRM data. This HRM data was the source of a large part data used to build their KPI dashboard. In this assignment, We’ve done some analysis to support their decision on whether they should move to the vertical reporting capabilities of the new application or change the current data warehouse and keep a central reporting repository.
ROC de Leijgraaf
ROC de Leijgraaf is a school. They use several vertical applications to administrate the student progress, validations for the government, etc. They had a management dashboard based on a cube in combination with Power BI. The goal of the project was to convert this to a data warehouse combined with a dashboard in a way that people of De Leijgraaf could maintain this. We’ve built ETL functionality in SSIS/T-SQL to load the data warehouse and created the dashboard in SSRS. To give teachers and the school management insight.
Dela has chosen to move to Azure and decouple applications through an event-driven architecture. In the old situation, the various vertical applications generated emails or printed documents to inform the customer about state changes or to request extra information. In the new landscape, this role is centralized in the, what they call, the output manager. The implementation of this application in Azure based on function apps connected to a SQL Managed instance was designed by us based on the input of business and guidelines from the enterprise architect. The output manager allows the business to connect events in combination with business rules to output and channels. As soon as there is a match, the output manager will gather the data from the vertical applications and orchestrate the document generation and delivery.
The Dela Intelligence Manager is a solution that was nominated for the DDMA Customer Data Awards 2019. The application is based on the Marketing Database and allows marketing intelligence specialists to target their audience precisely and automatically. It does that by separating the decision of whether an email will be sent from what the message will be. For instance, each day, the DIM decides to send emails to customers with children that are having their birthday that day. Based on whether the predictive model tells that it’s more likely that the customer will upsell or churn, the content of the email is chosen. Besides this event-driven messaging, the DIM will apply some business rules to determine whether it’s allowed to contact the customer according to the DELA standards, and it will gather response data to feed the Marketing database and predictive models.
“To support the marketing intelligence specialists, We’ve designed and built a SQL database on top of their Source Datawarehouse that allows them to query about the facts and behavior of their customers. This database is fundamental for their predictive models”.
Conneqt distributes mortgages for investors using a channel of independent financial advisors. Their goal is to improve their business due to a more data-driven way of working. To enable this, We’re building a Next Best Action Engine and a dictionary-driven DWH. This engine allows them to recognize trends in their data and execute actions based on this. Those actions can be marketing or sales-related but also activities for product management. The engine’s output is either email, publication to a website, or publishing actions on a dashboard. The engine relies on a single source of truth. This is created using the dictionary-driven DHW, they describe their DWH and sources in a dictionary (GUI), and the software generates the ETL and the database structure.
Code review, troubleshooting and performance improvement of complex time-consuming calculations.
Financial asset management data consolidation and quality.
Customer Data Platform development based on Azure Synapse Analytics, Azure Data Factory, Segment and Mode Analytics.