Laura H.-Sr. Data Expert & Project Manager

Design and Setup of an Enterprise Data Lake & Analytical DWH

Industry: Finance, Germany

As part of the Enterprise Architecture Transformation project – creating an analytical data platform to run analyses of customer service availability, quality of service, customer satisfaction etc.

Designing the Enterprise Data Lake & Analytical DWH:

• Analysis of the IT landscape
• designing data flows and ETL processes for data load into the Data Lake
• defining and implementing the KPI calculations
• designing the analytical DWH, data load
• DWH end-to-end automation.

Customer Churn Prediction

Industry: Telecommunications, Austria

Retaining existing customers is one of the central tasks for companies, since acquiring new customers would cost more. This company already had a churn prediction model in SAS, but it did not deliver very accurate estimates. In addition, the company wanted to reduce licence costs and migrate the model from the commercial software SAS to R.

Migrating the existing customer churn prediction model from SAS to R and optimizing it:

• Analysing the existing prediction model in SAS and recreating the same model in R
• extending the existing data model with additional data sources, adjusting and optimizing the prediction model for improved accuracy and performance
• setting the model into production.

Enterprise Architecture Design

Industry: Finance, Germany

Transformation of the Enterprise Architecture to be able to serve a higher number of customers in the future (expected volume: 10M). The solution must be based on the hybrid cloud strategy.

Designing the enterprise IT architecture:

• Analysis of the existing IT landscape
• identification of the IT architecture components to be changed (requirements engineering)
• modelling of the future data flows and data consuming systems (input / output for each system)
• modelling of interfaces for communication between systems
• creating a roadmap for the step-by-step implementation
• support with implementation.

Asset Metadata Ingest & Provide for a Streaming Platform

Industry: Media, Germany

One of the leading media platforms in Germany wanted to ingest and provide metadata for all content types into their platform, such that users can watch / listen to / read content according to their subscription type, media rights, youth protection, formats etc.

Coordinating the Metadata Ingest + Provide teams:

• Collaboration with stakeholders to gather requirements and write user stories
• coordinating the work of the Metadata Ingest + Provide teams
• alignment with other teams such as legal, rights, frontends, clients
• testing and acceptance of backlog increments
• development of a dashboard (in Tableau) to verify the completeness of data transfers.

Customer Satisfaction Index

Industry: Telecommunications, Austria

This telecommunications company had decided that it wanted to become the market leader in terms of customer satisfaction. To accomplish this, KPIs had to be defined so that progress towards the targets could be measured continuously and reliably. Designing and implementing the Customer Satisfaction Index:

• Requirements engineering in workshops with stakeholders
• workshops with network engineers to understand the network data
• workshops with customer service centre representatives to learn about the most common complaints
• development of an analytical data model to predict customer satisfaction
• validating the model against customer complaints
• setting the model into production.

Enterprise Reporting in Power BI

Industry: Public Sector, Austria

Previously, reporting had not been standardized in this organization, which meant that there was no consistent interpretation of the numbers. Different reporting tools were used across departments, some of them out-of-date and at the end of their product lifecycle. Our target was to standardize the enterprise reporting and bring it to the next level.

Designing and implementing the Enterprise Reporting Platform (Power BI):

• Defining the reports and KPIs together with stakeholders (requirements engineering)
• setup of the necessary infrastructure (Power BI Data Gateway), setting up a local DWH on the reporting server
• data modelling, adding new data sources to the data model
• organizing workshops with the usability expert for report design
• scheduling and end-to-end automation for standardized reports.

Customer Insights Analytics in Azure / Power BI / Qlik

Industry: Retail, E-Commerce, Germany

The company aimed to improve its understanding of customer behaviour and engagement, optimize marketing campaigns, and enhance revenue growth by implementing a comprehensive analytics solution on Power BI / Azure technologies. The project focused on analysing customer data, activation, campaigns, Google Analytics, revenue, clicks, and deriving other relevant metrics.

Developing reports for enhanced customer insights and engagement on Azure / Power BI:

• Collaboration with stakeholders to gather requirements and develop user stories for the reports
• Analysis of customer data from various sources, including Google Analytics, Google Cloud Platform, Salesforce CRM and transactional databases (SAP)
• Conducting A/B tests for customer data selection, development of a revenue attribution model
• Definition and evaluation of key performance indicators (KPIs), such as customer acquisition, activation, retention, revenue, click-through and churn rates
• Building analytical apps in Power BI and Qlik
• Alerting on data quality deviations using Power Automate.

Role Analysis and Role Modelling for Identity and Access Management

Industry: Finance, Germany

In an organization, users need access rights for various IT systems. One way to restrict access is to grant the rights for each user and each system separately. A better option is role-based access control (RBAC), where the entitlements are pre-grouped and assigned to a role, and only the role needs to be granted to a user. RBAC helped the company save 30% of the resources that were previously devoted to this task in IT support.

Role Mining and Modelling:

• Development of a clustering algorithm for grouping roles
• cluster analysis of the existing roles and entitlements (role mining)
• analysing the critical roles and entitlements which must not be in the same cluster, modelling of the new access roles, importing the new roles into the IAM system
• creation of a functional specification.