Hotline statistics

In the following example, one of our customers asked us to analyze their service hotline in order to evaluate its performance. For this purpose, relevant key figures were defined, data from various systems was queried and, of course, visualized with the help of state-of-the-art Power BI. The goal was to create a detailed evaluation of the service hotline in a central location for our customers.

Initial situation

Previously, reports from our customer were accessed and viewed in different systems, resulting in time-consuming report queries. The key figures were not calculated according to uniform definitions, which led to different levels of information for the company's report consumers.

The goal was to create a fast reporting at a central point (single point of truth). Furthermore, it was important to enable increased transparency in all reported areas. The variability of Power BI also ensures high availability on all devices (desktop, tablet & mobile).


Hotline statistics can be used to analyze the performance of call centers and ticket systems. For this purpose, among other things, the data of the telephone system is read out to determine key figures such as the accessibility of the hotline or the average call duration. By linking the ticket system, a detailed overview of various key figures such as the service level, the resolution rate or generally the total volume of tickets can be created. All key figures were defined and worked out with the respective persons in charge in order to ultimately provide a binding basis for decision-making.

In addition to the integration of CSV and Excel files via a file share, data from Google Analytics, Azure databases and a ticket system based on an on premises SQL server were integrated. A comment function was created via a Power App, which enables the area managers to evaluate the current development. Data for billing-relevant KPIs is stored in a data warehouse and the KPIs are calculated there to ensure traceability of the data and to create a single point of truth for all interested parties that can be tracked for years.


A categorized overview of various sub-areas is provided via the dashboard. The dashboard is intended to provide an initial overview of the most important KPIs. The current status, the evaluation of the status using a predefined traffic light system, and the trend over a defined period are displayed. The initial page thus conveys at first glance the areas in which action is required; jumping off points then take the user to the corresponding detailed pages, on the basis of which measures can then be taken.

Key figures configuration

Two additional power apps are linked to the dashboard. A parameter app and an app that rates comments and visualizes them via the dashboard.

Parameters app

On the one hand, the parameters of the traffic light system can be variably set via a power app, i.e. the threshold values at which the traffic light switches, as well as the respective target values and weightings of the individual key figures. These apps also write back to the Azure SQL data warehouse.


The second app was added in order to be able to evaluate and justify the individual overall key figures. The comments are recorded via the Power App by the respective key figure managers and visualized in the dashboard.

First-time fix rate

The first-resolution rate expresses how many customer inquiries/procedures were answered at the first contact. This means no forwarding and no repeated response were necessary. With its various filter options, the report page provides information on how the first resolution rate develops according to transaction type, input channel or keywords in the ticket. Among other things, a speedometer is used as visualization, which reflects the limit values that were previously defined via the parameter app. Furthermore, the development over time is displayed via a classic bar chart, which enables a display on year, month or day level via the drill function. The development between the previous month and the completed month as well as the resulting delta is visualized via KPI tiles, which immediately provide an indication of the development of the key figure.


With the help of Power BI, we were able to provide our customer with centralized reporting. Numerous key figures and data are now evaluated centrally, thus creating a great time saving for the report consumers. Furthermore, the following advantages could be achieved:

Time savings: Thanks to centralized reporting, individual data no longer has to be painstakingly searched for together.

Cost savings: Power BI's high level of compatibility eliminates the need for third-party reporting and data management tools.

Standardization of reporting: Uniform reporting standards in terms of content and visual appearance ensure greater transparency of data and lead to greater acceptance among report consumers.

Availability: The report is available to all authorized employees at any time.

Forschung und Vorbereitung

  • Einfacher Datenzugriff mit Pipelines, Lakehouse und Notebooks
  • Apache Spark und Python
  • Data Wrangler als Bestandteil von Notebooks

Experiment und ML-Model

  • Aufbau von Experimenten zur Überprüfung von Hypothesen
  • Aufbau von ML-Modellen auf Basis Azure Synapse ML
  • Einsatz von Notebooks mit Apache Spark und Python

Insights gewinnen

  • Speicherung von Vorhersagen in OneLake
  • Bereitstellung neuester Vorhersagen in Power Bl mittels Direct Lake Anbindung

Data Science Prozess

Dataflow Gen2


OneLake Shortcut


  • Erfassung von Daten mit Dataflow oder Datenpipeline

  • Shortcuts aus OneLake, Azure Data Lake etc.

  • T-SQL Read/Write

SQL-Endpunkt des Lakehouse

  • Automatisch aus Lakehouse generiert

  • Kompatibel mit SQL Server Management Studio und Azure Data Studio

  • T-SQL Read-Only

Dataflow Gen2


OneLake Shortcut

Event Streaming

  • Erfassung, Transformierung und Routing von Real-Time Events

  • No-Code Experience

KQL Database

  • Bevorzugte Datenhaltung für Event Streaming
  • Schnelle Speicherung und Abfrage von Daten
  • Basis für Power BI Reports

Dataflow Gen2


OneLake Shortcut


  • Speicher für strukturierte und unstrukturierte Daten
  • Basis für BiaData Transformationen und Analysen
  • SQL-Endpunkt und Basis für Auswertungen


  • Interaktive Compute für Transformation von BiaData
  • Entwicklung von Machine Learning
  • Modellen und Apache Spark
  • Python, R und Scala

Single Source of Truth.

Im Microsoft Fabric OneLake werden Daten aus verschiedenen Quellen in einem zentralen System gespeichert, das bietet zahlreiche Vorteile wie

  • Datenkonstistenz und hohe Datenqualität
  • Steigerung der Effizienz und Produktivität
  • Verbesserte Zusammenarbeit im Unternehmen
  • Erleichterte Datenintegration und -analyse
  • Kostenreduzierung
  • Compliance und Sicherheit

Citizen Developer.

Durch die Benutzerfreundlichkeit des Tools entstehen folgende Vorteile

  • Intuitive Oberfläche
  • Einfache Automatisierung und Datenintegration
  • Gute Kollaboration und Zusammenarbeit
  • Schnellere Entwicklungszyklen
  • Hohe Flexibilität und Skalierbarkeit
  • Fortschrittliche KI Komponenten

More Flexibility.

Eine leistungsfähige, integrierte und benutzerfreundliche Plattform, die es Unternehmen ermöglicht, ihre Daten effizient zu verwalten, tief gehende Analysen durchzuführen und fundierte Entscheidungen zu treffen. Dies trägt wesentlich zur Optimierung von Geschäftsprozessen und zur Steigerung der Wettbewerbsfähigkeit bei. Besondere Vorteile

  • Nahtlose Datenintegration durch einfache Datenpipelines
  • Flexible Verarbeitung von strukturierten und unstrukturierten Daten
  • Schnelle Anpassung an Veränderungen im Unternehmen
  • Konsistente Datenhaltung
  • Zentralisierte Datenverwaltung
  • Skalierbare Data Warehousing und Data Science Komponenten
  • Leistungsstarke Datenanalysen in Echtzeit


According to your wishes we create report designs according to the latest information design and UX/UI standards.

Our goal is to enable optimal report usage with the appropriate story telling.

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Our developers design customized reporting solutions.

Particular attention will be paid to the following:


Building Power BI Infrastructure

Create Power BI Reports

Workshops & Trainings

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With our broad expert knowledge in numerous business areas, we offer you holistic support in the development of key figures


Retail, Wholesale, E-Commerce.

Controlling + Finance

Accounting, sales controlling, investment controlling.

Supply Chain Management

Logistics, purchasing, production.


Websites, email marketing, social media campaigns.

Together with you, we define the necessary key figures and KPIs.


Unsere Data Engineering Experten generieren die richtige Kulisse für dein Reporting.

Mit der Microsoft Produktpalette entwickeln wir Data Warehouses, Data Lakes und Data Lakehouses nach neusten Standards.

Dazu nutzen wir unter anderem:

Azure Data Factory

Azure Data Lake

Azure Data Bricks

Azure Synapse

Azure SQL

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Microsoft Fabric

Central reporting

Single Point of Truth

For reporting, we create the basis for powerful data analyses.

At a glance

Create visualizations that draw attention to the essentials.


Easy to consume yet meaningful and accurate.

Customized design

According to the latest design standards and corporate identity guidelines - On all devices!

Enterprise Reporting & Self Service

One place

Meet your self-service BI and enterprise data analytics needs on a single platform.


Create & share reports, apps and data models. Relieve IT resources with true self-service.

Gartner Magic Quadrant

Microsoft is ranked as a Leader in the March 2022 Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for the second year in a row

Data management

Your data management in an individual data warehouse, based on the Microsoft SQL engine. With Azure Data Factory, Azure Data Lake Storage and Microsoft SQL Servers, Microsoft provides a comprehensive modern data warehouse solution for companies of all sizes, whether in the cloud or on-premises. 

Benefit from established techniques like SSIS and SSAS and leverage cutting-edge solutions like Azure AI & Machine Learning.

Efficient and centralized storage

Relief of the operational databases

Reduction of dependence on manual processes of data evaluation

Consistent database for reporting applications

Consolidation and aggregation of data

Creation of a single point of truth


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Azure Data Lakehouse

Azure Machine Learning Architektur

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Data sources

Beispiele für Power BI Datenquellen

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Data sources

Beispiele für Power BI Datenquellen

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Google Analytics​

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Adobe Analytics

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