For most organizations, it is a strategic priority to leverage the data assets generated during operations in a structured and efficient way to support business decision-making and competitiveness. To achieve this, many have already implemented various data warehouses, BI solutions, and analytics tools.

Our experts at Abesse evaluate your organization’s data management practices through a hands-on methodology built on international standards.

The assessment covers data management and data governance processes, the technological architecture and toolset, as well as organizational roles and responsibility matrices.

As a result of the assessment, we provide an objective view of the organization’s data maturity level and deliver tangible, prioritized recommendations to support the development of a more effective data-driven operation.

The foundation of reliable data-driven operations is high-quality, well-governed data. Abesse’s experts help design a framework that ensures data accuracy, consistency, and business interpretability.

Data quality rules and validation processes reduce the risk of incorrect decision-making, while metadata management and data asset inventory ensure transparency across data processes.

The governance framework encompasses the design of roles, permissions, data access rules, and auditing mechanisms.

By integrating tools such as Unity Catalog, Microsoft Purview, and OpenMetadata, we ensure compliance and long-term sustainable data management.

One of the greatest challenges in building an enterprise-level data-driven operation today is creating a unified data platform that can simultaneously support fast, self-service reporting needs and provide reliable, well-structured data for artificial intelligence and machine learning models.

Abesse’s experts support their clients in designing a highly scalable and flexible data platform that takes both business and technological aspects into account.

The foundation is a well-structured data architecture that ensures consistent, scalable, and business-meaningful use of data.

During the design phase, special attention is given to logical and physical data models, data quality, metadata management, and future analytical usability. The goal is to implement an architecture that can integrate dispersed raw data sources, ensuring data quality and consistency, and supporting various levels of data utilization — from operational reporting to predictive analytics. This can take the form of a complex, fully customized solution or a rapidly deployable, template-based approach.

A self-service analytics environment designed for business users significantly enhances the efficiency of data-driven decision-making. These solutions enable users to quickly access, filter, and analyze data without the need for IT intervention.

With the two-speed reporting architecture designed by Abesse, business teams can flexibly adapt to changing market conditions, while the IT backbone ensures system stability and compliance.

In our BI solutions, we ensure consistent application of governance principles to guarantee proper access management, unified data interpretation, and controlled versioning. We also treat reliable and optimized data loading processes as a top priority.

We support the design and implementation of ETL processes, ensuring data cleansing, normalization, and transformation according to business logic.

We complement the classic reporting environment with AI-driven analytics components that can generate forecasts, identify trends, and provide automated decision support based on historical data.

Machine learning models are integrated along business logic, enabling reports to provide not only descriptive insights but also predictive and analytical capabilities. In addition to traditional report and data visualization formats, insights can now also be accessed through a language-model interface as well.

Abesse has more than two decades of experience in developing business-critical applications.

In practice, it’s quite common that the long-term usability of the data asset becomes secondary when business needs are implemented through locally optimized solutions.

As a result, data extraction from applications often has to be carried out later through separate data projects, requiring significant technical effort.

Our experts design applications with future data usability in mind — for reporting, analytics, and AI-based decision support. These applications are not just data providers — they are fully integrated into the data platform, driving business logic and data-centric operations.

The end product of data solutions is therefore not merely a report or dashboard, but a business application that is built directly on data and operates with it — enabling real-time decision support, automated processes, and predictive capabilities.