Data Management Services

Maximize your business potential with Adastra’s advanced data management services — the key to unlocking insights and driving sustainable growth.

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We value the partnership with Adastra’s experts. Setup and integration of Microsoft Purview was designed around CETIN’s needs, and we greatly appreciate their flexibility in adapting to our needs while consistently delivering high-quality results.

Daniel Arnošt

Head of DW & BI

CETIN

Adastra helped us implement a data solution built on Microsoft Fabric and bring it into production within six months. We avoided technical uncertainty and significantly reduced the implementation timeline. The solution is now managed by our internal IT team.

Michal Gerthofer

Head of Data & AI

AGEL

We have been working closely with Adastra for years on the development of our data warehouses, which are essential for the daily operation of all BI layers used to manage the company. When development or support from Adastra is needed, I value that the collaboration is straightforward, open, and focused on long-term sustainability and further development. They are able to recommend direction, highlight potential risks, and propose effective solutions.

Vít Linhart

Project Manager/Business Consultant for BI

Albert

Adoki is process and cost efficient, and fully automated. It’s also easily scalable, so we’re gradually using it to manage more and more data transfers and increasing volumes of data. It gives us a single location from which to replicate data from multiple systems, the data warehouse, and more. It can be run by a single data specialist, and the tool alerts for any changes in the data in the source systems, preventing poor data quality.

Jiří Boček

Product Owner

Skoda Auto

Our partnership with Adastra helped us course‑correct early, get ingestion, transformation, and governance right, and build a unified data platform that moves our leaders from overload and gut feel to confident, data‑driven decisions.

Jon Steffey

Senior Director, Enterprise Software and Analytics

Tolmar

I can say with Adastra, from the very outset, I never had the feeling that I was talking to a vendor. Nor did I have the feeling that they were talking to just another client. We had open, transparent communication, and very good cooperation on a day-to-day basis and that was a big part of the success. For us, teaming up with Adastra for the data migration project was the right choice to make. I recommend Adastra to anyone who is looking into such a thing.

Christof de Mil

Country Manager of KBC Group Bulgaria and CEO of UBB

United Bulgarian Bank

Raiffeisenbank successfully acquired Equa Bank and fully integrated it under the Raiffeisenbank brand. Thanks to the dedication and professionalism of the Adastra team, combining the data from the two client bases went smoothly and according to plan. Adastra also ensured continuous regulatory reporting throughout the course of the acquisition. Adastra once again confirmed their excellence in data migration and working with data in general.

Miloš Matula

COO and Member of the Board of Directors

Raiffeisenbank

The IT reporting solution developed by Adastra provides the VW Group with a structured, scalable and automated technical foundation for planning, reporting, and data processing - combined with modern agile and DevOps practices.

Alexis Goss

IT Product Owner

Volkswagen

Adastra really understood how data-driven farming works, what we were trying to do, and had the expertise to put together a solution.

Juanita Moore

Vice President of Corporate Development

GoodLeaf Farms

Adastra’s recommendations enabled us to implement multiple quick wins within the same year, while also establishing a structured roadmap for long-term CRM development. Adastra’s work provided a solid baseline for continuous CRM improvement and measurable business outcomes.

Charlie Ford

Head of Platform Governance

Erste Group

Unlock Success with a Clear Data Strategy

The key to effective data use is to have a clear data strategy. Data governance provides a framework and set of tools to create, store, use, maintain and archive data. At Adastra, we are committed to supporting organizations by empowering them to effectively manage and control their data, enabling them to make confident and informed decisions.

The strategy to achieving success through robust data includes the following elements:

Consolidated Information

All data in one place, accessible to the entire organization. Uniform information for all.

Clean Data

Maximum control over data and data quality. Elimination of incorrect information.

A Quick and Single Source of Information

Data is available whenever you need it for further decision-making. No more waiting.

Our Partners

Adastra’s Data Management Craftsmanship

Data Governance

Data governance processes, policies and standards ensure that data across an organization’s entire system is trusted, accessible and understood. By implementing effective data governance, organizations can enhance data quality, mitigate risks, and comply with regulatory requirements.

Data Quality

Data quality emphasizes the importance of having accurate, consistent, and reliable data for critical business decisions. High-quality data allows organizations to avoid errors, reduce operational inefficiencies, and improve overall business performance.

Master Data Management

Master data management focuses on creating a single, authoritative version of key business data. It ensures that core data elements, such as customer information or product details are consistent and accurate across various systems and applications. By centralizing key data, organizations can improve data quality, enhance business processes and achieve a unified view of essential information.

Reference Data Management

Reference data management involves maintaining and synchronizing reference data. This provides context and standardization for other data within an organization. It also ensures data consistency across your organization and provides users with clear decision-making context.

Data Mesh

Data mesh is an architectural approach that aims to improve business analysis and outcomes by decentralizing data ownership and access. It involves breaking down monolithic data architectures into smaller, domain-specific data products that can be easily scaled and managed independently. Data mesh promotes agility, collaboration, and scalability in handling complex and diverse datasets within an organization.

Metadata Management

This process involves organizing and managing metadata to increase data literacy, make sense of your data, and overall maximize your organization’s business value. Effective metadata management supports data discovery, enhances data governance, and facilitates understanding of the data landscape.

Data Lineage

Data lineage involves visualizing and documenting the flow of data from its origin, through various processes and transformations, to its destination. Data visualization and documentation allow organizations to gain trust in their data for more confident decision-making.

Unleash Business Potential with a Tailored Data Management Approach

Data management plays a crucial role in enhancing data analytics and AI capabilities within enterprise organizations. The benefits of effective data management in this context include:

High-Quality Data for Analytics

Effective data management ensures that data is accurate, complete, and reliable. These are essential for high-quality data analytics. Accurate data leads to more precise insights and predictions, improving the outcomes of data-driven decisions.

Enhanced AI Model Performance

The effectiveness of an AI model relies on the quality of the data they are trained on. Implementing proper data management ensures that AI algorithms have access to clean, well-organized, and relevant data —a critical factor for developing effective AI models.

Scalability of Data Analytics and AI

As data volumes expand, effective data management practices enable organizations to efficiently scale their data analytics and AI initiatives. This includes handling larger datasets, seamlessly integrating diverse data sources, and deploying more complex models.

Robust Data Governance

A robust data governance framework is essential for analytics and AI, ensuring that data is used responsibly, ethically, and in compliance with regulations. Effective data management reinforces this framework by establishing clear policies and procedures for the responsible use of data.

Cost Efficiency

Efficient data management enables organizations to optimize storage and processing costs. By minimizing data redundancy and enhancing data quality, organizations can decrease the expenses associated with data analytics and AI initiatives.

Faster Insights and Decision-Making

With well-managed data, organizations can expedite the transformation of data into insights. This leads to accelerated decision-making, allowing businesses to respond rapidly to market changes and capitalize on new opportunities.

Improved Data Security

Proper data management involves implementing robust security measures. This is a vital step to protect the data used in analytics and AI against breaches and unauthorized access.

Innovation Capabilities

High-quality, well-managed data can drive innovation in AI and analytics. Enterprises can experiment with analytics approaches, develop advanced AI models, and explore emerging technologies such as machine learning (ML) and deep learning.

Turn Data into Meaningful Insights

Leverage Adastra’s frameworks to automate data flows and enable efficient, reliable, and scalable data processing.

Data Engineering and Automation Enablement

Adastra’s framework automates the process and transformation of data flows, enabling efficient, reliable, and scalable data processing. This allows for quick access to relevant and high-quality information for analytical purposes and business decision-making.

MetaCroc

This revolutionary tool streamlines the creation of functional data warehouses without the need for extensive developer involvement. This technologically versatile tool allows organizations to build data warehouses according to uniform rules and standards. It is widely applicable, user-friendly, and easy to implement. Its innovative features empower organizations to establish and manage robust data warehousing solutions effortlessly, allowing for seamless integration into existing workflows.

Adoki

Adoki, a data migration tool, enables organizations to facilitate scalable data transfers and replication across platforms. As data volume grows, traditional data warehouses may struggle to manage these large amounts of complex data. Adoki is a reliable, easily manageable, and scalable data movement solution that makes your data usable. It is simple, flexible, secure, and timesaving. Adoki efficiently replicates data between systems and can manage thousands of transfers to free up your team for more critical tasks.

Adele

Adele is a universal platform for simple data and code migration in the cloud. Organizations can simplify migrations, streamline on-premises platforming, and avoid coding errors. Adele’s powerful features —including data lineage and automatic pipeline generation —streamline your organizations code migrations. Adastra and ADELE will automate many of your organization’s migration tasks to accelerate transformation efforts.
Success Story

Adastra Helped a Leading Bank Optimize Operations and Implement New Data Management Concept

Our client wanted to enhance data-driven decision-making, optimize resource utilization, and ensure the data across all systems is trusted, accessible and understood. Adastra implemented a tailored data governance program that enhanced data management operations.

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Turn Your Data into a Managed Business Asset

We will assess your data strategy, quality and ownership and define the data management steps with the biggest business impact.

Data Management Services FAQ

How does data management improve analytics and AI?

Data management keeps data accurate, complete and reliable, which is the basis for precise insights and predictions. AI models perform only as well as the data they are trained on, so clean, well-organized data improves model results. Better data also leads to better data-driven decisions and outcomes across the organization.

Adastra covers data governance for trusted, accessible data, data quality for accurate and reliable data, master data management for consistency across systems, reference data management, data mesh for agility, metadata management for data literacy and data lineage for trust in decisions. Together they give you reliable data and operational efficiency.

Adastra uses its own tools to automate data processing. MetaCroc simplifies data warehouse creation and keeps rules and standards uniform, Adoki automates reliable and scalable data transfers, and Adele migrates data pipelines and code to the cloud. These tools reduce developer involvement and speed up data processing, transformation and migration projects.

A clear data strategy is the key to using data effectively. It gives you consolidated information in one place that the whole organization can access, clean data with maximum control over quality and no incorrect information, and a quick, single source of truth, so data is available whenever you need it for decisions.

Master data management creates a single, authoritative version of key business data, such as customer or product details, consistent across systems and applications. Reference data management maintains and synchronizes the reference data that gives context and standardization to other data, so users across the organization share the same definitions when they make decisions.

AGEL brought a Microsoft Fabric data solution into production within six months, with its internal IT team now managing it. CETIN set up Microsoft Purview around its needs, and Raiffeisenbank combined client data from Equa Bank smoothly during the acquisition, with continuous regulatory reporting throughout the whole acquisition and integration project.

Data lineage visualizes and documents how data flows from its origin, through processes and transformations, to its destination. When teams can see where a number comes from and how it was calculated, they trust it more and make more confident decisions. Lineage also supports governance and impact analysis when systems change.