Transform Your Business with Neontri’s Comprehensive Data Management Services

Maximize the value of your data and optimize your business operations.

Partnerships and certifications

Data is a valuable asset for any business, but managing it can be a daunting task. Neontri’s data management services provide a comprehensive solution to help you collect, store, process, and analyze data, giving you the insights you need to make informed decisions and drive business growth.

Our clients

We work for industry leaders and challengers. Our aim is to build long-lasting client relationships and become their vendor of choice. 100% client retention rate in 2022 validates our success.

Our data management expertise

NRT (near real-time) data analytics

With NRT (near real-time) data analytics, you can react swiftly to changing conditions, identify new opportunities, and stay ahead of the competition. Neontri specializes in NRT (near real-time) data analytics, empowering organizations to unlock the true value of their data as it streams in.

Data-driven decision-making

With NRT (near real-time) data analytics, you can react swiftly to changing conditions, identify new opportunities, and stay ahead of the competition. Neontri specializes in NRT (near real-time) data analytics, empowering organizations to unlock the true value of their data as it streams in.

Dataflow, streaming and batch data processing

Streaming and batch data processing is an essential part of managing your data. It involves moving and transforming data from one place to another, enabling you to access and analyze it in real-time.

Whether you need to process data in real-time, batch mode, or a combination of both, let us lend you a hand! Our solutions are designed to be scalable and reliable, ensuring that your data processing workflow operates smoothly and efficiently.

Data lake, data warehouse, or data lakehouse?

Managing and organizing data is an essential part of running a successful enterprise. That’s why we offer data management services that help you store and process your data in the most efficient and scalable way possible.

A data lake is a storage repository that can store structured and unstructured data at any scale. A data warehouse, on the other hand, is a system that stores and manages structured data from one or more sources. And a data lakehouse combines the best features of both data lakes and data warehouses, enabling organizations to store and analyze data from multiple sources in real-time.

Neontri can help you understand the differences between data lakes, data warehouses, and data lakehouses. Next, we can assist you with figuring out which one would be best suited for your needs.

Your pain points, our solutions


Lack of visibility into business operations

Many businesses struggle to gain a comprehensive view of their operations due to data silos and scattered information


Data security and compliance

With increasing data privacy regulations and cyber threats, it’s critical to ensure your data is secure and compliant.


Data quality and accuracy

Poor data quality can lead to incorrect insights and decisions, ultimately impacting your business operations and profitability.


Scalability and performance

As your business grows, managing large volumes of data can become a challenge, leading to performance issues and increased costs.


Limited internal resources

Many businesses may not have the necessary expertise or resources to manage data effectively.

Examples of our data management projects

Third-party integration
Data Integration

PSD2 Banking Hub

PSD2, a formidable challenge for banks and third-party payment providers, necessitated swift implementation and substantial investments in infrastructure. The creation of a cutting-edge PSD2 hub, developed in collaboration with KIR, seamlessly connected more than 300 Polish banks with third-party providers. The result? A game-changing platform that enabled timely compliance, fostered secure data exchange, and fueled innovation in the digital banking landscape.

Big data

The Alnova Archive, an offloading data system for PKO Bank Polski

Why people love Neontri

Neontri stood out with their commitment, technical competence, and understanding of KIR’s business needs.

Robert Trętowski
Vice President

A solution co-developed with Neontri has reduced the number of queries directed to the central system, resulting in lower infrastructure costs and customer service time.

Piotr Durakiewicz
Team Leader – Department of Electronic Banking Applications

The IT specialists they provided possessed the necessary experience and required competencies. We recommend Neontri as a trustworthy and reliable business partner.

Wojciech Moraczewski
Senior Manager

FAQ Data Management Expertise

Data management expertise refers to the skills and knowledge required to handle, organize, and govern data effectively. It involves the practices and methodologies used to collect, store, process, and secure data, ensuring its quality, accuracy, accessibility, and compliance with regulations.

Examples of data management include database administration, data cleansing, data warehousing, master data management, data integration, data governance, and data security. These practices ensure that data is properly collected, stored, and maintained, ready for analysis or other uses.

A data management specialist oversees data quality, integrity, and security. Tasks include creating and enforcing data policies, managing databases, performing data cleansing, ensuring data compliance with regulations, coordinating data integration, and working on data backup and recovery.

Data management focuses on handling and organizing data, including storage, governance, and quality control. Data analytics, on the other hand, involves analyzing data to derive insights, trends, or patterns. While data management ensures the availability and quality of data, analytics leverages this data for decision-making.

Key areas of data management include data governance, data quality, data integration, data security, data warehousing, metadata management, and master data management. These areas encompass the various practices needed to manage data effectively.


The 4 types of data management typically refer to:

    • Database Management: The use of databases to store and retrieve data.
    • Data Governance: Setting policies and standards for data quality and compliance.
    • Data Integration: Combining data from different sources for a unified view.
    • Data Security: Protecting data from unauthorized access or breaches.

Together, these aspects form a comprehensive approach to handling an organization’s data needs.

Marcin Dobosz
Director of Technology

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