Data Quality
Management Services
We eliminate errors and chaos in your data, allowing you to make faster, more accurate business decisions.
Let’s talk
We empower leaders:
Low data quality means
bad decisions!
Our services help increase the reliability of company data. Data cleansing plays a crucial role in ensuring data quality by standardizing and correcting data within various systems and processes.
Greater report and analysis accuracy
Knowing more allows you to make better decisions. Errors made during data entry can significantly impact report accuracy. Correct, consistent, and up-to-date data means greater reporting precision and improved quality of insights and predictions.
Time and resource savings
With always accurate data, you don’t need to manually verify its correctness or search for and correct errors. This helps your team save time, increase work efficiency, and achieve better results.
Regulatory compliance
Meeting regulatory and legal requirements, such as GDPR, KNF, or AML, depends on high-quality data. Establishing robust data governance mechanisms is crucial for managing and ensuring the quality of data across various platforms, which supports regulatory compliance.
This ensures compliance with regulations and minimizes the risk of fines and reputational damage.
Reduced risk of incorrect decisions
Analyzing discrepancies between systems, reconciling differences, and establishing the actual picture is key to reducing the risk of errors and increasing confidence in decision-making.
By aligning this process with business processes, we ensure data integrity supports operational efficiency and strategic goals.
Quality data is the foundation of being Data Driven
Let’s talkFrom needs analysis to reliable data
Step by step, we eliminate inconsistencies to provide you with high-quality data that supports your operational activities.
Knowledge and experience at every stage:
Diagnose primary data quality issues
Our experts identify areas that require improvement and determine which data is crucial for achieving business goals. Data profiling is a method we use to analyze and validate data, ensuring its quality and compliance with business rules.
We verify the accuracy of financial data in the systems by comparing it with the sources of truth.
Analyze the root cause of the problem
We search for the sources of identified data quality issues. We consider potential programming errors, configuration inaccuracies, problems in data integration processes, and inconsistencies between data sources.
Create an effective data cleansing and quality improvement plan
We develop a detailed corrective plan, precisely defining actions and timelines. Then, we implement the proposed solutions, including modeling information needed for direct acquisition, ensuring the data picture is as close to reality as possible.
Ensure continuous quality control
We introduce monitoring of key data quality indicators, including their accuracy, completeness, and timeliness. This is crucial to ensure adherence to business rules and data integrity during data profiling and ETL processes.
We regularly report results and continuously verify any deviations, ensuring data quality rules and control rules remain up-to-date.
Maintain new data management standards
We ensure efficient operations using the new data standard. Our experts support users in their daily work and help reinforce new, improved practices in data management.
Discover our clients’ success stories
The company increased conversion by 25% thanks to customer journey analytics
We mapped the user journey, identified critical drop-off points, and implemented a modern analytics stack (GA4, BigQuery, dbt, Tableau). This gave the client full visibility into their data and the ability to respond immediately.
The result? Up to 25% higher conversion, faster decision-making, and increased revenue without increasing the marketing budget.
We built a scalable BI environment for PŚO with daily operational reporting
We helped Polskie Światłowody Otwarte integrate data from multiple systems by building a multi-layered data model in Dataform and Google BigQuery. Based on this, we created a daily updated operational report in Power BI.
The result? A centralized data source, faster management decision-making, elimination of manual reporting, and full transparency of metrics for the Commercial Department.
We helped Celsium balance its heating data and save PLN 180,000 per year thanks to cloud analytics
We integrated data from meters, SCADA systems, billing systems, and weather systems into a single environment on Google Cloud Platform. We built a data warehouse, automated ETL processes, and dashboards in Tableau, enabling hourly heat consumption balancing and faster operational decisions.
The result? Four times lower analytical system maintenance costs, meter failure detection within one day, dynamic demand forecasts, and over PLN 180,000 in annual savings.
BoredPanda increased retention and campaign effectiveness with push analytics
BoredPanda automated reporting by integrating data from Firebase, Singular, and GCP. The implementation of push campaign analytics made it possible to measure the real impact of notifications on user behavior.
The result? The company improved retention, increased engagement, and manages its advertising budget more effectively based on reliable data.
We helped AMS build a data platform that increased advertising effectiveness and maintained its leading position
We integrated data from Proxi.cloud, CitiesAI, and AMS internal systems into a new BigQuery-based data warehouse. We created a data model, ELT processes, and dashboards that enable near real-time campaign performance analysis.
The result? Precise ad targeting, programmatic buying based on demographic and behavioral data, faster decisions, and a higher return on investment for the agency’s clients.
Your data holds great potential. Ask us how to make the most of it
Why should choose Alterdata?
We combine expert experience, extensive technical knowledge, and a flexible approach to collaboration to create data solutions that are truly tailored to your organization’s needs.
Comprehensive End-to-End Implementation
We manage the entire process: from consulting and technology selection, through data warehouse construction, to the development, maintenance, and optimization of solutions. This ensures that our clients receive consistent support at every stage of their data-related work, without having to coordinate multiple independent vendors.
Data Expert Team
We bring together the expertise of data engineers, analysts, data scientists, IT architects, and business consultants to address both technological and business needs. Our team helps translate an organization’s goals into concrete solutions that effectively support decision-making and business growth.
Technology Neutrality
We choose tools based on the goal, not the other way around. We work with popular cloud and analytics technologies, including Google Cloud, Azure, AWS, Snowflake, Databricks, Power BI, Tableau, and Looker. Thanks to our extensive knowledge of these tools, we recommend the solutions best suited to the client’s situation, rather than pushing a single technology.
Flexible Model of Collaboration
We offer support exactly when you need it, ranging from individual specialists to a Data Team as a Service model, without the need to build a full in-house team. This allows you to quickly expand your organization’s capabilities and leverage expert knowledge in a way that aligns with your current needs.
Business-Specific Solutions
We design services and architecture tailored to specific requirements, budgets, industries, company sizes, and business objectives. We treat each implementation as a unique case to ensure that the technology supports the processes, workflows, and priorities of the organization in question.
Secure Architecture
We create scalable, secure solutions designed to support organizational growth, handle increasing data volumes, and facilitate migration to modern cloud environments. We ensure access control, stability, and scalability so that the data platform can grow alongside your business.
Tech stack: the foundation of
our work
Discover the tools and technologies that power the solutions created by Alterdata.
Google Cloud Storage enables data storage in the cloud and provides high performance, offering flexible management of large datasets. It ensures easy data access and supports advanced analytics.
Azure Data Lake Storage is a service for storing and analyzing structured and unstructured data in the cloud, created by Microsoft. Data Lake Storage is scalable and supports various data formats.
Amazon S3 is a cloud service for securely storing data with virtually unlimited scalability. It is efficient, ensures consistency, and provides easy access to data.
Databricks is a cloud-based analytics platform that combines data engineering, data analysis, machine learning, and predictive models. It processes large datasets with high efficiency.
Microsoft Fabric is an integrated analytics environment that combines various tools such as Power BI, Data Factory, and Synapse. The platform supports the entire data lifecycle, including integration, processing, analysis, and visualization of results.
Google BigLake is a service that combines the features of both data warehouses and data lakes, making it easier to manage data in various formats and locations. It also allows processing large datasets without the need to move them between systems.
Google Cloud Dataflow is a data processing service based on Apache Beam. It supports distributed data processing in real-time and advanced analytics.
Azure Data Factory is a cloud-based data integration service that automates data flows and orchestrates processing tasks. It enables seamless integration of data from both cloud and on-premises sources for processing within a single environment.
Apache Kafka processes real-time data streams and supports the management of large volumes of data from various sources. It enables the analysis of events immediately after they occur.
Pub/Sub is used for messaging between applications, real-time data stream processing, analysis, and message queue creation. It integrates well with microservices and event-driven architectures (EDA).
Google Cloud Run supports containerized applications in a scalable and automated way, optimizing costs and resources. It allows flexible and efficient management of cloud applications, reducing the workload.
Azure Functions is another serverless solution that runs code in response to events, eliminating the need for server management. Its other advantages include the ability to automate processes and integrate various services.
AWS Lambda is an event-driven, serverless Function as a Service (FaaS) that enables automatic execution of code in response to events. It allows running applications without server infrastructure.
Azure App Service is a cloud platform used for running web and mobile applications. It offers automatic resource scaling and integration with DevOps tools (e.g., GitHub, Azure DevOps).
Snowflake is a platform that enables the storage, processing, and analysis of large datasets in the cloud. It is easily scalable, efficient, and ensures consistency as well as easy access to data.
Amazon Redshift is a cloud data warehouse that enables fast processing and analysis of large datasets. Redshift also offers the creation of complex analyses and real-time data reporting.
BigQuery is a scalable data analysis platform from Google Cloud. It enables fast processing of large datasets, analytics, and advanced reporting. It simplifies data access through integration with various data sources.
Azure Synapse Analytics is a platform that combines data warehousing, big data processing, and real-time analytics. It enables complex analyses on large volumes of data.
Data Build Tool simplifies data transformation and modeling directly in databases. It allows creating complex structures, automating processes, and managing data models in SQL.
Dataform is part of the Google Cloud Platform, automating data transformation in BigQuery using SQL query language. It supports serverless data stream orchestration and enables collaborative work with data.
Pandas is a data structure and analytical tool library in Python. It is useful for data manipulation and analysis. Pandas is used particularly in statistics and machine learning.
PySpark is an API for Apache Spark that allows processing large amounts of data in a distributed environment, in real-time. This tool is easy to use and versatile in its functionality.
Looker Studio is a tool used for exploring and advanced data visualization from various sources, in the form of clear reports, charts, and interactive dashboards. It facilitates data sharing and supports simultaneous collaboration among multiple users, without the need for coding.
Tableau, an application from Salesforce, is a versatile tool for data analysis and visualization, ideal for those seeking intuitive solutions. It is valued for its visualizations of spatial and geographical data, quick trend identification, and data analysis accuracy.
Power BI, Microsoft’s Business Intelligence platform, efficiently transforms large volumes of data into clear, interactive dashboards and accessible reports. It easily integrates with various data sources and monitors KPIs in real-time.
Looker is a cloud-based Business Intelligence and data analytics platform that enables data exploration, sharing, and visualization while supporting decision-making processes. Looker also leverages machine learning to automate processes and generate predictions.
Terraform is an open-source tool that allows for infrastructure management as code, as well as the automatic creation and updating of cloud resources. It supports efficient infrastructure control, minimizes the risk of errors, and ensures transparency and repeatability of processes.
GCP Workflows automates workflows in the cloud and simplifies the management of processes connecting Google Cloud services. This tool saves time by avoiding the duplication of tasks, improves work quality by eliminating errors, and enables efficient resource management.
Apache Airflow manages workflows, enabling scheduling, monitoring, and automation of ETL processes and other analytical tasks. It also provides access to the status of completed and ongoing tasks, as well as insights into their execution logs.
Rundeck is an open-source automation tool that enables scheduling, managing, and executing tasks on servers. It allows for quick response to events and supports the optimization of administrative tasks.
Python is a programming language, also used for machine learning, with libraries dedicated to machine learning (e.g., TensorFlow and scikit-learn). It is used for creating and testing machine learning models.
BigQuery ML allows the creation of machine learning models directly within Google’s data warehouse using only SQL. It provides a fast time-to-market, is cost-effective, and enables rapid iterative work.
R is a programming language primarily used for statistical calculations, data analysis, and visualization, but it also has modules for training and testing machine learning models. It enables rapid prototyping and deployment of machine learning.
Vertex AI is used for deploying, testing, and managing machine learning models. It also includes pre-built models prepared and trained by Google, such as Gemini. Vertex AI also supports custom models from TensorFlow, PyTorch, and other popular frameworks.
FAQ
How will I measure the effects of collaborating with Alterdata?
You can measure the results of our collaboration using key performance indicators (KPIs) that we will define together at the start of the project. Additionally, regular data quality audits and automated monitoring will enable you to continuously assess the effectiveness of the solutions we implement.
Why can an Alterdata analyst manage data quality processes more effectively than an internal team?
Our analysts have extensive experience across various industries and challenges, enabling them to transfer proven solutions between sectors. This allows them to quickly understand the specifics of your business and effectively ensure data quality, tailoring methods to the unique needs of your organization.
Does an external analyst or data engineer have access to all the information in our company?
We ensure complete data security. Access to information is strictly controlled, and our experts only have access to the data necessary to carry out the project, in accordance with the highest protection standards. We do not extract data; it is stored exclusively on the client’s side.