Data-driven solutions for Digital Natives companies
Our analytics means: better budget management, increased profitability and scalable data infrastructure
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We empower leaders:
Change the data,
into greater return and faster growth
Maximize profits with data – Intuitive dashboards show ROI, ROAS, LTV and trends, helping you optimally allocate budget, scale your business and grow your product on facts, not intuition
Data infrastructure that grows with your business – a flexible architecture provides consistent data, eliminates chaos and grows with your business, ensuring readiness for future needs and growth
Gain an edge with AI and ML – optimize budgets, automate processes, personalize offers and detect anomalies to scale business faster and make better decisions
Use data to grow your business
Free consultationWe have proven solutions
for your company’s challenges
Data platform ready for future growth
Data platform ready for future growth
We build state-of-the-art data platforms that support your business from day one, providing a solid foundation for dynamic growth.
With a well-thought-out architecture, you will avoid technology debt and costly refactorings, and your infrastructure will be ready for rapid scaling – from MVPs to millions of users.
Behavioral analytics to support decisions
Behavioral analytics to support decisions
Harness the power of data to better understand users and deliver personalized experiences. Create analytics solutions that allow you to segment, personalize and predict a user’s next action (Next Best Action).
This allows your platform to dynamically adapt to individual needs, increasing engagement and conversion.
We deliver the knowledge you need
We deliver the knowledge you need
A lack of core competencies should not hinder your business growth. Our team of experts fills in the gaps in analytics, data engineering or AI, giving you immediate access to strategic skills.
At the same time, we help you build these competencies internally so that your organization can grow independently and scale its capabilities for the future.
Data model to support growth dynamics
Data model to support growth dynamics
We design data models that keep up with the pace of your business. Structures based on events, user properties, and time series allow for fast analysis, accurate forecasting, and efficient scaling.
You gain flexibility that supports dynamic data work-from the first event to millions of operations per second.
GenAI in HR recruitment processes
GenAI in HR recruitment processes
Faster candidate selection saves the team time.
Our solution automatically processes resumes in various formats, catching key information – such as skills, experience or location and assigns them to the appropriate categories.
This prevents the HR team from wasting time manually reviewing documents.
Our knowledge
in theory and practice
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
1. Who are the digital natives Alterdata works with?
Digital natives are companies built in a digital-first environment—including SaaS, online platforms, marketplaces, fintech, edtech, and subscription-based businesses. These organizations generate large volumes of data and rely on data-driven decision-making.
2. What challenges do digital native companies bring to Alterdata?
The most common challenges include fragmented data, inconsistent metrics, manual reporting, scaling analytics infrastructure, and lack of clear insights for leadership teams. Growth often outpaces data maturity.
3. What types of data does Alterdata integrate for digital natives?
We integrate product, marketing, and financial data from analytics tools, advertising platforms, CRM systems, billing platforms, applications, data warehouses, and product analytics tools, creating a single source of truth.
4. How does Alterdata support scaling digital native businesses?
We design scalable data architectures, automate reporting, and build BI dashboards that grow with the business. This enables teams to test faster, optimize continuously, and make decisions based on reliable data.
5. Does Alterdata support founders and executive teams?
Yes. We deliver executive and operational dashboards that consolidate key KPIs into one clear view, removing conflicting reports and supporting faster strategic decision-making.
6. Are Alterdata solutions suitable for fast-growing digital companies?
Absolutely. We work with companies at different growth stages—from scale-ups to mature digital organizations—tailoring solutions to growth speed, data complexity, and business goals.