Introduction - Data is today’s strategic asset for every company
In the digital age, it’s not technology but data that provides a true competitive advantage. Whether you run an e-commerce business, a manufacturing company, a service provider, or a SaaS startup, your organization generates vast amounts of data—including various types of business data, such as transactional and analytical information. The problem is that this data is often scattered, unorganized, and difficult to access.
The cloud is therefore not a “magic box for files.” It is the foundation for building a coherent, scalable, and integrated data ecosystem within a secure and scalable cloud environment, where modern data management and analytics take place. Importantly, cloud solutions enable the storage of large volumes of data in efficient storage systems, which is crucial for handling both structured and unstructured data. In this article, we show why investing in the cloud makes sense from a data perspective and what risks you need to consider before starting migration.
Building a cloud data warehouse
Creating a modern cloud data warehouse is a process that requires a well-thought-out strategy and knowledge of best practices in data analytics. Key stages of this process include:
- Defining Business Requirements – Before starting implementation, define what types of data (e.g., historical data, streaming data, unstructured data) will be collected and analyzed, and what business goals you want to achieve with the data warehouse. This will help choose the right tools and ETL processes.
- Selecting a Cloud Platform – The choice of platform (AWS, Azure, Google Cloud Platform) should consider not only data storage costs but also the availability of advanced analytics features, integration with BI tools, scalability, and data security management capabilities. A cloud data platform enables organizations to ingest, integrate, manage, analyze, and secure data across multi-cloud environments and various data architectures within a single, self-managed portal.
- Designing Data Warehouse Architecture – At this stage, you create the data model, select appropriate data warehouses (e.g., BigQuery, Snowflake), plan data integration from various sources, and configure ETL/ELT processes. Platforms like Snowflake use virtual warehouses—scalable compute resources that can be independently managed to optimize query performance and concurrency. When considering storage, compare the data warehousing capabilities of solutions like Snowflake with traditional database storage options offered by cloud providers such as AWS, which provide a broader range of cloud computing services beyond just data warehousing. It is important that the architecture supports real-time business analytics and enables easy sharing of reliable data across the enterprise.
- Implementation and Configuration – This involves not only launching the cloud data warehouse but also implementing security policies, monitoring data quality, and automating operational reporting. This ensures that data is not only secure but always ready for analysis.
Building a cloud data warehouse opens new possibilities in business intelligence, machine learning, and advanced analytics, allowing you to make intelligent decisions based on reliable data from various source systems. Compared to an on-premises data warehouse, cloud data warehouses offer greater flexibility, scalability, and cost-efficiency, while traditional on-premises solutions often face higher costs, slower scalability, and increased maintenance challenges.
6 Reasons to keep data in the Cloud
1. A single source of truth - no more data silos
A data warehouse (e.g., Google BigQuery, Snowflake) enables combining data from various systems (ERP, CRM, e-commerce, marketing automation, production systems) in one place. A modern enterprise data warehouse aggregates data from different sources, providing a unified and comprehensive view of data for the entire organization. The cloud acts as a flexible environment that allows these warehouses to be easily launched and scaled. Centralizing data facilitates collaboration and analysis across different business units.
This allows you to:
- stop working with inconsistent reports,
- eliminate errors caused by system discrepancies,
- gain a real-time view of your business.
2. Scalable Analytics – available when you need it
Analytical systems don’t run continuously – often they are idle most of the time and then process complex metrics intensively in short periods. Cloud models allow flexible scaling of computing power exactly when needed, which means you only pay for resources used. Monitoring cloud usage is essential to optimize costs and ensure efficient resource consumption. Additionally, the cloud enables real-time streaming data analytics, which is crucial for modern analytical applications.
You can instantly:
- run demand forecasts for thousands of products,
- analyze customer journeys,
- deploy advanced ML/AI models without worrying about infrastructure.
3. Integration of marketing and sales data
A data warehouse allows you to combine campaign data (Google Ads, Meta Ads, LinkedIn) with actual sales results from CRM, e-commerce, or invoicing systems, and to analyze large volumes of customer data to gain insights and improve decision-making. The cloud plays a key role as an environment enabling rapid scaling of such integrations, real-time data availability, and automatic triggering of data processing in response to events (e.g., ad clicks, purchases, cart abandonment). The result?
- you measure real campaign ROI (ROAS vs margin, LTV vs CAC),
- you build customer segmentation based on transaction history,
- you can automate retargeting based on real events, using data continuously available and processed in the cloud.
4. Automation and real-time reporting
In the cloud, you can easily build automatic reports in Looker Studio, Power BI, or Tableau that pull data from multiple sources and update without human intervention. Data collection is the fundamental step in preparing data for analysis and reporting in the cloud, ensuring that information is gathered, aggregated, and ready for further processing. Cloud solutions also support operational reporting, enabling ongoing monitoring and analysis of operational data in real time. Thanks to the ability to scale computing resources in real time, you can create quasi-online analytics solutions that react almost instantly to new data. You gain:
- time savings for teams,
- elimination of “Excel errors,”
- access to up-to-date data 24/7 from anywhere in the world.
5. Data ready for Artificial Intelligence
If you plan to implement AI (e.g., chatbots, customer scoring, churn prediction), the cloud provides infrastructure and ready-to-use services. Using artificial intelligence in the cloud allows process automation and development of advanced data platforms:
- GPU servers and ML models in Google Vertex AI,
- ready tools for language processing, vision, and sound,
- integration with Jupyter, Python, and Data Science tools.
This is a huge step forward, especially when your data grows faster than your team.
Data analysis in the cloud also supports launching new products, enabling fast testing and optimization of business strategies. Cloud-based data warehouses are central to enterprise analytics, supporting data analysis, reporting, and business decision-making by consolidating large volumes of structured and semi-structured data from multiple sources.
6. Security and regulatory compliance
Modern cloud platforms meet numerous standards (ISO/IEC 27001, SOC 2, GDPR), and data is stored in data centers with the highest security levels. Compared to on-premises systems, the cloud offers greater flexibility and scalability in managing data security.
Moreover:
- you control data access (IAM, SSO, audit logs),
- you can encrypt data in transit and at rest, which is a critical security feature for regulatory compliance and ensures robust data protection through automatic data encryption and customer-managed encryption keys,
- you configure backups and retention policies automatically.
Data usage policies play a key role in controlling access and permissions for data across the cloud platform, supporting centralized governance and security.
5 Challenges to consider
1. Data storage and processing costs
The cloud is not “free.” Without controlling usage and optimizing data warehouse queries, bills can grow. You need:
- cost limits,
- monitoring of SQL queries and ETL pipelines,
- thoughtful data architecture (e.g., table partitioning),
- restrictions on access to sensitive data (e.g., roles, access levels, permission control, and audit logs).
2. Lack of data and cloud competencies
Building cloud data infrastructure requires specialists: data engineers, DevOps, cloud architects. Without a team, you risk delays, errors, and low data quality.
Proper training of users who will use cloud solutions is key to effective technology utilization.
Solutions include:
- outsourcing,
- team training,
- implementing data platforms with a technology partner.
3. Complexity of system integration
Connecting data from ERP, CRM, e-commerce, marketing automation, GA4 is not just “Export to Excel.” You must build and maintain ETL/ELT processes. Data integration often includes both unstructured and structured data that require standardization and transformation. Although integration is demanding, the cloud significantly simplifies it by offering ready services for data flow management (such as Dataflow, Glue, or Azure Data Factory), flexible job triggering based on events, and easy scaling of workloads. This allows you to respond faster to business needs without worrying about infrastructure.
Therefore, it’s worth planning from the start:
- a data map and dependencies,
- transformations and standardization,
- data quality monitoring.
4. Risk of chaos without a good data strategy
The cloud provides flexible and powerful tools for storing and processing data but does not impose any logical structure or order. Without a clearly defined data strategy (Data Governance), teams may create competing data sources, and decisions may be based on inconsistent reports. Lack of a coherent data strategy makes reliable business analysis and accurate decision-making difficult because business analysis requires trustworthy, consistent data and unified reports. Lack of control over data quality, naming, or access can quickly lead to chaos – only this time at the cloud level, not on a local server.
The cloud alone does not solve poor data quality problems. Without a data governance strategy, even the best infrastructure won’t help. You need:
- clearly defined data sources and owners,
- rules for data cleansing, validation, and standardization,
- consistent naming and data cataloging (e.g., with a Data Catalog).
5. Regulatory and legal requirements
For personal, financial, or medical data, you must ensure compliance with GDPR, HIPAA, DORA, and other regulations. This concerns, among others:
- data location (e.g., storage in the EU),
- data anonymization and retention,
- backup policies and auditability.
Who Should Treat the Cloud as a Foundation for Data Strategy?
The cloud works great for:
- companies that want to make decisions based on integrated, current data instead of gut feeling or incomplete reports,
- marketing and sales teams that want to combine campaign data with actual business results (e.g., sales, margin, retention),
- analytics departments that need scalable BI and ML tools,
- startups creating SaaS products based on user data.
Summary: Is the cloud right for you?
If you want to:
- integrate and organize data in one environment,
- improve analytics and reporting processes,
- use AI/ML (including generative AI) in business,
- make decisions faster and more accurately,
…then investing in the cloud with data in mind is not only justified but necessary. A modern cloud data warehouse is the foundation of an effective analytics strategy and supports business growth.
Don’t know where to start your data transformation in the cloud?
Contact us, and we will help you build a modern data architecture that truly supports your business.
