How Data Gains Value in the Cloud and What to Watch Out for During Migration

Data is your most strategic asset – but only if you know how to use it

In today’s digital era, it’s not technology but data that provides real competitive advantage. Whether you run an e-commerce store, a manufacturing company, a service-based business, or a SaaS startup – your organization generates vast amounts of data.
The problem? It’s often scattered, disorganized, and hard to access.

The cloud is not just a "magic box for storing files". It’s the foundation for building a cohesive, scalable, and integrated data ecosystem that turns information into actionable decisions.

In this article, we explore why cloud investment makes sense from a data perspective, and what risks you need to manage before making the move.

6 Reasons Why Data Works Better in the Cloud

1. A Single Source of Truth – No More Data Silos

A data warehouse (e.g. Google BigQuery, Snowflake) enables you to integrate data from various systems (ERP, CRM, e-commerce, marketing automation, production) into one place.
The cloud provides the flexible infrastructure needed to run and scale such warehouses with ease.

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 – When You Need It, Not Always-On

Analytical systems don’t run 24/7. Most of the time they’re idle, and then suddenly compute complex metrics in bursts.
Cloud models allow you to scale computing power on demand, meaning you only pay for what you use.

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 enables you to merge campaign data (Google Ads, Meta Ads, LinkedIn) with actual sales results from CRM, e-commerce, or invoicing systems.
The cloud plays a key role here – enabling fast scaling of such integrations, real-time data access, and automated processing triggered by events (e.g. ad clicks, purchases, abandoned carts).

As a result, you can:

• measure true campaign performance (ROAS vs. margin, LTV vs. CAC),
• build customer segments based on transaction history,
• automate retargeting based on real-time event data processed continuously in the cloud.
  

4. Real-Time Reporting and Automation

With the cloud, you can easily build automated reports in Looker Studio, Power BI, or Tableau that pull data from various sources and update without manual input.
Thanks to real-time resource scalability, you can create quasi-real-time analytical solutions that react instantly to new data.

You get:

• time savings for your 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're planning to implement AI (e.g. chatbots, customer scoring, churn prediction), the cloud provides a ready-to-use infrastructure:

• GPU servers and ML models in Google Vertex AI,
• tools for language, vision, and speech processing,
• integration with Jupyter, Python, and Data Science environments.
  

This is a huge step forward – especially if your data grows faster than your team.

6. Security and Regulatory Compliance

Modern cloud platforms meet international standards (ISO/IEC 27001, SOC 2, GDPR), and store data in highly secure data centers.

Additionally, you get:

• automatic backup and retention policies.
• access control (IAM, SSO, audit logs),
• encryption of data in transit and at rest,

5 Challenges You Need to Be Aware Of

1. Data Storage and Processing Costs

The cloud isn’t “free”. If you don’t monitor usage and optimize your queries, costs can escalate quickly.

You’ll need:

• cost limits and budgeting tools,
• SQL query and pipeline monitoring,
• well-designed data architecture (e.g. table partitioning),
• access control for sensitive data (roles, permissions, audit logs).
  

2. Lack of Skills in Cloud and Data Management

Building a cloud-based data infrastructure requires experts: data engineers, DevOps, and cloud architects.
Without them, expect delays, errors, and poor data quality.

You can address this by:

• outsourcing,
• training your team,
• partnering with experienced cloud and data specialists.
  

3. System Integration Complexity

Connecting ERP, CRM, e-commerce, GA4, or marketing automation tools isn’t just “Export to Excel”. You need to design and maintain robust ETL/ELT processes.
The cloud simplifies this significantly with tools like Dataflow, AWS Glue, or Azure Data Factory, allowing for event-driven execution and effortless scaling.

Be sure to plan:

• data flow maps and dependencies,
• data transformation and standardization,
• data quality monitoring.
  

4. Risk of Chaos Without a Data Strategy

The cloud offers powerful tools for data storage and processing, but it doesn’t impose order or logic by itself.
Without a clear data governance strategy, teams may create conflicting data sources and rely on inconsistent reports.
The result? Chaos in the cloud instead of in your legacy infrastructure.

You’ll need:

• defined data owners and sources,
• data cleansing, validation, and standardization rules,
• consistent naming conventions and data cataloging (e.g. via Data Catalog).
  

5. Regulatory and Legal Requirements

If you process personal, financial, or medical data, compliance with regulations like GDPR, HIPAA, DORA is a must.

This includes:

• backups and audit mechanisms.
• data residency (e.g. within the EU),
• data anonymization and retention policies,

Who Should Treat the Cloud as a Foundation for Data Strategy?

• Companies that want to make decisions based on integrated, up-to-date data, not gut feeling
• Marketing and sales teams that want to connect campaigns with real business outcomes (e.g. revenue, margin, retention)
• Analytics departments needing scalable BI and ML tools

Startups building SaaS products based on user datanty.

Summary: Is the Cloud Right for You?

If you want to:

• consolidate and structure data in one environment,
• streamline analytics and reporting,
• leverage AI/ML (including generative AI) in your business,
• make decisions faster and with confidence,
  

...then investing in cloud – with a data-first approach – is not just reasonable, but essential.Not sure where to begin your cloud data transformation?

Let’s talk - we’ll help you design a modern data architecture that truly supports your business.