Integrations make up 80% of the work involved in building a data warehouse. As companies are constantly evolving—launching new products, adopting new services, or even acquiring other companies—data integration work never really stops. On the contrary, the volume of data keeps growing, sources become increasingly diverse, and the data model grows more complex.

For example, companies often need to integrate data from marketing platforms (like Google Ads or Meta), CRM systems (like Salesforce or Zoho CRM), product analytics tools, and internal databases. Sales data might need to be refreshed daily, while user behavior or system logs could be updated hourly or in near real-time. Regular, automated integration ensures that teams always work with accurate and up-to-date information for decision-making. 

In most cases, data storage is understood as the “hardware” used to retain information, and usually, the choice is between the classic on-premise approach and cloud solutions. Since cloud storage is relatively inexpensive compared to computing power, it's often overlooked. That is, until the storage bill exceeds the budget several times over. To avoid overspending on something as seemingly simple as data storage, a lot of factors must be considered:

- How to organize the catalog so that unnecessary data isn’t stored
- What storage format is the most efficient
- How to implement versioning for stored data
- How to address data security and access control
- How to comply with regulations concerning data collection, storage, and intended use.

If you are uncontrollably storing things like large raw datasets that no one is currently using, multiple redundant backups, old model outputs, interim processing files, or uncompressed logs, there’s a high chance you’re incurring thousands of dollars in unnecessary monthly expenses. Efficiently managing formats (like using Parquet instead of CSV), setting up lifecycle policies, and tagging data based on usage can significantly reduce storage costs without compromising accessibility or availability.

Data transformation and cleansing is one of the stages of converting raw data into information ready for analysis, sharing with partners, selling, or transforming into various products. Before creating reports and visualizations, it is necessary to standardize the data, organize it, and remove errors (anomalies) that can significantly distort results and lead to incorrect data interpretation and wrong strategic decisions in the company.Properly selected tools and approaches can significantly reduce labor costs for transformations, make them transparent for the data owner, and effective for further use, as well as ensure lineage and coverage with metadata and tests. For this, we use tools such as:

- Cloud-based ETL / ELT solutions
- Data Build Tool, which has proven its efficiency and is widely popular in the data field
- Orchestration tools – modern orchestrators that allow control over the transformation process
- Specialized solutions for specific cases – for example, performing real-time transformations.

Modern organizations are very complex systems in which data is not stored in a single monolith but rather spread across multiple subsystems, often leading to data duplication or even repeated usage. If we imagine a very simplified model of an organization, it would most likely include the following systems:
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- Human Resource Management System
- Customer Relationship Management System
- Enterprise Resource Planning System
- Payroll System
- Accounting System
- Data Warehouse
- Payment hubs
- Websites / Landing pages
- A large number of specialized systems performing specific enterprise functions.

Proper data management requires identifying systems known as “sources of truth,” (SoT) because data in different systems can sometimes be misaligned, overwritten, lost, or end up contradicting one another. Choosing the wrong data source may lead to distorted reporting, inaccurate analytics, and poor decision-making.

We will help analyze your existing systems, determine which ones should be considered sources of truth, and minimize data flows from those systems that cannot guarantee data quality or distort it during processing. This way, you will achieve transparency in data flows, assign responsible data managers, and ensure that the “sources of truth” are properly maintained and consistently accumulate new data accurately and on time.