Data Architecture

data architecture

Data Modeling

Data Modeling starts with a conceptual description of all the information that is essential for the business to operate — who is whose client, who pays whom and how, who within the company can see this data, what it’s called, where it’s stored, and how it can be retrieved.
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Creating a unique Corporate Data Model that defines key business entities, their attributes, and relationships—as well as the rules for how this data is created, stored, and used—helps to understand the current business structure and identify options for improving it. It also ensures high-quality integration between systems, enables consistent terminology across business domains, reduces data redundancy, speeds up onboarding for analysts, and supports IT landscape planning.

Data Modeling also helps during M&A or system upgrades—when different systems must be connected without breaking logic or duplicating data. Whether it's choosing between normalized or denormalized structures, star vs. snowflake schemas, or aligning with data lake architecture, proper modeling ensures performance, clarity, and maintainability. We help select the most suitable data storage model and architecture, taking into account the technologies used within the company.

Data Migration Strategy

Let’s say you have a service where client data or other business-critical information is stored. At some point, you realize that the service you're using has become too expensive, or you've outgrown it, and its functionality is no longer sufficient. In most cases, moving away from the current service is quite difficult—you don’t want to lose historical data, many business processes depend on it, and shutting down those processes is highly undesirable, as it can lead to a decline in service quality or even loss of customers. We can help you migrate from one service to another, taking into account the specific constraints and requirements of your business.

Companies most often consider migrating services such as CRMs (e.g., moving from MicrosoftCRM to Salesforce), data warehouses (e.g., from Redshift to Snowflake or BigQuery), analytics platforms, or cloud storage providers. These migrations require careful planning: mapping and transforming legacy data, maintaining data integrity, ensuring minimal downtime, and re-integrating with existing tools and workflows. Our company can assist with developing a step-by-step migration strategy, automating data transfers, validating data accuracy, and ensuring the new system is fully operational with no disruption to business continuity.

Tech-Modernization

How do small companies grow and develop over time? At first, a useful or profitable service is purchased, then another one, and then a CRM system is implemented. The functionality of the CRM becomes insufficient, and a separate service for sending emails is purchased. And then another service. And another…

As a result, after several years of active growth, a company accumulates a large number of different technologies written in various programming languages. Later, when restructuring or changing the business model becomes necessary, it becomes problematic to transfer employees from one function to another — their retraining takes a lot of time, leads to significant costs, and causes dissatisfaction and demotivation in teams.

Many companies still use products like Excel, MS Access, and VBA for reporting — giants that once conquered the analytics world but remained at the level of 20 years ago in terms of quality and speed of analysis. Today, it’s not so easy to find hobbyist VBA specialists, and the younger generation of professionals is unwilling to work with outdated technologies.

We help companies transition smoothly to modern software solutions without failures or data loss, so that moving employees between teams isn’t painful, and the technologies used by the company keep pace with the times.

Data Solutions Cost Optimization

Many companies grow by acquiring small startups with the goal of further developing their potential. However, the technologies behind the “data products” of these acquired startups often turn out to be highly fragmented. To unify their tech stack, companies usually turn to cloud-based solutions tailored for analytics — but then face massive expenses. That’s because the most popular and effective cloud platforms require meticulous configuration and ongoing budget control, which often exceeds the capabilities of in-house staff.
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If you’ve also found yourself thinking that your “data solutions” and analytics are becoming too expensive, we can help identify weak spots and suggest ways to reduce costs without sacrificing quality—whether by optimizing existing processes on current platforms or by exploring alternative approaches that deliver the same results and quality at lower cost.

We have noticed that companies often overspend on cloud storage by keeping cold data in high-performance tiers or running inefficient SQL queries that rack up compute costs. Others might pay for redundant data tools across departments or use overpowered infrastructure for simple tasks. Our specialists can audit your architecture, redesign pipelines to reduce unnecessary processing, recommend cost-effective alternatives (like switching to Snowflake or BigQuery or using managed open-source tools), and implement monitoring tools to track usage and prevent budget overruns. These efforts can lead to savings of tens or even hundreds of thousands of dollars annually while improving operational efficiency.

boost your business with data-driven decisions
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