Most industrial companies still operate on systems created 10 years ago. These include accounting programs, outdated databases, or custom applications without APIs. Of course, such systems still work, but they require more money and time to maintain. At the same time, they hinder the scaling and integration of analytical models, slowing down business development. Therefore, database modernization is not a trend but a strategic step.
One of the most effective ways to modernize IT infrastructure is to combine it with a real-time data warehouse. This allows you to consolidate information, eliminate duplication, and move from chaotic reporting to real analytics. But the process is not limited to technical upgrades — it requires a change in approach to information management. Experts from company Cobit Solutions tell us more about what the step-by-step path to modernization looks like and seven common myths that prevent this transition.
Myth 1. If the system works, don’t touch it
This myth originated in the 2000s, when corporate IT systems were built on the principle of “if it works, don’t touch it.” Back then, the main goal was to ensure uninterrupted operations, rather than flexibility or data integration.
Companies invested large budgets in implementing ERP, CRM, and accounting software, so they considered them “long-term assets” rather than tools that needed constant updating. IT infrastructure was seen as an expense rather than a driver of growth, so any intervention seemed risky. When analytics, automation, and AI became a competitive advantage, these systems proved to be too restrictive.
What do we get now?
At first glance, your system may appear stable: reports are generated, accounting functions, data is stored somewhere. What do we do? Of course, we postpone the audit of our information systems until later. However, behind this “stability” lie many problems. Among them: fragmented databases, duplicate records, manual updates, and incompatible software versions.
To overcome these challenges, the company must take a systematic approach:
- Change its approach to IT assets. Start viewing information systems not as an expense, but as infrastructure that generates profit. Data must be accessible, integrated, and suitable for analytics — it is a new asset, not a burden.
- Order a technical audit. Before making any changes, you need to understand what exactly is outdated. Data warehouse consultants will help identify duplicates, scattered sources, slow processes, as well as outdated formats and systems without backup.
- Identify critical nodes. These are the systems that have the greatest impact on business operations: accounting, warehousing, production, and analytics. They are modernized first — either integrated with the Data Warehouse or gradually replaced.
It often becomes clear at this stage that the data cannot be used for analytics without prior cleaning. Such an audit is the first step in modernization. It shows how effective the working system is and whether it can continue to operate without a centralized repository.
Myth 2. Responsibility for data modernization lies solely with the IT department
Most managers consider data modernization to be a technical task, because historically, the IT department has been responsible for maintaining databases, updating servers, and implementing new software. In a corporate structure, this is perceived as an area of “technological competence,” while business units focus on finance, production, or sales.
In addition, most companies face a situation where the modernization initiative comes from IT — for example, due to a lack of capacity or integration issues. This leads management to believe that the technical team not only executes, but also determines the goals of change.
But what is the reality?
In reality, data modernization is a shared responsibility. The IT department provides the technological implementation, but only management and functional departments can determine which data has business value. They are the ones who understand which indicators affect profitability, efficiency, or risks.
Without this, the analytical system will remain an “empty shell”: it will collect data but will not answer questions that are important to the business. Therefore, in practice, financial analysts, operations managers, department heads—everyone who makes decisions based on data—are involved in the modernization process.
If the company lacks expertise, it is worth bringing in outside specialists. They help structure the process and avoid typical technical mistakes. In other words, external consultants are technical partners who work within the vision formed by a joint internal group.
However, there are cases when a company does not have its own analytical culture or clear data structure. In such cases, an external team (if it has certain experience and expertise) takes on the role of a facilitator. Such data warehouse consultants interview department heads and study what metrics they use, how they make decisions, and where data is lost or duplicated. Experts then help build a process of interaction between business and IT, explaining how to turn data into a management tool.
Myth 3. Only large corporations need a data warehouse
This myth is widespread due to confusion between the concepts of “data storage” and “large corporate platform.” Many believe that a data warehouse is too expensive, complex, and requires a separate IT department to support it. This perception was formed in the early 2000s, when companies built data warehouses in their own data centers. At that time, it was necessary to purchase servers, licenses, storage systems, and maintain a staff of administrators. This was too expensive for small businesses, so the technology seemed accessible only to large corporations.
What is the situation now?
Today, the situation is different. Cloud technologies allow you to launch a warehouse even for a single team or individual project, since the system is deployed in a remote environment provided by a supplier, such as Azure or Google Cloud. In other words, the warehouse is deployed on the supplier’s platform, and data from corporate systems is connected via ready-made connectors. This simplifies the launch and makes the technology accessible even to small companies.
They can start with a minimum number of data sources and gradually scale the system. Therefore, Data Warehouse is no longer a luxury for large corporations and has become a practical tool for any business that works with data.
Myth 4. Data migration always involves the risk of loss and downtime
In the 1990s and 2000s, database migration was a complex and risky process. Data was copied manually or in large batches, often without thorough verification. The system had to be shut down during the transfer, and any mistake could result in the loss of records or damage to the integrity of the database. As a result, companies approached migration with caution and postponed updates for years.
Modern approach
Today, the approach is different. Migration is performed in stages, with data streams duplicated and each step verified. Modern tools — Change Data Capture (CDC), ETL/ELT platforms (Azure Data Factory, Airflow, Talend, Databricks) — allow you to transfer only changed data instead of the entire volume. This allows you to update the repository in real time without stopping the old system.
In addition, before launch, good specialists create test environments where they check the logic, structure, and quality of data. After a successful test, the system gradually transitions to productive mode. This approach reduces risks and makes migration a manageable, predictable process.
How to start migration
Once a company has defined its business goals, formed a team, and understood what data it really needs, it can move on to planning the migration. Start with a data map — a document that describes all sources, formats, volumes, and update methods. This allows you to assess which systems can be integrated directly and which need to be cleaned up or transformed.
Next, the team chooses a migration approach. For large volumes, they use a phased approach — first testing individual blocks, then expanding coverage. For critical data, they set up replication so that the old and new environments work in parallel. Once the flow has stabilized, the old system can be gradually decommissioned.
In this way, migration is transformed from a risky operation into a managed process, where each stage is verified and has clear criteria for success.
Myth 5. Once the data warehouse is connected, the modernization process is complete
Many companies believe that connecting the Data Warehouse puts an end to the modernization process. This perception arises from a sense of accomplishment: the data has finally been collected, the reports are working, and the analytics are available. It seems that the system will continue to function independently, without intervention.
But a data warehouse is not the end point. It is an environment that only works when it is constantly maintained and developed. Data changes every day: new systems are added, formats change, and businesses launch new directions. If the structure of the warehouse is not updated, it quickly ceases to reflect the real state of the company.
What happens after connection?
After connection, the next stage begins — system operation and development. It includes data quality verification, performance monitoring, and model adaptation to new business tasks. Here, it is important not only to maintain technical stability but also to work with users: collect their requests, update reports, and optimize analytics processes.
Companies that perceive Data Warehouse as a “living system” get the most benefit from it. They see not just numbers, but dynamics, patterns, and trends that help them make decisions based on current data.
Is it expensive?
So, the repository needs to be updated, quality checked, access controlled, and adapted to changes in the business. And here the question naturally arises: won’t such constant work become too expensive? After all, we mentioned above that Data Warehouse is accessible even to small businesses.
However, there is no contradiction here. After all, when connecting a cloud solution, the company pays only for actual usage — data volume, queries, storage. Also, working with Data Warehouse does not require a large permanent team. At the launch stage, consultants and database developers may be involved, but further on, most tasks are performed by internal analysts. Cloud tools automate many routine processes, such as data updates, monitoring, and backups. Therefore, the company does not maintain a separate department, but only coordinates the work of the responsible specialists.
Scaling happens gradually, so maintenance costs grow along with your needs. Small businesses can start with a minimal set of reports and a single analytical module, then expand their infrastructure.
Data Warehouse support is an investment in the accuracy and speed of decisions, which the company controls independently.
Myth 6. You can do without AI because it is too complex and expensive
Many companies still believe that artificial intelligence is something complex, expensive, and unattainable for their level. This myth arose ten years ago, when AI-based solutions did indeed require powerful servers, specialized software, and a team of researchers. At that time, AI was associated with experiments by Google, IBM, and Amazon, rather than with everyday business operations. This created a persistent perception that the technology was only available to large corporations with their own analytical centers.
What now?
Today, the situation is completely different. Artificial intelligence tools are integrated directly into familiar analytical environments — Power BI, Azure, Databricks, Google Cloud. They help to analyze data faster, search for patterns, make predictions, and even create queries in plain language without code. This means that an analyst can ask the system, “Show me sales dynamics by region,” and get a ready-made dashboard without complex code.
AI does not replace people, but strengthens their work with data. It reduces the time spent on routine analysis, reveals hidden trends, and makes analytics accessible to all departments. Therefore, a modern data warehouse is no longer just a repository, but the foundation for building a “smart” data ecosystem.
How AI integrates into data processing and who really needs it
Modern artificial intelligence tools do not require complex infrastructure. Most cloud platforms already have built-in modules for analyzing, forecasting, and processing data. They can be connected directly to existing storage without changing the system architecture.
For small and medium-sized businesses, this has become a reality thanks to “AI as a Service” models, where companies use ready-made tools through a subscription. There is no need to purchase equipment or create a separate department. All you need is an analyst who understands business processes and knows how to formulate queries to the system.
The cost of such solutions depends on the scale, but is usually comparable to the license of a conventional BI tool. AI helps to prepare reports faster, forecast demand, control costs, and detect anomalies in data.
Artificial intelligence is no longer the privilege of large corporations. It is equally useful for chain retailers, manufacturers, and service companies.
Myth 7. New technologies automatically solve all data problems
Many companies expect that once they implement modern storage or integrate artificial intelligence, all their analytics challenges will disappear. This is a natural expectation: tools are becoming increasingly “smart,” processes are being automated, and advertising promises instant results.
But no technology works without human management, standards, and data discipline. Even the most advanced systems depend on how a company collects, verifies, and uses information. If the sources are chaotic—duplicates, errors, unsynchronized updates—no AI will make the analytics accurate.
True efficiency comes when technology is combined with a data culture. This means regular quality checks, defined access rules, transparent update processes, and responsible individuals. In such a system, technology truly helps—not “automatically,” but as a tool for controlled development.
Conclusion
Data modernization is not a one-time initiative, but rather an ongoing effort that shapes a business’s competitive advantage. Technology helps, but only when backed by the right attitude toward data.
For the system to work effectively, there are a few things to keep in mind:
- Start with a data culture. Explain to employees why analytics are needed and make data available to decision-makers.
- Assign responsibilities. Each department should have a person who monitors data quality and updates.
- Formalize processes. Determine how data is collected, verified, stored, and who owns it.
- Review goals regularly. Business changes — the analytics system must change with it.
- Develop cooperation between IT and business. This ensures that technology will truly work for results, rather than just existing in the background.
Data storage, artificial intelligence, and automation are just tools. It is up to you to use them effectively to develop your business at the pace of today’s world.
