At “ROOT CODE” JSC, we help the largest holdings build an effective data culture by providing businesses with transparency, relevance, and a unified understanding of metrics. Using a real-world case example, we'll explain why implementing a BI system won't work without a built-in data culture and what approaches allow an organization to make management decisions based on reliable information.

Why data culture is more important than technology

As part of one of the projects for a large holding company, our company was tasked with implementing a BI system for automating reporting. However, at the very first stage of data collection, the customer provided 12 Excel files with contradictory information.

1. The same fields were named in different ways ("ITN", "ITN of the organization", "customer code")
2. There were duplicates and different date formats.
3. The values for one region did not match in different files.

When clarifying which information was correct, we heard: "We have each division counting in its own way." This situation is typical for companies that do not have a data culture in place, and it leads to a loss of trust in analytics.

What do we mean by data culture?

Data culture is not just about having a BI platform or data warehouse. It is a system that includes:

• The data in the reports is accurate and clear;
• The data sources are transparent and have assigned responsibilities;
• It takes minutes to get up-to-date information, rather than days;
• And errors are analysed at the system level, rather than by individual employees.

Three main reasons for the failure of BI projects

In our practice, we often encounter three common scenarios that undermine the credibility of analytics:

1. Reports are manually generated in Excel (even with a BI system in place, employees continue to work in their familiar spreadsheets and send reports via email, leading to confusion between versions and inconsistent data)
2. Lack of accountability for data (different departments use their own databases, calculation logic, and formats, resulting in conflicting data)
3. Insufficient involvement of management (decisions are made based on experience, and BI is used for formal reporting rather than influencing management processes)

What really works: based on our experience

In projects with clients and internal initiatives, we have implemented proven approaches to creating a data culture:

1. Creating a single source of truth — Data owners are introduced for key data that control the logic of calculations and the description of entities. All data is automated, validated, and versioned. This eliminates discrepancies in the reporting.
2. Practical training for managers — employees are taught not just to work with BI, but also to understand the reasons for changes in indicators in specific business cases. Interactive sessions with real-life case studies are the most effective.
3. Regular analytical practices — we recommend implementing "data rituals":
   • Weekly newsletters with key facts and conclusions
   • Monthly meetings to discuss successful analytics cases
   • Quarterly dashboard revisions to update indicators.
4. Linking data culture to motivation — KPIs should take into account the quality of data (relevance, completeness) and the use of analytics in work. This creates a sense of responsibility for data at all levels of the organization.

Example: How data culture changed the course of a project

In one of the projects to develop a digital simulation platform for strategic infrastructure management, the key factor in success was the creation of a unified data culture. To achieve this goal, the following measures were taken:

1. End-to-end data normalisation system
2. Assignment of responsibilities for key entities
3. Automation of validation and visual control
4. Unified understanding of terminology and data structure.

This has enabled accurate calculations, increased the speed of analytics preparation, and transformed the platform into a comprehensive tool for strategic decision-making.

How to start building a data culture

To build a data culture in your company, we recommend the following:

• Stop postponing the issue of data quality until later.
• Find a specific business pain and solve it with analytics.
• Involve top managers in the data process.
• Make BI convenient and useful, rather than turning it into an Excel spreadsheet with graphs.

If your company is facing scattered data and lack of trust in reporting, "ROOT CODE" specialists will help you build a data culture and turn analytics into a reliable tool for business management. Contact us for a consultation.