BeHive Data

How to build company-wide reporting and a single source of truth, step by step

Project scope:

For mattress and bed manufacturer Materasso, we deployed Tableau as a production business intelligence tool that unified reporting across the key areas of sales and production. The goal of the project was to put the company's decision-making on a data foundation – replacing fragmented outputs from various systems with clear, interactive dashboards accessible to sales and management teams in real time. We built data sources connected to the company's systems, created calculations and metrics reflecting the specifics of the production and sales operations, and designed visualizations that give users an instant overview of performance. The result is a live reporting environment in Tableau that the client uses in day-to-day operations – from tracking sales indicators to analytics across the product portfolio. The project is also an example of deploying Tableau in a real-world production and sales environment within the Czech mid-market segment.

Client context:

Materasso is a manufacturer of medical and orthopedic mattresses, bed systems, and accessories for healthy sleep, with a tradition dating back to the mid-1990s and headquartered in Oravské Veselé, Slovakia. In its own production facilities, it combines craftsmanship with modern technology and ranks among the leading manufacturers of sleep systems in the region. The brand's offering covers a broad portfolio ranging from mattresses and beds to slatted bases, bedding accessories, and furniture, organized into several product and hotel collections. It supplies its products to the Czech, Slovak, and a number of other European markets.

Tech Stack:

MSSQL

Tableau

Python

DuckDB

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Implementing operational reporting in a manufacturing company

As the company grew, it became increasingly difficult for management to keep a detailed overview of day-to-day operations. Reports were available with a delay of up to two months, which meant that decisions were often based on experience and intuition rather than on current data. At the same time, each department worked with its own data, and management and owners didn't know which of those numbers were correct.

As a result, the company had limited ability to respond to operational situations in time, monitor financial risks, and systematically optimize production processes.

"Automating reporting and creating a single source of truth marked a significant shift in how we operate. Now we know we can trust the numbers, and in meetings we have room to discuss plans and strategy instead of arguing over whose numbers are the right ones."

Tomáš Chomát
Digital Manager & Co-owner

Highlights:

connecting production and financial data into unified views
automating manual repetitive tasks and reducing error rates
shifting the company-wide approach to data – from the production lines to top management
preparing data for predictive analytics and the use of AI agents

Our solution

Understanding business needs and decision-making processes

Before diving into technical implementation, we sat down with management to understand how decision-making actually works within the company. We explored what information people were missing, where decisions were made "by gut feeling," and where the lack of data had the greatest impact on the company's day-to-day operations. This allowed us to build the solution from the start on real needs rather than assumptions.

Consolidating fragmented data

Accounting and production data had until then lived separately in different systems, and connecting them was time-consuming and error-prone. We brought them together into a unified, consistent structure — a single source of truth from which reliable reports can be created across the entire company, without manual lookups and matching.

Designing a pragmatic reporting solution

Instead of an oversized system, we designed a solution that matches the company's real needs — without unnecessary complexity and within the tools the company already had in its architecture. No new licenses, no years of implementation: a fast path to value with what's already at hand.

Gradually rolling out reporting across the company

We started with reports for top management, where the impact was visible fastest. Once the solution proved its value, it naturally expanded further — into production, finance, and sales, at both the operational and managerial levels. Each new area built on the previous one, and the whole reporting setup grew as both we and the people at Materasso kept identifying more and more areas worth tracking.

One particularly interesting area we delivered as part of the project was the introduction of overview screens directly on the shop floor, which allow workers to monitor current performance and key metrics in real time. Every operator in production thus knows exactly what needs to be produced and when.

Results

The new reporting solution fundamentally changed the way the company is managed. Instead of delayed, manually assembled outputs, management now has an up-to-date overview of performance every day — and can respond to emerging problems the moment they appear, rather than with a week's delay. Decision-making has thus shifted from guesswork to numbers.

The project also had an unexpected but very valuable side benefit: it exposed weak spots in data quality and revealed room for optimizing processes in production and logistics. What used to remain hidden could suddenly be measured — and gradually improved. Reporting thus became not only a tool for oversight, but also a catalyst for further improvements across the company.

Data availability

D-1

Maximum data age

Data consolidation

3 → 1

Data sources

Reporting automation

-50h

Manual work per month

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Faster reporting

We cut data availability from several weeks down to regularly updated reporting for day-to-day management.

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A single view of data

We connected fragmented data into a unified view, allowing the company to work with consistent information across departments.

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Automation

We automated the manual preparation of reports and reduced the amount of manual work and the risk of errors.

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"We're nowhere near done — we keep coming up with more and more areas where we can mine and connect data within our company. We see the tremendous added value it brings us, and we want to make full use of its potential across every area, including predictive analysis laveriging AI technologies."