BIX Tech

Data Engineering

Data Lakehouse Implementation and Data Culture

How a major industry transitioned from manual processes and fragmented spreadsheets to a cloud-centralized data lakehouse with structured governance and data-driven decision-making.

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Industry & Manufacturing

Transforming a culture of manual bottlenecks into a data-driven organization with cloud architecture and evidence-based decisions rather than just experience.

The Challenge

A large industrial player sought to evaluate and consolidate its analytical culture to understand its current level of data maturity. The diagnosis was precise: although the team already made decisions based on information and showed solid maturity (with an overall score of 7.42 out of 10), the operation faced significant systemic bottlenecks. Key issues included manual adjustments in data repositories, heavy reliance on Excel spreadsheets for control and storage, a lack of unified calculation manuals for KPIs, and difficulty integrating information across departments. Structuring governance and centralizing processes became the priority to ensure data quality and support integrated decision-making.

I notice that data analysis is gaining momentum within the organization. Much is being done to ensure we have maximum quality information for more informed and accurate decision-making.

Manager, Industrial Sector Company

Our Work

To resolve these pain points structurally, BIX Tecnologia connected the strategic vision of analytical maturity with the technical modernization of the data infrastructure. By allocating data engineering specialists, a cloud data lakehouse was architected and developed using technologies such as Databricks, Python, Spark, and SQL. The project followed well-defined strategic stages: gathering requirements to integrate the cloud into the existing ecosystem, defining access and governance rules, and building robust ETL (Extract, Transform, Load) pipelines.

Results

The new architecture directly addressed the root causes identified in the initial diagnosis. With the data lakehouse in place, the company replaced decentralized processes prone to manual edits with a secure, unified, and scalable environment. Data that was previously siloed in departments or stored fragmentedly is now automatically extracted, processed, and made available for BI visualization tools. The result was the elimination of repetitive operational work, strengthened governance, and the consolidation of a centralized repository that sustains the continuous improvement of the company's data-driven culture.

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STEP 1

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STEP 2

Define project requirements and team structure

Within days, we will finalize your project specifications, define an engagement model, and select and onboard your team.

STEP 3

Start the project and track the results

Together, we will set the milestones and kick off the project. We will closely monitor progress, report updates, and continuously adapt to your needs.

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