Build your end-to-end data platform

Measure and enhance the reliability of your data for more relevant usage

Why ?

Obtain accurate insights into your business

You want to use a common, reliable, shared source of data for all business functions to make strategic decisions. However, you need to be vigilant about the various potential sources of errors.

Such potential sources of errors include missing data, abnormal data, outdated data, and data with different interpretations among users.

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Challenges

Make informed decisions

The expert

Imane L.

Data Quality expert

Data reliability is crucial for companies seeking to make informed decisions and improve their performance. Indeed, the accuracy and relevance of data are essential in guiding business strategies and choices. If the data used is inaccurate, incomplete, or outdated, the decisions made can be ineffective or even detrimental to the company.

By increasing the reliability of your data, you can obtain accurate insights into your business. This allows you to better plan your activities, understand the needs and expectations of your customers, identify market trends and respond promptly, and comply with applicable regulations.

The expert

Imane L.

Data Quality expert

Customized support

With our expertise in Data Engineering and Data Visualization, along with our strategic partners (Snowflake, Databricks, GCP), we are committed to guiding you in building the most relevant architecture that ensures quality, performance, and identification of key KPIs for the growth of your business.

Our customizable products

Technical challenge

During the technical challenge, we identify business issues and potential solutions for designing your data strategy. Then, we conduct an analysis of your existing infrastructure to verify technical feasibility.

ETL Creation

During the creation of your ETL, we are committed to building an efficient system for data extraction, transformation, and storage. Our objectives include :

Streamlining, synchronizing, and formatting data flows.
Aggregating and migrating data from multiple systems.
Automating manual data collection.
Establishing the foundations for Data Science and visualization projects.

Data Quality

Throughout the development of your solution, we implement data control processes to ensure its quality and business usage :

Quality and Observability: Monitor the end-to-end data health and eliminate improper usage by applying and automating checks for freshness, volume, format, and business rules. Ensure data lineage to track its origin. Implement standards and testing tools at each stage.
Accessibility: Facilitate data usage by providing a comprehensive, unified, and harmonized version of the "truth". Establish a data catalog and discovery tools with appropriate access levels and controls.

Data Product Discovery

The data product discovery phase aims to mitigate risks related to value, business viability, usability, and technical feasibility of a data product in the most cost-effective manner possible.

This approach is based on hands-on experience to overcome client biases and focuses on the data product strategy by:

Utilizing a multidisciplinary team to address all risks.
Employing an iterative approach to deliver value from the first release.

Let's work together

They have increased the reliability of their data

Data Engineering

Business Intelligence serving operational efficiency