Rediscover Analytics
We are committed to creating sustainable solutions with high added value, guiding the private and public sectors step by step in their digital transformation.
Why migrate to digitisation?
Stimulate innovation
Digitalisation offers a host of benefits, including greater operational efficiency, lower costs, improved decision-making, a better customer experience, access to new markets and adaptation to market changes.
Our Data Analysis Services
How can we help your business?
Application migration
Migrate legacy applications and data to a public or private cloud
Enterprise DWH design
Data architecture design for an EDW to create an enterprise view of the organization
Enterprise DWH implementation
Implementation services for enterprise data warehouse development
Data visualization and BI
Collection of business requirements, provision of BI reports, dashboards, provision of e2e business enablement
Data science
Design and develop an analytical model for the company's needs. Maintain and update previously designed solutions.
Data management strategy
- Understanding corporate strategy and governance policy
- Define data management charter and scope
- Data maturity assessment
- Roadmap (digitization/IT governance)
- Stakeholder engagement
- Stewardship and ownership
- Culture change
- Business execution
A data management strategy is a plan developed by an organization to effectively manage all its data throughout its lifecycle, from collection to archiving.
Process alignment
- New organizational processes and realignment
- Information architecture planning
- IT architecture planning
- Change management
Process alignment means aligning a company's operational activities with its strategic objectives. This involves adapting business processes, workflows and IT systems to effectively support the organization's overall objectives.
Implementation
- Throwing away the basics
- Data protection
- Security and confidentiality
- Metadata management
- Data quality management (DQM)
Implementation refers to the practical realization of a strategy, plan or project in an actual operating environment. This involves moving from theoretical conception to concrete realization, by putting in place the resources, processes and tools needed to achieve the set objectives.
Lifecycle management
- Plan & Design : Data architecture – Modeling – Design
- Activate and maintain : Big data storage – Data warehousing – Data lakes -Data integration
- Use and enhance : Data visualization BI – Data science – Data monetization
This process comprises several key stages, including data collection, storage, use, publication, archiving and deletion. The main aim of data lifecycle management is to optimize the use of data, while ensuring its integrity, security and regulatory compliance.