Introduction and challenges
Today, the vast majority of business teams rely on a suite of IT systems for the storage, utilization, and interaction with their data. The ability to unlock greater value from this siloed data distinguishes successful businesses from their competitors and plays a pivotal role in achieving success. With the proliferation of data sources, it becomes essential for each use-case to depend on a high-quality dataset that provides consistent information to various business teams while adhering to a range of IT constraints, including architectural best practices, storage capacity, memory usage, and technological strategy.
The data platform serves as the central nexus where structured and unstructured data, data management processes, supporting teams, and data-related governance converge. It establishes a common foundation to fulfill the requirements of both business and IT stakeholders, streamlining the creation of a unified pathway for each piece of information, from its origin to its ultimate application. It stands as the core element, acting as the singular source of truth for all business information, expediting use case development and enhancing user experience.
How we can help
We offer a range and services to support the design, build, scale-up and run phases of modern data platforms:
- Platform architecture: Outlining the components needed for data extraction, ingestion, storage, transformation, visualization and managing Machine Learning models
- Cloud provider and solutions selection: Assisting in selecting fit-for-purpose solutions, addressing the entire data value chain, starting with the base cloud providers (Google, Amazon, Microsoft…)
- Data collection: Starting with business use-cases and identifying the corresponding data objects and raw sources of data needed to build corresponding data catalogues and lineage between source and destination
- Data quality control: Helping get data quality under control by implementing systematic testing of the quality dimensions (freshness, completeness, validity, coherence), coupled with an efficient alerting and dispatch of remediation tasks
- Core model design: Designing an efficient domain-driven data model covering the objects most used by the business use-cases; supporting the setup of self-service BI and the reduction of development efforts for new use-cases
- Agile development and data product management: Helping adapt the agile framework to day-to-day processes and streamlining the ways of working between data teams, business teams and IT stakeholders
- MLOps and DataOps: Designing and refining the continuous ingestion/delivery processes for all data products, including the lifecycle management of Machine Learning models
- Self-service BI: Deploying Business Intelligence (BI) artefacts for self-service usage by business teams and training the business teams ‘power users’ on hard analytical skills
- User rights management: Framing and implementing a data sharing strategy that is compliant with regulations while allowing for visibility for the end-analysts