QUELLES SONT LEURS DIFFÉRENCES ?
Digital transformation means coordinating tasks within an integrated application ecosystem.
These tasks are often cross-functional and fall outside the scope of a clearly defined business solution. In this context, managing the flow of data to turn it into information is crucial.
The solution is known as a Data Pipeline: moving data from a source to a target while adding value that makes it useful.
In practice, the stages of a pipeline are very similar regardless of the type of data, and they rely on practices and tools that can be shared throughout the company.
Let’s describe the state of the art from these 3 angles: requirements, stages, and tools.
1. Requirements
- Data quality: consistency, accuracy, and uniqueness
- Data traceability, combined with process monitoring and, if necessary, system observability.
- Data usability: Data prepared to be queryable (structured or semi-structured).
- Real-time processing: Especially for event-driven data, so that information remains up-to-date.
- Availability: Achieved through high-performance processing and transformations.
2. In this context, the data processing steps are:
- Extraction followed by Ingestion
- Validation, Transformation, Uniqueness
- A storage model that makes data queryable for analysis and operational purposes
- Enrichment, including through machine learning
- Governance: who manages and who has access to what?
- Activation to expose directly usable data.
3. These tools fall into well-defined categories. Notably:
- Data centralization tools: MDM for referential data, Data Platforms to integrate transactional data, BI for reporting and analysis, CDP for marketing.
- Middleware or “run-of-the-mill” logic combines with APIs, while iPaaS adds cloud or hybrid integration capabilities.
- Tools for data quality, monitoring, and observability.
- Data processing tools, including machine learning.
However, best practices diverge according to two main objectives:
- Defining a common centralized data platform for the company.
- Governing data to serve the business.
We’ll delve into these two cases in a future issue. Stay tuned…