Data Warehouse & Business Intelligence

In Data Warehouses (DWH), data from multiple sources (including external sources) is integrated into specially designed models, creating a unified and aggregated view that provides reliable, high-performance access to Business Intelligence tools for analysis.

Market Challenges

In data integration, transformations and KPI precalculations are introduced to simplify business analysis for users, allowing them to focus on insights rather than the underlying transformation processes.

Feeding an Enterprise Data Warehouse is a time-consuming process, with challenges related to the time-to-market for new data availability requests and the costs involved in operationalizing them. The timeliness of data availability, particularly as the DWH scales, becomes increasingly critical.

The market has long sought effective solutions for both centralized data feeding in DWHs and for centralized governance in the design and maintenance of enterprise systems.

Scenarios

Some solutions are organizational in nature.

• Data Mesh, for example, is an innovative data architecture paradigm that shifts away from the traditional centralized model to a distributed approach. In this model, data is managed by specific business teams and domains, treating data as a product to be sold internally or externally and published in a catalog.
  The Data Mesh model fosters local governance and encourages teams to take ownership of their data, improving scalability and addressing key challenges. However, it also introduces governance and technological complexities that may not yet be fully mature.

In other cases, the solutions are architectural.

• The Logical Data Warehouse (LDW) is an architectural vision for data integration that combines the benefits of a traditional physical data warehouse approach with data virtualization technologies and real-time data integration. An LDW provides access to data from multiple sources without having to physically move or duplicate it. The data remain in their original systems and are presented to users as if they were present in a single virtual layer, thus hiding the complexity of the underlying distribution.
  We find this approach extremely attractive in the rapid prototyping stages to improve efficiency and accelerate time to market and to some extent to reduce data redundancy, but we have to consider that even the virtualization technology vendors themselves do not go so far as to claim that virtualization can completely replace physicalization in a traditional DWH. Instead, we can think about enabling a federation of smaller, better-managed, sectoral DWHs that are logically integrated by a virtualization and presentation layer.

We see this approach as especially valuable in rapid prototyping phases, improving efficiency and accelerating time-to-market. However, virtualization is not a complete replacement for traditional DWHs. It may instead support the federation of smaller, sector-specific DWHs, logically integrated through virtualization layers.

How We Operate

We work operationally with our clients in the following ways:

• Provide project and program management for DWH & BI initiatives.
• Assist in drafting RFCs, translating needs into requirements.
• Draft feasibility studies, defining alternative scenarios, evaluating costs, benefits, and selecting technology partners during the software selection phase.
• Assess production systems to identify inefficiencies.
• Support implementation and delivery of DWH & BI systems, and manage architectural evolution programs to design new solutions or optimize existing ones.
• Provide application management services for systems in production, managing day-to-day operations and corrective maintenance through service-level agreements.