The computation and storage aspects have evolved into significant considerations, encompassing both cloud and onsite expenses. The construction of extensive database reporting usually occurs gradually, aligned with the initial plans. Repetitive low-grain queries can incur costs in terms of both computation and time. Additionally, ensuring timely synchronization of the database and comprehensive incorporation of "company dimensions" for complete Integrated Development Environment (IDE) multidimensional use is essential. This allows for potential improvements or the adoption of a new structural approach.
The Orchestrated EDW Framework utilizes a streamlined enterprise data model, providing a versatile foundation for enterprise data science initiatives. It operates on SQL-based schemas and tables, seamlessly integrating R and Python.
Key EDW² process areas include Ingest, Catalog, Predict, Tune, Score, Compare, Vectors, and Numeric Levels. The accompanying image illustrates historical data utilization and the application of Data Science, where the red areas represent data generated with EDW².
Siloed analytic reports and dashboards pose a significant challenge to an enterprise's ability to effectively compare and contrast key metrics and KPIs in a unified view. The primary issue stems from the isolated nature of these reporting structures, where data and insights are compartmentalized across various departments or functions. This fragmentation hinders the seamless integration of key performance indicators (KPIs) and metrics, making it difficult to obtain a holistic overview of the organization's performance.
When analytics are siloed, it becomes arduous to draw meaningful connections and identify correlations between different aspects of the business. This lack of integration inhibits the ability to analyze cross-functional impacts and synergies, impeding the organization's capacity to make informed, data-driven decisions. Moreover, comparing metrics across disparate reports often involves manual efforts and may lead to inconsistencies or errors.
Critical for enterprise-wide high-speed computations: To address these challenges, it is crucial to compute and store all KPIs and metrics in a centralized database rather than relying solely on frontend analytics or potential layers. The ability of a compact central database that performs high-speed data science activities because the data is in one place further enhances this approach. Centralized storage allows for a unified and standardized approach to data, enabling a comprehensive and cohesive representation of key performance indicators. This approach facilitates a more efficient and accurate comparison of metrics, providing a consolidated view that supports better decision-making and strategic planning across the entire enterprise.
The Orchestrated EDW addresses this challenge to include more details than KPIs and metrics by implementing a streamlined enterprise data model focused on accuracy and speed, establishing a single source of truth for enterprise data that is ready for data science use. It also serves as a go-to resource for departmental analytics tools and supports the EDW² high-speed data science activities, incorporating automated enterprise-wide contrasting and comparing.
Departmental or silo analytics remain valuable due to their ability to provide:
- Specialized Focus
- Granular Detail
- Autonomy and Flexibility
- Targeted Decision-Making
- Rapid Response to Departmental Changes
However, a balance is necessary, as an enterprise unified KPI and metric database view provides:
- Holistic Perspective
- Consistency and Standardization
- Efficient Resource Utilization
- Cross-Functional Insights
- Ability to Perform Enterprise-wide Data Science activities
In conclusion, while departmental silo analytics offer specialized insights and autonomy, an enterprise unified view provides a broader perspective, consistency, and efficiency. Striking a balance between the two approaches is crucial to harness the strengths of both and maximize the organization's overall analytical capabilities.
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