Consistency, the integrity, updating and automation are some of the keys to a good warehouse management. The objective that companies should set themselves should be to achieve a maturity model, an approach that ensures optimal performance, not only the data storage system and its administration, but in addition to its interaction with business intelligence and results. of this and advanced analysis.
Between the warehouse management goals are the following:
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Provide effective access to corporate data and historical data for analysis and decision making.
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Provide a consistent and consistent representation of data across the organization, extending its use to any level.
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Enable a true usability environment for query applications, analysis and reports.
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Ensuring data security.
To make it, it is necessary to overcome technological and organizational limitations; design a strategy and be clear about business priorities, for which an impeccable knowledge of internal processes is essential.
Photo credits: “The background glow represents hi-tech and abstract” by Stuart Miles
Maturity Model for Warehouse Management
The keys to a mature model of data warehouse management have to do with the optimization of the following 4 aspects:
1. Architecture
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It should gather the services of data warehousing and business intelligence centrally, grouping the central data warehouse and other data sources and sources through a standard interface.
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Business rules need to be integrated, which must be previously defined and implemented correctly.
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You must understand metadata management through creating a central repository with built-in metadata, standardized and updated.
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It has to be integrated with corporate security requirements.
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It is imperative that you can deal with both unstructured data sources and web data sources.
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You must have specific software and hardware to get the most out of its performance.
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In ideal circumstances, should be able to provide real-time updates.
2. Data modeling
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Must use standardized data modeling tools for metadata design and maintenance.
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You must ensure automatic synchronization of all data models.
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The design of the logic levels must be guaranteed, physical and conceptual in all data models.
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The modeling standards should be extended to the entire organization, as well as its documentation.
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The level of granularity should be minimized as much as is feasible in everything related to the warehouse management.
3. ETL
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Generation of complete extraction processes, metadata transformation and loading.
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Real-time ETL capabilities.
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Daily automation.
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Specific data quality tools.
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Automatic restart and recovery system.
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Simple monitoring capabilities, advanced and in real time.
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Ability to manage metadata of all kinds.
4. Business intelligence applications
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Real-time and closed-loop BI applications.
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Availability of one tool for main BI and another for specific BI applications.
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Warehouse management business process oriented.
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Maximum update.
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Full metadata integration with BI applications.
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