Analytics and Data Warehouse (DWH)
Analytics and Data Warehouse (DWH)
The objective of Axional ERP Data Warehouse is to convert your business data into business intelligence. To achieve this objective, we use a prebuilt data mart structure that simultaneously offers flexible configuration.
Data structures are automatically fed ERP data in a consistent, reliable and moment-to-moment way, offering real-time information to the organization. Moreover, Axional ERP/DWH offers a large number of dynamic reports that let you obtain any kind of information quickly and efficiently with varying levels of detail breakdown.
ERP’s structured data warehouse allows you to turn your data into knowledge. Generally, the huge volume of information stored by a business management system makes it unmanageable. The use of a consolidation and ranking system is the solution to ensure that credible and up-to-date information is available anytime.
Axional ERP/DWH minimizes the risks, delays and overall costs that usually transpire in data warehouse deployment projects. Likewise, it guarantees that all end users will have high-quality analytic information at their disposal from all business areas of the company.
Obtaining information extracted directly from ERP, without being processed by a DWH consolidation system, offers the following advantages:
- Information is up-to-date. At the moment that any report is executed, the data obtained reflects the current situation.
- No other system is required to get reports. Users are accustomed to working within the ERP environment where they get their information, without the need for either specialized training or an independent system to manage security.
Axional DWH is not an independent warehousing and information analysis system; nevertheless, it is completely integrated in the ERP itself. It has mechanisms that reflect any change in the source documents in real time, without needing to rebuild all the information pyramids. Moreover, the information collection and report generation system uses the same reporting mechanisms as the reports obtained directly from ERP.
Users can access stored information through a battery of multidimensional analysis and extraction reports available in the system itself, or by using external Business Intelligence tools or report generators.
Data Warehouse characteristics
Information stored in the data marts is simplified, incorporating every piece of data and business attribute, calculated and accumulated. This allows you to leverage information and create new reports simply, as access to the ERP data model’s complex interwoven structures is not required.
Data marts can consolidate information provided by the ERP itself along with information supplied by external systems such as, for example, receipts in retail environments.
The word “report” usually connotes obtaining information from a static structure. However, Axional DWH information extraction systems let the user obtain dynamic reports in which they decide the analytic data they want to see, the filtering criteria, or the groups by which they want to break down information.
Past records of analytical attributes are stored in the DWH pyramids, allowing a historical analysis of the information regardless of the ERP’s current data. Typically in an ERP system, if a customer moves cities, address data is updated and the previous city is overwritten. The use of the DWH lets you analyze sales made to a customer in each of the cities where they have been.
All the data marts’ update mechanisms let you decide whether updates should be made in real time or according to a schedule in response to changes in the source documents. This helps avoid the data marts’ costly re-updating processes when documents already submitted to the data warehouse are altered.
Functionality for administrators
The system’s preconfigured data marts allow the automatic deployment of the data warehouse, offering a very simple data model to obtain information in a simple way, without the need for experts in the ERP’s relational structures.
The ability to set up the data warehouse in a system independent from the ERP ensures that obtaining information through report generation does not affect the performance of the ERP itself.
The data warehouse loading, updating, and operation system shares the same administrative environment as the ERP, simplifying administration and maintenance tasks and facilitating users’ security profiles configuration.
Data marts are sets of information that accumulate historical data for each area of a business and allow fast and efficient access to analytic information.
Each of the data marts’ pyramids constitutes an information pyramid that groups data into different time intervals:
Depending on the degree of detail required, the user can employ the corresponding pyramid level. For instance, if a grouped sales report for the last five years broken down by country is required, the pyramid yearly level will provide information much faster, improving user experience.
The purchases pyramid automatically collects all analytic information from the purchases area. It has data grouped by company or business unit attributes, items or item family attributes, and supplier attributes as well.
It automatically collects the volume of purchases by cost or units, gross amounts, discounts or bonuses applied, bulk discounts granted, etc. Moreover, the aggregation of new analytical dimensions can extend to cover user-specific needs.
With a similar structure to the purchases pyramid, the sales pyramid automatically collects analytical information from the sales area. It features groups of data broken down by company or business unit attributes, item and item family attributes, and customer attributes.
It automatically collects sales volume by cost or units, gross amounts, discounts or bonuses applied, bulk discounts granted, sales cost and gross profit, etc. Like the purchases pyramid, the sales pyramid can incorporate new analytical dimensions to fulfill user-specific needs.
Collects and groups information from warehouse activity, registering quantities and costs for each activity.
Allows users to configure the attributes and analytical dimensions that address user-specific needs, incorporating standardized attributes about warehouses, stock categories, or types of activity
The receipt pyramid is designed to seamlessly integrate retail purchases or sales data via the integration of individualized receipt data, allowing daily sales analysis or data mining by identifying loyalty cards.
The standard dimensions stored include sold units, sale amount and profit obtained, the evolution of sales vs. purchases, or the percent of variation between periods. Like the other pyramids, it has dimensional attributes which can be configured, allowing analysis matched to user needs.
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