FAST | Forget querying delays! Our DWH processes most complex data with exceeding performance – from highest aggregation to raw data level.
STATE-OF-THE-ART | We use the latest technologies to form a pioneering analytics framework.
SCALABLE | Don’t worry about growing data volumes and complexity – we designed our DWH to grow with you.
PROVEN | Our DWH has put its exceptional capabilities to the test for years – serving large numbers of customers.
The main components of our data model are measures and dimensions – reflected in the graphic's top and bottom row. If you put the data model to use and combine both entities, you'll get FULL INSIGHT into you business.
TOP ROW: MEASURES
We differentiate between five types of measures (or: key figures) that you see written on the boxes in the graphic's top row: Numbers, quantities, values, averages and rates. If you open a box, you'll find various groups of measures in it – e.g. discounts, revenues, margins, marketing costs, costs of good and more in the values' box as shown above. Each measure group again contains several individual measures – e.g. Order Value, Open Order Value, Cancelled Order Value, Revenue, Returned Revenue and Net Revenue in the "Revenues" group as shown above. In total, our data model comprises more than 800 measures.
BOTTOM ROW: DIMENSIONS
The graphic's bottom row shows the 17 dimensions that minubo's data model comprises. Dimensions are relevant analysis perspectives that offer focussed viewpoints on a given data set. A dimension comprises various attributes which represent its different characteristics and as such offer a specific breakdown of the available data or metrics within the analytical perspective provided by the dimension. In our graphic, you can see some sample attributes in the opened boxes of the dimensions "Customer" and "Product". In total, minubo's data model comprises more than 200 attributes.
Using minubo's data model via our frontend's various tools or via an API that you created with our data feeds, you can combine measures and attributes to get full insight into your business. Picking up on one of the two examples from the graphic, you could build a report showing how much returned revenue men and women generated – breaking up your chosen measure Returned Revenue across your chosen attribute Gender which is part of the dimension Customer. You could then go into more detail by further breaking your data up across more and more attributes: Which product categories did the returned revenue occur from with both men and women? Which sub categories? Which product, which SKU? minubo's data model allows analyses from highest aggregation to raw data level.
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COMPREHENSIVE | Our data model comprises everything you need to run broad commerce reporting and analytics – more than 800 measures, more than 200 attributes.
COMMERCE-SPECIFIC | Tailored to the particularities of commerce businesses, our data model is the best choice for every commerce company – from manufacturer across retail to marketplace.
BEST PRACTICE | Our data model has been shaped by the collaboration with 100+ commerce companies of all kinds and sizes.
DYNAMIC | Benefit from the continuous enhancement and expansion of our data model – and drive it forward yourself by having your custom requirements implemented.
In 2017, in cooperation with co-initiator Project A, we founded the CRS initiative to create an open discussion platform for topics around data-driven commerce. Topics we cover:
minubo's data model uses transaction metrics just as they are shown in the CRS transaction metrics matrix.