A data warehouse is a centralized location where information from many systems and applications, including ERP, legacy systems, and external ones, is stored. It gathers information from all areas of the company. Then, in order to help manufacturers make data-driven decisions, make it available for analysis, reporting, and other business intelligence (BI) tasks.
A subset of a data warehouse called a data mart contains structured data on just one topic or business area. It breaks up big datasets into smaller, easier-to-manage chunks. To boost data aggregation and agility without the requirement to retrieve data extracts from a centralized corporate database, use data on inventories or production.
The volume and/or range of data in the database distinguishes a data warehouse from a data mart. The data mart or the data warehouse can be implemented first. To determine which one should come first, the following considerations must be taken into account:
· What kind of data must be loaded — blobs are preferable to relational databases for images, for example — and why?
· Are there any legal requirements for the data or for the auditing that must be done?
· What information, such as Personally Identifiable Information (PII), is sensitive?
· If the data can be pushed or pulled inside the warehouse, are there any accessibility requirements?
· How often can the data warehouse/mart be loaded?
· How much data must be loaded, and how frequently does the data change?
· Time-series data will they be included? Should a summary be provided?
Both DW and DM are needed in big organizations which generate lots of data. DW and DM give value to relevant stakeholders. For example, Data warehouses are most useful to decision-makers since they can
· Without needing to comprehend the complexities of the data sources or the subtleties of data transformations, make decisions.
· Utilize BI technologies that can convey data more succinctly. A chart is more powerful than 1,000 words. Drilling into a chart’s specifics is quite valuable.
· They create and modify their reports to address new inquiries.
Whereas instead of spending their time, for example, altering the font on reports or PowerPoints, the IT department can construct self-service models and marts for customers. Compared to data warehouses, data marts are quicker and simpler to construct. The data marts concentrate on a single business division rather than the entire company. By enabling users to obtain the precise sort of data they most frequently need to examine and by presenting the data in a style that supports the group view of users, they decrease end-user response times.
The decision-makers in your firm profit from data warehouses and data marts. By providing the 5Cs of data and turning it into information, they enable confident decision-making regarding crucial matters.
(Data Warehouse vs Data Mart: Know the Difference, 2019; Takayoshi, 2021)
Data Warehouse vs Data Mart: Know the Difference. (2019, December 24). Guru99.com. https://www.guru99.com/data-warehouse-vs-data-mart.html
Takayoshi, L. (2021, August 20). Data Warehouse & Data Mart: An Overview for Manufacturers. Acumence. https://www.acumence.com/data-warehouses-and-data-marts-an-overview-for-manufacturers