Business intelligence systems are used primarily to facilitate decision making in a company. But nevertheless, if the data included in the data warehouse They have no quality, are not complete or have duplications, the reports extracted with your business intelligence, the only thing they will do is damage the business.
Reports pulled from a data warehouse should be smart. On the contrary, the work of all users slows down and decisions lose coherence and efficiency. In fact, according to Gartner, working with low-quality information leads to poor decisions, non-compliance with regulations and competitive disadvantages, among other problems...
A recent study revealed that 81% of executives view data as critical to achieving business results.
Source: Informatica.com
For a data warehouse to be smart, must be supported by master data management. Thus, decision making will be made on reliable and truthful data, which gives credibility to users and the business as a whole.
Two main approaches to MDM
There are two main approaches to managing master data: Operational MDM and Analytical MDM.
The first focuses on ensuring that the data is the same in the different operating systems.. Secondly, analytical MDM is generally associated with data warehousing and has been embraced by organizations looking to improve the speed and quality of their business intelligence reporting processes (WITH A).
The close relationship between MDM and data warehousing is not surprising, Since the “dimensions” from a data warehouse (for instance, customers or product hierarchies) they are essentially master data.
But these two important areas tend to be treated as completely separate from each other..
How to combine an MDM with Business Intelligence?
The first thing to note is that master data is represented as dimensions in BI systems and is not associated with facts (namely, transactions) in those systems. The introduction of a master data management system within the company has a positive impact on BI systems.
You may want to read: What is a warehouse and what should a modern data warehouse contemplate?
For instance, it is typical in an MDM system that the attribute data names and data definitions used to describe master data entities are the standard data names and company data definitions. These master data definitions are often called “shared business vocabulary” (SBV) for the company. The SBV is, therefore, a master metadata.
We can leverage a master data SBV in a BI system to enforce the reuse of the same data definitions across all dimensional models, BI tools business views and cubes to drive consistency between dimensional data. This way, adopting a master data SBV improves understanding of data presented in BI system reports, anal OLAP, dashboards and dashboards.
In addition to metadata Consistently, the arrival of an MDM system to the company can also affect the integration of data in the data warehouse of a BI system. If an MDM is not available, a BI system is based on a classic data warehouse architecture, where master data is split across multiple data warehouses on different lines of business operating systems. Therefore, to create dimensional data integrated into a BI system, a tool is generally used to integrate disparate master data held across multiple operating systems to build dimensions.
No wonder we get confused between the concept of a master data center and a data warehouse, when both integrate master data. However, Why do we need an MDM system when we already have a data warehouse? Or better, Why are we integrating master data into a BI system? Shouldn't master data already be integrated and treated as a data source by data integration tools used in a BI system??
In fact, this might be a better option because master data needs to be supplied not only to a BI system but also to operating systems. Master data can be supplied to the tools of data integration BI system in at least three ways:
- Through the use of integration and matching services of a data quality software SOA-enabled to supply master data directly to a data warehouse or for ETL processes that feed data warehouses.
- By using an MDM solution to create a virtual source of master data accessible through a BI system data integration tool
- Use an MDM data hub built or purchased as a persistent data source for a BI system data integration tool.
In summary, Master Data Management strengthens data storage systems / BI in the following ways:
- Provide master metadata for use in cubic and dimensional data models.
- Provide high-quality master data as a reliable data source for ETL processing.
- Provide federated views of master data through disparate reporting systems.
- Hierarchy version tracking through time.
- Automate the recreation of different versions of a dimension in a cube schema or star to reflect changes in hierarchies.
- Provide reliable data for reporting and analysis.
The quality, Accuracy and accessibility of data is essential for today's businesses. Trust in the organization and the effectiveness of the decisions made depend on them., avoiding risks and costs.
Do you want to know more about master data management (MDM) for your data warehouse?
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