We are using Microsoft MDS for our master data management, specifically within a project that requires identifying individuals. The use case involves handling multiple mobile numbers, changing addresses, and variations in how names are recorded, which is essential for our client, who requires accurate identification across different locations and name formats.
I use this solution to simplify master data management which makes it easy to create and maintain consistent master data without manual work. It supports data modeling, change tracking, and validation rules, all on the SQL Server platform. Unlike specialized tools, MDS is adaptable for various industries, making it a versatile choice for master data management.
We used MDS to maintain master data created for the sales team using lists from D&B. MDS is also used to create an inventory of all the processes and data elements created to help the mastering process. We integrated data from different sources for analytics. Some of the data elements have the same business value. However, the attributed names are different. After some cleansing in DQS, the data is persisted in MDS, where it is further evaluated against some business rules, which we cannot do in DQS. The data steward is also involved in analyzing the MDS errors, correcting, approving, and rejecting records. The output of this exercise is to create a single version of the truth for our dimensions for the EDW. We've used MDS to maintain reference data used by ETL processes during data integration and transformation.
We primarily use this solution to process external repos and MDS. The data model is useful to us and is created within our company by our data modelling team. The Microsoft MDS tool uploads the data into the schema from the JMD repository and then generates the MI reports.
We use it in our company. We use the product for data migration, production, and development. We deliver data sources to our customers using MDS. We are a data company.
It can be used for all the data cataloging, master data management, manufacturing, product management, data harmonization, data stewardship, for any kind of master data, and product catalog.
Sr. Database Developer at Hill Physicians Medical Group
Real User
2021-03-04T17:59:19Z
Mar 4, 2021
We use it to maintain our master data sets. We do a lot of setup files and crosswalk files. We are using SQL Server 2016. It is on-prem till the recent version because SQL Server 2016 is on-prem, but from SQL Server 2019 onwards, it is compatible with Azure. I did a POC last month on the new version to host it up in Azure. We had both public and private clouds.
Mainly, I do infrastructure support. We do fine-tuning, information, configuration, higher-level availability, and replication. Also, single and clustering solutions - both kinds. We do on-premises and cloud deployments. This is because some customers use Microsoft Azure, mainly in the financial sector, such as the Sri Lanka Government, who has many databases that cannot go on the cloud. The financial sector also works with on-prem databases. I am mainly using the SQL Server from 2019. That's the latest version since last January while our customers are mainly using the 2016 and 2017 versions. This is because we are not providing the latest version. We are testing some bugs now. In terms of functionality, I think the 2017 version is better. I have not fully tested the 2019, so I cannot give a recommendation for it.
We use it for analytical and operational enterprise reference data management including management of campaign, product and financial management hierarchies. We currently only use batch interfaces to feed or get data from it. We also use it to maintain source to enterprise data mappings that are fed to ETL via a generic RDM (Reference Data Management) design. We have a variety of different groups of data stewards who use the tool for data management and they get automatically notified when source systems feed new data that needs to have stewardship carried out.
Microsoft Master Data Services (MDS) is an SQL server solution for master data. MDS enables users to organize and manage a business's master set of data into models while also creating rules for updating the data and control over who can edit it. In addition, the master dataset can be shared with other people in your organization through Excel.
In Master Data Services, you create a model, which is the highest level container in the structure of your master data. This model can then be used...
We are using Microsoft MDS for our master data management, specifically within a project that requires identifying individuals. The use case involves handling multiple mobile numbers, changing addresses, and variations in how names are recorded, which is essential for our client, who requires accurate identification across different locations and name formats.
Microsoft MDS is simple to use. Compared to other databases, it is easier to manage and administer.
I use this solution to simplify master data management which makes it easy to create and maintain consistent master data without manual work. It supports data modeling, change tracking, and validation rules, all on the SQL Server platform. Unlike specialized tools, MDS is adaptable for various industries, making it a versatile choice for master data management.
We used MDS to maintain master data created for the sales team using lists from D&B. MDS is also used to create an inventory of all the processes and data elements created to help the mastering process. We integrated data from different sources for analytics. Some of the data elements have the same business value. However, the attributed names are different. After some cleansing in DQS, the data is persisted in MDS, where it is further evaluated against some business rules, which we cannot do in DQS. The data steward is also involved in analyzing the MDS errors, correcting, approving, and rejecting records. The output of this exercise is to create a single version of the truth for our dimensions for the EDW. We've used MDS to maintain reference data used by ETL processes during data integration and transformation.
It supports our production lifecycle, machinery management, registration, and distribution processes.
We primarily use this solution to process external repos and MDS. The data model is useful to us and is created within our company by our data modelling team. The Microsoft MDS tool uploads the data into the schema from the JMD repository and then generates the MI reports.
I'm using Microsoft MDS for configuring the storage for backups on the DPM server, and data protection manager.
We use it in our company. We use the product for data migration, production, and development. We deliver data sources to our customers using MDS. We are a data company.
I'm a distributor and developer of Microsoft MDS.
It can be used for all the data cataloging, master data management, manufacturing, product management, data harmonization, data stewardship, for any kind of master data, and product catalog.
We use it to maintain our master data sets. We do a lot of setup files and crosswalk files. We are using SQL Server 2016. It is on-prem till the recent version because SQL Server 2016 is on-prem, but from SQL Server 2019 onwards, it is compatible with Azure. I did a POC last month on the new version to host it up in Azure. We had both public and private clouds.
Mainly, I do infrastructure support. We do fine-tuning, information, configuration, higher-level availability, and replication. Also, single and clustering solutions - both kinds. We do on-premises and cloud deployments. This is because some customers use Microsoft Azure, mainly in the financial sector, such as the Sri Lanka Government, who has many databases that cannot go on the cloud. The financial sector also works with on-prem databases. I am mainly using the SQL Server from 2019. That's the latest version since last January while our customers are mainly using the 2016 and 2017 versions. This is because we are not providing the latest version. We are testing some bugs now. In terms of functionality, I think the 2017 version is better. I have not fully tested the 2019, so I cannot give a recommendation for it.
Our primary use case of this solution is to develop solutions for our clients.
Our primary use case is to have a database of customers. We also use it to manage accounts. We have an on-premises deployment.
We use it for analytical and operational enterprise reference data management including management of campaign, product and financial management hierarchies. We currently only use batch interfaces to feed or get data from it. We also use it to maintain source to enterprise data mappings that are fed to ETL via a generic RDM (Reference Data Management) design. We have a variety of different groups of data stewards who use the tool for data management and they get automatically notified when source systems feed new data that needs to have stewardship carried out.