appMDM™ makes the daunting job of Master Data Management easy. Chain-Sys understood that Master Data Management (MDM) is not just about data hubs, governance and data quality, but also the very important task of bringing the master data from the feeder applications and also sending the massaged record of truth to the consumer applications. appMDM™ is the only product that offers 2000 + ready built connectors (adapters) to major ERP and Enterprise Applications (SAP S/4HANA, SAP ECC, Oracle Cloud Applications, Oracle E-Business Suite R12, Primavera, Peoplesoft, JDEdwards, Siebel, Microsoft Dynamics, Salesforce, Procore, etc.)
Unlike some Master Data Management products, which are tied to one of the hubs, such as Customer Data Hub, Product Data Hub, Supplier Data Hub, appMDM™ offers all the above hubs as pre built standard hubs, and provides rapid drag and drop development of new hubs. The underlying technology platform, Chain-Sys Platform™, allows the rapid construction of Master Data Management hubs, governance and workflows. Governance and Data Quality measures can be extended to Transactional data such as Invoices, Sales Orders, etc.
appMDM™ addresses the main areas of Master Data Management:
|Master Data Governance|
|Data Quality Management|
|Master Data Simplification|
|Transaction Data Simplification|
|Operational Data Simplification|
|Building new Data Hubs|
|Data Profiling and Data Consolidation|
|Pre-built Connectors / Adapters to major ERPs|
|Pre-built Hubs for Customers, Products and Suppliers|
|Cloud or On-premise deployments|
Process of bringing data into common format across applications, e.g. addresses.
Ongoing process to identify and handle duplicate Master records. Rules are built continuously to identify potential duplicates. A decision would be made to keep, merge, or eliminate the duplicates. appMDM™ uses complex algorithms to facilitate identification of potential duplicates.
This is a core function of any operational MDM. Creating a single source of truth Master Record, from various applications and allowing usage of this record of truth by various consumer applications. Cross referencing information would need to be maintained for each application. Cross referencing is additionally helpful during the situations of merging duplicate master records. This cross referencing ensures that child data does not get orphaned while merging or eliminating a duplicate master (parent) record.
Process of analyzing Master Data and providing statistical summaries, e.g. minimum, maximum, average, median, number of unique values, distribution of values, etc. If 99% of the values for a column fall within the range of 0-100 and less than 1% have a value in excess of 1,000,000, that is a pretty good indication the outlier data needs to be evaluated for correctness and quality.
|Go multi domain beyond Customer, Supplier and Material|
|Build Operational and Analytic MDMs for SAP ECC, SAP S/4HANA, Oracle Cloud Applications, Oracle E-Business Suite, Salesforce, Workday, other Enterprise Applications and Custom Applications|
|Develop Screens, Workflows and Governance by drag and drop rapid development|
|Common Data Model|
|Built in Data Quality Management (DQM)|
|Ready domains/hubs for Customer, Supplier, Material/Item, Global Product Hierarchy, Pricing, Employee, Purchase Orders and more.|
|Analytical MDM → Customer 360⁰ and EDM|
|Quality Data to your Data lakes → Accurate Dashboards for decision making|
This would generally be done based on user defined rules. Once MDM is set up, corrections could be made in the source systems and pulled into the data hub, or data could be corrected in the data hub and pushed back into the source system.
Some legacy systems will not have as robust data as do more modern systems. Data Enrichment may be required to add missing information deemed critical.
The Operational MDM objective is to make the multiple Operational Applications work in harmony, without creating confusion about same master data information maintained in the multiple systems.
Many of the quality measures undertaken in an Operational MDM endeavor, can be easily be carried over to Analytical MDM. This results in reports generated from quality data, making decisions from such reports more accurate and meaningful.
Data Governance is the process of carefully crafting master data, by allowing newly created master data to be reviewed and approved by data stewards and business process stakeholders.