Master data management concerns itself with the control of an organization’s most critical and core data that describes objects and assets, such as customers, employees, or products. Complete and accurate master data contributes to better decision making, less cost, less re-work, higher process efficiency and complying to regulations. To gain these benefits from MDM, uvays has developed an approach which is organized into four building blocks; governance, processes, content & quality and systems & tooling. While growing your organization’s capabilities in these four building blocks, of which each will contribute to improved MDM, the focus in this article will lie on the processes that have potential for automation. Operational processes within MDM are primarily concerned with supporting the daily operations of business processes. Many opportunities exist in the maintenance of master data by means of creation, updating, deletion (archiving) or cleansing.

MDM Activity
Example Description
Creation A sales person has a new customer and must be added in the system.
Updating The address of the customer changes and this must be updated in the system.
Deletion (achieving) For over a year the customer has not bought anything from the company, therefore the status of the customer must be changed to inactive or be deleted from the system.
Cleansing Based on deviations identified in data quality reports the proactive cleansing of records can be initiated.

Understanding how your master data is supporting your business processes is a prerequisite for knowing if, and how, automation of master data operations can be leveraged into efficiency gains. Moreover, the number of changes and risk of errors in MDM processes must be considered as well, since not all master data operations are the right candidate for automation. Evaluating these factors thoroughly is key for an optimal investment in automation.


Key Industry Challenges with MDM

  • Continuous Data changes across organization
  • Data redundancy and data duplication
  • Changing Measures for Data Standards
  • Controlling the Data Entry Points
  • People dependency on specific data maintenance
  • Statutory Legal and Tax related changes make it harder to manage or organize data
  • Multiple Stakeholders for each data makes it harder to organize and maintain data
  • Data Governance measures become more person specific
  • Mergers, Acquisitions and Divesture leading to dis-organized data
  • Information is meaningful if available on time, authenticated and qualified to help making decisions. Ensuring this requires a discipline


The maturity of Robotic Process Automation has encouraged many organizations to start with the implementation of RPA within their business departments, such as in Finance, HR or IT. RPA is a low-code (business) solution that enables software robots to automate processes previously performed by humans. To successfully implement imdx, it is important to focus on manual and repetitive activities that follow clear-cut rules and make use of structured data. iMDX software robot uses the user interface of existing applications and includes techniques such as screen scraping, rules engines and workflows. When processes involve more complex logic or unstructured data, intelligent automation (advanced RPA) is our solution. By embedding smart add-ons, such as machine learning, chatbots, and algorithms, the scope of business processes eligible for automation increases.

One of the main advantages of using iMDX is that the implementation time is relatively short. In addition, iMDX is less expensive than human labor, an iMDX software license is available for a fraction of FTE related costs and can perform the work of 3-4 FTE. The development of a software robot within a standard business process can be finalized in a couple of weeks, resulting in a quick return on investment. Typically, iMDX is implemented through a Center of Excellence (CoE) consisting of both business and IT involvement within the business itself and is closely aligned with the process specialist. In comparison to more comprehensive IT implementation projects, this approach results in more business focused, tailor made solutions with attention to the details. Next to a quick return on investment and less implementation time, iMDX has multiple other benefits that are also relevant for MDM.



To leverage the benefits of imDX within MDM, one could consider looking at the main activities that often take place within operational processes.



a.   Auto correction / auto completion: a requestor of master data updates often makes use of a standardized form to submit their request. Even though auto completion and auto correction measures have long been applied to support users in filling out their request, iMDX can be used to validate input based on external data sources – offering the requestor details to help them fill in and submit their request.

b.  Unstructured data: OCR (Optical Character Recognition), pattern recognition technologies and intelligent automation help with extracting data from various formats, automating parts of manual data entry within master data requests.

c.  Validate the request for completeness and validity: the standardized format for requesting an update can be validated by the robot. If crucial details are missing or not in accordance with pre-set rules, the robot will inform the requester that data must be added, canceling the original request.



Adequate data validation controls to filter out incorrect input data can often be embedded in databases. However, for more enhanced validation such as cross-checking data against various internal and external sources, iMDX is an excellent solution to automate this task from end to end.



Data entry: requested data updates submitted by means of request forms that are not an integrated part of the source system. While more and more tools and systems provide workflow support and self-service solutions to enable business users to work directly with their data, ERP systems and other master data sources often require dedicated master data administrators to process business requests. The entire MDM function is based on supporting the business and making sure the master data is in line with business requirements. Automating the data entry, by directly entering the content from a request form into the target system, is a huge opportunity for making master data operations more rigid and efficient. By automating data entry with RPA, it is easy for the robot to perform certain input controls to verify whether data is complete, accurate and valid.



iMDX can be easily configured to update the requestor of the master data update by email. In addition, iMDX can also request for additional data which is still missing.


Ensuring the MDM Quality


Features Of iMDX

  • Enable data steward with various dashboard reporting (out of box) and customizable options to improve efficiency and analytics
  • Inbound connectors to source data from heterogeneous systems to MDX
  • Data harmonization and enrichment before sent to ERP
  • Built-in Data enrichment by checks on redundancies, de-dupes
  • Standard Outbound connectors to interface Master data with SAP

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