Top 8 Challenges in implementing Master Data Management

Master Data Management is a repository that stores every data in an organization as a single source for easy access. But creating and managing that master data is not simple. MDM uses all the data from the supplier to the consumer in the supply chain, data from different departments in an organization, even the data about employees, etc., to consolidate them under one roof.

It helps businesses to have an overview of everything without losing any vital information that might lead to loss or profit. Master Data Management needs a tool like Prosol to do its job. Managing data after consolidating in a tool goes through many challenges before putting them together as master data.

Challenges that can fail Master Data Management implementation:

Data consistency
Collecting data from multiple systems will often lead to mistakes and discrepancies. Replicated data can create duplicates, and data fidelity makes the data inaccessible and untrustworthy for anyone in an organization to handle. It will further cause the misplacement of product orders, excess inventory stock, and business loss. Hence it is crucial to focus on consistent data and dependable sources to be added to the master data.
Agile model
Master data management is not a one-time process. When a business goes through changes and evolves, the data grows too. So, the MDM tool needs to be adaptable to all the different changes in the systems. It’s also vital for an MDM tool to have an indefectible structure that can recognize complex errors in the long run and rectify them whenever they occur.
Data Standards
Standardizing data is one of the crucial steps in the initial stages of transferring data into a master data tool. This stage understands, combines, defines the characteristics of an entry, and determines the set it needs to be placed or exchanged depending on its nature. So, without a proper standardization process, the chance for data to get misplaced under a completely different component is high and might lead to compromises in data quality.
Data accessibility
Once the MDM tool organizes data from different departments in a single system, various people in an organization use it. It means the given data is not enough to be available but readily usable by any kind of person, even by someone with little knowledge about the tool.
Data security
Data security is keeping data safe from unauthorized & unintended sources from getting access to the master data. With managing master data and keeping everything together, there is more risk and possibility of harmful threats hacking into the system to retrieve valuable data. But this process of securing data is not wide enough to cover all other stages to prepare master data for protection.
Data Integration
Data integration is distinct from data security, even though both protect data from getting lost. A threat to data integrity may be caused by errors while transferring the data, bugs, and unauthorized systems inside the organization. Hence data integrity challenges involve inconsistencies, inaccuracy, and incomplete data sets. An exceptional MDM tool like Prosol handles these challenges efficiently by overseeing errors swiftly.
Data Governance
Data governance ensures the MDM tool follows all the above processes and makes the data accurate, private, safe, available, and usable to the decision-makers and owners in the given organization. This challenge usually covers every spot in the chain and stops nowhere until the data quality is guaranteed. Lack of data governance may cause imbalance leading to poor choices and unexpected business losses.
Data Stewardship
While data governance oversees the overall data in an organization, data stewardship is specific to ensuring data governance is adequately operated in various parts. Without a proper policy for data stewardship, the master data that is supposed to be helpful can cause more harm than intended. It will also consist of regulating rules and conditions to use data effectively.

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