Master Data Management involves linking all the data contained within an organization into a single file so that there is only one reference point of information. The data to be linked should be critical such that normal business operations cannot be undertaken without its availability.
The Master file enhances smooth access and sharing of information between different departments that have been set up. Additionally, when data is combined into a single file, it enables the enterprise to adopt variant types of computer applications, architectures, or platforms without interfering with the day to day activities.
Master Data Management takes care of scalability in the sense that as the organization grows and more departments are set up due to increased employee activity, smooth data access is still maintained without affecting the processes undertaken by older applications that had already been adopted. As for that reason, big enterprises that tend to have complex structures stand to be the biggest beneficiaries of MDM.
Also, when two different companies come together through a merger, there always tends to be confusion concerning how various processes and applications will operate in tandem. MDM plays a huge role in ensuring that contrasting activities are fused together, and operations are undertaken in a particular format.
The adoption of a new system into an organization always comes with its unique challenges since change always interferes with the culture that has already been established within a particular working environment. As for an MDM, three significant hurdles always come up in every organization.
Unlike a Customer Relationship Management, a Master Data Management program is a back-end system. Therefore, the number of users who are engaged in accessing the MDM hub are limited. Employees may end up becoming ignorant on the importance of the MDM as they believe that it is the responsibility of the IT personnel. Hence, it is important that an enterprise educates all persons on the advantages of a MDM system.
Adoption of a Master Data Management system brings about numerous alteration, especially at the managerial level. Such a situation can end up being very contrasting as the management may be repulsive towards change, yet their support is greatly needed to ensure that the MDM works. To overcome challenges that may be posed, it is crucial to work hand in hand with the managerial team so that they understand their privileges. Once integration with existing CRM systems is complete, smooth operations will continue to be undertaken.
In the course of bringing together two systems, some old features may become useless in the new system. For instance, some aspects of the CRM may need to be customized so that they can forcefully fit into the Master Data Management system. There is an immense need to ensure that such harmful decisions are not made in the process of integration, as this may end up affecting the process of data access in future and create a situation that cannot be undone.
Massive volumes of data are usually generated in an organization on a daily basis. Therefore, there is great need to ensure that the information is described and handled in a manner that will not create chaos in future as an enterprise grows. The concept behind Big Data ensures that data management can be undertaken more pragmatically. Under Big Data, there is Velocity, Volume, and Variety.
The amount of data that has been collected may range from terabytes to exabytes. Organizational data comes from different sources such as IoT, enterprise records, or sensors which capture real time information. Collection of data is important since it enables final analysis to be carried out using analytics tools.
Data that has been collected tends to be in different formats, which majorly include unstructured and structured data. The former exists in the form of document files while the latter is found in data stores such an SQL database, and they are all analyzed in a different manner.
The speed of data analysis creates the aspect of velocity. All projects associated with big data make use of gathered data, analysis of the collected information, and the final provision of results to the user depending on the type of information that was collected.