Characteristics of effective MDM
- Master data management
- Meta data management
- Data Preparation
- Data Advisory
- Data Integration
Before going into any MDM Project, an organization must understand the basic functionalities that a basic MDM solution must have and how can an organization make the most out of the MDM solution and guarantee the added-value it is providing.
Here is a list of 6 characteristics to help organization understand the concept behind an MDM solution and its benefits.
Characteristics of a Master Data Management Solution
1. Reference Data : Match and Merge
Apart from the reality that Master Data Management provides a centralized data repository, a valid MDM solution must be based on an efficient Match & Merge algorithm. Most organizations nowadays consume data from different sources (that are based on different models and follow different working methodologies) which is causing the forming of silos for data of the same domain. The bigger the number of silos, the higher the chances of having duplicate and un-clean data.
Here comes the power of the match & merge algorithm. The latter will take the available data and match the values within (based on well-studied matching criteria) and then merges the matched data to form a single source of truth or a Golden Record per result. This will allow organizations to have one correct instance of a value (example 1 record per Customer) that gathers all the information into one entity. This will not only create a clean data repository, but it will create a strong basis for analytics as you take it further. The whole purpose of a good MDM solution is to transform the data into valid and clean information to help business understand the actual amount of data they have and help them extract analytics cases for their future business plans.
2. Reference Data : Business Rules
It is very important to implement business rules on the incoming reference data: these business rules are defined once but act globally on all included systems. This will allow organizations to further refine the quality of the incoming data. Moreover, it will help define the data flow starting from the source system all the way to the MDM Hub.
This will give the technical team full control on the rolling of the data and the business team full awareness of the quality outcome.
3. Reference Data : Security
Before thinking of internal security measures that should be taken within the MDM Solution, it is very essential to obtain consent from all the systems to use their data at their terms. This will prove that external applications are aware that their data is being used and to what extent it is used and in what ways. A consent document should be provided before starting – this will help ensure everyone’s rights and help understand the scope of project overall and the scope of the needed security measures.
Data Domains that are handled in MDM can contain sensitive data – and securing this information is very essential. One way to implement access control and protect information is applying role-based rules: these rules require the creation of roles with different access levels. This role-based helps monitor who views what and who does what: Data availability and Actions on Data. Another form of data security within MDM, is respecting the General Data Protection Regulation (GDPR) that helps protect data based on geolocations and a defined set of rules. In addition, cryptography can be used by masking some values and avoid the wrong users to view or understand its meaning or by introducing some sort of authentication before allowing access.
All the mentioned security ways will help fortify your internal network against external threats coming in from the different systems that the process is dealing with and against any outside hazards.
4. Reference Data : Enrich and Refine available data
Another important purpose for MDM is to help enrich the available data to help form a 360 view of each data instance. In order to enrich the available data, the MDM solution must:
- Work on available data: apply business rules and transform the available data to conform with the new suggested design and data quality metrics.
- Integrate with external systems that have the same data domain that is available in the organization to help fill the gap of what’s there and confirm the righteousness of the data.
- Develop Data Quality Dashboards to help continuously monitor the quality of the data and highlight the importance of the cleaning exercise.
When the above is applied, accurate analytics dashboards can be extracted and business predictions and plans can be generated, providing a trusted source of information for the business personnel.
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