Master data management came into practice when businesses wanted to upscale the quality and consistency of their data assets such as customer data, product data, location data, asset data etc. Master data management or MDM is the tools, techniques and processes that ensure that the master data is coordinated throughout the organization. MDM gives a consolidated service for master data that gives accurate and complete master data consistently throughout the organization and other business partners.
To maintain clean master data, many changes are said to be introduced in the business processes fundamentally and subsequently, many issues of MDM are rather political than technical. Creation and maintenance of master bot come under master data management. Therefore, putting a lot of time, effort and money into developing a clean master data is a failed exercise unless the procedure to keep master data consistent and clean is also included.
Standardizing and cleaning of master data
It would help if you first understood the data model of your master data before you begin normalizing and cleaning it. You must have the contents defined for each attribute and proper mapping to the master data model from each source. You can leverage this information to illustrate the necessary transformations to clean the source data.
Cleaning of data and then transforming it into a master data model is quite similar to ETL (extract, transform, load) process used in data warehousing. If the tools of ETL and transformation are already defined, then it becomes easy to modify them as per the requirements of master data.
Some functions of data cleansing are:
- Standardize data formats- convert addresses, make sure all contact numbers look same and so forth on to a format that is common for all.
- Replacement of missing values- Insertion of defaults, looking up Dun & Bradstreet number and ZIP codes from the address.
- Regularize values- convert all the measurements into metric system, convert prices to a common currency, and change part numbers to industry standard.
- Mapping of attributes- Check the first and last names out of the contact name field, move Part# and partno to the PartNumber field.
Mostly tools put the data to not cleanse into an error table for hand processing and clean the rest of the data. The cleansed data will go into master table according to how the matching tool will work. You must check output as each source is getting clean to make sure that the process of cleansing is working properly.
Master data management can cover single (product, customers, locations and other) and multiple domains, which depends upon the technology used. Multi-domain MDM has it own benefits such as the ability to share reference data across various disciplines, consistent data stewardship experience, a minimized technology footprint, a lower total cost of ownership and a more significant ROI.
A strong MDM program needs to have a sound MDM strategy in place to manage data consistently. This strategy revolves around 6 disciplines:
- Governance
- Measurement
- Organization
- Policy
- Process
- Technology
Moreover, an MDM project is incomplete without the business users’ participation and support. Any project of MDM that doesn’t include the changes occurring in the processes for creating, maintaining and validating the master data is very likely to fail.
With the help of solutions provided by MDM, the quality of your data will improve, hence improving the business operations. Apart from that, we can also witness data visualization effects on MDM. It consists of data transformation, cleansing, integration and allowing companies to leverage accurate, consistent and up-to-date information.
