Relationship between Data Quality and MDM
Melissa IN Team | |
When businesses need to make decisions about their future, they rely on data. Data is what determines where the next store should be opened, how to segregate customers on email lists, etc. Businesses deal with a plethora of data and it doesn’t help that the data is varied and formatted in different ways. Decisions taken based on poor quality data will have a poor outcome. So, to make data useful, you need high-quality data and an efficient Master Data Management system.
What is Master Data Management and why is it important?
Master Data Management refers to the process of collating data from all input points and formatting it to create a single master copy. Anyone needing access to data will access the data from this record without creating copies so that it remains unique.
Master Data Management offers enterprises a single version of the truth and empowers them to understand customers better so that they can, in turn, improve their customer services. In the current scenario where product differentiation is minimal, customer service is often the factor determining a customer’s loyalty to the brand.
Master Data Management and data quality are intrinsically linked. For any Master Data Management initiative to be successful, improving data quality is a prerequisite.
Why is data quality a challenge for organizations?
Enterprises collect data from various points. Some data may be entered by the customer directly. For example, customers enter their names and email addresses when signing up for newsletters.
Customers also enter their addresses and contact numbers when confirming an order. When entering these details, customers can make typographic errors. A simple spelling error can have long term ramifications.
These errors may also be made if the data is being entered by a company representative. These representatives are usually sales interns or customer care representatives who don’t have many incentives for ensuring that they record high-quality data.
The problem is compounded when you take into consideration the fact that data is being collected simultaneously by different departments. An error in recording details can lead to duplication of records. For example, let’s say the customer signed up for a newsletter with the name ‘Soniya’ but the accounts team created an invoice with the name ‘Sonia’. As a result, the company will have two records for the same individual.
The situation could worsen when two companies merge and try to integrate their data sets or when one company is acquired by another.
Thus, raising the data quality standards cannot be ignored.
Implementing Data Quality for Master Data Management
While data quality initiatives can be run independently, every Master Data Management initiative must be preceded by implementing data quality measures or implemented in parallel. There are various aspects of data quality that must be addressed.
People may change their email addresses or shift homes thereby changing their addresses. In such cases, the old data contained in databases becomes obsolete and useless. When implementing Master Data Management initiatives you must ensure that the data is valid currently.
Completeness is an aspect of data quality that can vary from department to department. While one department may consider records to be complete with just the customer’s name and number, another department may consider it complete only if the record also contains the customer’s address. For Master Data Management, you will need to arrive at a decision for what defines completeness and ensure that all records adhere to this standard.
Data validity is tied to the way it is formatted. For example, if a customer enters his date of birth in a DD-MM-YY format when the required format is an MM-DD-YY format, the data becomes invalid. Thus, all data must be formatted to meet the preset formats.
All data must be accurate and error-free. It must be verified against reliable third party databases to confirm the reliability of the records.
The data stored in one database must match the data stored in other records. This means that the data held by all departments must be consistent in terms of accuracy and formatting.
Data integrity stands the risk of being compromised every time the data is transferred or copied. To maintain its integrity the data will need to be checked between updates to ensure that it remains intact and unaltered.
Who is responsible for Data Quality and Master Data Management?
Master Data Management is not an initiative that can be handled solely by the IT department or executed in isolation. The IT teams and business personnel need to work together to create data governance teams for Master Data Management projects. This is critical as it is only business users who can understand the type of data truly required by the business and, in turn, define data quality standards.
To maintain a high level of Master Data the initiatives must take a holistic approach and address the people, processes and technology used to improve Master data quality.
In terms of organization, roles such as data owners, data stewards, data managers, etc. need to be defined. Each role must be given clear tasks that are geared towards the company’s goals.
The ideal process for improving data quality starts with defining the company’s data goals and analyzing the current state of data quality. Data then needs to be cleaned and enriched to help with business processes. These different phases of the data quality cycle must be matched with individual roles to ensure high Master Data quality standards.
While data quality initiatives can exist independently, it is critical to any type of Master Data Management project. For it to be truly effective, the IT teams and business users must work in unison to ensure that the data maintains high-quality standards and is maintained as a single record.
Access to this data can be provided to anyone who needs it but systems must be put in place to ensure that it isn’t reproduced and the uniqueness of the record is maintained.