Data Quality and Master Data Management

Master data management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to a common point of reference. Master data management is intended to bring a systematic approach to data integration that ensures consistent use and reuse of data. The data management in any organization plays important role. As big data architectures find greater use, the types of data in organizations grow haphazardly in structure. With poor data management the problems like data duplication,incomplete and error prone data generates. So, to solve these issues, the concept of master data management emerged. Master Data Management is the technology-based discipline of business and IT to ensure consistent, accurate and responsible data objects of a system.

Data is generally considered high quality if it is "fit for [its] intended uses in operations, decision making and planning". Alternatively, data is deemed of high quality if it correctly represents the real-world construct to which it refers.The data is said only qualitiable if data posses following qualities:

• Completeness
  
  

• Validity
  
  

• Uniqueness
  
  

• Consistency
  
  

• Timeliness
  
  

• Accuracy
  
  

Data Quality is the issue where data captured in the systems is not of the highest quality due to human errors, in most cases. Normally, people in organizations doing data entry are not the highest paid people in the company. In addition, if they do not have incentives for capturing high quality data, they don’t care. For example, a single letter mistyped in any email address may lead to invalid email address.

Furthermore, as data volume increases, the question of internal data consistency becomes significant, regardless of fitness for use for any particular external purpose. People's views on data quality can often be in disagreement, even when discussing the same set of data used for the same purpose. Data cleansing may be required in order to ensure data quality.

To ensure data quality, number of vendors make tools for analyzing and repairing poor quality data. Most data quality tools offer a series of tools for improving data, which may include some or all of the following:

• Data Profiling
  
  

• Data standardization
  
  

• Geocoding
  
  

• Matching or Linking
  
  

• Monitoring
  
  

• Batch and Real time
  
  

Successful data quality and master data management initiatives require a universal approach. Some of the approaches which can be used are:

• Organizations need to address persons, processes and technology to implement the business demands on data quality.
  
  

• Organization should include clear responsibilities for data domains (e.g., customer, product, financial figures), as well as roles (data owner, operational data quality assurance / data stewards).
  
  

• Processes for data quality assurance can be defined by adopting best practices like the data quality cycle.