Data Refinement is a systematic process of converting a set of raw data into a usable, pertinent and integrated format for better understanding. It eliminates data redundancy and creates an organized and structured data source. Business analysts are benefited as they get to increase the overall productivity and efficiency of their work modules in a strategic way.
Data Refinement plays a very important role in data warehousing as the unrefined data can create havoc in the final statistical output from the data warehouse which the company will eventually use it as business intelligence. It does not apply only to one aspect in data warehousing but many stages starting from data modeling to final integration of systems in the data warehouse till the functioning of the entire business intelligence system.
In data modeling, data refining takes place when the semantics in the organization is being described at the time of developing the conceptual schema. All the abstract entity classes and relationships are identified and ensured that entities can be implemented on real-life events as a part of company activity. In case of data refining, all the unnecessary and irrelevant things are eliminated. The same thing is applied even in the logical schema where the tables, columns, XML tags and object-oriented are being described. Data refining ensures that the structures to store the data is well-defined.
In an Entity Relationship Model (ERM) data refining is used in order to ensure that all the relationship between the entities and their corresponding attributes are secure and accurate. It eliminated data redundancy and maintains integrity in which insert, update, and delete processes can be managed easily without compromising with the final data quality by broken integrity.
Data Refinement also plays an important role in database normalization in which technique is used in designing and relational database so data-duplication is reduced and the database is free from certain types on logical inconsistency.
Data massaging is a part of data mining in which the numbers, values, and statistics are extracted. The information that is found within the database predicts what the customer will do next.
Data Refining is the final action that is taken after data mining has completed all the stages in data collection or gathering.
Data Refinement speeds up the data procuring process and helps in finding the right data as per the business requirements, which makes the entire process simple. It is an added advantage in a structured business model as the data required for the analysis is directly available in a structured format which saves a lot of time and money for the organization. It also ensured that data which has been processed is updated periodically which keeps up the customer’s trust for the organization. Below are the reasons why Data Refinement is important
- Success rate improves in your business campaigns.
- Eliminates undesirable and unwanted data in the system.
- A good response is ensured in your email campaigns.
- Quality of the client database improves.
- Save money on marketing and data management.
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