Effective Data Modelling and Data Quality techniques
1. What is Data Modelling?
“The process converting unstructured, scattered and repetitive data into structured, meaningful and normalized data”.
2. Need of Data Modelling
a. Need to decrease redundancy of data and established the relation between data.
b. Need to Stored data in the normalized form.
c. Need to implement structured database management system.
d. Need to take backup and restore data when the system had the breakdown.
e. Need to operate data in a secured way.
3. Importance of Data Modelling in business.
Using data model we avoid repetition of data and stored in normalized form. Also, we can create a process for taking backup and restoring data. As well as we are making a database in secure and unbreakable mode.
4. What is Data Quality?
“The parameters for measuring nature of data, behavior of data, the fitness of data for given purpose of data.”
5. Need of Data Quality
Now a days, incorrect data and inconsistency of data is a problem as well. Eliminating data shadow systems and centralizing data in a warehouse is one of the solutions to the company can take to ensure data consistency. Also, lack enough time to deal with large-scale data-cleansing software. As well as Security concerns over sharing information, giving an application access to systems, and effects on legacy systems.
6. Data Quality Parameters
a. Accuracy: Data Accuracy is the parameter of data quality that deals with the data being exact when describing the physical characteristics of products
b. Completeness: Ensure that transactions are complete and valid. Validate data that were input, and edit or send back for correction as close to the point of origination as possible.
c. Update Status: Properly update relational data into the respective table in the database.
d. Relevance: Closeness between data consumer need and data provider output.
e. Consistency: Synchronization of data objects across the company.
7. Data Quality Strategy (Techniques) Outline
It’s Consist of Problem Definition, Data Issues Identification, Analysis, and Improvement. See Fig 7.1
1. Problem Identification
a. Review known Problems.
b. The scope of Problems.
c. Identify Problem Objectives.
d. Identify Experts.
e. Identify Documentation.
2. Identify Data Issues
a. Identify and Collect Data.
b. Flag Suspect Data.
c. Establish Quality Criteria.
d. Establish Metrics.
e. Perform High-Level Quality Assessment.
a. Apply Quality Criteria.
b. Identify Conformance Issues.
c. Assess Impacts.
d. Provide Recommendations.
e. Prioritize Conformance Issues.
f. Validate Conformance Issues.
a. Select Recommendations.
b. Implements Recommendations.
c. Document Improvements.
d. Monitor Improvements.