Understanding Data Quality Cycle

What is data Quality?????

The process of conditioning data to meet the specific needs of business users.
Data Quality ensures data is correct, consistent, and complete by monitoring, analyzing, and reporting.

Data Quality Life Cycle can be broadly classified into two steps

1. Data  Profiling/Data Quality Assessment
2. Data Cleansing

Data Profiling:Data Profiling is the analysis of data to identify data quality issues w.r.t various defined data quality attributes which are listed below.

1.Completeness
2.Conformity Of Data
3.Consistency
4.Accuracy
5.Duplicates
6.Address validity
7.Timeliness

Once the Issues are identified in the data w.r.t above attributes,we start the process of Data Cleansing.

Data Cleansing:Data Cleansing in the process of correcting the issues identified during the Profiling exercise.Few of the One-time cleansing activities are listed below.

1.De-Duplication:To identify the Survivor record among the identified duplicates and merge the duplicates into Survivor Record

2.Address Enrichment:To enrich or correct the address data.To enrich the addresses.

3.Field Level Cleansing:To apply cleansing logic specific to few fields.

4.Data Standardization:Standardizing the data w.r.t standards defined.Ex:Country Name Standardizing to ISO 3-Digit Format.Ex :United States to USA.

5.Parsing:parsing is the process of splitting the values in a field to multiple items.
Ex:Name to First Name,Middle Name,Last Name

Once the Data Cleansing exercise is complete,we prepare the data management Strategy.