Data Mining Data cleaning

1. DATA CLEANING
S. R. Thrisha | S.Juwariya Fathima | D. Vidhya | A. Zarifa Azra

2. ADVANTAGES:
◦ Improveddecision-making.
◦ More effective marketing and sales.
◦ Better operational performance.
◦ Increased use of data.
◦ Reduced data costs.
◦ Deletionof errors in the database.
◦ Better reporting to understand where the errors are emanating form.
◦ The eventual increase in productivity because of the supply of high – quality data in
your decision-making.

3. DISADVANTAGES:
◦ Analysts may lose out an actionable insights due to incomplete data.
◦ This is very common in case where missing observations and outliers are dropped.
◦ It may be lead to an even bigger problem when automated.
◦ Some automated data cleaning tools are not very smart and may end up mishandling
some observations in the data set.
◦ It is time – consuming
◦ Data cleaning may take a lot of time, especially when dealing with large data.
◦ The process is very expensive.

4. DIFFERENT BETWEEN DATA
CLEANSING AND DATA CLEANING:
◦ Data Cleansing:
Data Cleansing Is used more specifically to address removing dirt or germs,
especially viawashing, and is also used figuratively as seen in. It is called as “Data
Cleansing “.
◦ Data Cleaning:
Data Cleaning is used more generally to address everything from washing to
tidying up. It is calledas “Data Cleaning”.

5. REAL WORLD EXAMPLE:
◦ Data Cleaning is include the removal of columns that are unneeded for the data
model and the report.
◦ It mean a removing extra spaces from a fieldusing TRIM or CLEAN commands.
◦ It is making all states to letter abbreviation and even making them uppercase.

6. USAGE OF DATA CLEANING:
◦ Data Cleaning in Data Mining DataIntegration: Since it is difficult to ensure quality in
low-quality data, data integration has an important role in solving this problem. Data
Integration is the process of combining data from different data sets into a singleone.
◦ Data Migration: Data migration is the process of moving one file from one system to
another, one format to another, or one applicationto another.
◦ Data Transformation: Data transformationprocesses usually include using rules and
filters before further analysis. Data transformation is an integral part of most data
integration and data management processes. Data cleansing tools help to clean the
data using the built-in transformations of the systems.
◦ Data Debugging in ETL Processes: Data cleansing is crucial to preparing data during
extract, transform, and load (ETL) for reporting and analysis. Data cleansing ensures
that only high-quality data is used for decision-making and analysis.