Understanding the role Data Cleaning Tools
Posted: Nov 02, 2016
The Internet has truly revolutionized the way we do business. And a result the importance of the quality of data has gained prominence. All businesses and companies need to ensure that their data is up-to-date and error free. This is a must to meet regulation requirements, to manage customer relationships and for data integration. Data that is not well maintained needs maintenance and involves unnecessary expenses, apart from also damaging the reputation of a business and has a negative impact on customer experience.
That is why it is essential for a company to measure data quality. There is specialized software available to measure and monitor the quality of data. They are of immense significance in the banking, financial and insurance industries among various other domains. These different types of software are known as data cleansing tools.
The role of data Cleaning tools
To parse & standardize data: Even the smallest amount of dissimilarity in the value of data may cause for it to generate inaccurate or incorrect reports. Therefore to prevent any such occurrence it is vital to parse the data into segments or blocks which then is changed to a custom or standard format. Data cleansing tools help to parse data values and combine them into a unified format.
Beneficial in creating a data profile: The first stage when data is evaluated is that of analysis and exploration. For this data sets are measured using quantitative tools and analyzed. To help to profile data algorithms are used to statistically analyse and evaluate the quality of data. The tools also help to analyze a data set. The detailed analysis is then documented and used at a later stage to audit or monitor reports.
Identify similarities: The primary role of data quality management is to resolve two similar issues with a minor variation. At times records are actually present which appear as not being present. There needs to be techniques that help identify near matching records which ensures that similarity between various records could be identified. Such types of issues are resolved through "identity resolution". This helps to identify the levels of similarity between two records. There are data cleansing tools used in the management of data quality that use algorithms which restrict records with the probability of matches. It also assists in master data aggregation and cleansing.
Help to augment data: Data cleaning tools use methods such as elimination of duplicate data, address correction, removing redundant data etc. and also help in record linkage.
Helps in monitoring& evaluation: Apart from helping to identify and maintain data these tools help to monitor and evaluate data. This helps in meeting business goals and increasing profitability.The use of data cleansing tools and software is paramount at the investigation and evaluation stages of data quality management. These tools are beneficial in data analysis and helping to find any irregularities within the data. These tools help to maintain a very high standard of data quality that ensures authenticity of data.
My name is Sohel Ather, software engineer by profession and a content writer by passion, Technical writings, blogging etc are stuff that I do as hobby.