Improving data quality

Have you wondered about analyzing any sort of data that is full of discrepancies? Not a great idea right? You won't get the desired results. There comes the role of improving data quality. In the current business scenarios when data plays a pivotal role in every business because of data-driven decision making you need to consider improved and structured data for your analysis. There are two basic questions that may arise right now i.e What is meant by data quality and how to improve the quality of your data? Let's answer it in today's blog. When we talk about data quality the first thing that comes to my mind is how accurate and precise my data is. These are two broad aspects that will eventually hamper your insights and decision-making ability. If the data source is not reliable enough it will never generate useful insights. High data quality standards mainly work on several dimensions such as completeness, consistency, accuracy, format, and preciseness. These dimensions can vary from project to project.

Let's consider factors that can degrade the data quality. One of the major factors is the consolidation of data when an old dataset is combined with the new one sometimes it is possible that overlap occurs which can degrade the data quality. Along with it, when you receive real-time data you miss the opportunity to check the accuracy of the data which also leads to hampering the data quality. There are various tools that improve data quality such as Informatica Master Data Management, SAS Data Management, and Talend Data Quality. The main goal of all these tools is to reduce redundancies and errors in data.

The main question is how to improve the data quality. Let's consider different ways to achieve it-

• Data Profiling- As the name suggests it helps you in getting to know your data by taking the summary of your data into consideration. It is a key part of the ETL process. It normally functions by collecting the statistical summary of the data which helps in getting a clear glimpse of the data accuracy.
• Data Normalization- Data normalization or you can say data standardization which is a basic phenomenon to convert your data which is collected from different sources and in different formats to a common format. It helps you in eliminating data redundancy. There is a significant difference between normalization and standardization which depends on the distribution of the data.
• As we know different departments in an organization use the data differently which may lead to distortion of data to resolve that every organization needs a centralized system that can abide by business standards.
• Who likes redundancy in data? Data quality firewall helps in reducing data error and reduce data redundancy. It is a great aid for any business as it avoids the duplicacy of the data which eventually improves data quality.

These are some basic approaches to improve the data quality which will help you get a clear view of your data and you can design business strategies accordingly. It will provide you a consistent data throughout the organization which will reduce errors and gets better results. Good quality data is a boon for any business because you will make informed decisions and have great confidence about it.