“Financial services organizations are built on data, making data Quality a critical concern.”
Let’s get some knowledge regarding data quality which may not be a much popular buzzword like ‘Big Data’ but is often used in the data industry. Data analysts help others to remind that having data quality is essential to derive values from data. Data quality helps us to reflect accurate and clean data without any error.
What is data quality?
Data quality is a perception or an assessment of data’s fitness to serve its purpose in a given context. The quality of data is determined by factors such as accuracy, completeness, reliability, relevance and how up to date it is. As data has become more intricately linked with the operations of organizations, the emphasis on data quality has gained greater attention.
- Completeness:
Are values missing or is this duplicated data?
- Accuracy:
Does the data represent reality?
- Timeliness:
Does the data represent the required point in time at the right time? (The best data is useless if it isn’t available when you need it.)
- Validity:
Does the data match the rules?
- Uniqueness:
Is there duplicate data?
- Consistency:
Is the data consistent across various data stores?
- Availability and accessibility:
How accessible and available data is?
Key areas to consider when assessing data quality in your organization:
Data quality is a continuous process which helps the company to reduce their cost which might be incurred due to bad data. So, maintaining data quality is must specially for highly regulated organizations within the financial industry.
Keeping an eye on data on a continuous basis could be an advantage and assist in saving costs.
So, there is a need that urge for continuous check on data quality so that we can able to find out the loops and correct it as and when required.
Good data is a powerful asset and a source of opportunity for any organization but If you have bad or poor data quality it may result in increased costs and also may not produce good results when applying analytics, machine learning or AI. You may not able to find critical issues or opportunity areas to make your data more effective and useful further.
So, there is a need that urge for continuous check on data quality so that we can able to find out the loops and correct it as and when required.
Data quality problems do not arise from only one point in data workflow. Instead, there are many processes that you perform on your data or database as you aggregate, transform and visualize it can all introduce some new data quality issues. By enforcing data quality early and have a look into it continuously, you stand a much better chance of catching these problems and resolve issues as an when they occurred.
Data Quality is a big challenge or never-ending battle in data industry because data quality is defined in terms of a data set’s ability to serve a given task, the precise nature and characteristics of data quality will vary from case to case. Data quality may vary from organization to organization. What one organization perceives as high-quality data could be not of use in the eyes of another organization.
There must be understanding about how data quality changes based on the context is important because data quality is not something you just can simply obtain and keep it for future. As you may have data quality today but in some future period of time your goals may change and your data in its current state can no longer meet them.
It’s something you need to be constantly working on and improving to ensure that your data is ready to meet whichever tasks you throw at it. Data quality is major concerns which need to be continuously monitored and find the issues or loopholes so that they can resolve on time.
