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What is Data Quality?
Data quality is the assessment of fitness of data to fulfill its purpose in a perception or in any context. Below we will benefits and why data quality management needed for any orgationation. It describes several dimensions like:
- Data Correctness /Accuracy: Improvement and accuracy is a process in which the data is correctly captured, which describes the real-world unit.
- Data Consistency: Data consistency refers to the usability of data. Data throughout the organization should be sync with each other.
- Data Completeness: This is the extension for which the expected features of the data are provided.
- Timeliness: Right data to a right person at a right time is important in any organization for successful planning.
- Metadata: Metadata is a data that provides information about other data. There are many different types of metadata between these descriptive metadata, structural metadata, administrative metadata, reference metadata and statistical metadata.
Why Data Quality matters?
Good and consistent data would be the most valuable asset, however, if we collect bad data it would seriously harm your business and credibility. The quality of the data works on two aspects, accuracy and cleanliness. Which involves data consistency, completeness, and relevance.
Any management in the organization must need to think twice and re-examined the data or information before proceeding to upper level in the organization.
- What have you missed?
- When thing goes wrong
- Making confident decision
What is Data Quality Management?
Data Quality Management (DQM) refers to a business theory, which requires a combination of all people, processes and technologies, which are all with a common goal of improving the data quality measures, most to an enterprise organization.
What is the purpose of Data Quality Management?
First of all, any business situation improves with good quality data management, while outdated or unreliable data can lead to mistakes and wrongs. Any data quality management program can set up the framework for any department in the organization.
Secondly, accurate and reliable data give the actual picture of any business. Good data quality management gives you the real status of your company. It shows your upstream and downstream.
Data Quality Metrics Examples
- Percent of data is correct (correct data/ total data) (e.g. ZIP code, SSN)
- Percent of data is complete (e.g. phone number, address)
- Percent of data is consistent (such as business rules and logic rules)
- Percent of data is accurate timeliness (e.g. ages, education degree at special time or date)
- Percent of data is validity (e.g. first name, last name, suffix)
- Percent of data is uniqueness (e.g. primary keys, foreign keys)
Five Best Practices for Data Quality Management
So far, we have talked about quality data and quality data management. Now, we are going to tell which best practices should be kept in mind for data quality management.
Review the current data:
Smart organizations want to maximize their investment in data quality management, and this is to understand how poor data quality negatively affects the achievement of business objectives.
So, when you’re getting started with data quality management, do an audit of your current data. This phase should be completed with a formal report which explicitly lists the findings. The report can be circulated among the stakeholders, decision makers and therefore data quality improvement works are carried out.
Data Quality Measurement:
There are a variety of definitions of Data Quality Measurements, but data quality is usually measured against a set of criteria called ‘Data Quality Dimensions’, which assess the health of the data, such as completeness, or specification.
Put the Right People in Place:
So far, we have discussed what kind of data should be selected which benefit the organization. Now let us take a look at where we put people in the right place in the organization for profit.
Define roles, requirements and responsibilities in the company in a holistic manner and you can identify the right people for the job in a focused manner for specific projects. It can indicate the need for new rent or reprint of existing talent in some areas.
Establish Consistent Procedures:
If we talk about consistency, it is a process based on clearly documented steps that everyone follows. Create and follow procedural rules for dealing with data, and help to avoid common data quality issues.
Perform Data Quality Assurance Audits:
Do not wait for data quality problems Instead, you should do regular and recurring audits. It should be processed on a regular basis so that 1 percent of the mistake is not even a chance.
Benefits of Management Reporting – Read More
From this post, it is clear that good data quality matters, and the organization needs to focus on the high quality of data. It is also clear that data quality management is a critical process to keep your organization competitive in today’s digital market. However, a company that understands the true meaning of high data quality can only predict the success.
Abhishek is working as a Web Graphics Designer at EzDataMunch. He is involved in Maintaining and enhancing websites by adding and improving the design and interactive features, optimizing the web architectures for navigability & accessibility and ensuring the website and databases are being backed up. Also involved in marketing activities for brand promotion.