How vital is Data Quality for a successful Data Governance Framework?
Huge tons of data is generated every day from emerging and disruptive technologies such as Augmented/Virtual reality sensor technology, Robotics, the Internet of Things, and many other emerging technologies that help in Digital Transformation.
Data Quality can be defined as the degree to which the data is complete, consistent, timely, and accurate with the business needs and requirements rules. The hard fact is that it is a common practice for enterprises to commence data quality initiatives without Data Governance framework implementation, and this is why many data quality initiatives provide only short-term results.
To achieve the valuable data’s insights, it must be processed by enforcing the data quality best practices. Data is recorded and used for various purposes depending on the needs and requirements of the enterprise. Modern technologies such as Artificial Intelligence and Machine Learning allow enterprises to use data to generate significant value while focusing on privacy and control.
The data quality’s main critical components include consistency, completeness, validity, accuracy, uniqueness, and timeliness. With high-quality data, enterprises can become data-driven and can make impactful business decisions which ultimately helps them grab the best opportunities at the right time.
The main critical components of Data Quality.
- Consistency:
The types of data must be aligned with the expected versions of the data which is already collected - Completeness:
Data has to be collected by ensuring no gaps in it. - Validity:
Instead of the final results, validity should be obtained from the process. - Accuracy:
Ensure Data collected is relevant, correct, and accurate. The data should reflect the purpose it has been collected. - Uniqueness:
By preventing duplication within or across systems in any particular record, field, or data set, uniqueness can be achieved. - Timeliness:
The main reason for maintaining timeliness is that the data should be received and available at the expected time to serve the data’s purpose and be used efficiently.
What includes in Data Governance that will help Data Quality?
Data Governance includes vital areas such as people, process, and technology. For instance, the framework of the Data Governance assigns responsibility and ownership of the data and defines the methods for managing the data, and leverages the technologies that will enable processes and people.
People: The team that collaborates is vital for the success of Data Governance. The people will be necessary for managing all the enterprises’ data to help them define their roles and responsibilities.
Process: The next important step in building a data governance framework is defining how the data will be monitored, controlled, and audited. This helps the data to be consistent, accurate and serves the purpose. There are many processes involved in the Data Governance processes, such as master and reference data management, security, and risk management.
Technology: Technology helps streamline both people and processes and helps your enterprises make sound data-driven business decisions. Technologies include things such as standardization, verification, collaboration, monitoring, identity resolution, and reporting tools, to name a few.
Undoubtedly it is vital to use quality data that to gain a competitive edge, and there are many benefits, such as
- Better Audience Targeting
- Informed Decision-Making
- Improves Relationship with Customers
- Simple and Effective Implementation of Data
Undoubtedly, Data Quality and Data Governance are vital for enterprises, and focusing only on Data Quality without Data Governance will lead only to short-term results.To know more information and any queries, please contact +1 888 840 0098, and you can email us at sales@amurta.com. We will be happy to assist you.
