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Best practices of Data Cleansing

  • Edwin Walker 


Data Accuracy:

Data accuracy is the biggest challenge for many businesses, having accurate data is useful in all its stages to use. Data results in inaccuracies when it is created, collected or during the clean-up, or when being stored. The inconsistencies from any of the sources make the data useless or less valuable. Discrepancies make it difficult for organizations to correct them at later stages of the data, as it becomes expensive and very tedious.Data cleansing removes inaccuracies and makes data useful in all and even stored for future use.

Data Security:

Best way to ensure security and privacy is ensuring that an organization has a data governance model. A good data governance model defines how data will be used and moved from one stage to another stage. Ensuring data security, encryption may be done to avoid a breach. When encryption is implemented with measures, such as a strong firewall, it keeps the data safe and secure.

Data Performance and Scalability:

With the rapid growth of data, a data pipeline experiences a challenge in scalability. A good data pipeline engine is sufficiently extensible and effective as well as robust. It processes data in real-time and never gets overwhelmed.

A scalable data pipeline is one with a good architecture that is built to anticipate changes in volumes and the diversity of data and data types over time. The latest data curation platform such as DQLabs employs such possibilities and has a highly scalable data pipeline engine.


Data Governance is the management of data with regards to data ownership, accessibility, accuracy, consistency, data quality, and data security in an organization. It enhances data integrity and quality. This is through solving data issues such as errors, inaccuracies, and inconsistencies that may exist between various data sets.
Through data governance, an organization is able to maintain compliance with relevant data laws and regulations.


In today’s world, the biggest challenge with data is its security. There are so much confidential data in organizations, this poses a high risk. Data encryption involves encoding so only the intended recipients can decrypt. This is a best practice in data cleansing.

Right architecture:

Today, data is spread almost everywhere, there is a high likelihood to have data chaos. This creates a need for good architecture, this informs data collection, enhancement. It helps to understand existing data and make sense of it. It is also the backbone in the management of data throughout its lifecycle.
The right data architecture lays out the foundation for a data governance structure, it’s, therefore, worthwhile to invest in the right architecture as early as possible.

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