Ensuring Data Quality in the Era of Big Data – Three Challenges to Overcome

Ensuring Data Quality in the Era of Big Data – Three Challenges to Overcome

Many companies now gather, exchange, and make data accessible to all employees in an efficient manner as a result of data socialising. While most organizations profit from having access to such information resources, others are concerned about the accuracy of that data.

There’s no denying that data makes the world go round – and with increased innovation and automation affecting people’s daily lives, it’s now feasible to capture and store more data than ever before, making data access easier than ever.

However, for many companies, ensuring corporate data quality proves to be a significant challenge, with a vast number of factors contributing to data quality issues.

Poor data not only costs businesses money, but it also has an impact on customers. Therefore, it’s understandable that companies all around the world desire to enhance their data quality processes. At the end of the day, analysis is incorrect, data insights are unreliable, and if data quality isn’t up to par, data assets can soon become liabilities.

Here are a few data quality issues to avoid in the workplace.

Data that is duplicated

Duplicated data is unavoidable when numerous, siloed systems are present. It’s critical that the organization and its data provider have a proper data verification procedure in place, including data deduplication technologies to search through the data and find duplicate records.

Security concerns

Confidentiality, availability and integrity are the three essential principles that govern data security. An organization’s business-critical data, as well as personal information, must be protected. A strong data security policy differentiates the protection of the organization’s data assets, prioritizing the protection of the most critical data.

Human error

Human error is likely the most difficult obstacle to overcome when it comes to getting excellent data quality. Employees are prone to mistakes like typos and missed alpha numerals, which can lead to data quality difficulties and even inaccurate data sets.

Also Read: Four Key Big Data Challenges to Watch Out For in 2022

The most effective strategy to address this problem is to reduce human effort while increasing data input. Every day, the usage of AI makes automation more possible. Human error can be minimized through the use of advanced algorithms and AI-based systems.

Data quality, analytics and data governance

Implementing an enterprise data intelligence platform with a broad and strong set of capabilities is the best method to improve and safeguard data quality. Companies can gain more value from their data and ensure the quality of the data across systems and processes by combining data governance, analytics capabilities and data quality.

Having a data intelligence platform in place creates a strong data governance framework, making it easier to streamline operations aimed at ensuring data quality throughout the supply chain.

Companies expect and require data to be easily understood, accessed, and trusted by stakeholders at every stage of the journey when it is initially created. This enables the data to be used to provide actionable insights and guide business decisions.

Businesses must first evaluate the lineage, attributes, metadata, and quality of data in order to comprehend what it signifies and its quality. Data governance capabilities of a robust data intelligence platform can deliver these vital capabilities as well as integrated data quality features that help monitor and improve data.

The platform should also make it easier to get a complete picture of a company’s data landscape. This knowledge helps all users to have confidence in the quality of all their data, ensuring regulatory compliance and allowing them to make confident business decisions.

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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.