By Nikhil Sonawane - September 22, 2022 4 Mins Read
As businesses are accelerating towards a digital-first world, poor data quality will significantly result in reducing the business value and negatively impacting financial performance. Hence, it is crucial to gather, store, and high-quality process data throughout the organization to optimize the business efficiency.
The data quality depends on its ability to enable businesses to achieve their business goals.
If the gathered data does not serve the requirements of its organization, it is poor-quality data, and it seems useless. Enterprises spend a substantial amount of finances, efforts, and energy to gather, store and process the data assets, and all of that will go in vain if the data has multiple errors and is not maintained properly. CDOs need to ensure that they have effective KPIs in place that evaluate the data quality of the business.
Here are a few data quality metrics that DataOps can leverage to improve overall data health:
One of the most significant strategies to enhance data quality is to embrace the shift-left approach in the entire data operations throughout the organization. Data ops should consider designing effective strategies to integrate, transform, join, and make data accessible and ready to use. This approach will evaluate and remediate data quality concerns so that all the data analytics, visualization, and machine learning tools are operating on consistent and high-quality data channels to improve overall performance. Accuracy, Completeness, Consistency, Timeliness, Uniqueness, and validity are a few commonly used data quality metrics.
Many organizations find it challenging to promote a data-driven decision-making approach because they do not trust the data they process.
CDOs should consider prioritizing enhancing the data quality metrics accuracy, completeness, and usability to ensure that the workforce works on authentic data that drive valuable business insights.
DataOps teams can evaluate their data management practices and set KPIs to measure the accuracy, completeness, and usefulness of data gathered through various channels. Generating trustworthy data enables the data ops teams with their current operational efficiency and agility that offers the resources with data-driven insights to optimize business outcomes.
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IT leaders put a lot of effort into gathering reliable data so that it can be used for decision-making, analysis, and prediction. But to ensure that, it is crucial to have an effective data network and systems for accessing the crucial data sources that are readily available and trustworthy. Another crucial data quality metric that data ops teams should embrace is data system availability. Enterprises today have multiple channels through which they generate data, and it is crucial to ensure the data channel is working at its optimum levels to serve its purpose. Measuring the system performance and availability of each data channel will ensure the data gathered is useful and consistently generated. Organizations can even set KPIs to measure the time required to detect and resolve a data downtime to understand the performance and availability. Evaluating downtime will enable enterprises to determine the financial impact of downtime and data quality. Data ops need to overachieve basic data quality and availability metrics to enhance real-time capabilities. Data timeliness is another important data quality metric that allows businesses to track the end-to-end data flow throughout the organization. Timeliness metrics makes it easier to analyze and establish internal and external SLAs and eventually offer a direct line to enhance and accelerate data analysis.
Nikhil S is a Tech Journalist with OnDot Media. He is a media professional with eclectic experience in communications for various technology media brands. He brings his eye for editorial detail and keen sense of language skills to every article he writes.
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