The rise of the Internet of Things (IoT) as a primary data contributor in big data applications has posed new data quality constraints, necessitating the creation of an IoT-inclusive data validation ecosystem. Read more: https://www.cigniti.com/blog/quality-assurance-big-data-analytics-iot/

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Quality Assurance in Big Data Analytics: An IoT Perspective

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Quality Assurance in Big Data Analytics: An IoT Perspective
The rise of the Internet of Things (IoT) as a primary data contributor in big data applications has posed new data quality
constraints, necessitating the creation of an IoT-inclusive data validation ecosystem.
In a big data testing, standardized data quality methodologies and frameworks are available for data acquired from a range of
sources such as data warehouses, weblogs, social media, and so on.
Because IoT data is so different from traditional data, the issues of assuring its quality are likewise distinct, necessitating the use
of a specially built IoT data testing layer.
IoT engineers and executives must ensure that their IoT deployments have a solid data governance program in place, one that
addresses the necessary data quality measures as well as how to ensure that they are met and maintained.
Any conclusions or forecasts based on facts that do not satisfy a predetermined criterion are faulty, and when predictions skew
in the wrong direction, they can cost an organization money.
Precision, record completeness, data set completeness, authenticity, reliability, originality, coherence, accuracy, usability, and
availability are all objective properties or dimensions that data scientists can measure to assess data quality. Usability,
credibility, interpretability, and objectivity are examples of subjective attributes.

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Quality Assurance in Big Data Analytics: An IoT Perspective
Data quality exists on a spectrum, and the techniques and tools used to acquire, combine, store, and analyze data vary along
that spectrum. Identifying the nature of the data collected and the reason for which it will be used will dictate the shape of
the solution employed to ensure that the quality is appropriate for the purpose.
Data from endpoint devices has far higher data quality requirements than data from an IoT deployment intended to improve
efficiency.
The majority of current scientific and industry efforts in IoT are focused on developing integrated platforms in order to realize
its commercial potential. The present IoT and Big Data setting poses a variety of issues, such as ensuring sensor data quality
defined by accessibility and veracity. Traditional signal processing methodologies are no longer enough, necessitating a
combination of architectural and analytical approaches.
The rapid expansion of IoT testing brings with it a slew of new challenges that must be addressed.
Data created by internet-connected devices is growing at an exponential rate, and the storage capacity of Big data systems is
limited, thus storing and managing such a massive volume of data has become a major difficulty. To collect, save, and process
this data, some procedures and frameworks must be designed.

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Quality Assurance in Big Data Analytics: An IoT Perspective
Cigniti offers independent quality engineering and a wide range of software testing services and solutions for the next
generation enterprises and ISVs across the globe. Our experienced and deep-skilled quality assurance professionals have a
hands-on, end-to-end understanding of the challenges faced by enterprises while on the path of digital transformation.
We implement the best possible software testing methodologies and applications, a Testing Center of Excellence, and
world-class software testing Labs to deliver on our promise of Quality Engineering, Quality Assurance, and Digital
Assurance.
Whether you have desktop, mobile or next-gen-based applications, our software testing specialists work with a focused
approach to help you get more out of your testing efforts and improve time to market, and thus, your ROI.
Get in touch with our Quality Assurance experts to learn more about quality assurance in big data analytics from an IoT
perspective.
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