Quality Assurance in Big Data Analytics - An IoT Perspective

1. Quality Assurance in Big Data Analytics: An IoT Perspective

2. 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. 

3. 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. 

4. 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.   Read Full Blog at: https://www.cigniti.com/blog/quality-assurance-big-data-analytics-iot