The need of big data testing for digital customer experience

1. The need of big data testing for digital customer experience

2. The need of big data testing for digital customer experience In today’s world of digitalization, the top motive for enterprises is to ensure a robust digital customer experience. However, tracking this experience has been a challenge for eons. The advent of emerging technologies such as Artificial Intelligence (AI)/Machine Learning (ML), 5G, Internet of Things (IoT), and Big Data & Analytics, has made measuring customer experience possible now. While AI/ML, 5G, and IoT can indirectly help gauge the customer experience, Big Data & Analytics is going to play a big role in measuring the profundity of customer experience. Measuring Customer Experience Gartner in its recent study emphasized on 5 types of metrics such as Customer satisfaction (CSAT), customer loyalty/retention/churn, advocacy/reputation/brand, Quality/operations, and employee engagement to measure customer experience. While all these metrics quantify the measurement of customer experience, it is imperative to understand that only those metrics need to be taken into consideration that are relevant to the business. Recent studies by industry experts suggest that it is only the correct analysis, meaningful use of data collected, and holistic approach that can help improve the digital customer experience.

3. The need of big data testing for digital customer experience Big Data Testing for better data quality According to Evans Data Corporation, “19.2% of big data app developers say quality of data is the biggest problem they consistently face”. Data quality is a vital characteristic that determines the trustworthiness of decision-making. It helps you manage and cleanse data while making it available across the enterprise. With Big Data comes bad data. So, it is imperative that the processed data adheres to the highest standards. Big Data testing is completely different when compared to the typical testing around traditional data warehouses or databases that revolve around structured data and use Structured Query Language (SQL) to accomplish the testing. Big Data deals with not only structured data, but also semi-structured and unstructured data and typically relies on Hibernate Query Language (HQL). Data Validation, Process Validation, and Outcome Validation are the key components of Big Data Testing. While validating the data, the collected data is ensured to be accurate and not corrupted. This collected data is sent to Hadoop Distributed File System (HDFS) for validation where it is partitioned and thoroughly checked.

4. The need of big data testing for digital customer experience It is imperative to identify core business priorities first to ensure that business opportunities are driving your analytics investments. While many firms become more data-driven with business models that depend on data, deprived quality data will gradually become a systemic problem. Cigniti’s Big Data Testing framework helps clients measure and improve digital customer experience using Big Data Analytics. Cigniti leverages its experience of having tested large scale data warehousing and business intelligence applications to offer a host of Big Data testing services and solutions such as BI application Usability Testing. Need help? Talk to our Big Data Testing and Customer Experience experts to know more about Big Data Testing for superior digital customer experience. Read Full Blog at: https://www.cigniti.com/blog/big-data-testing-digital-customer-experience