On the Evaluation, Management and Improvement of Data Quality in Streaming Time Series

Gómez-Omella, Meritxell; Sierra, Basilio; Ferreiro, Susana

doi:10.1109/access.2022.3195338

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…Research on the framework supporting these data quality inspection, monitoring and control processes is an important axis of research in the sense that systematic data quality management and error predictability can be guaranteed. Some researchers focus on several metrics to measure data quality in streaming time series data in IoT applications [34]. They propose a set of metrics for measuring data quality (DQ) in streaming time series, and implement and validate a set of techniques and tools to monitor and improve the quality of information.…”

Section: B Framework To Support Data Quality Assessment Process (Incl...mentioning

confidence: 99%

Exploring the Impact of Data Quality on Business Performance in CRM Systems for Home Appliance Business

Suh

2023

IEEE Access

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In customer relationship management (CRM), high-quality customer data is at the heart of reliable data analysis and is the foundation for data-driven decisions that impact business goals. To find performance indicators of data quality to maximize the effectiveness of CRM, we need to devise an approach to identifying and managing "business-relevant" information quality metrics. Therefore, this paper deals with the discovery and validation of the Data Quality Dimension (DQD) in terms of meaning and utilization value of data values other than the aspects such as syntax criteria or data format. We design the quality index and scoring logic of the customer integration profile and prove its usefulness by applying it to actual CRM data. A sample of real business operations data of approximately 1 million CRM customers was used to analyze the relevance between the DQDs and business performance indicators. As business performance indicators, we used both the company's purchasing loyalty index and the performance of past promotional campaigns. We analyzed the significant impact of each DQD on purchase loyalty and promotional campaign success rate. Next, we confirmed the effectiveness of DQDs in terms of providing analytic ease for predictive analysis such as target marketing in CRM. In addition, we showed some possibilities to consider improving data quality by analyzing the granularity of a specific attribute based on a certain DQD. Through these verification results, the validity of the DQDs of the customer profile was confirmed in the context of 'suitability for use' of customer data that affects business activities critical to the company in the CRM system.

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Section: B Framework To Support Data Quality Assessment Process (Incl...mentioning

confidence: 99%