A Review on Enhancing Data Quality for Optimal Data Analytics Performance - Rangineni, Sandeep; Bhanushali, Amit; Suryadevara, Manoj; Venkata, Srinivas; Peddireddy, Kiran (International Journal of Computer Sciences and Engineering, 2023)
This intellectual researches into the realm of data quality and its profound impact on data analytics. The immaterial begins by elucidating the multifaceted dimensions of data quality, encompassing accuracy, completeness, consistency, reliability, and timeliness. Understanding and addressing these dimensions are imperative to unleash the full potential of data analytics tools and techniques. Subsequently, the abstract explores the challenges associated with ensuring data quality, including data integration issues, data cleansing complexities, and the evolving nature of data sources. Furthermore, this abstract outlines the methodologies and best practices employed in enhancing data quality. Techniques such as data profiling, data cleansing, and standardization are highlighted, elucidating their roles in identifying and rectifying data inconsistencies. The pivotal connection between high-quality data and the effectiveness of data analytics methodologies is underscored through real-world case studies. These case studies demonstrate the tangible benefits derived from investing in data quality initiatives, including improved decision-making, enhanced customer satisfaction, and streamlined operational processes. Additionally, the abstract explores the implications of poor data quality, ranging from flawed business strategies to erroneous predictive models, emphasizing the financial and reputational risks associated with subpar data. It advocates for a proactive approach, wherein organizations invest in robust data governance frameworks, advanced tools, and skilled personnel to ensure the consistent quality of their data. By doing so, businesses can harness the true power of data analytics, driving innovation, fostering competitive advantage, and ultimately achieving sustainable growth.