Mis Disc1 Please Answer With 300 Words Minimum And APA Stand

Mis Disc1please Answer With 300 Words Minimum And Apa Standard1 Wha

Mis Disc1please Answer With 300 Words Minimum And Apa Standard1 Wha

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The significant risks associated with poor data quality in businesses are multifaceted, affecting operational efficiency, decision-making, and overall organizational reputation. First, inaccurate or incomplete data can lead to flawed business decisions, which can result in financial losses, missed opportunities, and strategic misalignments. For example, businesses relying on erroneous customer data may target the wrong audience, leading to ineffective marketing campaigns and wasted resources (Kwak et al., 2017). Second, poor data quality can escalate operational costs due to the additional resources needed for data cleansing and revision. Organizations spend considerable time and money correcting errors, which could otherwise be directed toward strategic initiatives (Ross et al., 2018). Third, legal and compliance risks are heightened when inaccurate data leads to violations of data privacy regulations such as GDPR or HIPAA. Failure to maintain data standards can result in hefty penalties and damage to reputation (Piprava et al., 2020).

Data mining is a process engaged in extracting valuable, hidden patterns from large datasets by analyzing their structure and relationships. It enables organizations to discover insights that are not immediately perceivable through traditional analysis. Data mining involves techniques such as classification, clustering, association rule learning, and regression analysis, which help in making predictive analyses, segmenting markets, and improving product recommendations (Han et al., 2011). It also facilitates informed decision-making by transforming raw data into usable information.

Text mining, on the other hand, involves extracting meaningful information from unstructured text data. Given the explosion of textual content across social media, customer reviews, and corporate documents, text mining enables the identification of themes, sentiments, and trends from vast volumes of textual data (Feldman & Sanger, 2006). It employs Natural Language Processing (NLP) algorithms to decipher language nuances, categorize content, and infer sentiments, providing businesses with insights into customer opinions, emerging issues, and market trends (Gupta & Aggarwal, 2019). Both data and text mining are pivotal in harnessing big data for strategic advantage amidst the digital era (Aggarwal, 2015).

References

  • Aggarwal, C. C. (2015). Data Mining: The Textbook. Springer.
  • Feldman, R., & Sanger, J. (2006). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press.
  • Gupta, P., & Aggarwal, S. (2019). Sentiment analysis and opinion mining: A review. Journal of Intelligent Systems, 28(3), 337-352.
  • Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann.
  • Kwak, H., Lee, C., Park, H., & Moon, S. (2017). Data quality issues and measurement in big data environment. International Journal of Data and Knowledge Engineering, 1(1), 1-12.
  • Piprava, P., Bajaj, N., & Mishra, M. K. (2020). Data privacy and security in healthcare systems: A review. Journal of Medical Systems, 44(8), 145.
  • Ross, J. W., Beath, C. M., & R.g., Pt. (2018). Data quality management and governance. Harvard Business Review.

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