Challenges and Applications of Data Analytics in Social Perspectives

Data analytics has the potential to be a competitive advantage, but machine learning has not been able to surpass challenges such as lack of better methods, deeper understanding of problems, and advanced tools. Challenges and Applications of Data Analytics in Social Perspectives provides insights into these challenges and focuses on machine learning and deep learning techniques to improve practice and research.

\n Format: Hardback
\n Length: 330 pages
\n Publication date: 30 November 2020
\n Publisher: IGI Global
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With the exponential growth of data in real-time, there is an immense opportunity to leverage data as a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made significant strides, it has yet to overcome several challenges that hinder its full potential. These limitations include the need for improved methods, deeper understanding of problems, and advanced tools.

Challenges and Applications of Data Analytics in Social Perspectives offers innovative insights into the prevailing challenges in data analytics and its application on social media. It focuses on various machine learning and deep learning techniques to enhance practice and research. The content within this publication examines topics such as collaborative filtering, data visualization, and edge computing. This research is invaluable for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academics, researchers, and students.

By leveraging data analytics, organizations can gain a deeper understanding of their customers, market trends, and operations, leading to more informed decision-making and improved business outcomes. However, there are several challenges that need to be addressed to fully realize the potential of data analytics.

One challenge is the lack of standardized data formats and structures, which can make it difficult to integrate and analyze data from different sources. This requires organizations to invest in data management and governance practices to ensure that data is accurate, consistent, and accessible.

Another challenge is the complexity of data analysis, which requires specialized skills and expertise. This can be particularly challenging for small and medium-sized businesses that may not have the resources to invest in advanced analytics tools and technologies.

Additionally, there are concerns about data privacy and security, particularly in the age of big data and the increasing use of artificial intelligence. Organizations need to implement robust security measures to protect sensitive data and ensure that it is used responsibly.

Despite these challenges, there are many applications of data analytics in social perspectives. One area of application is social media analysis, which involves using data to understand the behavior and preferences of social media users. This can be used to optimize social media marketing campaigns, improve customer engagement, and identify potential risks or threats.

Another area of application is healthcare analytics, which involves using data to improve patient outcomes and reduce healthcare costs. This can be done by analyzing patient data to identify patterns and trends, developing personalized treatment plans, and improving the efficiency of healthcare operations.

In conclusion, with the exponential growth of data in real-time, there is an immense opportunity to leverage data as a competitive advantage. While machine learning has made significant strides, it has yet to overcome several challenges that hinder its full potential. By addressing these challenges and applying data analytics in social perspectives, organizations can gain a competitive edge and improve their business outcomes.

\n Weight: 1108g\n
Dimension: 223 x 290 x 27 (mm)\n
ISBN-13: 9781799825661\n \n