The evolution of the internet was a major spark plug for the rise of contemporary data concepts like data analytics, data science, big data. Cloud computing and Artificial Intelligence. The internet facilitated the uninterrupted communication of individuals and devices on a 24/7 basis therefore creating large complex volumes of data that historical tools were incapable of handling laying the groundwork for modern management of data and its analysis.
Innovation has become essential in any industry across the world and with many embracing technologies in various sectors the assimilation of data analytics and science has transformed industries universally. This essay looks into how data science and analytics are transforming industries today, how it is applied and also the ethical considerations affecting data science and analytics in organizations
Introduction
Data science and analytics houses a wide range of methods and tools necessary in the extraction of actionable insights from large datasets leading to the making of informed decisions and innovation. The insights gained through data analytics are also crucial in the driving of organizational strategic actions hence enabling organizations to stay ahead of the competition and adapt their strategies based on changing market dynamics. Data science and analytics is a systematic that involves several stages involving data collection, data cleaning, data exploration, data modelling and eventually interpretation with each contributing to understanding the data set and driving actionable conclusions.
Healthcare Industry
The healthcare industry is the spine of any community as it supports the well being of the population at large. Other industrial sectors majorly depend on the healthcare sector, education, energy, sports, security, banking and telecommunications cannot operate effectively without it being functional. Data science and analytics have made a notable difference in the industry necessitating the development and adoption of predictive machine learning models that have aided disease identification, the personalization of treatment plans culminating in the enhanced patient results.
Data science machine learning algorithms have transformed the healthcare industry through the early detection of pathologies, utilizing patients genetics and history in order to suggest the right and needed medications in the form of either a remedy or therapeutics. The healthcare industry has also been transformed through wearable devices that have aided in the detection of deviation from normal vital signs. Data analytics has revolutionized the sector through analyzing of behavior’s such as sleeping, physical activities and understanding patterns associated with mental health.
Security Industry
Data science machine learning models in the form of intrusion detection systems are transforming the security industry through the highlighting of suspicious network traffic aiding in the stopping of internal security threats. Data analytics has made it possible for security agencies to spot threats early by deploying behavior analytics for actions tracking. Mapping and the prediction of crime has become essential with predictive policing tacking center stage.
Predictive policing entails the utilization of math’s, predictive data analytics techniques in law to pinpoint potential crime activity. Geospatial analysis comes into play within predictive analytics whereby algorithms are utilized to analyze large datasets containing historical crime data to aid in crime prevention now and in the future.
Data science machine learning models have been assimilated in the security sector to help in fraud detection by flagging unexpected purchase behavior, analyzing transaction patterns to highlight fraudulent activities in banking and ecommerce. Data analytics being crucial for business intelligence and providing the necessary information for organizations to make informed decisions, security teams are able to utilize dashboards and visualization to aid in the understanding of new trends therefore improving the speed and accuracy of how needed actions are taken during an emergency.
Education Sector
Schools and learning institutions in the education sector have been greatly transformed through the adoption of data analytics and data science. Many institutions are now able to come up with better operational efficiency mechanisms and allocation of resources adequately based on an analysis on the data they with regards on the number of classes available and students who are enrolled. Data analytics has made the budgeting across various school departments less cumbersome owing to the simplicity of the process.
Data analytics has transformed learning by helping teachers analyze the performance of their students, engagements within the class environment accompanied by the feedback they get to get an understanding of when and how to update the learning materials needed. With home learning a common concept in the contemporary world, instructors are now able to personalize learning paths based on a student’s strength and weaknesses. Ultimately data analytics and data science prediction models have made it possible for teachers to easily predict students who are at risk based on their performances therefore offering an intervention plan to aid in their learning.
Energy Sector
The adoption of the Internet of Things (IOT) in the energy sector has made it possible for energy companies to monitor the status of their equipment by utilizing data on their sensors therefore rendering it easy to predict any mechanical failure of either its transformers and turbines where needed. This utilization of data analytics culminates in the reduction of downtime if any as the equipment is always monitored.
With many countries focusing on renewable energy, data analytics has made it possible for organizations in the industry to be able to provide communities and businesses with patterns of how the use energy together with suggestions on how to save energy after undertaking behavior analysis.
Sports Industry
The sports industry has been transformed greatly by incorporating both data science and data analytics. Data analytics is used on a daily basis to analyze individual player performance by using key attributes such as speed, strength and the distance covered. With sports being a team initiative managers utilize analytics to identify where his or her team needs to improve through an analysis of its strengths and weaknesses. Additionally, the utilization of medical records is integrated to help in the creation of individual training programs. Key performance indicators are also set after analysis to help the team based on how the players perform.
Recruitment that is well carried out is essential for any team either players or the team staff. Data driven analytics are consistently undertaken in order to scout for talent, machine learning models are also deployed to aid in the process based on what the team needs. With many sport companies and teams targeting to gain revenue the optimization of the same is made possible through analyzing the sponsorships return on investment (ROI), the sale of merchandise during game days, normal days and during the offseason.
Ethical Considerations
Data science and data analytics have greatly transformed a wide array of industries however concerns have been greatly raised over data privacy and the amount of data collected by companies and individuals as many people are not conversant with how their data is being used as well as the companies collecting it. With the human factor in play during the training of the science models algorithms may be biased on either racial or economic basis based on the location and information input into them. Data accuracy and the integrity of data is another ethical consideration owing to the fact that if the data used to make an analysis is poor it automatically leads to the drawing of harmful conclusions that could have detrimental effects.
Conclusion
Innovation and increased performance within the key industries such as healthcare, security, education, sports and energy have been greatly attributed to the incorporation of data analytics and data science over the past few years however ethical considerations surrounding the privacy and fairness of the process need to be thoroughly looked at in order to avert the erosion of trust and the bolstering of societal biases. Institutions ought to give precedence to ethical standards whenever data analytics and data science is involved.
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