Advantages and disadvantages of big data for the society

 

2.  What are the advantages and disadvantages of big data for society?




Big data is growing in a number of industries, and healthcare is no exception. 

There are many positive ways to use big data in a personalized healthcare.

The program may sound powerful, but it doesn’t come without risks. While big data has many advantages, the disadvantages should also be considered before making the jump.

Advantages:

Higher-Quality Care

Because big data draws from a number of sources, including previous doctor and pharmacy visits, social media, and other outside sources, it can create a more complete picture of a patient. Similarly, a doctor may be able to see underlying causes for a health issue that wouldn’t be easily visible with just basic health information.

Early Intervention

The overall goal of big data in healthcare is to use predictive analysis to find and address medical issues before they turn into larger problems. Big data definitely makes the entire process more efficient.

 

Disadvantages:

Privacy

One of the strongest negatives relating to big data is the lack of privacy, especially when it comes to confidential medical records. To be effective and get the full, comprehensive look at a patient, big data must have access to everything, including private records and social media posts.

 

Replacing Doctors

While some people see the ability to predict future medical issues as a positive, big data also poses the risk of replacing doctors. Big data simply isn’t at the point yet where it can be used on its own, and it definitely lacks the personal touch of a human doctor.

 .

.

.

https://www.healthtechzone.com/topics/healthcare/articles/2016/11/18/427248-pros-cons-big-data-the-healthcare-industry.html

Comments

Post a Comment

Popular posts from this blog

Technical requirements of big data

Image
2.       What are the technical requirements of big data? This data boom presents a massive opportunity to find new efficiencies, detect previously unseen patterns and increase levels of service to citizens, but Big Data analytics can’t exist in a vacuum. Because of the enormous quantities of data involved in these solutions, they must incorporate a robust infrastructure for storage, processing and networking, in addition to analytics software. 1.       Storage Solutions Often, organizations already possess enough storage in-house to support a Big Data initiative. (After all, the data that will be processed and analyzed via a Big Data solution is already living somewhere.) However, agencies may decide to invest in storage solutions that are optimized for Big Data. While not necessary for all Big Data deployments, flash storage is especially attractive due to its performance advantages and high availability.   2. Processing Power   Servers intended for Big Data analytics must have enoug
Read more

Limitations of traditional data analysis

Image
 7 .      What are the limitations of traditional data analysis? Traditional statistics is about analysing and summarizing data, and suited to processing lesser amounts of linear, repeatable data to find a solution. It’s very useful in environments where data, and the relationships between the data, are relatively stable. It is still by far the most commonly used methodology and continues to be good for mid to long term forecasting based on prior sales history, where a few hundred or even fewer data points may generate a reasonable forecast. The important limitations of statistics are:   Statistics laws are true on average. Statistics are aggregates of facts, so a single observation is not a statistic. Statistics deal with groups and aggregates only. Statistical methods are best applicable to quantitative data. Statistics cannot be applied to heterogeneous data. If sufficient care is not exercised in collecting, analysing and interpreting the data, statistical results might be misleadi
Read more

Traditional data analysis

Image
 6 .      What is Traditional data analysis (descriptive and inferential)?          Traditional data is the structured data which is being majorly maintained by all types of businesses starting from very small to big organizations. In traditional database system a centralized database architecture used to store and maintain the data in a fixed format or fields in a file. For managing and accessing the data Structured Query Language (SQL) is used. 3 Characteristic of Traditional Data: ·        Traditional analytics is static.   Traditional data analytics typically relies on dashboards composed of visualizations. These dashboards are based on common business questions and are predefined well in advance.   ·        Traditional analytics answers “what.”   Traditional analytics answers a series of “what” questions and leaves the user to determine “why” through their own analyses.   ·        Traditional analytics is driven by hypotheses.   Dashboards are typically predefined based on common
Read more