Big Data by Ketjona

 7 .       What do you think big data can be used for in the future? Many people agree that big data is here to stay and not a mere fad. Something that is not so clear-cut to everyday individuals concerns the future trends of big data analytics. These technologies are quickly evolving. What does that mean for the businesses that use them now or will soon?   Augmented Analytics Will Speed Decision-Making It involves applying technologies like artificial intelligence (AI), machine learning and natural language processing to big data platforms. This helps organizations reach decisions faster and recognize trends more efficiently. Cloud Data Will Shape Customer Experiences Cloud computing is a major topic of discussion when people weigh in about big data trends. Here are a few things those in the know expect to see regarding what’s happening now and what could occur soon when users combine big data with cloud computing. . . . https://www.smartdatacollective.com/6-important-big-data-future-

 6 .       Give two examples of contemporary applications of big data in society  There are many positive ways to use big data, including weather prediction, forecasting natural disasters, urban and community planning, traffic management, logistics and machine efficiency, personalized healthcare, customised learning, autonomous vehicles etc.   1. Public health Big data allows scientists to unravel entire DNA sequences in minutes. This means increased ability to predict patterns in diseases and mutations. Data from smart watches and wearable devices increases the amount of data health experts can collect. Some hospitals are already using big data techniques to monitor premature and sick babies by analyzing every heartbeat and breathing pattern of every baby. Scientists can now develop algorithms that can help predict infections based on that data -- hours before physical symptoms appear. 2. Poverty reduction For organizations like UNICEF, big data is key to their success. They can deliv

  5 .       Give two examples of contemporary applications of big data in science Data science has evolved as a way to make sense of big data. These analyses allow researchers and companies to make data-driven decisions. Getting trained in data science can help researchers analyze and manage their data sets. Data science can also be used to better allocate research resources.   ·         The NASA Center for Climate Simulation (NCCS) stores 32 petabytes of climate observations and simulations on the Discover supercomputing cluster.   ·         Google's DNAStack compiles and organizes DNA samples of genetic data from around the world to identify diseases and other medical defects. These fast and exact calculations eliminate any "friction points", or human errors that could be made by one of the numerous science and biology experts working with the DNA. DNAStack, a part of Google Genomics, allows scientists to use the vast sample of resources from Google's search server

4.       What are the limitations of big data predictive analytical software? Big data is seen by many to be the key that unlocks the door to growth and success. However, although big data analytics is a remarkable tool that can help with business decisions, it does have its limitations. 1.   Security As with many technological endeavors, big data analytics is prone to data breach. The information that you provide a third party could get leaked to customers or competitors. 2.   Transferability Because much of the data you need analyzed lies behind a firewall or on a private cloud, it takes technical know-how to efficiently get this data to an analytics team. Furthermore, it may be difficult to consistently transfer data to specialists for repeat analysis.        3.   Inconsistency in data collection Sometimes the tools we use to gather big data sets are imprecise. For example, Google is famous for its tweaks and updates that change the search experience in countless ways; the results o

 3 .       Give two examples of software used to collect big data. Companies use the best big data analytics tools to predict and determine behavior on a large scale to help you make decisions and see how they will affect this grouping. These can help companies reduce operating costs, offer better products and services, and see how their consumers are spending that result in a more profits and growth.   1.    Tableau Tableau is extremely powerful. The fact that it is one of the most mature and powerful options available shows as soon as you see the available features. It’s a bit steeper to learn this platform, but once you do it is well worth it. 2. Zoho Analytics Zoho Analytics is a really nice system. They’ve been around for a long time when it comes to CRM and predictive data. They can handle everything from analytics to sales. . . . https://www.vssmonitoring.com/best-big-data-analytics-tools/

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

1. Two examples of contemporary applications of big data in business The organizations have realized the need for evolving from a knowing organization to a learning organization. Businesses want to be more objective and data-driven, and so they are embracing the power of data and technology.   1.       Using Big Data Analytics to Boost Customer Acquisition and Retention The use of big data allows businesses to observe various customer related patterns and trends. Observing customer behavior is important to trigger loyalty. Theoretically, the more data that a business collects the more patterns and trends the business can be able to identify. In the modern business world and the current technology age, a business can easily collect all the customer data it needs. This means that it is very easy to understand the modern-day client. Basically, all that is necessary is having a big data analytics strategy to maximize the data at your disposal. With a proper customer data analytics mechanis