Big Dat The Advantages And Disadvantages Of Big Technology

For Payers - Since heath data can be made available to consume and analyze, organizations can take advantage and reduce their existing costs related to patient care. Adoption of digital heath data is estimated to save about $77 billion per year for both inpatient and outpatient care[5]. Better decisions can be made by Payers by taking the benefit of intelligent Algorithms which predict disease possibilities and provide more insights on target audience to better understand the entire healthcare system.

Big Data Analytics is the process of analyzing large amounts of raw information generated and stored. In today 's fast paced technologies, we are inundated with in a tsunami of data before us. All applications, in a broader range are depending on data in a remarkable way. BDA is driving almost every field in our society from Retail, Manufacturing and Mobile applications to life and physical sciences. The Data Analytics techniques are performed to uncover hidden patterns, unknown correlations and other useful information. Earlier, Data Analytics were based on guessing and inaccurate data models but currently this can be done directly. Big Data has truly revolutionized scientific research (Computing Research Association 2014).

According to the article, 2.8ZB of data has been created and replicated in 2012. The proliferation of devices such as PCs and smartphones worldwide, increased Internet access within emerging markets and the boost in data from machines such as surveillance cameras or smart meters has contributed to the doubling of the digital universe. IDC projects that the digital universe will reach 40 ZB by 2020, an amount that exceeds previous forecasts by 14%. Thus, data is not narrowed to big only; it is actually huge. Like, 40 ZB data is the equivalent of 1.7 MBs of new information created by the every single human for every second of the day. Developing countries like China and India are currently covering 36% of digital universe; the prediction says it will be increased up to 62% by 2020. So the companies will have numerous scopes to dig out more data and analyze them as per their requirement. Despite the unprecedented expansion of the digital universe due to the massive amounts of data being generated daily by people and machines, IDC estimates that only 0.5% of the world’s data is being analyzed.

"The ever-increasing appetite of businesses to embrace emerging big data-related software and infrastructure technologies while keeping the implementation costs low has led to the creation of a rich ecosystem of new and incumbent suppliers," said Ashish Nadkarni, IDC program director, enterprise servers and storage, in a prepared statement. Nadkarni co-authored the IDC report with Dan Vesset, program VP, business analytics & big data. "At the same time, the

Big data has been around for a long time. With the introduction of the Internet of Things, this technology has caused a rapid evolution for big data. The IoT now contributes the biggest percentage of data for big data applications. As data continues to grow at exponential rates and the Internet of Things become more a part of our everyday world, they both have become intimately interconnected. Wherever there is the IoT, you will find big data, and wherever you find big data you will find the IoT.

the ‘big Data era’ has arrived — multi-petabyte data warehouses, social media interactions, real-time sensory data feeds, geospatial information and other new data sources are presenting organisations with a range of challenges, but also significant opportunities. IDC believes that as CIOs start to adopt the new class of technologies required to process, discover and analyse these massive data sets that cannot be dealt with using traditional databases

Big Data is becoming more meaningful with the ever more powerful data technologies, which enable us to derive insights from the data and help us make decisions. Big Data also creates new courses and professional fields such as the data science and data scientist, which are aimed at analyzing the ever growing volume of data. Some might think this exaggerated because data analysis, after all, not a new invention. However, we might all agree that the progress of digitization associated with the generation of ever larger amounts of data have totally changed the ways we deal with data.

But data is expanding faster than ever and now facing big data challenges too. In this paper we are

This chapter considers the challenges and opportunities around storage, sharing and usage of data – particularly large datasets. An increasingly important area of IT/Computer Science/Informatics is data science and as big data has become more prevalent across many industries and sectors there has been the emergence of larger numbers of highly skilled data scientists to support the challenges of data storage, sharing and usage. Such experts can provide far more comprehensive advice and expertise than such a short chapter, however, the key considerations of a data scientist are set out in this chapter to help explain the value that such expertise brings.

Big data refers to the large amount of data that is impossible to handle by using traditional or conventional methods such as relational databases or it is a technique that is required to handle the large amount of data that is generated with advancements in technology and increase in population. Big data helps to store, retrieve and modify these large data sets. For example, with the advent of smart technology there is rapid increase in use of mobile phones due to which large amount of data is generated every second, so it is impossible to handle by using traditional methods hence to overcome this problem big data concepts were introduced.

Every year the data transmitted over the internet is growing exponentially. By the end of 2016, Cisco estimates that the annual global data traffic will reach 6.6 zettabytes. The challenge will be not only to “speed up” the internet connections, but also to develop software systems that will be able to handle large data requests in optimal time. For the definition of the Big Data, there are various different

Five years ago, few people had heard the phrase ‘Big Data.’ Today, it’s hard to go an hour without seeing it implemented practically in our daily life. The promise of a highly accurate data-driven decision-making tool is an attractive lure for any organization in any industry. However, big data is not without its own problems.

Big Data Now : 2016 Edition is a collection of big data and data science blogs and excerpts written by various O’reilly authors. It brings forward the knowledge of executing big data project and creating scalable solutions.

We are living in data age, around twenty one zetabytes of data is predicted to be there till 2020. Recent years have witnessed a dramatic increase in our ability to collect data from various sensors, devices, in different formats, from independent or connected applications. This data flood has outpaced our capability to process, analyze, store and understand these datasets. Today people are totally into social networking sites

We are living in data age, around twenty one zetabytes of data is predicted to be there till 2020. Recent years have witnessed a dramatic increase in our