Introduction of the Big Data

In this paper, the main focus will be around explaining various aspects of Big Data. The difference will be tried to identify between Big Data and Data Science. In today’s modern business world and with increasing role of technology, the role of Big Data is also increasing, so companies have to come up with new ways to handle their Big Data. It is important to understand that why the use of big data is needed in future and how it can be done.

Data science of the Big Data:

Here, we explain the difference between big data and data science. Dealing with structured as well as unstructured data, related to data cleansing; data science is a field that comprises of everything, preparation, as well as analysis. The humongous volumes of Big Data cannot effectively be processed with the traditional applications, which currently exist. Big data is also defined as to analyze something with great insights, which could lead to improved decision as well as business strategic moves. The data science is the combination of statistics, programming, mathematics, capturing data as well as problem solving, and having the ability to look differently, as well as the cleansing the activity, aligning the information. The umbrella technique is very simple, which is used to extract the vision as well as data from information. The skills required for data science are as follow; in-depth knowledge, python coding, platform Hadoop, database SQL/ coding as well as data of unstructured work (Monnappa, 2018)

Big Data:

The humongous volumes of information, the big data refers to the data that couldn’t be effectively managed with the traditional application that exits. When raw data processing of big data is started, it is mostly impossible to store it in a single computer. For immense volume of information, a buzzword that is utilized to describe, on a day-to-day basis big data inundates a business, both structure as well as un-structured. Gartner give the definition of big data, High volume in big data, as well as velocity is high otherwise information assets high-variety that demand cost effective, innovation forms of information processing, which enables enhanced vision, making of decision, and automation process. Another author describes it as, problems related to data handling as well as management of Big Data is a technique to resolve all the unsolved problems. With the help of this tool we can easily understand the need of customer as well as better understanding of their needs. The following skills which are required for big Data like Analytical skills, creativity, Mathematics’, statistical skills, computer science and business skills (Monnappa, 2018)

Big Data + Data Science = Big Data Science

Literature Survey of the Big Data

Big data:

A research was conducted by the Author M.S & et.al (2015), which shows that how research direction can be changed in business model by big data by providing services with product. Through different applications like social media, smart devices and wireless sensor technology, more data can easily be shifted. In this report, it is emphasized on the improvement of new technology rather than the older one, and upcoming challenges & expected trends are also discussed in this paper. Moreover, the aspect which provides awareness about internet environment is also discussed. IoT is aimed at collecting information from smart objectives of different domains. For integration, collection, processing, transmission & delivery of context information, IoT is best suited infrastructure (M.S & et.al, 2015)

Data Scienec & the Big Data:

A research study was conducted by Stanton & et.al (2012), which stated that Data science passed on the emerging areas of network, which are concerned about the collection, management, analysis as well as visualization for the large collection of information. The database area along with the computer science is related to the Data science with the different skills involving the mathematical skill. The data science is great for analyzing the data, where various people entertain the analysis of data, which is happily spend for the histograms as well as the average of peoples should allow data science activities. The transformations of the data is more valuable for the use of decisions makers to know about that in what way the data is transformed  (Stanton & et.al, 2012)

Why is the use of big data needed?

Big data plays an important role in our management or data handling techniques. It increases the number of data sources as well as the variations along with the volume of information for analysis, which is useful. From big data, a non-relational framework to produce analytics could be used, before it is consolidated into a data warehouse otherwise to pre process big data. We would look at industry research, at present; it gives three main reasons that why we require Big Data as well as analytical strategy. In many ways, Big Data as well as analytics strategy welfares your organization:

Creating smart, leaner organization

To have cross channel conversation equipping your organization

For the inevitable future preparing for your organization

Execution of Big Data and analytics strategy is ultimately to develop the organization into more efficient as well as smarter one. Nowadays, in many industries from criminal justice to health care, Big Data is being leveraged with powerful outcomes to real estate. To economic forecasting, the same logic is being applied. For example, from one quarter to the next turns the number of Google queries regarding housing as well as real estate out to expect more accuracy in the housing market and what’s going to happen than any team of expert real estate forecasters.”There are some appealing benefits of big data for example:

in obtaining vision, it is quick & valuable

in the performance of a business, it performs with better and improved analysis

in better decision-making, it reduces the risks (White, 2012)

Example of Big Data usage is real world application:         

In many propelling industries, Big Data assistance is playing an important role:

PUBLIC SECTOR

Through Big Data, some of the main facilities delivered to public sector revolve everywhere. The government sectors are facilitating in areas for example recognition of deceit, investigation of power, economic investigation promotion, and protection of ecological. In the areas the main influences are such as:

Analysis of Economic

Sharing of Data (from organizations to citizens gathered information is sharing)

Agencies TAX

Cyber Security(Munné, 2016)

HEALTH CARE & the Big Data

In the field of health care as well as medicine the importance of Big Data could not be neglected. It is important to understand that healthcare data can be huge, and for better healthcare performance, it is necessary to handle big data with data analytics. It can enable doctors, surgeons and healthcare providers to keep track of patients on regular basis. Moreover, data analytics can also help to extract some research data out of patient’s records. Moreover, the doctor could easily attain his patient’s background and history, as big data analytics will allow to retrieve all relevant information. Moreover, the data can also be more safe and secure. The information could be stored for a lifetime. In the future, the doctors have the authority to reach the information anytime.

There are lots of technological devices deployed and that means that they have very much enabled the field in big data orientation process. Nowadays, on the basis of reports to their patients, doctors are able to prescribe medicines completely that through various tech devices. From tech devices all the data obtained are secured as well as deposited with the benefit of Big Data (Lebied, 2018)

Benefits of the Big Data:

Big data provides many benefits to its users. For instance, in the health care center, big data can assist doctors and medical staff in data management to save the complete medical record of a patient that they can use in future to provide the best treatment according to the medical history of the patient. Through big data analytics, doctors can also use the data of an old patient having similar issues and complications of a current patient to provide better treatment. Through this, they can easily understand the case of a patient with the guidance of the previous patient’s report. They can suggest the most effective medicines by viewing the big data analysis results. However, in organization, big data also support the management process and develop the competitive advantage for the organization.  

Challenges of the Big Data:

In using big data analytics, the biggest challenge is to section useful data from gatherings. Organized as well as unorganized data for analysis, the data compulsory is a combination of both. This is the shortage of talented personnel who have enough knowledge to give the sensible data. Big data analytics comes with many challenges as possibilities from recruitment to training and from budgeting to strategizing. Big data sometimes causes to data breaches and data theft that negatively impact on the image of the organization and sometimes results in the misuse of data. Big data management also requires professional workers to work on it. Therefore the organization needs to provide employee's training that increases the budget of the organization. Big data also requires synchronization across disparate sources of the information and data.

Conclusion of the Big Data:

It can be concluded that both data science and big data are gaining more importance in every business space, so companies have to understand its crucial role, and make sure that they adopt new ways to deal with challenges of big data analytics, and be able to handle their big data more effectively and efficiently.

References of the Big Data

Lebied, M. (2018). 12 Examples of Big Data Analytics In Healthcare That Can Save People. Retrieved September 6, 2018, from https://www.datapine.com/blog/big-data-examples-in-healthcare/

M.S, S., & et.al. (2015). Big Data – Literature Survey. International Journal for Research in Applied Science & Engineering.

Monnappa, A. (2018). Data Science vs. Big Data vs. Data Analytics. Retrieved September 6, 2018, from https://www.simplilearn.com/data-science-vs-big-data-vs-data-analytics-article

Munné, R. (2016). Big Data in the Public Sector. New Horizons for a Data-Driven Economy , 195-208.

Stanton, J., & et.al. (2012). It is conducted that by the Author.

White, C. (2012). What Is Big Data and Why Do We Need It? Retrieved September 6, 2018, from https://www.technologytransfer.eu/article/98/2012/1/What_Is_Big_Data_and_Why_Do_We_Need_It_.html