Introduction of 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 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. To humongous volumes Big Data refers data effectively cannot be processed with the traditional applications that exist. Big data is also defined as to analyze something insights which could lead to improved decision as well as business strategic moves. Now we briefly discuss what they are. In ingenious ways data science is the combination of statistics, programming, mathematics, capturing data as well as problem solving, at things the ability to look differently, as well as the cleansing the activity; aligning the information as well as preparing. 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 is as follow in-depth knowledge, python coding, platform Hadoop, database SQL/ coding as well as data of unstructured work (Monnappa, 2018)

Big Data:

To humongous volumes of information big data refers that couldn’t effectively proceed with the traditional application that exits. With the raw data the processing of big data begins that is not aggregated as well as in the memory mostly it is impossible to store of a single computer. For immense volume of information a buzzword that is utilize 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 that enables enhanced vision, making of decision, as well as automation process. Another author describe it as  problems related to data handling as well as management Big Data is a technique to resolve all the unsolved problems,  some recent industries was used to live with such 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 as well as business skills (Monnappa, 2018)

Big Data + Data Science = Big Data Science

Literature Survey of Big Data

Big data:

It is conducted that by the Author M.S & et.al (2015), the research study shows how the research direction can be changed in business model by big data to providing services with product. Through different applications like social media, smart devices and wireless sensor technology can shift more data. In this report emphasizes on the improvement of new technology rather than the older and upcoming challenges and expected trends are also discusses in this paper as well as that aspect which provide awareness about internet environment. IoT is aimed as to 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 Science of Big Data:

It is conducted that by the Author Stanton & et.al (2012), Data scienec pass on the emerging area of network which is concered about the collection ,mangement , analysis aswell as ivsualization for the large collection information . the database area along with the computer scienec is related to the Data scienec with the avriopus skills invling the mathematicals skill.  The data scienec is great analzying the data ,where the various people entertain the analysis of the data which is happily spend for the histrograms as well as the averages of the peoples should allow the the data scienec activites . At last the data scienetist involved to obtained the data ,for the collection of data which creat the great reusable that is think about the data cruations duriong the challges of the difficulites. The transformations of the data  is more valubale for the use of decsions makers to know  about, what way the data is tarnsform in addition to summazrie. (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 preprocess 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 your organization.

Execution of Big Data and analytics strategy is ultimately to create the organization more efficient as well as smarter. 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 accurately in the housing market what’s going to happen than any team of expert real estate forecasters.”There are some appealing welfares of big data for example:

in obtaining vision it is quick valuable

the performance of a business it performs better analysis as well as improved

in better decision-making it reduces the risk as well as helps (White, 2012)

Example of Big Data usage is real world application:         

In propelling many industries Big Data’s assistances are playing an important factor for instance:

Public sector of Big Data

Through Big Data some of the main facilities delivered to public sector revolve everywhere government sectors facilitating in areas for example recognition of deceit, investigation of power, economic investigation promotion, 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 of Big Data

In the field of health care as well as medicine could not be neglected the importance of Big Data. It enables doctors it helps in a way, surgeons etc. their complete patient’s Background to keep a reliable track. Such as, the doctor could easily attain his patient’s background, if one of the patients comes to see the doctor. To the doctor the data is only accessible as well as the patient it remains safe as well as protected. The information could be stored for a lifetime. In the future the doctors have the authority to reach the information anytime.

There a lot of technological devices are deployed and that means that they have very much enabled the fields 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 they attain. From tech devices all the data obtained are secured as well as deposited with the benefit of Big Data (Lebied, 2018)

Benefits of 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 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 that is comprehended to very hard. 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 require 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 require synchronization across disparate sources of the information and data.

Conclusion of 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 their crucial role, and make sure that they adopt new ways to deal with challenges of big data analytics, and e bale to handle their big data more effectively and efficiently.

References of 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