Data Handling, Business Intelligence, and Current Industry Trends

This essay delves into the critical role of data handling and business intelligence within modern business operations. It explores the fundamental processes of data handling, emphasizing its importance in managing and processing large-scale information to derive meaningful conclusions. The essay examines current trends in data analysis, including self-service analysis, customization, and big data applications leveraging technologies like Hadoop. It provides real-world examples, such as Facebook's data management strategies, to illustrate the practical applications of data handling. Furthermore, the essay discusses the role of business intelligence, its tools, and technologies in improving decision-making and planning. It also considers the role of data handling for small and medium-sized businesses and its impact on operational efficiency. The content highlights the significance of data handling in various business contexts, emphasizing its contribution to data analysis, prediction, and overall business strategy. References to relevant books, journals, and online resources are included, providing a comprehensive overview of the subject.

Data Handling

Topic: “ Data Handling And Business Intelligence.”
Data handling refers to the process through which large scale information can be stored.
It helps in generating conclusion from huge amount of data. This is considered as a process
through which information can be managed and processed easily (Atashrouz, Mozaffarian and
Pazuki, 2015). Data handling is one of the important process in business for managing data
effectively. It provides platform to the organisation, through which they can perform various
operations on data. These activities includes data analysis, prediction, customizable
visualization, API deployment, Real time data analysis etc. This essay is based on the role and
importance of data handling into business (Georgsson and Staggers, 2016). It includes details
related to currents trends, role and application big data in companies using Hadoop along with
example.
Current trend of data handling.
Data handling has completely turned the information analysis procedure in business
companies. It provides various features to the firm including operational guidance and large-
scale analysis of data. It enables the companies in implementing effective business tactics. The
currents trends of data analysis are considered as self-service analysis, customization, API
deployments and big data (Henze, Hummen and Wehrle, 2013). To handle the large scale of data
concept of SQL technologies is considered. Hadoop is based on the same technique and provided
with hoc queries. It can be implemented in the physical as well as virtual machines. To introduce
this service in virtual environment, company should implement cloud services first (Illingworth,
2014).
There are various companies that are using this concept, are Facebook, Twitter, Linkedin
and so on. Everyday approx. 950 million of users are uploading various information and nearly
540 million new users registered. To manage information of millions of data, this technology is
used by the organisation (Kimura, M. and et. al., 2014). Facebook has developed a data center,
all the applications for managing information are developed by the staff. The data center of
Facebook also consists of virtual system, due to this, users are able to access their information
across the worlds. Before, storing they do not distribute data into segments. All the information
is stored in the basis of their extension, along with this compression of data also takes place for
reducing the storage space. However, company needs to develop huge data centers and also
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requires lost of manpower to handle these machines (Mamun, A. S. M. A. and et al., 2016). It
will directly impact on the expenses of the companies.
The business intelligence
Business intelligence refers to the process in which various software applications are provided to
handle data effectively. It consists of various tools and technologies such as data mining, online
processing of data, reporting and query. It helps in improving decision making and planning of
the organisation. Previously, the data ware houses consist of ODS and OLAP. These components
were providing effective performance and interactive analysis, but processing become very
complex (Michael, K. and Miller, K.W., 2013). Hadoop is one of the advance technologies for
handling large amount of data effectively. To implement this system a distribution system is
required known as Hadoop Distributed File System (HDFS).
It provides effective transnational of raw material and produces great analysis results.
The reporting capability of business intelligence is limited by Hadoop, due to which batch
oriented process is considered by the software. The performance and interaction of the process is
increased by introducing the hoc queries. Along with this various open source software are also
involved for effective performance (Padilha, C. D. A. and et. al., 2015). Some of them are
Apache and impala. This helped in producing interactive and feasible results. According to the
research of 2015, companies are implementing Hadoop into their organisation. As it provides
flexible operations and able to manage extremely large amount of data. Along with this it also
provides storage facilities at cheap prices. Therefore, most of the companies are considering their
services.
Practical application
Data handling is having various practical applications such as storage, analysis,
operational and manipulation of data. There are various companies that are implementing big
data concept into their data warehouse. One of the biggest practical example of big data
implementation is Facebook. To manage data of approx. 10 billion users that are providing
information of various types including pictures, messages, likes and shares. Requires huge
amount of storage area (Price, D. K., Plante, D. F. and Duffy, M. O., Hartford Fire Insurance
Company, 2016). All the information of Facebook is stored effectively with big data. On the
basis of stored information in the form of cookies, tag, suggestions, likes and dislikes. They are
able to analyses behavior of user. It helps the company for referring advertisement to attract
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visitors. Along with this they also provide features like flashbacks and celebration prides. It
enables the users for sharing movies and videos of their previous share.
There are various surveys and researches are also being conducted by the company with
the help of stickers. All the data are effectively stored in the data house, users are able to access
when every they required. The stored information should be provided to the users without
making much delay, for this company considers virtual machines known as cloud service. It
provides access to the users from any location (Samuelsson, J. and Sjoberg, R.,
Telefonaktiebolaget Lm Ericsson (Publ), 2017). The information of Facebook is stored at cloud,
due to which accessing time get reduced gradually. All the services that are provided by the data
handling are consists of safety features. Any kind of issues like data redundancy, loss and
inconsistency does not occurs. It maintains quality and safety of services. Therefore, it is one of
the most preferable data storage service.
Role of DH in enterprises and small and medium business.
Role of data handling for small and medium business groups is equally important. It
provides facility through which they can easily store online and offline information. There are
various software available for reducing complexity of large data including ClearStory Data,
Kissmetrics, InsightSquared and Google Analytic. The data handling plays a significant role in
business organisation. It supports the company by providing effective information related to
planning and profit. The quality of decision- making can be improved with the help of this, as it
provides conclusion of huge amount of research information (10 Business Intelligence trends for
2016. 2016). It reduces the calculation and operational time of data, due to which speed of
processing gets reduced automatically. It helps in maintaining the consistency and quality of
data, due to which companies are able to maintain standard of the information. The small and
medium organisations are not having large amount of data for processing, due to which results
generated by data handling is not much effective. Lots of modifications are also required into the
existing system, which increases the expenses of the firm.
On the basis of above essay, it is inferred that data handling is considered as an essential
concept for the organisations. It provides a platform through which they can analyses and
operate data effectively. It also helps the companies by managing huge amount of information
effectively. This is considered as widely implemented concept by the firms. Companies of
different sizes are considering this technique. In the above practical application of Facebook is
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provided. They are implementing this concept this concept for managing data of users
effectively. On the basis of that various features are also provided by the company including
flashbacks, videos and so on. Along with this it plays a significant role in decision making and
planning processing. Therefore, role of data handling is considered essential in business firm.
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REFERENCES
Books and Journals
Atashrouz, S., Mozaffarian, M. and Pazuki, G., 2015. Modeling the thermal conductivity of ionic
liquids and ionanofluids based on a group method of data handling and modified
Maxwell model. Industrial & Engineering Chemistry Research.54(34). pp.8600-8610.
Georgsson, M. and Staggers, N., 2016, September. A Practical Method for Data Handling in
Multi-Method Usability Research Studies. In MIE (pp. 302-306).
Henze, M., Hummen, R. and Wehrle, K., 2013, May. The cloud needs cross-layer data handling
annotations. In Security and Privacy Workshops (SPW), 2013 IEEE (pp. 18-22). IEEE.
Illingworth, R. A., 2014. A data handling system for modern and future Fermilab experiments.
In Journal of Physics: Conference Series (Vol. 513, No. 3, p. 032045). IOP Publishing.
Kimura, M. and et. al., 2014. A survey aimed at general citizens of the US and Japan about their
attitudes toward electronic medical data handling. International journal of environmental
research and public health.11(5). pp.4572-4588.
Mamun, A. S. M. A. and et. al., 2016. A comparison of missing data handling methods in linear
structural relationship model: evidence from BDHS2007 data. Electronic Journal of
Applied Statistical Analysis.9(1). pp.122-133.
Michael, K. and Miller, K. W., 2013. Big data: New opportunities and new challenges [guest
editors' introduction]. Computer.46(6). pp.22-24.
Padilha, C. D. A. and et. al., 2015. Prediction of rhamnolipid breakthrough curves on activated
carbon and Amberlite XAD-2 using artificial neural network and group method data
handling models. J. Mol. Liq.206. pp.293-299.
Price, D. K., Plante, D. F. and Duffy, M. O., Hartford Fire Insurance Company, 2016. System
and method for efficient data handling across multiple systems. U.S. Patent 9.465.883.
Samuelsson, J. and Sjoberg, R., Telefonaktiebolaget Lm Ericsson (Publ), 2017. Extension data
handling. U.S. Patent 9.554.129.
Online
10 Business Intelligence trends for 2016. 2016. [Online]. Available through:
<http://uk.pcmag.com/cloud-services/73741/feature/10-business-intelligence-trends-for-
2016>. [Accessed on 31st July 2017].
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