Data Management and Governance: 3G, 4G, 5G Technologies and Analysis

This report provides a comprehensive analysis of data management and governance, addressing several key concepts and technologies. It begins by defining and explaining essential terminologies such as IS concepts and classification, big data analytics, change data capture, ETL, RDBMSs, Hadoop and MapReduce, Internet of Things, and mashups. The report then delves into a comparative study of 3G, 4G, and 5G technologies, highlighting their differences in terms of technology, bandwidth, and internet services. Furthermore, the report examines the implications of drug development failures and the factors that have made biomedical analytics feasible, particularly focusing on how big data analytics can be leveraged to improve drug discovery and development. The report also addresses the justification for investments in big data analytics by drug makers and explores the reasons why pharmaceutical companies might be willing to share data despite industry competition. This report is based on the assignment brief that requires a deep understanding of data governance and data management.

Running head: DATA MANAGEMENT
DATA MANAGEMENT
Name of the Student
Name of the university
Author note

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Question 1
IS Concepts and Classification
IS concepts and classification emerged as basic requirement to growth of
organization. This means about management of hardware as well as software of the
organization. At present the organizations are using distant techniques of infrastructure
management. It helps also in reducing the operational expenses.
Big Data Analytics
Big Data Analytics is said to be as a complex process that helps in examining large
and varied sets of volumes of data as well as big data for covering information like customer
preferences, market trends as well as unknown correlations which could help organizations in
making decisions of business (Wamba, et al. 2017). It examines huge quantity of data for
analyzing the data of the organization.
Change data capture
Change data capture (CDC) is a design patterns of software that is used for
determining as well as tracking data which has changed for action could be taken by use of
changed data. This is approach of data integration which is dependent on identification,
delivery and capture of changes that are made to the data sources of organization.
ETL, RDBMSs
ETL
It is the short form of extract, transform and load, the database functions which are
combined in one tool for pulling data of a database and shift it in other database. The process
to read data from database is known as extract. The process to convert data extracted from

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previous form to form that is required is known as transform. Load is process to write data in
target database.
RDBMSs
DBMS that are mainly designed for relational databases are called RDBMS. Hence
RDBMSs are subset of the DBMSs. In a relational database, data is stored within structured
format, and the structure makes use of rows as well as columns (Wu, et al. 2016). It makes
this easy in locating as well as accessing specific values in database. This is relational as the
values of all tables are related with one another.
Hadoop and MapReduce
Hadoop
Hadoop is software of an open source framework to store and run applications over
commodity hardware’s clusters. This provides huge storage of any type of data, huge
processing power as well as ability in handling limitless virtually concurrent jobs or tasks.
Hadoop is key consideration having data volumes as well as varieties increasing constantly.
MapReduce
MapReduce refers to program model as well as processing technique for the purpose
of distributed computing which is based upon java. The algorithm considers two essential
tasks: Map and Reduce. Map accepts data sets and converts this in another data set (Hashem
et al. 2016). Output from map is taken by reduce task as the input and then combines the data
tuples in smaller tuples set.
Internet of Things

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Internet of Things (IoT) is computing devices’ system that are interrelated, digital and
mechanical devices which are provided unique identifiers as well as ability for transferring
data on network without any need of human-computer interaction or human-human
interaction (Khan and Salah 2018). IoT’s definition has evolved for convergence of several
technologies, machine learning, embedded systems, analytics of real time and commodity
sensors.
Mashup
There is creation of mashup from the modular components which end users could
assemble as well as reassemble as the desired for serving current requirements. In enterprise
mashup, the product is combination of applications having external sources data and
corporate internal data.
Question 2
The 3G standard makes use of Universal Mobile Telecommunications System or
UMTS as its network architecture. It combines different elements of the 2G network with
modified or new technologies as well as protocols so that faster data rate can be delivered.
Speed of 3G is up to 2Mbps operating at a range of 2100MHz with a bandwidth of 15 to 20
MHz (Ezhilarasan and Dinakaran 2017).
Data rate is different in 3G and 4G technology. The technologies behind 4G include
MIMO or Multiple Input Multiple Output and Orthogonal Frequency Division Multiplexing
or OFDM (Ezhilarasan and Dinakaran 2017). Examples of 4G standards are LTE and
WiMAX. This supports interactive multimedia, videos as well as voice. 4G provides a speed
up to 20 Mbps or it can be said even more than that.

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The main difference in between 4G and 5G is the peak capacity and the latency. The
peak capacity of the 5G UWB sector is in Gbps in comparison to Mbps in 4G.
Comparison 3G 4G 5G
Introduced in the year 2001 2009 2018
Technology used WCDMA LTE, WiMAX MIMO, mmWaves
Type of switching Packet switching
having the exception of
air interference
Packet switching Packet switching
Bandwidth 25 MHz 100 MHz 30 GHz to 300 GHz
Internet services Broadband Ultra broadband Wireless World Wide
Web
Advantages High security and
international roaming
Speed, Global mobility
and high speed
handoffs
Low latency and
extremely high speeds
Application Video conferencing,
GPS and mobile TV
High speed
applications, wearable
devices
High resolution video
streaming, robots,
medical procedures,
control of vehicles
Table 1: Comparison (Gopal and Kuppusamy 2015)
Question 3
The major consequence of drug development failure is loss of money and time. Lot of
money is invested as well as time to test the drugs thus it can be said that failure can be
associated with loss of money and time. Positive thing in this is that failure will certainly
provide some ideas as to how could the drug be modified so that it passes.

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The factor that has made biomedical analytics feasible is rapid modernization in
technology and the need to identify behavioral patterns of consumers (Vicini et al. 2016). In
the healthcare sector if behavioral patterns can be understood then it can help in various
things such as implementing treatment plans and other such things like personalized medical
care.
By making investments into big data analytics, drug makers can certainly avoid
developing those kinds of drugs that mainly target the wrong biological pathways thus saving
the money which could have otherwise been wasted in the research.
Drug makers if willingly share the data inspite of the fierce competition in their
industry only because of social responsibility so that they can prove their patient centricity.
Patient centricity means thinking of the patients first and then of the profit (Auffray et al.
2016). This should be the motive of every drug maker as they are the ones vested with such
as big responsibility to save lives of people. By sharing data they can help bring
improvisations in the arena of healthcare.

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References
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wireless applications. IOSR Journal of Electronics and Communication Engineering, 10(6),
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Vicini, P., Fields, O., Lai, E., Litwack, E.D., Martin, A.M., Morgan, T.M., Pacanowski,
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discovery and development. Clinical Pharmacology & Therapeutics, 99(2), pp.198-207.
Hashem, I.A.T., Anuar, N.B., Gani, A., Yaqoob, I., Xia, F. and Khan, S.U., 2016.
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Khan, M.A. and Salah, K., 2018. IoT security: Review, blockchain solutions, and open
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Ezhilarasan, E. and Dinakaran, M., 2017, February. A Review on mobile technologies: 3G,
4G and 5G. In 2017 second international conference on recent trends and challenges in
computational models (ICRTCCM) (pp. 369-373). IEEE.