Big Data Analytics: Trends and Applications

This assignment delves into the rapidly evolving field of big data analytics. It examines key trends shaping the landscape, such as the rise of cloud computing, NoSQL databases, and deep learning. The discussion encompasses preprocessing methods for managing massive datasets and highlights the diverse applications of big data analytics in domains like business intelligence, smart cities, and healthcare.

Running head: BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS
MODELS
Leveraging big Data for enhancing decision making and creating business models
Name of the Student
Name of the University
Authors note

1BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
Executive Summary
The following report consist discussion about the identification, creation of business strategy
and aligning different business operations of an organization to leverage the benefits of big
data in the organizational decision making process. In addition to that, the required
technology stack and support of NoSQL in this process is also discussed in the different
sections of this report. In addition to that, the role of social media on organizational decision
making process is also provided in this paper.

2BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
Table of Contents
Introduction................................................................................................................................3
1. Identify, create and discuss Business Strategy for a Big Data use case.................................3
2. Identification and aligning business initiatives, objectives and tasks with the developed
Business Strategy.......................................................................................................................4
3. Identify and discuss the required Technology Stack..............................................................5
4. Discussion on Data Analytics and MDM to support DS&BI................................................6
Data Analytics to support DS & BI............................................................................................6
MDM to support DS&BI...........................................................................................................7
5. Discuss support of NoSQL for Big Data Analytics...............................................................7
6. Discussion on different NoSQL Databases and its use in Big Data......................................9
7. Role of Social media in organisation's decision making process.........................................10
8. Big Data Value creation process..........................................................................................11
Conclusion................................................................................................................................13
References................................................................................................................................14

3BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
Introduction
In an unpredictable, complex, uncertain and agile business environment the capability
of business organizations to make real time business choices and decisions is basic for its
sustainability and growth in the competitive market (Zakir, Seymour and Berg 2015). Precise
processing of the expansive, continuous growing amount of data about different business
operations, customers, business contenders in the market in order to enhance the quality of
the business strategies decisions which will help the business organization apart from the
other organizations in the market.
The following report contributes to the identification, creation and discussion about a
Business Strategy, aligning the business initiatives, objectives and tasks with the developed
strategy, required Technology Stack for the strategy. In addition to that, discussion on Data
Analytics and MDM to support DS&BI, use of different NoSQL Databases in developed
Big Data use case as well as role of the social media in the decision making process are also
provided in the different sections of this report.
1. Identify, create and discuss Business Strategy for a Big Data
use case
Turning into a data analytics driven business organization enables an organization in
reducing the costs for the different businesses operations, enhance the amount of revenues
and improved competitive advantage in the markets (Amini, erostathopoulos and Prehofer
2017). This is the reason for which business analytics and intelligence keep on going to be
one of the most important needs for any organization. Numerous business decisions in the
organizations are yet not based on the data analytics, thus, the organizations are searching for
approaches in order to reduce the time required for conveying business intelligence with the

4BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
goal that they can grow the utilization of the business analytics for a wide range of business
data.
In this report we are considering the E-commerce personalization use for the online
E-commerce platforms (Zakir, Seymour and Berg 2015). The online Retail industry is
moving on to the personalized and targeted offers for their customers. In fact, independent
retailers are considering the data personalization as one of the top business drivers in order
to increase revenue from the competitive market.
2. Identification and aligning business initiatives, objectives and
tasks with the developed Business Strategy.
Business initiatives: Data mining technique helps to extract useful knowledge to
successfully accomplish the project and to carry out business operations by means of
scientific algorithms and scientific computing. The big data assists in decision-making
procedures; it also helps to extract large chunks of data from the database in agile and
start with
The
Strategy
Focus on
specific
areas
Business
issues that
needs to be
addressed
Find the
data source
that can
help in
resolving
the issus
Determine
what data is
available
Find out if
the cost for
data
processing
is justified.

5BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
effective manner (Lu, Setiono and Liu 2017). The DBMS tools are not so effective compared
to data mining techniques, this data mining techniques can scale up the business activities of
the enterprises. Since the company can get the probable business solutions beforehand it can
greatly influence the company’s profit and the company’s success.
Task: The organisations after making an effective decision must take the initiative to
carry out the plans accordingly (Mondal, Bhattacharya and Sarkar 2017). The organisations
must have a clear objective to accomplish those goals. A clear objective can solely create a
platform for the embellishment of the business operations which they want to undertake.
Objectives: The organisations must have made distinct plans to execute the project.
Data mining thus can create an impact on objectives, data mining helps to create predictive
data models and techniques which ease the set of goals which they plan to carry out to
conduct their business operations and earn a profit (Zakir, Seymour and Berg 2015). It is
absolutely necessary to set business goals and the goals must be carried out sequentially to
get the desired outcome or profit.
3. Identify and discuss the required Technology Stack
In order to implement the personalization of the products and services for their
customers it is important use a specific stack of technology that helps the organization to
efficiently manage, process and have insights from the coming data from different sources
(inside as well as outside sources of the organization). The following are the details of the
technology stack that are required to able
The Microsoft BI is selected for this scenario. This technology stack aligns well
with the business needs as well as different prerequisites of the e-commerce platforms
(Amini, erostathopoulos and Prehofer 2017). The tools in this stack make the process easier

6BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
to exploit the operation and customer data from different sources by utilizing the existing data
architecture and existing skill sets.
The responsible tea in the origination utilizes various inbuilt administrations tools to
deal with data mart or the data warehouse which includes the following,
• SSAS (SQL Server Analysis Services): This is important for conveying and
managing the data cubes and models (mainly tabular model) inside the data warehouse or
data mart.
• SSIS (SQL Server Integration Services): This is important ETL packages. i.e. for
extracting, transforming and load different packages for configuring data feeds from outer
sources (Data Sources outside the organization).
• SSRS (SQL Server Reporting Services): This is important for making reports after
analysing the data
•Microsoft Visual Studio* with BI Development Studio (BIDS). In addition to that,
SSDT (SQL Server Data Tools). This helps the development in the organization to compose
BI applications, including ETL packages and tabular data models, reports etc.
4. Discussion on Data Analytics and MDM to support DS&BI
Data Analytics to support DS & BI
Data Analytics works upon algorithms to yield future prospects of a particular project.
The Data Analytics has the ability to predict the possible outcome of a project and project
execution beforehand. The Data Analytics consists of certain tools like data modelling, online
analytical processing (OLAP), data mining and data forecasting (Shmueli and Lichtendahl
2017). Thus analysing both the modern data patterns and previously generated data patterns
help to analyse the probable improvement of a particular project. The tools work

7BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
independently and are executed separately for varied purposes, but their main sole purpose to
give predictions. Thus the company can predict the overall gain they can acquire. Along with
that, they can predict their growth in the market share.
MDM to support DS&BI
MDM has to offer three Business Intelligence solutions
i. The Data Warehouse to keep track of the records of the operation history.
ii. The Enterprise Management solution to assure quality data being transmitted to the Data
Warehouse.
ii. The BI applications that use DW and MDM data to send genuine data to the enterprises
that require it to enhance their business activities.
Thus with the absence of the MDM, there is every possibility that the project or the
solutions fail on which they are working upon. MDM assists to take correct decision and this
effective decision-making approach assists to mitigate the risks associated with the solution
(Ng et al. 2017). MDM helps to provide the accurate data flow of master data throughout the
entire project. This data flow signifies the actual operations of a particular enterprise and it
can be also acknowledged and can be assured that the data is actually utilising BI tools.
5. Discuss support of NoSQL for Big Data Analytics
NoSQL databases concentrate on analytical processing of the huge amount of
collected data in the data warehouse, offering expanded scalability over the data servers.
Computational and capacity prerequisites of uses, for example, for Business Intelligence, Big
Data analytics over peta-byte datasets cannot be completed centralized data bases (Zakir,
Seymour and Berg 2015). This prompted the advancement and use of NoSQL databases

8BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
which are appropriated and evenly versatile, such as Google's Bigtable and its open source
execution HBase. The development of different distribute key-value data stores, for example,
Voldemort and Cassandra, demonstrates the productivity and cost adequacy of their
approaches. The main impediments with databases are they are difficult to scale with Data
warehousing, Web 2. 0, and with cloud applications.
The strictness of the relational databases can be a load for big data applications which
comprises of very different sort of characteristics. Content, pictures, recordings, ongoing
information and other quick changing data must be put away inside various tables. Since the
e-commerce like web applications are extremely deft, fundamental database must be
adaptable and dynamic too keeping in mind the end goal to help simple data analytic. NoSQL
frameworks show the capacity to store and file discretionarily Big Data sets while
empowering a huge measure of simultaneous user requirements. Principle points of interest
of NoSQL are the accompanying viewpoints: 1) Rapid Reading and writing data to the data
base information. 2) Support mass capacity; 3) Easy to scale; 4) minimal effort.
The Applications and their databases need to select scale-out either approach or
scale-up way to deal with concurrent access by the users. Scaling-up approach refers to the
approach in which the functionalities are included to existing server due to the increase in the
number simultaneous user access (Zakir, Seymour and Berg 2015). Scaling-out indicates to a
circulated design, rather than adding functionalities to the current servers the product servers
are added to meet the necessity of worldwide clients. NoSQL utilizes scale-out approach on
the three-level web design and worked extremely well. In case the huge numbers of users
utilize the application, greater product servers are added to the application/web level, and
execution is accomplished by appropriating the heap on expanded number of circulated
servers for the application.

9BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
6. Discussion on different NoSQL Databases and its use in Big
Data
The NoSQL databases are mainly categorized in four types which are key value
based, graph databases, document databases and column-oriented databases. In this context
only the key value based and document databases them are discussed briefly in the following
sections as they seem the best fit for the selected use case.
Key-value based databases: This databases can deal with expansive number of
records. They can also support high volumes of state changes per second with a huge number
of synchronous clients through conveyed handling and capacity. Key valued databases
depend on their in excess to confront the loss of capacity hubs and to secure applications.
They are exceptionally helpful for both putting away the consequences of scientific
calculations, (for example, state considers as a real part of monstrous quantities of records)
and for creating those outcomes by means of reports. In any case, Key-value based databases
acquire one downside of NoSQL databases. They do not give any sort of customary database
abilities. Along these lines, to guarantee exchanges atomicity or the consistency of numerous
parallel exchanges, clients ought to rather depend on the application itself.
Document Based databases: This kind of databases were intended to deal with the
storage as well as the administration of extensive scale of user data. This sort of database
appoints a key an incentive to every document. Documents are encoded in a standard
information trade arrangement, for example Javascript Option Documentation (JSON) or
BSON (Binary JSON), XML etc. Record or document based databases are perceived as an
effective, adaptable and nimble tool to store and analyse Big data. This databases are not the

10BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
same as key-value based databases (Zakir, Seymour and Berg 2015). While key-based
databases empower to look for information just by key values, document bassed databases
enable clients to look for information in view of the substance of documents. Example of
such Databases are CouchDB and MongoDB.
7. Role of Social media in organisation's decision making process
Social media by its nature is made to bring the people closer to each other. In the
organizations, it is utilized to construct a group for representatives to cooperate for basic
leadership process (Zakir, Seymour and Berg 2015). As organization grow internationally,
the remote employees and accomplices additionally increments. Online networking gives an
extension to separated representatives to meet up, and empowers them to be the part of the
bigger authoritative group, where they can team up as well as take part in an association's
execution, culture and qualities.
Different organizations dispatch online campaigns on Twitter, Facebook and
MySpace and attempt to focus the greatest number of important gatherings of people as they
can. In traditional way, a business organization needed to pay a huge investment for
commercials on different mediums, in daily papers and on radio to achieve their clients.
However with online networking, organizatinons can either take up paid ads on destinations
or make pages or gatherings in which individuals intentionally enlist and get refreshes about
new items. The utilization of online networking stages for promoting and showcasing
increments associations' range drastically.

11BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
The data coming from social media has changed the way organizations interface with
their customers. Organizations are dynamic on existing web-based social media sites, for
example, Facebook and Twitter where they frequently screen and post content identified with
new items or modifications on existing items, drawing in clients with different offers and
advancements. This type of promoting has the points of interest of bearing a minimal effort
when contrasted with paid promoting on TV and focusing on the suitable segment of
customers as opposed to besieging everybody with paid commercials (Lu, Setiono and Liu
2017). Operations are another key perspective in which online social media can help an
organization to achieve critical change in an organization. In order to really comprehend the
issue of operations inside an association. Notwithstanding that, Social media can possibly
change initiative in two distinct regions: key knowledge and execution (Zakir, Seymour and
Berg 2015). Officials can dissect the failure or success of new items by getting input from
Facebook and Twitter platforms. Customers instantly post their reactions and surveys of new
items via web-based networking media sites well prior to the complaint division finds out
about them.
8. Big Data Value creation process
There are mainly four techniques that can be used to create value for a specific
business which are namely, data democratization, contextualization, experimentation and at
the end data execution.
In data democratization stage the business data collected across different sources
inside and outside the organization that enable a wide range of users. This is helpful for the
users to access and comprehend data acquired from different sources whenever it is needed
by them.

12BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
In this process sheer volume of the acquired data is considered as challenge, but this
can be improved when users inside as well as outside from the organization can be benefitted
from the insights from the collected business data (Amini, erostathopoulos and Prehofer
2017). Use of the business intelligence can help in the acquiring the relevant knowledge
important for different business operations to enable the organization for better application of
the insights is positively associated with better value creation for the business organization.
The next stage is, data contextualization, it is the capacity to appoint significance as
a method for deciphering the information inside which an activity is executed (Lu, Setiono
and Liu 2017). Firms gather a critical sum and diverse sorts of information, including
information on client conduct, showcase request, moving inclinations and changing customer
needs. The capacity to recognize the logical hints to pick up an all-encompassing perspective
of customers is decidedly connected with better business value creation
The business condition that continually and quickly changes, future expectation turns
out to be more vital than the basic representation of verifiable or current points of view for
the organization. For viable future predictions for the business, data analytics utilizing
measurable and prescient demonstrating procedures might be connected to improve and
support the organizations chosen business strategy. The accumulation and collection of the
sheer amount of business data, and other data from outside the organization, empowers the
business to build up their own analytic capacity.
The data experimentation is about experimenting with the collected business data to
have improved insights from the data. Information experimentation is an organizations ability
to advance 'experimentation' and persistently exploring the changes related to the collected
information and screen different insights from the data by applying them in different
operations (Lu, Setiono and Liu 2017). This stage is considered as a trial and error culture to

13BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
get better insights from the collected data, when combined with a more prominent level of
information accessibility in the organization. This helps in having better opportunities to
change an incentive from huge information inside the organization.
Data execution is the ability to change insights from the collected data into operations
that promptly helps the organization to find out business opportunities that helps in better
customer engagement in this while creating value through the different business operations.
Different researchers like Amini, erostathopoulos and Prehofer (2017) discovered different
ways in which the business organizations execute data execution from the collected insight
from the use of business analytics.
Conclusion
For the business organizations, the big Data is a disruptive force that will influence
the organizations regardless the industries, departments in the organization. In order to
leverage the benefits from the big data and business analytics not only the IT infrastructure
need to change to cop up with it, yet every department inside an organization will
experience changes in order to leverage the benefits. Business analytics will change as well
as will become a part of different business procedure rather than a distinctive operation that
can be performed just by the professionals. The efficiency of the organization in handling
will helps the organization to provide the ability users throughout the organization to work
with different data sets to collect better insights to create value in the different business
processes.

14BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
References
Amini, S., Gerostathopoulos, I. and Prehofer, C., 2017, June. Big data analytics architecture
for real-time traffic control. In Models and Technologies for Intelligent Transportation
Systems (MT-ITS), 2017 5th IEEE International Conference on (pp. 710-715). IEEE.
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J.M. and Herrera, F., 2016. Big data
preprocessing: methods and prospects. Big Data Analytics, 1(1), p.9.
Kambatla, K., Kollias, G., Kumar, V. and Grama, A., 2014. Trends in big data analytics.
Journal of Parallel and Distributed Computing, 74(7), pp.2561-2573.
Lu, H., Setiono, R. and Liu, H., 2017. Neurorule: A connectionist approach to data
mining. arXiv preprint arXiv:1701.01358.
Mondal, K.C., Bhattacharya, S. and Sarkar, A., 2017. Comparative Study of Parallelism on
Data Mining. In Proceedings of the First International Conference on Intelligent Computing
and Communication (pp. 195-204). Springer Singapore.
Moniruzzaman, A.B.M. and Hossain, S.A., 2013. Nosql database: New era of databases for
big data analytics-classification, characteristics and comparison. arXiv preprint
arXiv:1307.0191.
Ng, S.T., Xu, F.J., Yang, Y. and Lu, M., 2017. A master data management solution to unlock
the value of big infrastructure data for smart, sustainable and resilient city planning.
In Proceedings: Creative Construction Conference 2017, June 19-22, Primošten, Croatia.
Shmueli, G. and Lichtendahl Jr, K.C., 2017. Data Mining for Business Analytics: Concepts,
Techniques, and Applications in R. John Wiley & Sons.

15BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS
Zakir, J., Seymour, T. and Berg, K., 2015. BIG DATA ANALYTICS. Issues in Information
Systems, 16(2).
Sharda, R., Delen, D. and Turban, E., 2013. Business Intelligence: A Managerial Perspective
on Analytics. Prentice Hall Press.
Wixom, B., Ariyachandra, T., Douglas, D.E., Goul, M., Gupta, B., Iyer, L.S., Kulkarni, U.R.,
Mooney, J.G., Phillips-Wren, G.E. and Turetken, O., 2014. The current state of business
intelligence in academia: The arrival of big data. CAIS, 34, p.1.
Erl, T., Khattak, W. and Buhler, P., 2016. Big data fundamentals: concepts, drivers &
techniques. Prentice Hall Press.
Loebbecke, C. and Picot, A., 2015. Reflections on societal and business model transformation
arising from digitization and big data analytics: A research agenda. The Journal of Strategic
Information Systems, 24(3), pp.149-157.
Chen, C.P. and Zhang, C.Y., 2014. Data-intensive applications, challenges, techniques and
technologies: A survey on Big Data. Information Sciences, 275, pp.314-347.
Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and
Muharemagic, E., 2015. Deep learning applications and challenges in big data analytics.
Journal of Big Data, 2(1), p.1.

16BIG DATA FOR ENHANCING DECISION MAKING AND CREATING BUSINESS MODELS