Enhancing Decision Making and New Business Models with Big Data

This report delves into the strategic utilization of big data to enhance decision-making processes and foster the creation of novel business models. It commences by defining big data and its role in organizational success, emphasizing the importance of a well-defined business strategy and technological infrastructure. The report then outlines a framework for leveraging big data, including data types, business objectives, and the integration of business intelligence tools. It explores the alignment of business initiatives and objectives with the big data strategy, highlighting the required technology stack, including cloud computing and mobile applications. Furthermore, the report discusses data analytics, Master Data Management (MDM), and the support of NoSQL databases for big data analysis. Various NoSQL databases are examined, along with their specific applications in big data storage and retrieval. Overall, the report provides a comprehensive overview of how businesses can harness the power of big data to drive informed decisions, optimize operations, and develop innovative business models.

Leveraging big Data for enhancing decision making and creating new business models 1
LEVERAGING BIG DATA FOR ENHANCING DECISION MAKING AND CREATING
NEW BUSINESS MODELS
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Introduction
It is the anticipation of every individual or a business organization that gets into a market
that after a short period they will make profits which will be a reward of the activities they plan
to undertake. There are incidences of businesses that have made in the world of business after
coming up with a particular solution to existing real-world problems due to considerations made
before and entry commences. Before a business introduces its products in the market research is
conducted to ensure that what is provided meets the expectation of the customers. A feasibility
study is carried out to gather information to identify a gap that is in the market in which a
product can be introduced to fill the gap. All factors considered that can lead to the introduction
of a particular solution are stated so that when a conclusion is made the customers are the central
point of focus. With the introduction of technology in the 21st century, a feasibility study has
been made easier. Technology does not only apply in the starting of the business but also in the
course of operation. Data about an organization is updated after every short time when an
improvement has been made. The result is big data formation which helps the organization in the
process of decision making. As a result of improvements in the decision making processes, new
business models are invented which translates to a more efficient way of running a business
leading to success that keeps it competitive. In the course of this work, the discussion will be
based on how big leveraging data enhances decision making and creates new business models.
Business strategy for a Big Data use
Big Data is defined as a capability that enables companies to gather large volumes of data
which is used in the functioning of the business to make decisions accordingly. Like any other
capability that is acquired by an organization, it requires to b technologically stored as well as
being analyzed and processed to enhance the governance. According to IDC through their

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research that was conducted related to big data services, when it is appropriately utilized through
the incorporation of technology, it enables the market to grow by double digits. In the figure
below there is a presentation of a framework that illustrates how big data strategy is used in
improving the business.
The whole process of utilizing big data aims at accomplishing a business objective. In the
process of developing big data capabilities, businesses are usually engaged in a measurement or
experiment activity to weigh whether the data that is gathered contributes to accomplishing
business goals in realizing the value and potential. In the process of measuring or testing, an
organization develops questions for hypothesis purposes and are subjected to scientific
approaches to verify them. On the second section of the framework, the data type is becoming of
importance. In the course of business functioning, organizations collect data that that are specific
to particular areas of operations such as sales which are stored in the databases which have been
formed with schemas or structure in place. This data is called transactional data. In other cases,
organizations collect data that comes from different sources other than transactions which are

Leveraging big Data for enhancing decision making and creating new business models 4
unstructured with an example being social media. When Business objective and Type of data are
combined, they form a quadrant with data exploration, social analytics, performance
management and decision science.
In the performance management section, an organization aims at understanding the
meaning of the data through multidimensional analysis and pre-determined queries. The data that
are most applicable to the performance management is usually transactional which mostly covers
the how customers have been doing the purchasing in a particular year, the levels of inventory
and the results regarding profit. The managers of a particular organization can be engaged in a
question and answer forum trying to gather information such as the various customer segments
that results to profitability. In the course of asking the various question, they get real-time
answers directly from the customers which help in making decisions that are short term. The
short-term decisions that are made in a particular, organization contribute to the creation of long-
term plans of the organization. The formulation of the plans is usually attributed to business
intelligence tools that are used. The tools have been designed with an interface that enables the
users to select which queries they can run through filtration and rank the output according to
various dimensions such as regions. After the performance data is keyed in the system and
analysis is conducted the managers are in a position to generate reports which are essential to
make decisions regarding the future of various aspects of the business such as customer service,
sales, marketing, human resource, and manufacturing.
Identification and alignment business initiatives, objectives and Tasks with the
developed Business
Strategy

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The business intelligence tools have evolved in the past couple of decades and have
experienced great improvements. The managers' roles in the system have been improved as they
interact with multiple business transactions and know their completeness as they follow the rule
of garbage in garbage out in computing (McAfee, et al. 2012). This analysis helps the managers to
be aware of the direction of the business and change accordingly whenever they find they are
heading the wrong direction. Identification is achieved through the use of analytic tools that
subject it to statistical as well as experiments depending on the answers that were previously
given. The approach leverages modeling technique that is predictive in that they can know what
the future of the organization will look like in days to come from their actions in the current or
previous state of operations.
Due to the increased emphasis on digital analysis and inbound marketing, they run
experiments regularly and data stored in the databases which give the organization a chance to
update when new changes are noticed. Different variables are combined which gives the
organization a room for testing a variety which in the different results and conditions are tested.
Through the testing of the various variables, the organization can be in a position to determine
which combinations result in giving the best results.
Required Technology Stack

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In the modern technological applications, the data is being integrated to communicate
with both the desktop as well as mobile computing devices. The storage of the devices is either
stored in the cloud or even synchronized to ensure that there is communication that is achieved
with ease. Big data cannot be stored in the devices due to the limitations of cost as well cost that
is out of control of the devices. The devices require an additional storage facility that can only be
achieved when the devices are synchronized to the external sources through a network which
structured or unstructured. Cloud computing has turned out to one of the cheapest ways of
storing huge amounts of business data that can be accessed by devices as long as they are
connected via the internet (Bughin et al. 2012). In the cloud, there have been applications that have
been developed which facilitate data analysis which implies once the user knowledge of what of
what is going on or not, they will have to the data. The limitation that has been identified to
hinder the success of the connection is the internet which many at times require 3G and 4G
strengths. This limitation hinders the organizations to have their branches extended to remote
areas through internet service providers and device manufacturers are hands-on to ensure that
they can make the data available to all parts of the globe. The use of mobile applications that are
specific to certain services such as google cloud has enabled users to access the data in whatever
the location they are in. This has reduced the cost of operation for businesses thus maximizing
profit.
Discussion on Data Analytics and MDM to support DS&BI
The presentation of an ace information administration framework inside the venture
ought to positively affect BI frameworks. For instance, it is normally the case in an MDM
framework that the characteristic information names and information definitions used to portray
ace information elements are probably going to be the standard information names and

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information definitions for the undertaking. These ace information definitions are regularly
alluded to as a shared business vocabulary (SBV) for the endeavor. The SBV is along these lines
ace metadata.We can exploit an ace information SBV in a BI framework to implement the reuse
of similar information definitions over every dimensional model, 3D shapes and BI instrument
business sees keeping in mind the end goal to drive consistency crosswise over dimensional
information. Receiving an ace information SBV along these lines can just enhance the
comprehension of the information introduced in BI framework reports, OLAP investigations,
dashboards, and scorecards. It likewise adds to the interest of consistency and the impression of
put stock in BI
Support of NoSQL for Big Data Analytics

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Over the last couple of years, databases have been incorporated in the functionality of the
organization as they offer data analysis through calling of various functions that are incorporated
giving eliminating the chances of installing different analysis software. New forms of databases
have been introduced to the technological arena which has resulted in the advancement of the
various data storage functionalities. NoSQL is a challenging type of a database which is when it
comes to relation database dominance. A relational database has dominated the storage industry
as they provide the mechanism for storing data with persistency control concurrency,
transactions and have interfaces that provide the users with a chance of integrating data as well
as generating reports. There is an aspect of cracking in the dominance of the relational database.
In the design of the NoSQL database, there have been integrated more than one
mechanisms of storage that can be used. The variety in the storage need is usually as a result of
incorporation of the needs which are considered for the various users. NoSQL has been used
because it does not use the relational model, it operates well on clusters, mostly open source
which makes it accessible for an organization with minimum cost and it has fewer schemas that
do not require one to be experienced.
The reason as to why various users have opted to use the NoSQL database is the fact that
there have been prior frustrations with the impedance mismatch between the relational and in-
memory data structures of the software. With NoSQL databases give a chance to the developers
to create a database without necessarily converting in-memory structures to relational structures
(Duda, 2012). NoSQL has been designed with the capability that it can facilitate movement away
from utilizing the database standing in between for integration purposes as it favors
encapsulation between the databases and the external applications.
Different NoSQL Databases and its use in Big Data use

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In the course of designing the various aspects of the databases using the NoSQL
technology, there have emerged various databases which are applied in the storage of Big Data.
a. Key value Database
As illustrated above in the key value database, data is stored in key-value data stores
which can be used for any API. The client can input the fundamental values or delete as per the
needs. The data stored in this form of databases store the values without necessarily considering
what is inside it. The API that uses the data is given the responsibility to know what is stored in
them and how they will use it. When a user wants to access the data stored uses the primary key
as it can be easily filtered.
There are different variations of the databases depending on the needs of the users. There
are databases such as Memcached which can store nonpersistent data when certain needs of the

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clients are different at all levels. There are also APIs which need data to be refreshed after a
particular period of time which is not achieved in a particular format.
b. Document database
In this form of a database, the documents become the central concept that is used for
storage. The databases are required to function in such a way that they can retrieve data
documents in the form of XML, BSON, JSON and many others depending on what is stored in
the system. These documents are self-describing and are in hierarchical structures forming a tree
which may contain maps scalars and collection values (McCreary and Kell, 2014). The document
databases are stored in such a way that they are similar to those in key value which implies that
the two can be integrated and work in unison. The difference comes in because the documents
are examinable and the database can allow transition from the relational database with ease
which means they can link with APIs that were not initially designed for this type of the database
c. Column family stores

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The data in this form of the databases are designed in such a way that they form columns
families which are presented as rows. Column rows in this format of the database are groups of
data that are related, and often they are accessed together. Each of the columns is consist of a
columns map in that there are major columns that accommodate others. The column that
accommodates the others is called a super column.
d. Tree database

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In this type of the database, the users can store the entities and relationships of those
entities. Entities can be seen as the nodes that store the properties. In traversing the relationship
is usually very fast. The relationship between the nodes is not calculated at the time of query but
maintain the persistence of the due to the relationships. When traversing the persistent
relationships is much faster than doing it at the query level.
When storing data using the various types of NoSQL, there are aspects of consistency
that have been ensured in that there hope for integration. In the 21st century majority of the
organizations have shifted their way of operating to web platforms. Due to this change, there
have risen a need to have a database that supports clustering as well as storing large volumes of
data. The limitation of relational databases to run on clusters have made them inefficient and
inapplicable to operate on web servers. Organizations also today have integrated the use of ERPs