4Emerging Trends in Business IntelligenceITS 531.docx

4
Emerging Trends in Business Intelligence
ITS 531-20 Business Intelligence
Emerging Trends in Business Intelligence
By
Vivek Reddy Chinthakuntla
Soumya Kalakonda
To Professor Dr. Kelly Bruning
University of the Cumberlands
Table of Contents
Abstract..................................................................................
.....................................................4
Business Intelligence with Data
Analytics.................................................................................
...............6
Partial Application of BI with Data
Analytics.................................................................................
..........7
Future of BI and Data
Analytics.................................................................................
................................8
Positive and negative impacts of BI
...............................................................................................

...........9
Recommendations
...............................................................................................
.....................................9
Cloud Computing with
BI...........................................................................................
............................10
Practical
Implications............................................................................
..................................................10
Future of Cloud Computing with
BI.................................................................................. .........
.............14
Advantages and
Disadvantages.........................................................................
.......................................15
Recommendations....................................................................
................................................................15
Introduction to Business Drive Data
Intelligence.............................................................................
........16
Data Governance of Self-Service BI
...............................................................................................
.........19
Future of BI depends on Data
Governance.............................................................................
.................19
Conclusion..............................................................................
..................................................................20
References..............................................................................
.................................................................. 22
Abstract:
This paper is based on the proposition used, and the outcomes
attained, using data management to expedite the changes in the

operation from a conventional old-fashioned practice to an
automatic Business Intelligence data analytics system,
presenting timely, reliable system production data by using
Business Intelligence tools and technologies. This paper
explains the importance and productivity of different modeling
procedures.
Business Intelligence and analytics have evolved as the most
crucial area to design new applications by decision-makers by
evaluating the effect of business organization's issues related to
data. This is multiplying billion-dollar advancement and has
been famous around the globe. This research provides an
opportunity to identify emerging trends in Business
Intelligence.
As like many other trends in Business Intelligence Cloud
computing is one of them. The data can be stored in the Cloud
environment. There are few companies which offer the cloud-
based data warehouse to store the data information. The DAAS
(Data as a Service) is a platform for the Cloud based Business
Intelligence (Demirkan and Delen, 2013). The DAAS is the
service oriented which provides the business process
architecture and infrastructure for process of accessing data
despite of the data in the local machine or in the server. The
purpose of the DAAS is that we can access the data wherever it
resides. The data cleansing and enhance the data in different
application and server used by the organization irrespective of
their network.
Increase in the data, focus on the accuracy and data
validation are some of the major factors of the data governance
helps to improve the organizational performance. But there are
few pro and cons about the use of the Data Governance in the
Organization. To overcome few of the drawbacks of the data
governance the Business Intelligence is in use. The Business
Intelligence and reporting system to provide a necessary
advantage to the organization. Business Intelligence is the
bridge that leads for the better data governance (Hugh J.
Watson).

Business Intelligence with Data Analytics
Business Intelligence is defined as a bunch of applications
and technologies that are put together to gather, analyze, store
and share the data with the companies which would help them to
implement some strategic decisions to increase the revenue(Ali,
Crvenkovski, Johnson ,2016). First of all, I would like to
provide a general insight in to how companies use Business
Intelligence to improve their service and generate high
revenues. In our day to day lives, we see customers responding
to calls from the service centers, different surveys popping up
on the websites and social media sites and reviews on some
major websites. All the raw data is collected and filtered by
using different kinds of applications and lastly the extremely
important data is shared with the companies for them to know
about their customers likes, dislikes, interests and their views
towards the products or services of the company. Let’s take a
look at an example related to this data, when customers are
interested in buying something from Amazon, first thing they
tend to do is look out for reviews and based on that the

customers would take a decision whether to buy the product or
not. Positive reviews help in the sales of the product, these
companies they collect the data from the reviews to analyze it
and implement some robust decisions which would help in the
increase of the product quality, this would help in getting some
positive reviews from the customers on the websites and would
have an impact on the sales.
Different components of Business Intelligence platform are
1) External data source: These are not considered as the part of
BI environment; however, these play an important role in the
application of data analytics solution. Usually these are external
databases, which consists of structured and non-structured files.
2) Data staging area: The staging area can be defined as copy of
systems that store data. It can help in storing the historical as
well as real time data, real time data plays an important role in
this process as we have to take different emerging trends in to
consideration to attract the customers. Some of its
characteristics are structured and denormalized form of data
model.
3) Multi-dimensional data warehouse: Data warehouse can be
defined as a repository which stores data in a non-volatile,
subject oriented and time variant manner. It can be considered
as the major database which has all the required data to
implement some effective decisions in the company. Data
warehouse plays a crucial role in providing the Business
Intelligence solution (Ali, Crvenkovski, Johnson ,2016)..
4) ETL (Extract, transform and load): Once we are done with
setting up the data staging and warehouse, all the data is
collected and transferred to the Business Intelligence platform
where extraction, transformation and loading process takes
place. It makes sure that the data is loaded in a right manner
without any issues.
5) OLAP (Online Analytical processing) cubes: It can be
defined as technique which is used in the analysis process with
high success rate. It is a multi-dimensional array which has data
sets.

6) Semantic layer: The process of reporting can be considered
as crucial in applying the data analytics solution, the important
goal of the BI solution is to have some set of tools that are used
for reporting and analytical purposes.
7) BI portal: The best solution for the access based on the single
source of information is to develop an instinctive BI portal. It
would be easy for the organization to extract the specific set of
information from all the raw data.
8) Data Mining: It is defined as a technique which involves
finding out the unknown patterns with the help of automation
process.
The implementation of BI solution has been increasing lately, it
is being applied
in various kinds of organizations like health industries,
colleges, universities, etc., It also depends on the geographical
locations, not many people have an idea about Business
Intelligence but day by day it’s use is increasing because of the
success that lot of companies are seeing after incorporating the
Business Intelligence solution. Data collection is considered as
a crucial part in applying the BI solution. Some of the
companies are not using this solution because they don’t have
access to the customers data. Many companies are coming
forward to invest in the tools which would help them in the data
collection process. Various techniques have been developed to
help companies in their success by attracting a wide range of
customers. These solutions can also be implemented in
marketing the products and services.
Practical implications of Business Intelligence with Data
Analytics:
Business Intelligence has a direct impact on any strategic
decision-making of a company. Business Intelligence with Data
Analytics has restructured the interrelations with in a company,
and art of business (D. P. Acharjya, Kauser Ahmed P, 2016,
page: 3). Data Analytics enables a business to measure, analyze
and manages the performance of marketing analytics. With new
BI tools like PowerBI, RStudio, Tableau etc. it is possible to

perform data analysis in real time instead of holding to history
data for prediction. It helps to project the data with dashboards,
generate reports, graphs and visuals to show business nature and
performance.
Example: A healthcare industry provides services to people
across the country, for this it involves enormous volume of
money transactions which makes it an attractive fraud target. To
avoid fraud transactions, healthcare industry uses Hadoop data
warehousing which allows distributed processing of data sets to
avoid any kind of fraud transaction and keeps all the customers
information private (Prajna Dora, Dr. G. Hari Sekharan, 2013).
With Data Analytics it helps healthcare industry to analyze
every transaction and projection of the company for a better
future from its competitors.
Example: A grocery store owner has a practice of keeping track
of all the items with its sales and inventory, using powerBI.
This helps him to understand what and when to order items
when the inventory is low. As the primary objective of a store
owner is to make sure everything is available in his store, so
that customer do not have to go to some other store for some
items. Using BI tools frees his complex work and allows to
produce the reports of profit and loss based on each item and
also helps to analyze how to price each item to with stand the
competition from other stores.
Future of BI and Data Analytics
Now-a-days BI is necessary tools all the businesses to run for
analyzing, producing reports, statistics and dashboards. In
marketing sector in order to market a product, they need to
analyze the market trend and tweak the product and then release
it to customers. As Business Intelligence expanded its use even
in small scale companies, which enabled to advance in other
fields like data mining and data modelling. Data Mining allows
a business to analyze data sets to find unsuspected relationships
and extract implicit, unknown and potential useful information
(Dina Fawzy, Sherin Moussa and Nagwa Badr, 2016).
In data Mining, Neural networks are also used in classification

because of their ability to extract meaningful information from
complex data, they are applied to detect patterns that are
considered to be too complicated to be performed by humans.
Business Intelligence helps to integrate numerous data sources
into one and allows to manage the data flow easily with cloud
based environment. With Cloud based environment it is possible
to process real time data and can be visualized from any device
around the world with data integrity.
Positive and Negative impact of BI
With focus on improving business value, companies have been
using BI to gather the data for predicting the present and future.
Predictive Analysis helps any kind of business to forecast how
to modify the business model to withstand from its competitors.
To predict future, businesses needs to consider lot of factors
from past, today and also from other company’s trends to
anticipate and make changes to its strategy. Data Visualization
of current statistics and future projections using different kinds
of charts and dashboards helps business analysts and also
investors on a business performance. Data Analytics helps to
understand the consumer so that they can optimize and ease
customer experience to maintain healthy relationship.
The negative aspect of Business Intelligence and data Analytics
is that for better data analyzing, businesses collect too much
information, which can be viewed by the parent company and
also some marketing company’s shares data for mutual benefits.
Lack of sufficient data and making conclusions with it for
business-decision can affect the business reputation adversely
(Remigiusz Tunowski, 2015).
Recommendations
With the ever growing different tools for BI, the key application
is to focus on how business meets the customer. In business
sector, staying ahead with your competitor is a vital role.
Defining real time statistics using data visualization techniques
allows the organizations to monitor logistics, sales and
productivity. Summarizing the data by reporting helps to
monitor business performance. Before picking a BI tool, first

business requirements needs to be thoroughly analyzed, after
that based on the use cases particular tool is chosen as different
vendors specialize in different niches within BI field.
Cloud Computing with Business Intelligence
Cloud computing is the most popular and broadly utilized
innovation nowadays. Organizations use cloud-based data
management and business intelligence answers to manage and
analyze the data rapidly and viably. Huge data, storage
capabilities, and inadequate analysis are challenging that many
organizations are facing today, and flawless data management
methods and analytic models are required to actualize an
integrated business intelligence arrangement. Cloud computing
has instigated another desire for prospects of business
intelligence (Thomson, and Van-der, 2010). Nonetheless, in
what manner will business intelligence be executed on Cloud
and by what method will the traffic and demand profile
resemble?
Practical Implications
Undertakings are thinking about substantial interest in Business
Intelligence hypotheses and innovations to maintain their upper
hands. business intelligence allows massive differing data
gathered from infection sources to be transformed into helpful
information, allowing increasingly successful and proficient
generation. This paper quickly and broadly investigates the
business intelligence innovation, applications, and patterns
while gives a couple of stimulating and innovate hypotheses and
practices. The landscape of business intelligence is developing
exponentially because of the new advances and ideas in the
business. The conclusion cloud computing will be the cause of
the following leap forward, and a conceptual framework for a
savvy business intelligence arrangement as a service. A short
explanation of what business intelligence accomplishes and then
taking note of especially that the utilization of business
intelligence frameworks inside smaller companies with asset
constraints is low (Gameiro, 2011). The fact of the significant

expense barriers and intricacy of in-house skill. The proposed
framework joins attributes of information innovation re-
appropriating, traditional business intelligence, cloud
computing, and choice hypothesis to present consolidated
perspectives on cloud business intelligence.
Cloud business intelligence is a revolutionary idea of conveying
business intelligence capabilities as a service utilizing a cloud-
based architecture that comes at a lower cost at this point has
faster organization and adaptability. Software as a Service
business intelligence is a conveyance model for business
intelligence in which applications are typically sent outside of a
company's firewall at a facilitated location and accessed by an
end-client with a protected Internet association (Pyae, 2018).
Example 1 to Banking Sector: Business Intelligence with Cloud
Computing in banking is characterized as the utilization of
analytics software, or software as a service, to create data
visualizations that are interactive and can be created at the work
area level by end-clients for banks and financial service
companies. Regularly utilized banking business intelligence
software incorporates Microsoft Power business intelligence,
Tableau, Tibco Spotfire, and Domo. Banking business
intelligence applications can be facilitated on the cloud and
designed to run private dedicated servers for financial services
companies that are exacting on data security prerequisites
(Amster, 2017).
Business Intelligence with Cloud Computing arrangement offers
a great decision in picking the management required and level
of security and subsequently is suitable for almost any business.
Although there is no magic projectile that can meet all the
prerequisites, cloud computing offers several advantages to the
financial establishments. These advantages include (Amster,
2017):
Cost-saving: The large straightforward capital use can be
transformed into continuous, smaller operational expenses with
no mass interests in new software and hardware.
Business progression: In cloud computing, the service supplier

manages innovation, and banking firms can have more elevated
levels of fault tolerance, data insurance, and disaster
recuperation. Cloud computing also offers an elevated level of
back-up and redundancy at a lower cost.
Usage-based charging: Institutions can pick and pick services
based on a pay-as-you-go basis.
Business agility: As the cloud is available on-demand, the
infrastructure venture is limited, saving the ideal opportunity
for initial set-up. The improvement cycle for the new items is
diminished, leading to an increasingly effective and faster
reaction to the clients.
Business center: Financial firms can move non-critical services,
for example, software patches, maintenance, and so forth to the
cloud, and can concentrate on their center business areas, not
information innovation.
Green IT: Transferring banking services to the cloud decreases
carbon impression and vitality utilization, and there is limited
inactive time with increasingly proficient utilization of
computing power.
Example 2 in the Aviation Industry: Aviation and aerospace
businesses are moving towards the cloud for analytics, plan, and
testing. To store data proficiently and safely manufacturers
advance toward the cloud as an answer because of its high
scalability feature as far as storage and register. Cloud
arrangement has turned into a critical factor in the aviation and
aerospace fields. It addresses the challenges aerospace,
aviation, and safeguard companies face and gives faster answers
for the changing condition. Cloud computing avoids companies
to put resources into the whole infrastructure and paves a way
to pay just for services they use. With the cloud, it ends up
easier to simulate each aircraft segment rather than structure a
physical model (Vagdevi, and Guruprasad, 2015). Operations
and management in air businesses mainly rely upon immense
arrangements of data. Gathering, ranking and extracting these
data are major challenges and these can be addressed by a
cloud-based database. Cloud computing is utilized to host

services on the ground station which at that point gives services
to aircraft moving in a range. At the point when aircraft moves
to start with one geographical system then onto the next, the
Virtual Machine will also be moved to start with one hub then
onto the next hub on an alternate cloud in another geographical
area (Vagdevi, and Guruprasad, 2015). It also depicts an aircraft
data coordinate with a virtual private cloud where cloud
services are given via the IPSec burrow.
Hong Kong Airlines is one of the famous airline companies
globally. To adequately rival other airline ventures and to
expand its services to clients Hong Kong Airlines manufactured
its own advanced, dedicated center information technology
framework for reservation management, baggage management,
finance, safety, client relationships, and group booking. With an
increase in the team, flights, and information, traditional
dedicated center postures many disadvantages. The framework
was less effective, if low security and expended a lot of
intensity. Along these lines, Hong Kong Airlines had to move
from a traditional information technology framework to Cloud
Computing with assistance from Huawei. It is currently utilizing
Huawei's FusionCloud work area cloud arrangement (Vagdevi,
and Guruprasad, 2015). The arrangement has a cloud-based data
focus also called a work area cloud virtualization platform. The
data focus has about 500 virtual machines and is proficiently
managing the airline.
Future of Cloud Computing with Business Intelligence
Business Intelligence, and latterly data analytics, have been
distinguished as major commercial and technological
improvements that cloud computing can host and enhance. Both
of these advancements give ways of analyzing data in a
meaningful manner to facilitate basic leadership and are aimed
at increasing efficiency and enhancing business performance.
The increased network between information frameworks across
the world has developed globally integrated databases with
higher complexities than the traditional organizational archives.
Cloud business intelligence is the idea of conveying business

intelligence and data analytics capabilities as a service. With
cloud business intelligence arrangements, business clients will
have the option to keep better fiscal command over information
innovation extends and have the adaptability to elastically scale
up or down usage as requirements change (Al-Aqrabi, Liu,
Richard, and Nick, 2015). In any case, in the shared domains of
cloud computing, business intelligence and data analytics
services are presented to security and privacy threats by
endeavors, eavesdropping, circulated attacks, malware attacks,
and other realized challenges to cloud computing.
Business intelligence is required to enter many complex
domains (business and non-business related) which were
incomprehensible for it in a self-facilitated condition.
Applications like setting aware, location-aware automation,
massive scale semantics, advanced science and innovation
databases, real-time disaster and emergency management, city
management, global finance, and economy revealing, and global
checking of ventures and sectors are not many such areas where
business intelligence or business intelligence like frameworks
have gigantic potential on Cloud computing (Olszak, 2013).
Advantages and Disadvantages
Regarding business intelligence in the cloud computing
condition, it ought to be perceived that cloud computing offers
great open doors for business intelligence. Although the
advancement of cloud computing innovation is still in its
infancy, there are as yet many issues to be fathomed. Along
these lines, a few challenges may be found, although these two
advances are regularly viewed as unpredictable innovations.
This can be explained by the fact that the two innovations are
currently facing levels of popularity, especially their integrated
variants. Thusly, the lack of hardware or software capacity is
just because of an inadequate spending plan for related
activities (Olszak, 2013). Cloud computing crushes the financial
matters of business intelligence by utilizing the hardware,
system, security, and software expected to create data

warehouses on demand, on a pay-per-use basis. In contrast,
cloud business intelligence presents significant dangers to the
performance of the business. It is truly vulnerable to the
external condition in fact, and although the innovation can
handle large amounts of data, it cannot be considered at the
outset. Subsequently, it usually takes quite a while frame to
make legitimate optimizations. Along these lines, this
integration isn't suitable for each company in small to medium
ventures. This sort of business is arranged towards other
strategic goals, so perplexing innovations will impede them
from reaching the appropriate target range.
Recommendations
Cloud computing plays an important job later on for Business
Intelligence. Business intelligence in the cloud has been created
to enhance the adaptability of implementation, availability,
scalability and increased performance of business intelligence
software. Here I talked about the importance of cloud
computing business intelligence for two sectors: banking and
aviation, and referenced a few considerations that must be
considered before picking between business intelligence as-a-
service contributions (Olszak, 2013). It also illustrated how
business intelligence functions and indicated business
intelligence segments and architecture. Also, the advantages and
challenges of Cloud business intelligence were talked about, the
contrasts among open and private cloud illustrated, and how to
pick the best one for an organization.
Introduction to Business-Driven Data Governance
Data governance is not a new topic, but it is evolving new day
by day. Its information systems exist as long as the data
governance exists. These days Data turned as an important asset
for corporate organizations. Most of them are struggling to
administrate their data in efficient way. This mainly happens
due to the lack of design, structure, and strategies that are
applying to information. Many Organization are using data
governance, but some organizations still resisting it. Especially,
Data governance is very important to business intelligence

because many organization in the world depend on the BI and
also they depend on the reporting systems which are used to
provide an advantage of competition. There is a little change in
Business intelligence without a infrastructure of data of high
quality, secure, accessible data. The directors who understood
the business and Information technology, they are in the
position to help building a bridge which leads to a better data
governance. This will help the entire governance and also
Business intelligence. Some of the business persons had done a
great job by defining data governance.
What is Data governance?
Everyone as their own opinion on data governance. When the
conversation about data raises the term data governance is used.
It often means different thing to different organization people
because of the broad scope of the data governance. The data
used in the organization can leverage it as an asset of the
organization, this consists of people, technologies and processes
which are used to manage and use data. There is a direct impact
on Business Intelligence by encompasses issues that indicates
the business strategy and also securing customer data. Because
of many facets of data governance organization which touches a
wide range varieties of ways, which makes it appeal as an
overwhelming.
Business-Driven starting point
It is very difficult to start because of wide scope of data
governance especially when the organization maturity curve is
not so far with the data governance. The important thing we
came to know with the years of Information technology change
is splitting the large projects into small projects with the same
data collection. This will help to deliver quick successes with
the division of small projects. There is little tolerance from
senior management in which the promise of the projects which
is delivered only far into the future. The same formula is
applied Data Governance which is to identify the business areas
which are effected with the organizational pain. This
organizational pain is because of the poor execution of data

governance in the organization. This is the time where
management commits the resource of organization.
Contingency Theory Data Governance Organization:
The contingency theory states the relation between effectiveness
and characteristics are determined by the contingencies. The
contingency refers that each organization requests a Data
Governance arrangement that matches with a gathering of
setting factors, a lot of extraordinary attributes that depict this
authoritative condition and exhibit the connection between the
plan of the Data Governance model and the potential
achievement of this model in regard of the results. The Author
proposes seven contingency factors that plays a key role to gain
success in Data Governance design and its performance.
Performance Strategy, Organization Structure, Competitive
Strategy, Diversification Breadth, Process Harmonization,
Market Regulation and Decision-Making Style.
Data Governance (DG) activity structuring customized Data
Governance jobs and advisory groups appropriate for its
authoritative particularities, it is extremely normal that a Data
Governance system, for example, the ones looked into in the
past section of this proposition is utilized as an establishment of
the DG plan and gives a benchmark on what might be center
spaces and segments of the DG program. A system is nothing
else than the determination of Data Governance as an idea with
various lower-level segments that create a technique for
overseeing information as a hierarchical resource. As it were, a
DG system is an approach to present an essential deliberation
layer that helps associations and DG specialists and
professionals to compose, present and impart DG ideas in a
simpler for no expert crowd to comprehend the way.
Financial organizations may face more pressure from industry
guidelines that should be converted into necessities for their DG
structure lastly plan and create explicit strategies and
procedures that vary to a huge degree with the ones set up in an
organization of an alternate industry, for case retail deals. For
this situation, information detectability for review purposes

from the source frameworks to the end-clients applications is a
higher organized goal for the budgetary association, and this
must be delineated in the standards and components of the DG
plan.
In this aspect, another model could be recognized for the
situation that an association expects to execute DG with regards
to a particular task or activity, for example, Master Data
Management or a Business Intelligence venture, with the last
offering likenesses to this contextual analysis and the trigger
for DG happening from the expectation to convey the Qlik
Sense information perception stage. Contingent upon the
association's exceptional prerequisites with respect to DG, the
lower-level components of the execution subtleties that identify
with explicit DG areas, for example, Data Quality Management,
Metadata and Master Data Management, Data Analytics, etc
should take an alternate structure and speak to requests and
needs that stem out of the objective authoritative and
information scene.
Since it is commonly acknowledged that DG is subject to
possible factors and no "enchantment formula" or "one size fits
all" approach exists, at that point likewise the chose for
execution DG structure ought to likewise speak to authoritative
particularities and necessities of the educational setting. Along
these lines correspondence of DG ideas to the individuals of the
association can be more straightforward and powerful as they
can relate DG ideas and lower-level parts in the structure with
the information scene of their association and the data working
society as of now set up and perhaps even in a split second
perceive the improvement potential and the stuff to arrive.
Data governance of Self-service Business Intelligence
When an organization in need of self service in data discovery
of explorative and Business Intelligence, Data Governance come
in lime light to help organization which is ordinary. In this
ordinary business, users in it who feel shy away from difficult
data models for Business Intelligence. For the main stream
users it Data governance acts as seamless, data analytics

environment.
Future of BI depends on Data Governance
Business users can transform the analytics future through self-
explorations, when they are powered with the direct access to
consistent and data source and when they have data discovery
tools. The confusion will be eliminated by the self-service
Business Intelligence. It delivers the actionable and instant as
the users need it. The data solution vendors turn the business
users to data evangelists by eliminating the data engineer,
highly tech data scientists.
Conclusion:
This paper introduces some introductory knowledge of health
care system and its fraudulent behaviors, examines the
properties of health care records. For future studies, some
preparations have been pointed out and various methods may be
explored to enhance analytics and efficiency of exposure of
fraud. However, to become aware of and eliminate the cases of
fraud is the ultimate intention, in order that fraud may be
averted within the conclusion. Second, because both fraudulent
and logical models in health care statistics may additionally
trade over the years, health care fraud detection method must be
effective and satisfactory to evolve these modifications. Hence,
future researches can try to develop self-evolving fraud
detection strategies. All this results in the belief that the best
resolution for identifying fraud within the medical health
insurance design for now is a decision tree and naive Bayesian,
each in terms of technology and in phrases of models of
analysis.
Cloud business intelligence has been created to enhance the
proficiency and profitability of business intelligence and
increase the performance of business intelligence software. It
helps in shorting business intelligence implementations, a
decrease in cost business intelligence applications. Cloud
facilitates testing and upgrading of business intelligence
programs. Regardless of these undoubted advantages, there are

various dangers and vulnerabilities during cloud business
intelligence utilizing. Security, data insurance, lack of control,
and several different barriers anticipate widespread adoption of
the business intelligence cloud.
Data Governance can significantly improved by Business
Intelligence. It incorporates the Business Intelligence which
will help to improve the end users. As the data consistency and
slow adoption is low in the data governance which is improved
by adapting to the Business Intelligence. Overall Business
Intelligence helps the organization to make better and faster
decision , provide insights into data quality. Therefore by
implementing these changes they can see the metrics in increase
of the effective data governance.
References
Al-Aqrabi, H., Liu, L., Richard, H., and Nick, A. (2015). Cloud
BI: Future of business intelligence in the Cloud. Journal of
Computer and System Sciences Volume 81, Issue 1, Pages 85-

96.
Amster, A. (2017). Applications of Business Intelligence in
Banking and Finance. Retrieved from
https://www.qubole.com/blog/business-intelligence-and-finance/
Gameiro, C. (2011). Implementation of Business Intelligence
tools using open source approach. Proceedings of the 2011
Workshop on Open Source and Design of Communication
(OSDOC '11). ACM, New York, NY, USA, 27-32.
Olszak C. M. (2013). Assessment of Business Intelligence
Maturity in the Selected Organizations. Retrieved from
https://annals-csis.org/proceedings/2013/pliks/139.pdf
Pyae, A. (2018). Cloud Computing in Business Intelligence.
Retrieved from
https://www.researchgate.net/publication/333037033_Cloud_Co
mputing_in_Business_Intelligence
Weber, K., Otto, B., & Osterle, H. (2009). One Size Does Not
Fit All- A Contigency Approach To Data Governance. ACM J.
Data Inform. Quality 1, 1, Article 4
Emerging Trends in Data Analytics and Business Intelligence
Team Members

Student Name
Student Name
Student Name
Student Name
Introduction
Increasing Operational Efficiency with Business Intelligence
and Analytics
Data Analytics and BI in Cloud computing
Location Based Analytics
3
Business Intelligence and Analytics

Why do Organizations need BI?
Solve Business problems
Improve Customer relationships
Increase Operational Efficiency
5
Business intelligence and analytics are in high demand as
organizations seek to use information assets to improve
business outcomes, customer relationships, and operational
efficiency. Yet, it has perhaps never been more challenging to
keep up with the changing demands and expectations of a
growing BI and analytics user community. IT-driven application
development, limited access to historical data, and canned
business reports are no longer satisfactory. Users want more
control, better visualization and analysis capabilities, and faster
development cycles
Applications of BI
Enterprise Reporting
Ad hoc Reporting

Self service Analytics
Real time Analytics
Mobile Analytics
6
Evolution of BI
Analytics as a service
Migration of traditional BI to cloud BI
Use of AI to automate real time decision making
Integration of AI and visual analytics
7
Predictive Analytics, data mining leads new analytics software
change
Data Analytics and BI in Cloud Computing

Why do we need Cloud Computing?
Building Infrastructure is difficult
Utilize cloud computing for powerful analytics
Highly scalable and secure applications
9
It is very difficult to build infrastructure for highly reliable
data. So, we utilize cloud computing for powerful analytics
How companies can adopt Cloud Computing?
Understand the determinants of Cloud Computing
Build Strategies by analyzing cost
Study Applications
10

I
Advantages
Cost
Performance
Security
Pay as you go
PROS AND CONS
Disadvantages
Compliance
Migration of mission critical applications
11
Cost – no upfront cost. Capital to operational
Performance: analyze big data

Security: in-build security is provided by the vendor
Disadvantages: Loss of critical information when the servers go
down
Applications
Geo-based analytics
Postal services
Web Applications
Marketing, Sales and Retail
13
Advantages
Real time tracking of deliverables
Provide personalized experience to customers

Pros and Cons
Disadvantages
Compliance
Migration of mission critical applications
14
Cost – no upfront cost. Capital to operational
Performance: analyze big data
Security: in-build security is provided by the vendor
Disadvantages: Loss of critical information when the servers go
down
Recommendations
More improved network connectivity
Educating consumers with the usage of location based services
15

16
THANKS!
References
Hung, S.-Y., Huang, Y.-W., Lin, C.-C., Chen, K.-c., &
Tarn, J. M. (2016). Factors InfluencingBusiness Intelligence
Systems Implementation Success in the Enterprises. Pacific
AsiaConference on Information Systems.
Wattal, Suneel & Kumar, Ajay. (2014). Cloud computing -
An emerging trend in informationtechnology. 168-173.
10.1109/ICICICT.2014.6781273.
17

EMERGING TRENDS IN DATA ANALYTICS AND
BUSINESS INTELLIGENCE 1
List Names of The People in your group
Business Intelligence (ITS-531-20)
University of the Cumberlands
Professor Kelly Bruning
Table of Contents
Introduction
...............................................................................................
...................................... 3

...................................................... 4
and Analytics ................................ 5
Business Intelligence
.............................................................................................. .
................... 5
Practical implications of BI
...............................................................................................
......... 7
Example
..................................................................................... ..........
................................... 7
Future of BI
...............................................................................................
.................................. 8
............................................................................................ 9
Recommendations
...............................................................................................
........................ 9
Data Analytics and Business Intelligence in Cloud computing
.................................................... 10
Practical
Implications............................................................................
.................................... 11

Example
...............................................................................................
................................. 11
Example
...............................................................................................
................................. 12
Future of Cloud Computing
...............................................................................................
....... 13
Positive and Negative Impacts
...............................................................................................
... 14
Recommendations
...............................................................................................
...................... 15
............................................................................................. ..
.............. 15
Real time implementation of location
analytics........................................................................ 18
Example
...............................................................................................
................................. 18
Example
...............................................................................................
................................. 18

Predictions....................................................................... .......
................................................... 20
Negative and Positive Effects on Business Organizations
....................................................... 20
Positive Effects
...............................................................................................
.......................... 20
Negative Effects
...............................................................................................
......................... 21
Recommendations
...............................................................................................
...................... 21
Conclusion
...............................................................................................
..................................... 21
References
...............................................................................................
...................................... 22
Introduction

The emerging trends in the field of Business Intelligence and
Data Analytics has noticeably
advanced with several innovations, evolutions in the modern
data driven era. There are several
technologies currently trending which are being adapted by
numerous organizations. For
instance, machine learning, data science, advanced data
analytics, data governance, Apache
Hadoop, Apache Spark, internet of things, no sql, blockchain,
virtual reality, geo-spatial location
analytics and business intelligence on the cloud etc. These
emerging trends help organizations
with improved customer management, improved cost
management, operational excellence, data
quality, security, customer data confidentiality, better use of
business data through Business
Intelligence and data warehousing.

The following are the three trends discussed in this paper
1. Increasing Operational Efficiency with Business Intelligence
and Analytics
2. Data Analytics and Business Intelligence in Cloud computing
3. Location Based Analytics
and Analytics

Business Intelligence
Business Intelligence incorporates many technologies, tools,
applications for analysis and
best practices are inherited to integrate, collect, analyze, and
display raw data of business
organization for creating actionable and insightful business
information. BI as a process of
technology-driven and as a discipline is made up of numerous
linked activities, comprising
online analytical processing, data mining, reporting, and
querying. BI tools comprise of business-
driven data, to provide supporting documents and reports useful
for business decision making.
With BI tools, business persons may start examining the data
themselves instead to wait for
Information Technology to run analytical and compound
reports. This information access
benefits operators back up commercial decisions with solid
numbers, rather than gut anecdotes
and feelings. BI in business aims to support executives
understand their business needs to make
improvements, plan budgets, provide managers with their team
performances and upgrades to
make business decisions. Organizations also use BI tools to run

their budget reports for cutting
costs, modifying existing applications by upgrading to latest
versions and specify incompetent
operational procedures.
BI maintains and improves working efficiency and benefits
companies to increase
executive productivity. The software of Business intelligence
deals with several benefits,
comprising influential data and reporting analytics abilities.
Using BI’s data visualization tools
such as real-time dashboards, directors may generate
instinctive, clear reports that enclose
relevant, unlawful data. Business Analytics is the course of
discovering reports and data in order
to remove expressive insights, which may be used to high
understand and increase the
performance of the business (Hung, Huang, Lin, Chen, & Tarn,
2016).” BI deals as an objective
management function. Managers are capable to program data
depend on goals, which can

include sales objectives, financial objectives, or productivity
measures, regularly. The features of
BI subsidize to the objective of offering an awareness of present
commercial practices. The
software of Business analytics is used to analyze and explore
current and historical data. It
exploits statistical analysis, quantitative analysis, and data
mining to recognize past commercial
trends.
According to Robles-Flores and Kulkarni (2013), the rapid rise
in data volume in
businesses has meant that comprehensive data gathering is
barely likely through manual means.
BI solutions may help here. They offer tools with proper
technologies to contribute to the
integration, collection, editing, storage, and study of existing
data. Though almost only big
companies were involved in this matter a few ages ago, it has
temporarily also developed
necessary for start-up businesses, and so the marketplace for BI
has been increasing for years. He
focuses on the overall potentials of consuming BI in the
beginning (Kulkarni & Robles-Flores,

2013). First, it will be observed which workers of result that are
appropriate for beginning and
what chances exist for realizing BI systems in the beginning.
Then it will be revealed to what
amount BI has succeeded in the beginning, in which parts the
methods of BI are practiced in
start-ups, and what drive BI has in the beginning. Finally, the
critical success factors for the
projects of BI, in the beginning, are considered.
With growing globalization of marketplaces, aggressive
competition, growing the speed
with variations in customer needs and market conditions, all
market members and businesses
look new challenges. In the long run, businesses will be capable
of recognize themselves, who
may adapt to these situations, who may respond quickly and be
flexible to changes though at the
similar time consuming their costs under the device. For this
purpose, a precise knowledge of the
present corporate and marketplace situation is crucial. To

safeguard this and to offer
management with the data needed in their decision-making and
planning, cultured information
and communication schemes are practiced. Like the 1960s,
numerous approaches have been
industrialized for the systems, which have to develop known
under numerous diverse names like
Decision Support Systems, Management Information Systems,
and Executive Information
Systems. Today, the word BI has become recognized both in
research and in practice. BI defines
methods like collecting, processing, storing, analyzing, and
offering company data.
Practical implications of BI
BI has a direct impact on the business strategic, operational,
and tactical business results.
BI confines fact-based decision making to consume historical
data instead of assumptions. The
tools of BI perform business data analysis and generate reports,
dashboards, summaries, graphs,
maps, and charts to offer users with complete intelligence about
the business nature. BI supports
on data visualization that improves the data quality and the
decision making.

Example: An owner of the hotel practices analytical
applications of BI to collect
statistical information about average tenancy and room rate. It
benefits to discover aggregate
income generated in each room (Wieder & Ossimitz, 2015, pp.
1163-1171). It also gathers
statistics on marketplace share and information from consumer
surveys from every hotel to
choose its competitive situation in numerous markets. Through
analysing these trends every year,
every month, and everyday supports management to provide
discounts on hotel room rentals.
Example: A bank provides certain level of access to branch
managers to multiple
applications of BI to evaluate employee performances,
operational data and compare it to the
other zones. It helps the branch manager to govern who the
supreme profitable consumers are
and which consumers they must work on. The usage of BI tools
frees IT staff from the challenge

of producing logical reports for business departments. It also
provides employees with rich
source of data with certain access levels to look over the
percentage of commissions they are
earning monthly, run market share reports and look over risk
profiles.
Future of BI
The key future trends of business intelligence is forecasting and
development of the
digital BI world into space where platforms and tools will
develop more wide-spectrum and
finally, highly collaborative (Bach, Jaklič, & Vugec, 2018, pp.
63-86). The development of BI
has been intensive on small form-factor strategies, but the
emphasis will shift to actual big touch
devices. “This will permit team colleagues to function towards
business decisions by the side-by-
side data exploration in actual thought time.” Numerous vendors
are functioning toward this
enlarged integration, with application programming interfaces
permitting for business data
analysis in users’ current systems. The BI industry has extended
exponentially in current years

and is probably to endure growing. If you want to create the
business data analysis in a
recognized or newly approved BI system, your team must be
data-driven. Businesses must focus
on how and why they are consuming data. With these goals in
mind, business leaders may design
a strategy for BI usage specialized for their team, provide them
with cloud based environment to
store structured and unstructured data and create a data-driven
environment. The software of BI
will become more accessible as the business grows. This
development will also drive a more
informed user base. However, along with these developments,
commercial leaders are essential
to take on the duty of educating their staff.

The businesses have focused on BI for gathering important
competitive data from past
data and inspecting it in graphs and dashboards. Though static
data is no longer necessary for
creating informed results. In the present competitive
marketplace, businesses need a view of not
only the past and today's consequences, but also what is
probable to occur in the future so they
may anticipate and strategy for change. Rather than BI, in the
year 2019, the focus will be on
commercial insights, where businesses judge the performance
on data-driven analytics and
measuring business analytics as per the results, and forecasting
outcomes depend on past data. It
will all be around the value that data may create for its
operators, instead of dashboards and
reports.
The negative impacts of BI come when user does not have a big
pool of correct data from
which to compete for conclusions (Kulkarni & Robles-Flores,
2013, pp. 15-17). When this
occurs, decision-makers will often create wrong decisions as
they are creating their decisions off
data that is incomplete or inaccurate. It is essential in BI to

extrapolate numerous elements and
factors to go along with the data that is gathered in order to
derive to a complete picture that is
required to create business decisions.
Recommendations
As per the recommendations, BI is facing new technologies ad
approaches, proposing
both disruptions and opportunities for buyers and suppliers. BI
consumers are now challenging
solutions that are easier to deploy, buy, use, and integrate to
support mobile computing and
social or collaborative capabilities. Business Intelligence is
very vital for business organizations
for distributing useful data from the great volumes of data being
composed. There are numerous
BI tools accessible, but no tool is correct for each user’s
requirement. Organizations are to
understand the emerging trend of BI to better their operational
performance (Islam, 2018) and

integrate the most efficient tools by concentrating on the budget
assigned to their development
team and allow them to come up with Data accuracy and
compliance and be transparent to
identify and eliminate the gaps that leads to improve customer
satisfaction.
Data Analytics and Business Intelligence in Cloud computing
Recent days the emerging trend is Cloud Computing. Cloud
Computing is described as a
type of computing which relies on the shared resources. In that
case there will be less usage of
local servers or personal devices to handle different type of
applications. All the applications are
mostly accessed via web. When accessing via web the services
are delivered and used for the
internet and are paid by the cloud customer (Gupta, Mittal,
Joshi, Pearce, & Joshi, 2016). Cloud
computing is the place where the hardware and software is
located and the way it works will not
matter but the user will be somewhere up cloud that represents
the internet.
Cloud computing are very popular the reason is to reduce cost
and complexity of
operating computers and networks. Cloud computing is

considered as efficient as it allows
organizations to focus on innovation which helps in the product
development. Mainly cloud
computing is used for unlimited storage in the cloud, where the
cloud is cheaper than the drive
storage space (Gupta, Mittal, Joshi, Pearce, & Joshi, 2016).
There are some providers who
introduce unlimited storage. There are some reasons to use
cloud computing for data protection
and there is a flexibility for this. The main reason is to access
the data anytime and anywhere by
using the services.
According to (Yang, Huang, Li, Liu, & Hu, 2016) it was noted
that the organizations face
three basic private cloud paths which build internally with
developer focused tools and the
infrastructure which is defined by software. Most of the
organizations use cloud computing for
the business services. The organizations will start their own
digital application platforms that will

include server less and event driven services and form the basic
foundation for the business to
run in core level. Cloud computing is mainly used to deploy,
monitor and upgrade the
technology for the organization to run in a good profit which is
very helpful for the business.
According to Han, Liang, and Zhang (2015) the integrating
mobile sensing and cloud
computing which helps in forming the single idea of mobile
cloud sensing. For the mobile
platform the data is provided as data –as –a –service in the case
of cloud. It is a powerful
computing for the mobile devices to connect regarding network
resources. There are solutions
which have been investigated to connect mobile devices with
the most powerful cloud
computing where the network and its capabilities (Hung, Tuan-
Anh, and Huh 2013).There is an
example for the cloud based mobile Augmentation which is the
emerging trend of threat mobile
computing model to increase and enhance the storage
capabilities of mobile devices. Soyata et al.
(2012) Mobile cloud-based Hybrid Architecture is proposed for
mobile cloud computing

applications. Cloud computing offers unlimited demand
processing power. There is a limitation
of cloud computing network bandwidth by which the efficiency
of computation will be impacted
over large data volumes Virtualization of cloud computing is a
challenging task to ensure the
data and to support the data processing (Huang et al. 2013).
Practical Implications
Example: AWS is an example in which cloud computing is used
as emerging trend their
different types of services. Amazon Web Services hosts a cloud
conference which is AWS
relevant. The conference lasts for a week and it provides
opportunities to gain some information
about the Amazons products and the latest launches and the
services which has to be provided to
all the skill levels. In next two years Amazon web services
(AWS) will be reaching a high
revenue of all cloud services. It clearly shows that Amazon

leads ahead of Google and Microsoft
in cloud computing. If there is an issue, to address that issue a
set of new machine learning
models were released when there is a cloud conference which
will be helpful to the developers
and scientists to deploy and manage machine learning models.
There are some latest services
which are used is Amazon Recognition, Amazon Polly.
In Amazon server less cloud computing allows the developers to
develop and run the
applications and services without any complex infrastructure of
servers. Server less is one of
the emerging trends in cloud computing. There is another
service which is called AWS Server
less Application Repository which is designed to be in the
publication, discovery and
deployment of server less applications. These products and
services will be widely used.
Example: The other example where the cloud computing is an
emerging trend is in the
healthcare sector. In the healthcare systems there are cloud
based Electronic Medical Records
(EMR). These records are done electronically and secured and
that data will be centralized in a

storage location. EMRs can bring the healthcare systems
together in which the information will
be accessed across the other healthcare system, they are
connected through the Application
Programming Interfaces (API) that will be there in the cloud
infrastructure. The developing
infrastructure in the health care system will working hard to
connect to different type of trusts
,clinics and other hospitals through the cloud network. This
cloud network can monitor the types
of cost effective services which are offered to the patients.
There will be the communication
through the cloud where the doctors stay connected with the
cloud based phone system. For the
high quality research which is very important in the case of
patients cloud communication is
used. Sometimes if need doctors can communicate on phone
where that process make some sort
of sense and this results in the best treatment for the patients.
For the doctors the cloud –based

phone system would make very easy.
In the case of streamed collaboration this communication results
in streamlined patient
care. This is a positive news where the patients need not visit
the hospital when they can take
care of themselves for the first time. By cloud technology the
whole medical team will be able to
easily communicate and share the information. This
communication will be very easy in today’s
world because the cloud network can be used anywhere and
anytime. Sharing data between
pharmaceutical giants with the help of cloud network will help
the researchers in choosing the
best option. The most recent witnessed is when the clinical trial
big data revealed which is used
curing the lung cancer. Flatiron Health center said that most of
the potential data of cancer
patients is yet to be analyzed. By the cloud network mainly the
communication will be easy to
the doctors and the data of the patient can also be checked
anytime or anywhere when in
emergency. This was one of the emerging trends of cloud
computing in the Healthcare system.

Future of Cloud Computing
In future Cloud network solutions will be increasing affordable
and will have a sparking
interest from businesses who seeks the availability in the case
of security for the data and
systems. Most of the enterprise IT organizations will be
committing to hybrid cloud architectures
in future IT World. Some of the most innovative companies will
be starting investigating and
offering hybrid cloud services to different industry sectors. The
Companies like Amazon,
Microsoft are the currently are the two top companies which are
using the cloud network and the
tech companies like Oracle and Google are in the way to reach
the goal in the using the cloud
network. Amazon’s AWS stack and Microsoft high level service
offerings are ahead in today’s
competition where both offer infrastructure and platform of
service. Market share is likely to
shift to Google which introduces new cloud services and has a

focus on modifying their cloud
strategy.
Many leading organizations will be able to design, build and
operate cloud services
which is used to help in reducing management complexity and
operating costs. Cloud architects
will need broad skills in infrastructure design and optimization
with deep security. In future
cloud computing will be enabling individuals and organizations
of all sizes to work with data in
inspiring ways. Cloud computing will be impacting virtually in
every aspect of technology from
corporate capabilities.
Positive and Negative Impacts
Many enterprises still consider cloud computing as the concept
of large number of
computers connected to the internet in real-time (Deshmukh &
Shah, 2016). With the increasing
number of people who becomes aware of storing the data and
accessing the same data. There are
some positive and negative impacts of cloud computing. The
positive ones is cost reducing
which is most significant cloud computing benefit. The cost

includes IT cost and non-IT cost.
The other positive is flexibility which refers allowing
employees to be flexible of work practices.
Employees can access anything stored in the cloud and web-
enabled devices such as smart
phones, laptops, and notebooks. Cloud computing will enhances
the function of remote working.
Some of the negatives is cloud computing has benefits for which
many enterprises allows
to concentrate on their core business than IT and infrastructure
issues (Deshmukh & Shah, 2016).
These shortcomings are mainly related to smaller business
operations. The providers may offer
to compensate the outage where the customers will not be
satisfied.
Recommendations
Cloud computing shows that IT professionals every IT
professional should be aware of

the upcoming trends emerging in cloud computing, adapt it to
their data infrastructure and make
sure their application developers and architects are aware of
how to utilize and manage data on
the cloud environment.
Location based analytics is a tool that enables the business
organizations to make decisions in
context to the geographic location of either the consumer or the
consumer base. Often the
location data is coupled with Geographical Information Systems
to provide the clear
understanding of how the data is impacting the organization’s
business. Geographical
Information Systems provide the ability to visually analyze the
data obtained from various
sources. Geographical Information Systems which form the
critical tool for visually analyzing
the effects of data in topographic, environmental, and
demographic perspectives. Integrating the
location data with the business data provides the reliable,
accurate predictions of the businesses
and better business decisions. According to Heesung,W.,2018

.Location analytics or the Location
Intelligence is the tool facilitating the pictorial representation
of the data like the Heat Maps on
the Map. Location intelligence is the combination of the geo
spatial data warehouse and various
geo spatial Online Analytical processing tools (OLAP).
According to Turban et al , 2015, “Location or the Geospatial
analytics is the combination of
visualizing tools, the business factors and the key performance
indicators (KPIs) to achieve the
visual presentation of the information to make decisions for
sustaining and thriving business”.
The location analysis would provide the advantage of exploring
the opportunities of a specific
region. Geospatial analytics enable the entities or organizations
to analyze the data on the basis
of the location in addition to the dimensions offered by the
tradition business analytical
technologies (Wang,Z., Hu., & Zhou, W. 2017 ) .One of the

major sources of the spatial data is
Geographic Information Systems (GIS). Location data doesn’t
provide the complete analysis
whereas the combination of the Enterprise data warehouse and
geospatial data would serve as an
reliable source of Business intelligence. Location based
analytics require the Spatial data
warehouse and the location based data is input through the
various sensor technologies, global
positioning systems (GPS) or through installation of Radio
Frequency Identification (RFID)
devices in various logistic businesses (Hirve,s., Marsh, A.,
Lele, P., Chavan, U., Battacharjee, T.,
Nair, H., Campbell, H., & Juveskar, S. 2018). Spatial or
location data added to the Enterprise
data ware house facilitates the business organizations to
perform calculation required to perform
the data analysis with more productivity and unveiling the
various trends and patterns. According
to Sachan et al. 2016, the results enable the business to
establish the relationship between the
location data and the business Key performance indicators in an
organization. The location
intelligence employs different data visualization techniques like

Heat maps besides the
traditional data analysis visual techniques like bar graphs, pie
graphs, and tables (Farney, T. A.
2011). The Geographic Information System data viewers are
used for the visual representation of
the location data. According to Ziming et al, (2016) The
Location data analytics carried out in
following steps.
a. Enrich: The data various sources in the context of location is
collected in relation to various
other Key Performance Indicators (KPIs). The data collected is
stored in an integrated data
ware house to achieve the holistic effect on the organization.
b. Analyze: Analysis of the huge data with data points or Key
Performance Indicators (KPIs)
from data warehouse in relevance to the location data.
c. Map and Discover: After analyzing the data, it is further
visualized through various visual
analytical dashboards. The data patterns and correlations are

identified by applying various
algorithms.
d. Predict: Identifying the patterns to identify the causes,
improve the process to meet the needs
of consumers, identify the areas of improvement and explore
new fields of growth for the
business expansion.
Figure 1. Categorization of Geospatial analytics-based
applications. From classification of
location-based analytics, Sharda, R., Turban, E., & Delen, D.
(2014). Business Intelligence and
Analytics: Systems for Decision Support, Global Edition.
London, England: Pearson.
Real time implementation of location analytics

Example: Location based analytics employed at Great Clips:
According to Turban et al, (2015)
Great clips has used location analytics tool provided by Alteyx
to analyze the location-based data
and integrated customer data to explore new locations for
starting new saloon site locations. By
implementing the Location intelligence provided by Alteryx
great clips achieved reduction in the
time to analyze the data, improved performance by reduction in
workforce, and make smart
decisions.
Example: Location Analytics at Intergalactic telephone Corp
(ITC): Inetrgalactic Telephone
Corp offers telephone services to its customers across USA.
There were number reported
incidents of dropped call across USA (Turban et al, (2015). The
company has decided to do
Location analytics with the assistance of Teradata in the North
Eastern USA.

Figure.2. Zoey and Jake (2012). BSI Teradata: The Case of the
Dropped Mobile Calls
[PowerPoint slides]. Retrieved from
https://www.slideshare.net/teradata/bsi-teradata-the-case-of-
the-dropped-mobile-calls.
Predictions
Following are the predictions of location analytics,
Location intelligence would be used in all kinds of industries
like Information Technology,
Health care, Retail, Pharma, insurance, telecom, and fast food
chain industries. I would predict
the usage of analytics in food industry to cater the needs of
different demographic categories

depending on their food habits. Location analytics has a great
scope in the Information
technology. Location analytics in IT would help develop web
applications, mobile applications
to cater the personalized view to people of different regions.
Negative and Positive Effects on Business Organizations
Location analytics has both positive and the negative effects on
the entities. The following are
the positive and negative effects.
Positive Effects
1. Location intelligence helps organizations to serve the needs
of people on location centric
basis.
2. Geospatial analytics helps organizations to plan future events
based on the previous data.
Find helpful in Weather prediction and climate patterns.
3. Geospatial analytics solves various issues associated with the
business organizations and
increased productivity and business performance.

Negative Effects
One major negative effect is the loss of customer privacy which
further would raise lots of legal
issues.
Recommendations
I would recommend the organizations to develop applications
that would gather the location data
in compliance with the rules or legislatures of respective
countries where they operate their
business. Develop an application that would report the patterns
and trends by analyzing the
different kinds of analytical data like demographic data, gender
data, Age data, mortality etc.
Conclusion
The need to increase the business value is paving the way for
the rise of emerging trends in
business and data analytics. The question that arises is how to
measure the business value. This

paper discusses in detail about measuring business value based
on the needs of the business user
and data accessibility using analytic and visualization tools. To
make the analytics more
meaningful to the business user, highly appealing visualizations
are built that reveal deeper data
insights based on user preferences. The analytic applications are
being embedded in enterprise
applications. Issues such as data integration, data storage, data
analysis are being extremely
critical during the system design (Kohavi, Ron & J. Rothleder,
Neal & Simoudis, Evangelos,
2002, p.7). To enhance the effectiveness of analytics, the
business analytics solutions are being
extended beyond customer focused to sales, marketing and other
business supporting functions.
In the end, to achieve the optimal results, a number of analytic
solutions exist today that provide
mechanism to provide deeper insights and a method to measure
the key performance indicators
in an actionable manner.

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3958
Residency Project
Write a research paper describing THREE emerging trends in
data analytics and business intelligence that were ALSO
addressed in your text. Some examples of emerging trends are
discussed in Chapter 14 of the course textbook, including
location-based analytics, recommendation engines, data-as-a-
service, and analytics-as-a-service. You can also find topics
through your own research online and in the University of the
Cumberlands University Library with peer reviewed journals.
Your paper should address the following points regarding each
of the three emerging trends. The paper should address one
emerging trend at a time with the subject of the emerging trend
bold as a subheading for APA.
· Describe the emerging trend in a way that would be
understandable to a nontechnical business manager.
· Articulate how the author addressed the subject matter in the
text
· Provide at least two examples of how the trend is being
applied in organizations currently.
· Predict how the trend is likely to develop over the next 5
years.
· Analyze how the trend may impact business organizations in
the coming years, including both positive and negative impacts.
· Recommend what you think interested business organizations
should do in regards to this trend.
Guidelines

Residency Research papers must be 12-15 pages in length
(excluding the title page and table of contents nor the reference
list). This averages 4-5 double spaced pages per technology
addressed. The paper is to be in 12-point, Times New Roman
font; be double spaced; and include a title page, table of
contents, introduction, body of the paper, summary or
conclusion, and references.
· Papers must follow APA format. Please review and follow the
APA resources in the Syllabus.
· References are very important. At least five authoritative,
outside references are required for EACH of the emerging
trends (15 total references). Anonymous authors are not
acceptable. Web sources, if used, must be authored by
recognized experts in the field. At least three references must be
peer-reviewed, scholarly papers from the University of the
Cumberlands University Library. All should be listed on the last
page, titled references.
· Appropriate intext citations are required. I would expect to see
4-7 intext citations for each technology trend. Remember that
for each reference you use, there needs to be an intext citation
and for every intext citation there needs to be a reference.
· All University of the Cumberlands University policies are in
effect, including the plagiarism policy.
· Interim deliverables for this project are the annotated
bibliography assignment. That serves as a research repository
for your group on articles to draw from in writing the final
paper.

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