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Saeed,F., Mohammed, F., & Gazem, N. (Eds.). (2019). Emerging Trends in Intelligent Computing and Informatics: Data Science, Intelligent Information Systems and Smart Computing (Vol. 1073). Springer Nature.

According to (Saeed, Mohammed, & Gazem, 2019) the role data plays in the livelihood of humanity is just unimaginable and even beyond comprehension. Nobody today in the 21st century can dispute the impact intelligent computing and informatics have had on their lives since virtually everything today is connected to information systems. Today it is virtually impossible to imagine a world without technology as everything that sustains humanity in one way or the other require an input from technology world. In order to bring to context the role technology has and is continuing to play in the livelihood of humanity and also the world of business, I will take you back in time shortly. In the 7th and even until 16th century the idea of an airplane was only a misconception and few people like the Wright brothers could imagine that. Today aviation industry is one of the most leading industries in the transport sector which have open the world to unlimited opportunities. The same case can be said for many success stories and revolution technology has brought to the world order of today (Banister, 2007). According to Apple Company founder and the then CEO the late Steve Jobs, he argued that in the recent future technology will form an integral part of the community and it will be hard for one to resist its eventual occurrence. Jobs sentiments were also shared by many other players in the tech world like Elon Musk, Mark Zuckerberg, Warren Buffet and Bill Gates. They all pointed to a future where even the business world won’t be able to do without continued cutting edge technologies like digital twin technology, digital ethics and privacy and lastly automation and AI driven development.

Digital Twin technology

Before its discovery industries like manufacturing had issues of increasing efficiency in their operations so that product and service delivery would meet the international standards and also make manufacturing sustainable. Also instances of failure along the manufacturing line were hard to detect and properly diagnose due to lack of a way to monitor data from each manufacturing point and coordinate the data to arrive at a well informed decisions (Saeed, Mohammed, & Gazem, 2019). All these challenges almost seems to be forgotten with the invention of digital twin technology (DT) which has streamlined the manufacturing sector. With this kind of technology sensors and data transmission technology were able to be install at all point of the production or manufacturing line, all with an objective of collecting critical data of the process and analyzing it. From the data collected it was possible to use data analytics skills in determining areas where if adjustments are made the all manufacturing like could be optimized. Further this technology answered the problem of data and data analytics in influencing the entire manufacturing division.

Today it is possible through simulation to integrate almost all physical and virtual features of a system and this can be done for an entire manufacturing line. For instance, if the Boeing company experiences a problems with one of their airbuses as it was with the Boeing 737 max airbus, all they need is to go back to the whole production line or cycle and determine where could the problem have arose from. And this has been made possible through data analytics facilitated by the DT technology. Although one of the challenges of smart manufacturing been with finding a seamless way to integrate physical and virtual spaces together, progress seems to be underway especially through simulations, data communications and even acquisition (Saeed, Mohammed, & Gazem, 2019). Moreover, in 2017 the manufacturing industry in the US and China experienced a rise in their overall returns with the estimation ranking the figures at close to 12 trillion US dollars as the value of the manufacturing industry globally. This figure represents a 12.56% rise from the previous figures reported in 2007.

Digital Ethics and Privacy

With continuous increase and advancement in technology, issues like cybercrime and data privacy have been of great concern to many players particularly those handling large volume of data. Cybercrimes have been on a constant rise over the last ten years with estimations putting the total cost of global cybercrime annually at a whooping one trillion US dollars. Just from the figures one can be deduce how cybercrimes is of concern to the world. The issue of data security and compromise of data privacy was evident recently in the case where Facebook Company was accused of selling private data of its clients to Cambridge Analytica. This issue brought uproar with many people accusing the management of Facebook Company for compromising the integrity of their data. That is just one example, to heighten the adrenaline what do you think would have if the US, Russia and even China launch codes for their nuclear weapons were compromise? The aftermath could be catastrophic, and for this reason and many more digital ethics and privacy technology aims at reducing instances of data been compromised and at the same time coming up with technologies that would ensure cybercrimes is managed if not totally eradicated. In addition, the challenge various technologies in digital ethics and data privacy like data masking is facing is dynamisms to deal with different forms of replicating

Automation and AI development technologies

Automation have and is continuing to change the world of manufacturing and business significantly. Previously before invention of autonomous robots and invention of artificial intelligence (AI) manufacturing and even service delivery in some industries and companies were greatly slowed down. For instance, the Toyota Company before automation or introduction of autonomous robots in their production factory, the company was only able to assembly 15 cars a day. But with AI technology and robotics in play, the company have been able to improve its efficiency greatly and also the turnover of cars assembled per day from just 15 to 140 a day. It therefore follows that the fusion of technology in business and human lives has created more opportunity and improved the way of life.

Data Governance and Embedded Data Encryption

Many institutions are facing imminent threats of protecting the data they have against any external invasion. Data is crucial to any organization, and firms spend millions of dollars investing in effective security system only to realize the problem they thought of cyber-attack vulnerability is not the only problem the firm faces. It is then advisable to normally do a background on all security systems from households to firms in order to ensure there are no problems hidden behind the visible threat. Some of the trends in data analytics and business intelligence includes data security and embedded data encryption which helps to deal with the challenge of data insecurity and integrity (Saeed, Mohammed, & Gazem, 2019). Also the use of various technological solutions like data masking, data encryption and data resilience systems that would prevent unwarranted access of the company’s data are on continually on increase to combat data insecurity amidst Big Data emergence.

Data masking is one of the effective technologies used largely in corporate word to provide security to the data of a company. Data masking which is normally known as data obfuscation is the process of hiding the native data of the company through modified content which creates so many steps and decryption process in order for one to be able to access those data. The sole reason for carrying out data masking to data system is to protect the data that is top level security for instance the coke formula of the Coca cola company is a classified data that the company would not want to fall in the wrong hands or at worse its competitors for they will be out of business as a result of that. For this technological solution to be considered effective it must be consistent in its function which is providing top level security to the data when various tests are done to the system.

Process of data masking often involves various steps with the first step in data masking includes finding the data which is normally the first step which involves identifying the data that is classified and grouping it from the data that is not classified. This is often carried out by most company’s chief information specialist or data security analyst who puts together a detailed list of all classified data that needs to be protected from unauthorized access. Secondly assessing the situation is usually the next step and at this stage the company needs to an oversight from the security administrator of the company on the in the data security information status of the company with regard to security since this will determine the type of data masking technique to be adopted by the company (Saeed, Mohammed, & Gazem, 2019). He or she will also offer suggestion to be best data location and to what level does the data should be masked. Further the implementation of masking is normally the third stage and after carrying out the assessment of the situation and finding the data that requires masking the next step is to implement data masking technique which the company finds appropriate to use. Remember that for big organization that deals with large volumes of data, it isn’t feasible to assume that a single or just using an easy to incorporate system can be used in the entire company’s data (Saeed, Mohammed, & Gazem, 2019). Rather implementation must take into account effective and proper planning so that in the process no data is lost and therefore securing the entire data as a whole. Lastly testing data masking results which is the last step in data masking process since through various leaks might be identified early and their remedy found. Conducting the test ensures that the masking configuration yield the expected outcome. If it doesn’t then the DBA will restore the database to the pre-mask state. Tweaks the masking algorithms and completes the data making process from the start. Some of the common data masking techniques includes; Encryption, Character scrambling, Nulling out or deletion, Number and data variance, Substitution and Shuffling.

Data resilience is one of the technique that is normally used to protect data and only avail it when it’s needed especially in the production line of a company or factory. There exist several technologies which address the data resilience and they include: Logical replication

which is a technique a widely used multisystem data resiliency topology for high recoverability in space normally the IBM space. Logical replication is normally deployed through a product that is provided by a high availability independent software vendor (ISV). The replication is then run through software approaches on objects, the changes to the objects are replicated to often a backup drive or copy. In addition, most logical replication solutions allow for the additional characters or modification beyond the object replication ascertaining solid proof systems.

Data encryption is one of the new and rapidly technologies that many companies are using to secure their highly classified data from been misused by those not meant to handle those data. Data encryption can be made where the data is guarded by several embedded security passcodes which can only be bypassed using the right encryption key (Saeed, Mohammed, & Gazem, 2019). This technique which has been adopted by many large data firms have help in safeguarding the data integrity and reliability.

Big Data conditioning has also an impact on the quality of the final data obtained from any raw data collected. In addition, a condition applied in data analysis will largely depend on data type. An example includes classifying your data on the basis of gender is an option in the clustering of your data as either male or female which in R script can be abbreviated as (‘gender’, ‘F’} or (‘gender’, ‘M’). Putting such conditions helps in grouping the data in recognizable and easily understandable clusters for analysis and visualization (Saeed, Mohammed, & Gazem, 2019). Also data condition approach can include nationality, economic status and even religion. Data types which are easily recognizable in R- programing language includes integers, numeric and factor. Alternatively data types can be classified into discrete and continuous where discrete includes things like numerical such as 1, 2 and 3 while continuous data type includes integers, and also logical data type besides infinite possibilities. In addition R data structures can include matrix, vectors and data frame, all these data structures and type have proven to be very comprehensive in clustering and performing data analytics. Despite R been widely preferred in simulation the data size capability is very limited, at the time of building the scripts and running of R scripts uses libraries that are restricted to 32-bit integers. And this means that a section of the vectors and indices are constrained to 32-bit as stated earlier. Also it is possible to find some data frames run out of space during the process of executing and running R even on a powerful sizeable memory PCs. And lastly overly data issues faced from a programing standpoint in any type of data analysis and visualizations includes missing values, having leading and trailing spaces which interrupt the flow of data and dates which aren’t properly interpreted properly in terms of time.

Data cleaning is equally important as conditioning of data since not all raw data either collected or mined is useful, meaning that all the data which might be of little significance needs to be syphoned out and only vital once left for analysis and visualization. Therefore data cleaning involves the back and forth operation of converting the raw data into reliable statistical figures which can be used to make reasonable decisions. Some of the biggest data firms such as Facebook and Amazon receives large bits of data in their servers that analyzing the data can be a problem unless the data is first cleaned and the properly structured (Saeed, Mohammed, & Gazem, 2019). Moreover performing data cleaning helps in increasing the data reliability and increases the content base of the data. Some of the data cleaning techniques used in R includes removing duplicates in the data so that the remaining data can be void of repetition that might reduce the data’s reliability and integrity. In addition, checking your data for any errors, normalizing it and even fixing and bringing in new inputs helps in converting the raw data from been technically correct to consistent data. Another data cleaning technique includes performing error highlighting so that all possible lines of codes or even extra spaces that might exist in the data that consume unnecessary space. These data cleaning techniques helps sieve the data so that the remaining exercise of data analysis and visualization effective leading to production of reliable data.

The most effective and world-wide used statistical technologies or technological tools in analyzing and possibly visualizing data includes the statistical package for the social science commonly abbreviated as (SPSS). Besides this statistical tool makes it possible to perform and compile various descriptive statistics including parametric and even none parametric analysis. But the only limitation of this statistical tool is that analysis of a wide range of data is impossible hence data cleaning and conditioning is necessary to improve the reliability and accuracy of the data been analyzed (Romero, & Ventura, 2013). Another statistical tool is the R which is the foundation for statistical computing also this tool of analysis although been very preferred has its own inconsistencies especially of data size it can handle as mentioned above. Other statistical tools includes the Microsoft excel and mat lab which equally are very detailed in analyzing any type of data as long as it’s numerical.

The results obtained indicates that statistical analyses depended more on the type of data been scrutinized and the way the data has been classified. From the data finally generated it was properly structured and organized as compared to the original or rather raw data. There can be possible ways in which one could discretely misrepresent a data especially when performing cleaning and analysis on the same data at hand. This could be as a results of either negligence or just wrong techniques been used in the analysis otherwise the misrepresentation shouldn’t be expected.

In conclusion data science is vital in the era of today be it in business, technological world or even in the medical research field since quality data helps in deducing proper conclusions and steps to be taken in improving any situation at hand. But all these can only be possible if the approaches used in the analysis process are appropriate and context related. Further the role of business intelligence in shaping the future of businesses and even various sectors of the economy is just insurmountable. Although data analytics isn’t something new its continued evolution especially amidst advent of Big Data has proven very helpful especially in analyzing the vast data and making information extraction from such data possible.

EMERGING

TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 1

Emerging Trends in Data Analytics and

Business Intelligence

List Names of The People in your group

Business Intelligence (ITS-531-20)

University of the Cumberlands

Professor Kelly Bruning

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 2

Table of Contents

Introduction ……………………………………………………………………………………………………………………. 3

Emerging Trends in Data Analytics and Business Intelligence ……………………………………………… 4

Increasing Operational Efficiency with Business Intelligence and Analytics ………………………….. 5

Business Intelligence …………………………………………………………………………………………………… 5

Practical implications of BI ………………………………………………………………………………………….. 7

Example …………………………………………………………………………………………………………………. 7

Future of BI ………………………………………………………………………………………………………………… 8

Positive and Negative impact of BI ……………………………………………………………………………….. 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

Location Based Analytics ………………………………………………………………………………………………. 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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 3

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 4

Emerging Trends in Data Analytics and Business Intelligence

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 5

Increasing Operational Efficiency with Business Intelligence 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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 6

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 7

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 8

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 9

Positive and Negative impact of BI

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 10

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 11

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 12

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 13

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 14

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 15

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

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

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 16

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 17

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 18

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 19

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 20

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 21

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.

EMERGING TRENDS IN DATA ANALYTICS AND BUSINESS INTELLIGENCE 22

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