Dr. Kelly Bruning
University of the Cumberland’s
Table of Contents
Abstract....................................................................................................................................................3
Business Intelligence with Data Analytics................................................................................................4
Partial Application of BI with Data Analytics..........................................................................................6
Future of BI and Data Analytics................................................................................................................7
Positive and negative impacts of BI and data Analytics...........................................................................8
Recommendations ...................................................................................................................................9
Cloud Computing with BI.......................................................................................................................9
Practical Implications..............................................................................................................................9
Future of Cloud Computing with BI........................................................................................................13
Advantages and Disadvantages................................................................................................................14
Recommendations...................................................................................................................................15
Introduction to Business Drive Data Intelligence....................................................................................15
Data Governance of Self-Service BI ........................................................................................................17
Future of BI depends on Data Governance..............................................................................................19
Conclusion................................................................................................................................................19
References................................................................................................................................................ 21
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.
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, 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. 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
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 must 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.
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 Power BI, 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 1:
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 2:
A grocery store owner has a practice of keeping track of all the items with its sales and inventory, using power BI. 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
Business Intelligence 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 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 in BI and data analytics
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 Tarnowski, 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 need to be thoroughly analyzed, after that based on the use cases 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 (Amstar, 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:
1. Cost-saving: The large straightforward capital use can be transformed into continuous, smaller operational expenses with no mass interests in new software and hardware.
2. 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.
3. 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 Fusion Cloud 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 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 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 if 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 (Dora & Sekharan, 2013). 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
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 (Helen & Osama , 2016). 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 securing customer data. Because of many facets of data governance organization which touches a wide range variety 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 (Gamerio , 2011). The same formula is applied Data Governance which is to identify the business areas which are affected 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 (Boris, Oesterle, & Weber, 2009). 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.
Example:
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-client’s 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 (Alvaro & Carlos, 2019). 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 (Boris, Oesterle, & Weber, 2009). 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, its Data governance acts as seamless, data analytics environment.
Future of Business Intelligence 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 improve 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.
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