International Journal of Technology Management and Information System (IJTMIS) eISSN: 2710-6268 [Vol. 4 No. 4 December 2022]
Journal website: http://myjms.mohe.gov.my/index.php/ijtmis
CLOUD BUSINESS INTELLIGENCE ISSUES AND CHALLENGES
Hasnain Sultan1, Lee-Kwun Chan2*, Pei-Hwa Siew3, Chen Kang Lee4, Cheng-Qin Cheah5 and Jin-Yew Yong6
1 2 4 5 6 Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar,
MALAYSIA
3 Faculty of Creative Industries, Universiti Tunku Abdul Rahman, Sungai Long, MALAYSIA
*Corresponding author: [email protected]
Article Information:
Article history:
Received date : 9 December 2022 Revised date : 20 December 2022 Accepted date : 26 December 2022 Published date : 30 December 2022
To cite this document:
Sultan, H., Chan, L. K., Siew, P. H., Lee, C. H., Cheah, C. Q., & Yong, J. Y.
(2022). CLOUD BUSINESS INTELLIGENCE ISSUES AND CHALLENGES. International Journal of Technology Management and Information System, 4(4), 75-85.
Abstract: Businesses constantly look for ways to improve their intelligence and gain a competitive advantage through the use of business intelligence (BI) solutions.
How organisations manage data is a new business intelligence phenomenon. Cloud computing is one of the tools that can increase the accessibility of BI tools. Using BI in cloud computisng, users can access data from all over the globe. To quickly and easily access business operations in the cloud, BI applications are available.
Organisations face several challenges when implementing such technologies, including sensitive data security, organisational trends and demands, data latency, data integration, system performance, and a lack of skilled IT staff. After carefully analysing what type of analysis is required, organisations should be cautious in selecting the appropriate BI. Furthermore, before moving to the BI cloud, organisations must follow cloud computing best practices, focus on strong governance, and discuss possible concerns and issues. As a result, it is essential to anticipate current cloud BI technology challenges and issues. This paper discusses the issues and challenges of cloud BI and can be used as a source of data and information for leading cloud BI organisations and giving vendors a suggestion of what customers want from them when they go for implementing cloud BI systems. In this way, the chances of successfully implementing cloud BI technology increase. The issues that businesses
1. Introduction
Over the last decade, the Internet, communications technology, Internet-of-things and Web-based, and integrated services have grown rapidly, with many organisations effectively implementing them, resulting in the creation of massive amounts of data (Patil & Chavan, 2020). Cloud computing is an extension of the concept of distributed computing, which is the process of running a programme or an application across a network of computers (Salem, 2021).
The primary goal of business intelligence (BI) is to deliver information at the right time and in the right format so that the organisation can make the best decisions more quickly and accurately (de Sá Mussa et al., 2018). The competitive edge business intelligence offers, there are a few factors that prevent organisations from making investments in it. Therefore, the idea of providing cloud- based BI solutions has emerged in enabling organisations in their economic growth journey and to enable them to enjoy the benefits of BI (Mondal, 2022). This enables organisations from implementing any tools on-site, provides scalability during peak times, and is cost-effective to the user as it uses a pay-as-you-use model. Cloud-based BI (or cloud BI) refers to the process of transforming data into actionable insights in a cloud environment, either partially or entirely, without the cost or hassle of physical hardware, cloud BI provides organisations with the information they need to make data-driven decisions (Mondal, 2022).
Cloud BI systems are getting more attention and focus from many business organizations around the world as the organizers or top management has started to realize that it is capable of delivering business needs. In a cloud BI system, there are many components such as Online Analytical Processing (OLAP), real-time BI and so on. Complex information can be handled and processed efficiently with cloud BI, and thus its value to a business organisation is unpredictable. Not only that, but it also assists organisations in increasing profits while remaining competitive. On the other hand, because there are so many cloud BI tools on the market, many organisations are having difficulty selecting the right cloud BI tools to solve short and long-term business requirements.
Thus, the discussion of this paper is divided into two sections. The first section discusses the demand and challenges of cloud BI, followed by the attempts and methods for successful cloud BI projects and implementations. Lastly, a conclusion will be included after all sections. Business leaders, managers, and other operations staff members are supposed to use corporate information to make wiser decisions for the organization. Businesses also employ predictive analytics to find new future opportunities, reduce expenses, and find ineffective company procedures.
currently face in BI using old approaches plus shifting technological trends are also discussed in this article.
Additionally, it would offer innovative strategies for implementing cloud BI technologies in organisations.
Keywords: Cloud Computing, Business Intelligence, Cloud BI implementation, OLAP, SAP HANA, NoSQL, Cloud BI mobility.
2. BI Demands and Challenges 2.1 Industry Trends and Demand
Cloud BI system demand is currently increasing gradually in many industries, including the manufacturing industry (Elshibani, 2022). In the 2020s, the manufacturing industry is undergoing significant changes and manufacturing companies are doing their best to comply with the industry regulations with the implementation of cloud BI systems in factories (Elshibani, 2022). Due to the nature of the manufacturing industry which is highly reliable on quantitative data, the importance of BI systems can be explained easily.
Companies and industries were forced into emergency mode as a result of the COVID-19 pandemic as they tried to understand the situation. Many companies were compelled to take a close look at their current cloud BI strategies. Companies realigned their budgets in order to make room for the transition to cloud BI and analytics platforms because on-premises solutions were unable to handle the demands of a workforce that was largely distributed.
As a result, there are numerous concerns that need answers from cloud BI systems. For example, how can we make better business decisions? How can machine downtime be reduced and production processes optimised? How can inventory turnover rates be efficiently controlled? How can supply chain management be improved? How do you keep your company's finances in order?
2.2 Data Security Risks
Nearly 75% of Chief Information Officers and IT professionals rank security as the top risk associated with cloud BI integration when it comes to potential dangers (Kasem & Hassanein, 2014). Data is stored and accessed online using cloud computing. Organisations use BI systems to improve efficiency and the ability to obtain a large amount of information quickly. However, BI systems can expose organisations to a variety of data security risks, including loss of data. For example, for cloud-based BI tools, BI is heavily dependent on the security of third-party providers (Al-Aqrabi, 2021).
Many organisations used the Work from Home (WFH) method for their employees during the Covid-19 pandemic. Attackers may take advantage of this and gain access to sensitive company information. As a result, cybersecurity mesh architecture is one approach to assisting cloud BI systems in data security. Cybersecurity mesh is a type of security control that can be assembled and scaled. Its primary goal is to safeguard the digital assets that exist in cloud BI systems (Majstorovic & Stojadinovic, 2020).
2.3 Data Consolidation Challenges
Data consolidation or data integration is the process of physically combining data from various systems and storing it in one location (Sreemathy et al., 2020). Reducing the amount of data storage locations is the primary goal of data consolidation. It is a critical step before performing data analysis and displaying information in report form. It allows users to control various types of data from a single point of access and converts raw data into information that can be used to make business decisions.
2.4 Data-latency
The duration takes a user to receive source information from a warehouse or BI dashboard is known as data latency in cloud BI. Many businesses prefer to use systems that provide real-time data and information (Patil & Chavan, 2020). In such a case, cloud BI should provide real-time data access. As a consequence, the analysis supported data that is frequently out of date by the time it is shared with top management to form decision-making. As a result, real-time integration should be on hand to enhance data.
2.5 Lack of expertise
Lack of expertise is the deficiency of people with the ability and knowledge in cloud BI and IT technical areas (Mishan et al., 2017). One of the problems organisations and businesses today face is a lack of expertise. Olszak and Ziemba (2012 as cited in Mishan et al., 2017) asserted that BI professionals are decisive because they are in charge of resolving and clearing up any technical issues. According to Mishan et al. (2017), organisations rely too heavily on skilled individuals.
Organisations are migrating to the cloud, and cloud technology is advancing quickly. Factors like the lack of expert employees make it difficult for businesses to keep up with technology and create a shortage of cloud experts. These difficulties can be overcome by educating and advancing IT personnel (Patil & Chavan, 2020). The ability of IS employees to use cloud BI capabilities is crucial, and in this case, expertise and knowledge are needed to confirm that an understanding of cloud BI could add value to the organisation (Madyatmadja et al., 2022).
2.6 Performance Bottleneck
When we use cloud BI systems to turn raw data into usable information, we anticipate that the information will be analysed, distributed, and reported to us in a neat and organised manner. In order to help us make wise decisions, the information must also be correct and presented in the proper format. The following list of techniques can be used to improve analytical performance (Armbrust et al., 2013; 2020; Aziz, 2014; Liu & Zhou, 2018; Nicholson et al., 2022):
• Techniques for online analytical processing (OLAP) caching to retain complex query results and OLAP processes in cache for optimum query performance
• Using in-memory analytics
• Data needs collaboration
• Democratisation of data
• Delete undesirable data after analysis.
• Leave out pointless information
• By merging data from numerous sources to create several dashboards, you may divide the work.
• Utilise the query and cache monitors
• A delta cache
• Use database layer computations instead of application layer calculations.
• Caching methods and Least Recently Used (LRU) algorithms
• Query execution using parallel processing techniques
• Utilise approaches for managing aggregation and data
2.7 Data Automation
Automation has been a hot topic since the 2010s and many experts claimed that it will be the trend in the 2020s (Leonard et al., 2017). The reason behind that is because more and more complex data with high volume emerge nowadays, automation is essential to eliminate manual data processes that are redundant and time-consuming (“Business Intelligence Trends 2022.”, 2022).
No matter if it is predicting sales volume or guessing the value of customers might be able to bring to the organization, predictive analytics and reporting are one of the most significant advances in cloud business intelligence. It can be done by analyzing existing data patterns thus knowing future business trends.
2.8 Cost of Ownership
The cost of ownership varies for different cloud BI systems from time to time. This is because, at the earlier stage, there were fewer business application vendors offering cloud BI systems. Hence, users had limited choices the cost of ownership was higher at that time. Since the 2020s, cloud BI systems have gained a lot of popularity and many vendors are providing their services too (Mondal, 2022). Business application vendors offer cloud BI systems with different prices according to the functionalities equipped in their applications. Generally, the cost of ownership of a cloud BI system includes implementation cost, business administration cost, Information Technology (IT) cost, administration cost, storage cost and license cost. Other than that, other aspects like product quality and ease of integration. Single-user implementation cost, ease of modifications for developers and ease of understanding the user manual have to be considered for ownership.
Nowadays, cloud BI is experiencing rapid change as new technology is coming into the field every year (Indriasari et al., 2019). As cloud BI users, we have to think about the changes it brought to us and reconsider the value of any cloud BI system before owning them to avoid any mistakes.
Furthermore, from the point of view of profit or loss, we, as business users have to do some financial analysis to compare the cost of ownership of a cloud BI system with the value that it is able to create for us for minimizing loss or maximizing profit.
2.9 Economic Challenges
The trend of economics has a great impact on many organizations. If the economy is on a great trend, then it has a good impact on organizations and vice versa (Llave, 2017). As a business owner, it is a must to keep an eye on or monitor the economic trend tightly so that they are well prepared to provide BI solutions to overcome economic challenges based on the organization’s budget and scope.
• Economic recession (the demand for cloud BI decreased a lot)
• Cloud BI deployments are reduced
• Large projects are forced to stop as demand decreased
• Organizations may venture into different fields for gaining profits
• Cut cost policy will be implemented by organizations
Smaller projects will be preferred as organizations can generate short-term profits quickly to ease daily operations.
3. Cloud BI Projects
We investigate the difficulties with cloud BI implementation that have been noted and discussed in the existing literature. Lennerholt et al. (2018) claimed that cloud BI is more than just a software programme. Organisations should instead plan how to guarantee easy access to and utilisation of data so that timely, accurate decisions can be made. The first thought that should cross the minds of cloud BI managers when they are establishing a brand-new cloud BI project, cloud BI migration, or cloud BI integration project is how to overcome cloud BI trends and challenges that are constantly changing due to many factors like technology advancement and so forth. The areas listed below should be given preference to ensure that cloud BI systems are effective and successful:
• Define the implementation's overall scope.
• JIT (Just in Time) modelling
• Calculate the overall risks for the cloud BI initiatives both within and outside.
• Architecture and technology should both be flexible.
• Use a bottom-up and top-down strategy.
• Iterations must consider the time involved.
• Plan the project budget carefully in advance.
• Conduct several tests as the projects progress.
• Keep all documents secure and organised.
• Adopt a standard for organisational development
• Think about the scope, timing, and cost considering the organisation's capabilities.
• Create a worldwide staffing strategy.
• Create progress-related milestones for the project.
4. Cloud BI Implementations
It is challenging for organisations to complete their BI initiatives because the majority of cloud BI software deployments fail. Currently, between 70% and 80% of cloud BI implementation projects fail as a result of various issues and challenges (Villamarín, et al., 2017). The following are a few crucial factors for a long-term adoption of cloud BI:
• Plan the implementation of cloud BI.
• Specify the group in charge of adopting cloud BI.
• Decide on a Key Performance Indicator (KPI) that cloud BI will examine.
• Locate a trustworthy software supplier
• Select the best cloud BI tools.
• Think about the infrastructure.
• Prepare the data.
• Processing of migratory data
• Establish a feedback loop
• Broader implementation of cloud BI
• Worries about privacy and information technology (IT) security infractions
• Several data sources that should not be joined
• Business standardisation or analysis
• Several “Source Systems” being included in the SLA Management, Vendor, and Outsourcing Models
• Stakeholders can get access to current information
4.1 NoSQL in Future
Database systems were not designed for today’s modern real-world applications or being utilized for customer experience owing to concerns with capacity and performance, despite the fact that they are still valuable given the vast development of data. Although NoSQL technologies have been available for quite some time, they are now filling the void. By 2026, the NoSQL industry is anticipated to have grown tenfold, reaching roughly about US$22.08 billion (Nikiforova et al., 2022). For success and development, it is worthwhile to use NoSQL to accommodate the genuine, multi-region apps that run our everyday operations (Rasheed et al., 2019).
The biggest advantage of implementing NoSQL includes high scalability, distributed computing, cheaper cost, scheme flexibility, semi-structured data and no complex relationships. Therefore, looking into the future of NoSQL, people move towards fresher and novel data requirements and they increasingly talking about databases like NoSQL systems and other databases to address these needs.
4.2 Drives the New Data Warehouse
The building style of data warehousing must be revamped in the present to reflect more cutting- edge state-of-the-art technological ideas, such as SOA, Big Data, social media analytics, mobility, cloud, NoSQL, and data visualization, for more sophisticated BI solutions with sophisticated analysis as well as access to paid abilities and better implementation methods (Garani et al., 2019).
In the past two decades, Data Warehouse has massively grown to transform into a unique infrastructure of modern enterprise architecture. It develops on the concept of ETL which integrates data from different sources into a repository for analysis and reporting. Since companies have embarked on embracing better organizational agility, Data Warehouses has emerged in vitality to assist in decision-making.
For IT businesses, the rapid expansion of data traffic brought on by social networks, e-commerce, businesses, and other data sources has become a crucial and difficult problem. Between 2013 and 2020, this growth rate—which rose from 4.4 to 44 ZB—doubles every two years and increases tenfold (Mansouri et al., 2017). The difficulties brought on by this increase in data can be overcome with the help of cloud computing services. Pay-per-use cloud computing provides the appearance of an infinite pool of extremely reliable, scalable, and flexible computing, storage, and network resources. Storage as a Service (StaaS) is one of the key elements of the infrastructure-as-a-service (IaaS) category.
4.3 SAP HANA
A multi-model database called SAP HANA (High-performance Analytic Appliance) keeps the information in memory rather than on a disc. Users can execute powerful analytics and increase efficiencies within the same platform thanks to the column-oriented in-memory database design (Chawda, 2020). A crucial step in the organization's journey toward digital transformation is the cloud-hosted SAP HANA platform. Many customers are migrating their on-premise SAP systems to the cloud in order to increase productivity and reduce spending (Figueiredo, 2022). As more businesses adopt the cloud, it is getting more difficult for them to support on-premise implementation. The IT landscape is streamlined and made simpler with scalability and cost savings when SAP HANA is moved to the cloud.
Table 1: SAP HANA Benefits
Benefits Additional notes
Total Data Management The security, as well as the availability of all the information from the SAP ERP are regularly checked and enhanced.
High Speed Even within large-scale systems, questions may be answered in less than one second.
Rapid Scalability The capacity to grow quickly is dependent on the number of concurrent users and also the volume of accompanying data across various network settings Consistent Efficiency Give a very affordable way to manage massive data volumes.
Powerful The capability of quickly storing and handling a wide range of information formats, including diagrams as well as files.
Unique flexibility Gives users a variety of deployment alternatives so they may adjust to every circumstance or information challenges your organization could encounter.
Artificial Intelligence Being capable of integrating these AI capabilities into SAP and other apps Airtight security The solution excels in data and app privacy, secure configurations, and thus
more.
Proved simplicity All of your company's data is presented by SAP HANA in a widely obtainable and useable way.
Functional versatility SAP HANA allows spectral methods and composite transactions regardless of the data format.
4.4 Cloud And Mobility
In terms of cloud mobility, it is indeed important to balance the prices and resources of various cloud services, whether they are private or publicly accessible. It is a new development that aims to simplify workload transfer across platforms. Inside a cloud environment, mobility facilitates the completion of tasks with client needs (Omar Al Zitawi et al., 2013). Additionally, by relocating data towards the intended host, it improves operational system performance, supports cloud evolution, lowers communication usage, and resolves load-balanced issues (“Mobility Drives Transformation…”, 2015). The establishment of policies for automated approved transmitting data to a selection of servers must be possible with an efficient cloud mobility system.
There are two types of cloud and mobility which are weak and strong mobility. In weak mobility, it enables code to move around networks. The code may occasionally have starting data given but no execution states. In cases of poor mobility, all processing states of the programmes do not transfer. In strong mobility, the software and execution state are given permission to restart at a
different resource. Time duration, CPU, inputs, and programme counters may all be saved inside this way.
5. Conclusion
Businesses are looking for a platform that is cost-effective, quick, and efficient for gaining insight and enhancing the quality and pace of business decisions. Cloud BI is just the service that can provide such a platform to businesses. However, many obstacles and issues must be overcome before this technology can be implemented in various organisations and businesses. This article's discussion of issues and challenges as well as its recommendations for using cutting-edge tools, approaches, and methodologies will be helpful to readers. Cloud BI service providers are working nonstop to advance technology and provide a complete out-of-the-box solution with a range of methodologies and tools. One of the challenges of business intelligence is integrating data from various source systems. As the number of information sources expands, many organisations will need to gather data from a variety of databases, big data platforms, and enterprise applications to be analysed. Cloud BI is more than just software; it's a way to guarantee a real-time view of all relevant business information.
Adopting cloud BI has many benefits, from improved analysis to increased competitive advantages. The dependability of data clarity, improved customer usability, and an increase in employee satisfaction are just a few benefits of cloud BI. By contrast to 2021, it is anticipated that the size of the worldwide market for cloud BI tools would increase to millions by 2028 (“Cloud BI Tools Market…”, 2022). Cloud BI gives predictive analytics infrastructures access to virtually infinite computer resources, storage space, and memory; it has an impact on how advanced analytics projects are managed. This study's findings could be very useful for future cloud BI innovation.
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