Agile Business Intelligence Research Articles

Proposed Metrics for Agile Business Intelligence

Business Intelligence has become the front of demanding technologies. The large or midsize companies need technology to adapt to changing requirements, to deal with a rich amount of data; to get correct information, to analyze data, and finally to quickly support decision makers. Developing an agile BI application can be defined as a short-delivering software application. This process is based on frequently adapting to changing requirements, involving customer participation, and delivering high-quality applications. Agile teams need a set of reliable metrics to measure their performance at different three levels: functionality, content, and scalability. This paper proposes Goal Question Agility Metrics that can be used in measuring the performance of agile teams working in developing business intelligence applications. Goal Question Agility Metrics is based on the Goal Question Metrics method that was developed by Victor Basili in 1994 [19]. In addition, Goal Question Agility Metrics depend on agile and BI concepts. GQAM proposes a set of metrics that are used to measure performance. The GQAM will be subjected to experiments using a real data set in the future.

Systematic Literature and Expert Review of Agile Methodology Usage in Business Intelligence Projects

Background: Agile methodology is known for delivering effective projects with added value within a shorter timeframe, especially in Business Intelligence (BI) system which is a valuable tool for informed decision-making. However, identifying impactful elements for successful BI implementation is complex due to the wide range of Agile attributes. Objective: This research aims to systematically review and analyze the integration of BI within Agile methodology, providing valuable guidance for future projects implementation, enhancing the understanding of effective application, and identifying influential factors. Methods: Based on the Kitchenham method, 19 papers were analyzed from 288 papers, sourced from databases like Scopus, ACM, IEEE, and others published in 2016-2022. Meanwhile the extracted key factors impacting agile BI implementation were validated by qualified expert. Results: Agile was discovered to provide numerous benefits to BI projects by promoting flexibility, collaboration, and rapid iteration for enhanced adaptability, while effectively addressing challenges including those related to technology, management, and skills gaps. In addition, Agile methods, including tasks such as calculating cycle time, measuring defect backlogs, mapping code ownership, and engaging end users, offered practical solutions. The advantages included adaptability, success, value enhancement, cost reduction, shortened timelines, and improved precision. The research additionally considered other critical Agile elements such as BI tools, Agile Practices, Manifesto, and Methods, thereby enhancing insights for successful implementation. Conclusion: In conclusion, the research outlined Agile BI implementation into seven key factor groups, validated by qualified expert, providing guidance for BI integration and practices, and establishing a fundamental baseline for future applications. Keywords: Agile Methodology, Business Intelligence (BI), Expert Judgement, Kitchenham, Systematic Literature Review (SLR)

Business Intelligence Agility, Informing Agility and Organizational Agility: Research Agenda

In assuring organizational agility, informing activities play a key role by sensing the environment for important changes. A special part has to be assigned to business intelligence (BI) activities, since their exact task is to monitor the environment and detect important issues to provide ground for well-informed responses. In turbulent business environment, BI function has to adjust dynamically to maintain the quality of produced insights. A survey on published research has shown that many sources stress the importance of agile BI on organizational agility in general. However, the importance of managerial and especially cultural factors for the role of informing in building up agility competencies is under-researched. The technology factors of agile informing that create preconditions for organizational agility are much better researched and evaluated than their human and managerial counterparts. There appears to be a need for consistent research approach in favor of the less researched human and managerial factors. The goal of this paper is to define research framework by addressing agility issues at three levels: organizational, information and BI agility. This framework is going to serve as a foundation for planned research on the factors of BI agility.

COVID-19: Optimizing Business Performance through Agile Business Intelligence and Data Analytics

The current COVID-19 pandemic has led to a devastating socio-economic predicament, which has resulted in the temporary closure and collapse of thousands of businesses across the globe. The quicker companies can respond to the current pandemic situation, the more likely their chances of surviving in the short or long term. Businesses around the world are compelled to make significant changes to their business operations, such as downsizing, product, and service diversification. To address these changes quickly, companies need to adopt or capitalize on their business intelligence strategies through agile risk management, artificial intelligence systems, and data analytics to help make informed decisions to enhance business operations amid COVID-19. This article outlines some practical and theoretical recommendations of business intelligence strategies for organizations and their service supply chain network on how to be adaptive, flexible, and innovative to survive and stay competitive during these challenging times by leveraging agile dimensions, artificial intelligence systems, and data analytics.

Building novel capabilities to enable business intelligence agility: results from a quantitative study

The class of business intelligence (BI) systems is widely used to guide decisions in all kinds of organizations and across hierarchical levels and functions. Organizations have launched many initiatives to accomplish adequate and timely decision support as an important factor to achieve and sustain competitive advantage. Given today’s turbulent environments it is increasingly challenging to bridge the gap between establishing a long-term strategy and quickly adopting to the dynamics in market competition. BI must address this field of tension as it was originally used to retrospectively reflect an organization’s performance and build upon stability and efficiency. This study aims to understand and achieve an agile BI from a dynamic capability perspective. Therefore, we investigate how dynamic BI capabilities, i.e., adoption of assets, market understanding, and intimacy as well as business operations, impact the agility of BI. Starting from a literature review of dynamic capabilities in information systems and BI, we propose hypotheses to connect dynamic BI capabilities with the agility to provide information. The derived hypotheses were tested using partial least squares structural equation modeling on data collected in a questionnaire-based survey. The results show that the lens of dynamic capabilities provides useful means to foster BI agility. The study identifies that technological advancements like in-memory technology seem to be a technology enabler for BI agility. However, an adequate adoption and integration of BI assets as well as the focus on market orientation and business operations are crucial to reach BI agility.

Comparative study of Agile Business Intelligence and Agile Data Warehouse

In a rapidly changing business oriented economy, Business Intelligence solutions have to become more agile. This paper attempts to compare and contrast between Agile BI and Agile DW based on different analytical Agile driven technologies. Agile software development is a set of principles and practices that was influenced by practitioners of Extreme Programming, SCRUM, DSDM, Adaptive Software Development and others. It was driven out of the need for an alternative to documentation driven, heavyweight software development processes. Agile development processes can take a lot of pain out of building data warehouses and enable project teams to deliver functionality, and business value, on a rolling basis. Rapidly gaining in popularity, the Agile approach to data warehousing solves many of the thorny problems typically associated with data warehouse development most notably high costs, low user adoption, ever-changing business requirements and the inability to rapidly adapt as business conditions change. The Agile approach can be used to develop any analytical database, the two mechanisms that uses agile approach are Data Warehouse and Business Intelligence. Also, this paper briefly looks at technologies that can be used for enabling an agile BI solution.

Agile Business Intelligence: Collection and Classification of Agile Business Intelligence Actions by Means of a Catalog and a Selection Guide

Based on a literature review, the article presents a catalog of 31 agile business intelligence actions suitable to increase the agility of business intelligence systems. The actions are classified by the categories “principles,” “methods,” “techniques,” and “technologies” and by their implementation level. In addition, the actions are assigned to business intelligence agility types, resulting in an agile business intelligence actions selection guide. Both artifacts provide benefits for scientists (identification of research areas) and for practitioners (implementation support).

Agile BI � The Future of BI

In rapidly changing economy, Business Intelligence solutions have to become more agile. This paper attempts to discuss some questions which help in creating agile BI solution such as: What is Agile? Why agile is so well suited for BI? Which are key elements that promote agile BI solution? Also, paper briefly looks at technologies that can be used for enabling agile BI solution.Keywords: Agile Business Intelligence, Agile Business Analytics, Agile Development Methodologies, In-Memory Bi Approaches, Data Virtualization Server1 IntroductionBusiness Intelligence (BI) was defined in different ways. The Data-Warehousing Institute has defined Business Intelligence as the tools, technologies and processes required to turn data into information and information into knowledge and plans that optimize business actions [6], Turban has defined BI as a broad category of applications and techniques for gathering, storing, analyzing and providing access to data to help enterprise user make better business and strategic decisions. [23], The range of capabilities that can be defined as business intelligence is very broad. The spectrum of BI technologies is presented in [16].Most enterprises have hundreds of internal and external data sources such as: databases, e-mail archives, file systems, spreadsheets, digital images, audio files and more. 80% of organizational data are unstructured and semi-structured data.Traditional Business Intelligence systems use small fraction of all data available. Also, traditional BI systems use only structured The core components of traditional BI architecture are: ETL tools, enterprise data warehouse with metadata repository and business analytics (Figure 1).Traditional BI systems use ETL tools for extracting data from multiple sources and temporarily storing those datasets at staging area. Organizations use data warehouses to aggregate cleaned and structured Business analytics/BI tools include enterprise reporting tools, ad hoc query tools, statistical analysis tools, OLAP tools, spatial-OLAP analysis tools, dashboards, scorecards and advanced analytics. Advanced analytics typically refer to: data mining tools, text mining tools, predictive analytics, artificial intelligence, and natural language processing. But architecture is unable to get adapted to change. A study by Aberdeen Group [24] showed that this style ofBI is predominantly controlled, driven and delivered by corporate IT. Often, only static views of data are available and any changes or enhancements must be made by IT organization. These characteristics are in contradiction with frequently changing business and big data. Big data typically refers to following types of data: semi-structured data (XML and similar standards), unstructured data, Web data (social data, Web logs) and real-time data (event data, spatial data, machine-generated data).Table 1 briefly summarizes main disadvantages of traditional BI systems.How to eliminate these problems? By building agile BI solution. A study by TDWI Research [22] showed that many traditional business intelligence systems are not agile:* 33% of organizations needed more than three months to add new data source to existing business intelligence system* developing complex report or dashboard with about 20 dimensions, 12 measures and 6 user access rides took on average 7 weeks in 2011The next section presents briefly concept of agile BI and key elements that together promote agile BI solution.2 Agile BIAgile means ability to be adaptable. Agile BI was defined in different ways. The Forrester Research defines agile BI as an approach that combines processes, methodologies, organizational structure, tools and technologies that enable strategic, tactical and operational decision -makers to be more flexible and more responsive to fast pace of changes to business and regulatory requirements [9], According to Data Warehousing Institute agile BI addresses broad need to enable flexibility by accelerating time it takes to deliver value with BI projects. …