Considerations for an Extended Framework for Interactive Epoch-Era Analysis

AbstractEpoch-Era Analysis (EEA) is a framework that supports narrative and computational scenario planning and analysis for both short run and long run futures. Currently EEA is being applied to frame problems faced by the DoD's Engineered Resilient Systems (ERS) efforts. Because of the large amount of data that must be analyzed when extending EEA to large-scale problems, such as those posed by DoD, a “big data” problem is introduced. This motivates the need for extensions to EEA methods that overcome the computational and human cognition issues that arise as a result. The research presented here describes exploratory development of Interactive Epoch-Era Analysis (IEEA) methods, including human interface and reasoning considerations for epoch and era characterizations, as well as single and multi- epoch and era analyses. Visualization techniques and methods for mitigating computational resource restrictions that facilitate improved decision-making are also presented

Building Business Intelligence & Analytics Capabilities - A Work System Perspective

Although enterprises believe that they can achieve a competitive advantage with big data and AI, their analytics initiatives’ success rate still lags behind expectations. Existing research reveals that value creation with business intelligence and analytics (BI&A) is a complex process with multiple stages between the initial investments in BI&A resources and ultimately obtaining value. While prior research mostly focused on value generation mechanisms, we still lack a thorough understanding of how enterprises actually build BI&A capabilities. We explain the process in our research using work system theory (WST). Based on case studies and focus groups, we identify four prevalent BI&A capabilities: reporting, data exploration, analytics experimentation, and analytics production. For each identified BI&A capability, we derive patterns for BI&A resource orchestration, using the WST lens. Our findings complement the BI&A value creation research stream by providing insights into capability building

Une nouvelle approche mixte d'enrichissement de dimensions dans un schéma multidimensionnel en constellation Application à la biodiversité des oiseaux

International audienceLes entrepôts de données (DW) et les systèmes OLAP sont des technologies d'analyse en ligne pour de grands volumes de données, basés sur les be-soins des utilisateurs. Leur succès dépend essentiellement de la phase de conception où les exigences fonctionnelles sont confrontées aux sources de données (méthodologie de conception mixte). Cependant, les méthodes de conception existantes semblent parfois inefficaces, lorsque les décideurs définissent des exi-gences fonctionnelles qui ne peuvent être déduites à partir des sources de don-nées (approche centrée sur les données), ou lorsque le décideur n'a pas intégré tous ces besoins durant la phase de conception (approche centrée sur l'utilisa-teur). Cet article propose une nouvelle méthodologie mixte d'enrichissement de schémas en constellation, où l'approche classique de conception est améliorée grâce à la fouille de données dans le but de créer de nouvelles hiérarchies au sein d'une dimension. Un prototype associé est également présenté

Chronological evolution of the information-driven decision-making process (1950–2020)

The version of record os available online at:https://doi.org/10.1007/s13132-022-00917-yThe decision-making process (DMP) is essential in organizations and has changed due to multidisciplinary research, greatly infuenced by the progress in information technologies and computational science. This work’s objective is analysing the progressive interaction between DMP and information technologies and the consequent breakthroughs in how business is conducted since 1950 to recent times. Therefore, a chronological review of the information-driven DMP evolvement is presented. The major landmarks that defned how technology infuenced how information is generated, stored, managed, and used for making better decisions, minimizing the uncertainty and gaining knowledge, are covered. The fndings showed that even if current data-driven trends in managerial decision making have led to competitive advantages and business opportunities, there is still a gap between the technological capabilities and the organizational needs. Nowadays, it has been reported that the adoption of technology solutions in many companies is faster than their capacity to adapt at managerial level. Aware of this reality, the “Circumplex Hierarchical Representation of Organization Maturity Assessment” (CHROMA) model has been developed. This tool makes it possible to evaluate whether the management of organizations is making decisions using the available data correctly and optimizing their information systems.Peer ReviewedPostprint (published version

Business intelligence to support NOVA IMS academic services BI system

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceKimball argues that Business Intelligence is one of the most important assets of any organization, allowing it to store, explore and add value to the organization’s data which will ultimately help in the decision making process. Nowadays, some organizations and, in this specific case, some schools are not yet transforming data into their full potential and business intelligence is one of the most known tools to help schools in this issue, seen as some of them are still using out-dated information systems, and do not yet apply business intelligence techniques to their increasing amounts of data so as to turn it into useful information and knowledge. In the present report, I intend to analyse the current NOVA IMS academic services data and the rationales behind the need to work with this data, so as to propose a solution that will ultimately help the school board or the academic services to make better-supported decisions. In order to do so, it was developed a Data Warehouse that will clean and transform the source database. Another important step to help the academic services is to present a series of reports to discover information in the decision making process

Application of augmented intelligence in business intelligence tools

Proširena inteligencija je kombinacija ljudske i strojne inteligencije. Treba ju razlikovati od umjetne inteligencije koja po svojoj definiciji zamjenjuje ljudsku inteligenciju, dok proširena nadopunjuje. U ovom radu cilj je prikazati dio ponude mogućnosti proširene inteligencije u alatima poslovne inteligencije na tržištu kako bi se prikazao kratak uvid u što danas sve mogu alati poslovne inteligencije. Napravljena je studija slučaja sa jednom od prikazanih aplikacija gdje se pokušalo doći do detaljnih zaključaka o tome što i u kojoj mjeri je jedan alat poslovne inteligencija sposoban. Rezultati su pokazali da postoje određena ograničenja u radu ovog alata, no isto tako da postoji veliki potencijal da se jednog dana aplikacija unutar tog alata počne koristiti u realnom svijetu u mjeri u kojoj se koriste i druge aplikacije unutar istog alata.Augmented intelligence is a combination of human and machine intelligence. It should be distinguished from artificial intelligence, which by definition replaces the human intelligence, while the augmented complements it. In this paper, the aim is to present part of the augmented intelligence offering in business intelligence tools in the market to give a brief overview of what all business intelligence tools can do today. A case study was made with one of the applications presented, where detailed conclusions were drawn as to what and to what extent a business intelligence tool is capable. The results have shown that there are some limitations to the usability of this tool, but there is also a great potential that the application within this tool could be used in the real world to the extent that other applications within the same tool are used

Application of augmented intelligence in business intelligence tools

Proširena inteligencija je kombinacija ljudske i strojne inteligencije. Treba ju razlikovati od umjetne inteligencije koja po svojoj definiciji zamjenjuje ljudsku inteligenciju, dok proširena nadopunjuje. U ovom radu cilj je prikazati dio ponude mogućnosti proširene inteligencije u alatima poslovne inteligencije na tržištu kako bi se prikazao kratak uvid u što danas sve mogu alati poslovne inteligencije. Napravljena je studija slučaja sa jednom od prikazanih aplikacija gdje se pokušalo doći do detaljnih zaključaka o tome što i u kojoj mjeri je jedan alat poslovne inteligencija sposoban. Rezultati su pokazali da postoje određena ograničenja u radu ovog alata, no isto tako da postoji veliki potencijal da se jednog dana aplikacija unutar tog alata počne koristiti u realnom svijetu u mjeri u kojoj se koriste i druge aplikacije unutar istog alata.Augmented intelligence is a combination of human and machine intelligence. It should be distinguished from artificial intelligence, which by definition replaces the human intelligence, while the augmented complements it. In this paper, the aim is to present part of the augmented intelligence offering in business intelligence tools in the market to give a brief overview of what all business intelligence tools can do today. A case study was made with one of the applications presented, where detailed conclusions were drawn as to what and to what extent a business intelligence tool is capable. The results have shown that there are some limitations to the usability of this tool, but there is also a great potential that the application within this tool could be used in the real world to the extent that other applications within the same tool are used