AI Techniques in Information Management

By: U. Yadav

Information management has evolved through record storage and processing to a company’s strategic opportunist. Modern advanced information management systems enable streamlining and automating things and mostly repetitive document-based procedures. Through the use of AI, technology is among the significant factors of such automation. AI technologies will justify and consider as mainly, here the learning performed from previous experiences. Furthermore, artificial neural networks can quickly sift across vast amounts of information to spot trends and labels [1]. That was evident from the basic filing method: the individual should inspect the document, specifically identify and allocate it to other enterprise systems, and then manually save it, ideally appropriately. The same could be said about records, tweets, messages, and every other medium we gather data from nowadays. Raw data is still mainly handled manually, so skilled workers are overwhelmed by a massive amount of data. This is precisely where AI should begin now to boost autonomous disorganized or unstructured information processing opportunities. AI usually follows mainly two steps for managing unstructured data, which are classification and contextualization. For the variety of information, various machine learning classifiers are used as these classifiers have self-learning capability, learn from experience, and automatically classify data into other groups. For contextualization number of AI, techniques are used which are shown in figure 1 [2].

Individuals, technologies, and procedures are the three key pillars of organizational learning [3]. Individuals are considered the most significant aspect of information management, accounting for 70 percent of its performance. This is really attributable to the reality that humans are the medium of communication because they constantly develop and spread it. Furthermore, procedures that account for 20 percent of the total define all or most of the activities happening in the practical administration of information, such as producing, collecting, distributing, exchanging, and applying knowledge. Information management is becoming less expensive, standardized, pervasive, and far more successful in meeting the requirements of individuals in the context of rapid technological advancements [4]. The characteristics of Ai Technologies successfully assist in elevating the innovation process to the next step. It enables workers to obtain focused information in a brief moment and improves main decision taking. This also helps companies enhance their capacity to manage procedures and records efficiently. Tech companies specifically need to incorporate additional automation into development products. Digital lending networks have efficient strategies as applications for significantly improving the management of unstructured data. We require a modern, increasingly thoughtful framework for information management. This special issue aims to give researchers a medium and opportunity to develop the scope of Artificial intelligence-based information management systems along with security techniques and provide a clear summary of the present tactics and challenges involved in this area [5].
Open research Issues and challenges:

● AI-based techniques for unstructured data organization
● Artificial Intelligence Secured networking architectures for information
● Application of artificial intelligence in information management systems
● Role of AI in data management systems
● Deep learning and AI automation features in the management system

References

1. Sanzogni, L., Guzman, G., Busch, P.: Artificial intelligence and knowledge management: questioning the tacit dimension. Prometheus 35(1), 37–56 (2017).
2. Aljaaf, A.J., Al-Jumeily, D., Hussain, A.J., Fergus, P., Al-Jumaily, M., Abdel-Aziz, K.: Toward an optimal use of artificial intelligence techniques within a clinical decision support system. Sci Inf Conf (SAI) 2015, 548–554 (2015).
3. Geisler, E., Wickramasinghe, N.: Principles of Knowledge Management: Theory, Practice, and Cases: Theory, Practice, and Cases. Routledge (2015).
4. Lee, K.-W., Lanting, M.C.L., Rojdamrongratana, M.: Managing customer life cycle through knowledge management capability: a contextual role of information technology. Total Qual. Manag. Bus. Excell. 28(13–14), 1559–1583 (2017)
5. Shahid, N., Rappon, T., Berta, W.: Applications of artificial neural networks in health care organizational decision-making: a scoping review. PLoS ONE 14(2), e0212356 (2019).

Cite this article:

U. Yadav (2021), AI techniques in Information Management, Insights2Techinfo, pp. 1

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