Enterprise Data Management Research Articles

Redefining Enterprise Data Management with AI-Powered Automation

In today's rapidly evolving digital landscape, the volume of enterprise data has surged exponentially, posing significant challenges in effective data management. Traditional data management techniques are becoming increasingly inadequate to handle the complexity and scale of modern enterprise data. This paper presents an innovative approach to revolutionize enterprise data management through AI-powered automation, a solution that enhances accuracy, efficiency, and decision-making processes within organizations. By leveraging advanced artificial intelligence technologies, such as machine learning, natural language processing, and predictive analytics, our proposed system aims to streamline data processing, ensure data quality, and provide real-time insights. This paper will discuss the limitations of existing data management systems, illustrate the novel methodologies integrated within our AI-driven framework, and demonstrate the system's efficacy through empirical results. The transformative potential of AI in automating data management processes not only addresses current challenges but also sets a foundation for future advancements in the field. As enterprises strive to maintain a competitive edge, the adoption of AI-powered automation for data management is not merely an option but a necessity for sustaining growth and innovation.

Enterprise Data Governance : A Comprehensive Framework for Ensuring Data Integrity, Security, and Compliance in Modern Organizations

Enterprise data governance has emerged as a critical function for organizations seeking to maximize the value of their data assets while mitigating risks associated with data management. This article presents a comprehensive framework for implementing effective data governance strategies in modern enterprises. It examines the key components of robust governance structures, including clear policies, well-defined roles and responsibilities, and the establishment of data quality standards. The article explores methods for ensuring regulatory compliance and enhancing data security, emphasizing the importance of risk management and data protection measures. By analyzing case studies across various industries, the article identifies best practices and common challenges in data governance implementation. Additionally, it investigates the impact of emerging technologies such as artificial intelligence and blockchain on data governance practices. The findings underscore the necessity of a holistic approach to data governance that aligns with organizational objectives, fosters a data-driven culture, and adapts to evolving regulatory landscapes. This article contributes to the growing body of knowledge on enterprise data management and provides practical insights for organizations striving to improve their data governance capabilities in an increasingly complex digital environment.

Unveiling Causality in Stateful Enterprise Knowledge Graphs: An Exploration of Process-Driven Object Relationships

The exponential growth of enterprise data generation demands innovative approaches for efficient information management across highly interconnected domains. Particularly, the process of systems engineering involves inhomogeneous data sets requiring a high degree of flexibility and traceability throughout its lifecycle. While structured data resides comfortably in dedicated systems, managing unstructured information within annotations, documents and emails often comes with challenging holistic comprehension of linking, context and revisioning. Graph technology holds promise in establishing adaptable connections among decentralized data sets. However, leveraging graph databases comes with challenges. Schema standardization, crucial for framing complex processes, is a difficult undertaking due to evolving data interrelationships and changing requirements. This paper proposes a two-fold solution. First, we introduce a conceptual schema that decouples stateful object descriptions from its headers and furthermore documents transactions. This allows flexible object associations, conditional state versioning and improved traceability while maintaining manageability. Second, we leverage the Business Process Model and Notation (BPMN) for process design, service orchestration and automated graph manipulations. Through application to the publicly available Northwind data set using neo4j and Flowable BPM, we illustrate our methodology’s efficacy and the seamless integration of type-safer data science into dynamic knowledge graphs, contributing to the future of enterprise data management.

The Impact of Big Data Technology in the Financing Field and Countermeasures Analysis

Against the background of promoting the sustained recovery of the national economy, the State has gradually increased its support for small and micro enterprises. In addition to policy support, enterprises as the main body should actively seek changes to resolve financing difficulties, combined with the current digital economy and the development of big data technology, enterprises should also from the external and internal perspectives of their own data construction and access to financing channels to make thinking. This paper analyzes the impact of big data technology on enterprise financing to draw out the opinions and suggestions on enterprise data management and system construction, reveals the mechanism of the impact of big data on enterprise financing, and expands the research on the application of big data.

Evaluation of enterprise accounting data management based on maturity model

Abstract The study of enterprise management driven by accounting data is not only of great strategic significance, but also of great practical value with distinctive characteristics of the times. Therefore, based on the maturity model, this paper establishes a three-level evaluation index system of accounting data management (AAM), divides the maturity level and key process areas and constructs an evaluation model of AAM. In the process of transforming qualitative indicators into quantitative indicators, analytic hierarchy process is used to determine the weight of indicators, and the fuzzy comprehensive evaluation method is applied for organisational evaluation. Finally, the evaluation model is introduced in the case enterprise, and the evaluation value of AAM is calculated by evaluating the AAM ability of the case, which is helpful to improve the ability of enterprise data management scientifically and efficiently.

Data Management and Decision-Making Process Using Machine Learning Approach for Enterprises

Currently, Machine Learning (ML) seems very attractive since it may speed up business functions in enterprises, lower costs for supplying goods and services, and manage information to promote enterprise efficiency. Essential technological domains nowadays are the explosive period of growth in enterprise solutions, which are progressively used in almost all business platforms. The ML sessions will receive a thorough summary, and the relevant organizations will be shown procedures for relevant business processes. The data management unit is already been striving to solve related issues in ML applications for more than a generation, creating numerous customized analytical techniques. The approach described in the study uses a weighted directed graph displayed in an industrial environment to identify the core part of the neural network structure and then trains them using the relevant data source. The article proposed ML-assisted Enterprise Data Management (ML-EDM) for identifying the trade-off between ML growth in the financial sector and its consequences in precision and interpretability. According to the experimental findings, the ratio of AI for decision-making is 84.25%, the Speed and Agility proportion is 92.70%, the result of Earlier Prediction Management is 93.80%, the Infrastructure Development is 85.46%, with Data Efficiency is 84.5% and Performance efficiency of the system is 90.14%.

Understanding data governance requirements in IoT adoption for smart ports – a gap analysis

ABSTRACT Maritime shipping is considered the backbone of the global supply chain. It is expected that future fully automated smart ports will be data-driven, harvesting power from advanced analytics and artificial intelligence. Under the hood, Internet of Things (IoT) technologies are considered as an enabling foundation of new data sources for innovations and high-level automation. Thus, enterprise data management becomes critical when implementing IoT projects. However, the implementation of data management needs to be governed. Although the IoT adoption in smart ports receives significant interest from the port authority, the effectiveness of the IoT adoption lacks proper evaluation and support of comprehensive data management policy and guidelines. By using the systematic literature review and a case study to analyze the current gaps in IoT data governance research, this research shows that IoT data governance still needs to catch up in smart ports. In particular, compared to other industries, data lifecycle management and data quality management are two key data governance areas that should receive increasing attention from the port authorities. It is believed that with adequate data governance, smart ports can truly harvest the power of IoT to improve operational efficiency and sustainability with an increasing level of automation.

An Overview of Data Warehouse and Data Lake in Modern Enterprise Data Management

Data is the lifeblood of any organization. In today’s world, organizations recognize the vital role of data in modern business intelligence systems for making meaningful decisions and staying competitive in the field. Efficient and optimal data analytics provides a competitive edge to its performance and services. Major organizations generate, collect and process vast amounts of data, falling under the category of big data. Managing and analyzing the sheer volume and variety of big data is a cumbersome process. At the same time, proper utilization of the vast collection of an organization’s information can generate meaningful insights into business tactics. In this regard, two of the popular data management systems in the area of big data analytics (i.e., data warehouse and data lake) act as platforms to accumulate the big data generated and used by organizations. Although seemingly similar, both of them differ in terms of their characteristics and applications. This article presents a detailed overview of the roles of data warehouses and data lakes in modern enterprise data management. We detail the definitions, characteristics and related works for the respective data management frameworks. Furthermore, we explain the architecture and design considerations of the current state of the art. Finally, we provide a perspective on the challenges and promising research directions for the future.

Intelligent Financial Data Analysis and Decision Management Based on Edge Computing

In this era of data explosion, in order to make better use of data, people need to collect information to analyze and judge future decisions. For enterprises, the management of time, capital, and decision-making can directly affect the acquisition of enterprise profits. The sooner we analyze financial data, the earlier we use an intelligent system to assist management and promote operators to make reasonable business decisions. Traditional enterprise data management is very backward and has few functions. In order to achieve the purpose of optimal allocation of enterprise resources, information-based financial management. According to the financial status and functional requirements of enterprises, the specific functions of the system are designed. The method based on edge computing has a high reference value. This paper puts forward a financial management system of intelligent analysis. It can provide rich solutions to financial problems. Data mining and analysis of financial situation provide support for decision-making management of enterprises. The research results show that (1) after 10 times of inspection and adjustment, the system grade is all D grade, and there is no defect. (2) There is no abnormality in system response time; the average response time of F1, F2, F3, and F4 functions is in line with expectations. The fluctuation of time range is no more than 3 s; the overall average response time is 1 s. (3) After CPU detection, the CPU of the system is less than 70% of the expected utilization rate. The system is qualified. (4) The whale algorithm is used to combine new and old tasks. After comparison, it has an excellent performance in computing resources and delays energy consumption. Some follow-up improvement work needs to be optimized.

Data Integration and Interoperability: Towards a Model-Driven and Pattern-Oriented Approach

Data integration is one of the core responsibilities of EDM (enterprise data management) and interoperability. It is essential for almost every digitalization project, e.g., during the migration from a legacy ERP (enterprise resource planning) software to a new system. One challenge is the incompatibility of data models, i.e., different software systems use specific or proprietary terminology, data structures, data formats, and semantics. Data need to be interchanged between software systems, and often complex data conversions or transformations are necessary. This paper presents an approach that allows software engineers or data experts to use models and patterns in order to specify data integration: it is based on data models such as ER (entity-relationship) diagrams or UML (unified modeling language) class models that are well-accepted and widely used in practice. Predefined data integration patterns are combined (applied) on the model level leading to formal, precise, and concise definitions of data transformations and conversions. Data integration definitions can then be executed (via code generation) so that a manual implementation is not necessary. The advantages are that existing data models can be reused, standardized data integration patterns lead to fast results, and data integration specifications are executable and can be easily maintained and extended. An example transformation of elements of a relational data model to object-oriented data structures shows the approach in practice. Its focus is on data mappings and relationships.

Research on dynamic and secure storage of financial data based on cloud platform

Traditional financial data storage methods are prone to data leakage and narrow data coverage. Therefore, this paper proposes a dynamic and secure storage method of financial data based on cloud platform.In order to improve the ability of enterprise data management, the paper constructs a financial cloud computing platform, mining financial data by rough set theory, and analyzing the results of frequent pattern mining of financial data by fuzzy attribute characteristics.According to the granularity theory, the financial data is classified and processed, and the CSA cloud risk model is established to realize the dynamic and secure storage of financial data.The experimental results show that. The maximum data storage delay of this method is no more than 4.1 s, the maximum data leakage risk coefficient is no more than 0.5, the number of data types can reach 30, and the data storage coverage is improved.

Financial Data Security Management Method and Edge Computing Platform Based on Intelligent Edge Computing and Big Data

This paper mainly studies the financial data management method and edge computing platform based on intelligent edge computing and big computing. This paper first introduces the theoretical basis of edge computing and big data computing, analyzes the collaborative mode of edge computing model and cloud computing, and discusses the cloud data storage scheme. In this paper, edge computing platform is built, and EC Master and Docker private image warehouse are mainly deployed in the data center. In this paper, the edge computing platform constructed was tested. In the mobile edge computing business processing time test, when there was a big gap in computing amount between business 1 and business 2, the total service time of MEC instances was around 0.3 s, showing a good user experience. In the edge acceleration effect test, before and after the adoption of CDN edge acceleration, from the perspective of the overall web page, the average time consumed to open a web page decreased from 1615 ms to 403 ms. From requesting a single HTTP access request, the average time decreased from 520 ms to 74 ms. The financial data management edge computing platform designed in this paper can effectively manage financial data and make an effective contribution to enterprise data management.

Knowledge graph construction based on knowledge enhanced word embedding model in manufacturing domain

Manufacturing industry is the foundation of a country’s economic development and prosperity. At present, the data in manufacturing enterprises have the problems of weak correlation and high redundancy, which can be solved effectively by knowledge graph. In this paper, a method of knowledge graph construction in manufacturing domain based on knowledge enhanced word embedding model is proposed. The main contributions are as follows: (1) At the algorithmic level, this paper proposes KEWE-BERT, an end-to-end model for joint entity and relation extraction, which superimposes the token embedding and knowledge embedding output by BERT and TransR so as to improve the effect of knowledge extraction; (2) At the application level, knowledge representation model ManuOnto and dataset ManuDT are constructed based on real manufacturing scenarios, and KEWE-BERT is used to construct knowledge graph from them. The knowledge graph constructed has rich semantic relations, which can be applied in actual production environment. Other than that, KEWE-BERT can extract effective knowledge and patterns from redundant texts in the enterprise, which providing a solution for enterprise data management.

A framework for effective data management outsourcing in investment management

Investment firms have historically engaged in outsourcing a range of back, middle and front office functions to specialist providers. Anyway, operational data management, until recently, remained within the purview of the in-house data team and managed on-premises. This state of affairs is changing. Over the past decade, firms have attempted to centralise data governance — here defined as the process of managing the availability, usage and integrity of data through internal standards, policies and processes — in order to meet a range of data challenges including poor data visibility, rising data costs and regulatory requirements. While many of these attempts have resulted in expensive and inflexible enterprise data management systems, they have helped make the case for outsourcing data management. Firms are now in a position to draft appropriate service-level agreements (SLAs) for outsourced data management services, and many see the value that comes with outsourcers’ consumption-based models and agile approaches to data management. In most investment firms today, there is a recognition that there are various different types of data and various data processes, some of which are core and some of which are non-core. Firms are coming to realise that the foundational, but not differentiating, activities of operational data management is expensive and time-consuming and that by outsourcing it they can free up time and resources to focus on what is core and proprietary — data analysis, insight and using that data and insight as a factor in business decision-making. As firms set about migrating to managed data services, they need to consider the core boundaries of their business, the commercial models of available services and how they can best ensure oversight of their outsource partners. Those that get it right will form strategic partnerships that can unlock value from data.

INFORMATION SYSTEM OF CONSTRUCTION SERVICE WEB BASED ON PAPUA GAPEKSINDO ASSOCIATION

Information technology is currently growing rapidly. Computers as information technology tools play an important role in solving data processing problems. Therefore, many companies are using information technology to improve enterprise data management. One of them is GAPEKSINDO Association. It is the organization of companies in construction services. In collecting data and processing business entity data, the companies will apply for Business Certificate, Gapeksindo Association in Papua have the problems, they are data collection and data processing is still using Excel. The weakness use Excel is the data storage can be accidentally deleted and search data will take a long time. Based on the problems above, information system is needed to support Papua Gapeksindo Association in collecting data and processing business data. It can provide convenience in collecting data and processing business data, ensuring security in storing data and it will be stored in database. This information system based on web by using the Unified Modeling Language (UML) design method. The development method using the Waterfall method and the testing method using Black Box.

Application of Java Technology in Dynamic Web Database Technology

With the development of Internet technology, Java has been applied to a variety of software design, including Database technology (hereinafter referred to as DT), web technology, which can enhance enterprise information and data management. DT can improve the efficient operation of information system, which needs to ensure the access performance of database. With the rapid increase of data volume, the data transmission between dynamic web pages is busy, which will lead to more and more database data access. Therefore, we have higher and higher requirements for dynamic web DT, which requires fast and efficient data interaction with server database. Java is one of the most commonly used programming software, which can solve many problems of dynamic web database. Therefore, more and more attention has been paid to the research of database optimization technology by Java.

BI warehousing system based on big data

In the wave of informatization, the data generated by enterprise operations has increased rapidly, prompting the intelligent development of enterprise warehousing systems. In the development of BI warehousing systems, the application of big data technology can promote the rapid development of business intelligence warehousing systems. The application of big data technology in the BI warehousing system can improve the service quality of the data intelligence of the warehousing system. Based on data, it provides support for corresponding decision-making, thereby improving the enterprise data management system. Therefore, this article mainly conducts research and analysis on the construction of BI warehousing system under the application of big data technology, and aims to provide a certain reference value for similar events in the future through a detailed explanation of the current situation of BI warehousing system construction and big data technology application.

Innovative Uses of OData Services in Modern SAP Solutions

OData (Open Data Protocol) has emerged as a pivotal framework in the realm of modern SAP solutions, facilitating seamless data integration and interoperability across diverse platforms. This abstract explores the innovative applications of OData services within SAP environments, highlighting their transformative impact on enterprise data management. By leveraging OData, organizations can enhance data accessibility, allowing developers to create rich, interactive applications that draw data from multiple sources. The protocol supports RESTful architecture, making it easier to consume and manipulate data via standard HTTP methods. This adaptability enables businesses to implement real-time analytics, fostering agile decision-making and improving overall operational efficiency. Furthermore, the integration of OData with SAP Fiori applications empowers users with intuitive interfaces, streamlining workflows and enhancing user experience. As companies continue to embrace digital transformation, OData services offer a robust solution for connecting cloud and on-premise applications, enabling enterprises to harness the power of data-driven insights. This paper aims to showcase various innovative use cases of OData services in modern SAP solutions, including their role in developing APIs, enhancing mobile applications, and driving business intelligence initiatives. By examining these applications, this research underscores the significance of OData as a catalyst for innovation and efficiency in the evolving landscape of enterprise resource planning (ERP) systems. Through practical examples and case studies, we demonstrate how leveraging OData services can lead to a more responsive, integrated, and data-centric approach in SAP implementations.

Intelligent physical systems for strategic planning and management of enterprise information

Recently developed advanced metering infrastructure in an intelligent physical system (IPS) sorts large quantities of data available in design, whereas the potential electricity system is implemented for profits and aid in the client’s transition from inactive to an active role. This paper investigates the usage of Deep Reinforcement Learning (DRL), in Intelligent Physical System for Strategic Planning in Enterprise Information (IPSSPEI) has been proposed to achieve the online schedule optimization for building energy management of Enterprise Data in an intelligent grid context. Here, two methods such as, profound R-learning and deep policy gradient, have been proposed to compute and examine the learning procedure which performs several actions simultaneously to overcome the scheduling problems. Hence, this high-dimensional database contains information about the generation and utilization of energy by photovoltaic power cars and smart buildings, with advanced metering infrastructure. Moreover, the electrical energy forecasting approaches could be used to give a real-time input to the consumers, facilitating the better application of electricity in an intelligent physical system to overcome the scheduling problems.

Arsitektur Enterprise Data Management Untuk Unit Kegiatan Mahasiswa & Organisasi Mahasiswa di STMIK Komputama Majenang

The development of Private Universities from year to year is growing. This is directly proportional to the development of student organizations and student activity units. One of the private universities is STMIK Komputama Majenang. STMIK Komputama consists of several Student Activity Units and Student Organizations. STMIK Komputama also has 3 campuses in which each campus has several Student Activity Units and the same Student Organization. With the existence of these 3 campus units, STMIK Komputama Majenang has difficulty in terms of coordination and also the synchronization of the Activities Agenda of the Student Activity Units and Student Organizations. The Enterprise Data Management Architecture is used to connect 3 campuses belonging to STMIK Komputama Majenang. Enterprise Data Management Architecture is the application of architecture used to carry out data management in a Student Activity Unit and Student Organization. In this research, an architecture that is able to synchronize and also produce a good planning of activities in 3 campus of STMIK Komputama Majenang