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Master data governance is not a new field, and it has existed since the early 90s (8). However, the demand for master data governance has been rising recently due to the ever-increasing demand for cost optimization, faster product innovations, compliance with the set rules and regulations, and competitive advantage in the business field. One of the areas in which companies can achieve master data governance is through maintaining consistent data quality. Handling data is one of the pain areas in most companies due to the complicities involved in managing data. Still, companies that succeed in handling data compete well in their business since data leads to optimal decision-making. Data mismatch is putting so many companies projecting to accelerate their growth on brakes. This paper outlines the best practices of master data governance, which can help the companies solve and improve their odds in data governance and improve their business quickly, predictably when planning and implementing the decisions made.

Many companies have implemented technology in conducting their business, one of them is by using information systems in managing their business. The business process that runs on the Konveksi A&N is still manual because the Konveksi A&N has not implemented or is familiar with information systems like other companies has. This causes all the data collection process of goods, production, and transactions are all documented in different books, making it possible for errors in recording and calculating the data. In addition, the owner also needs to come directly to the location to find out new data or updated data where this takes more time. This research aims to establish a web-based system that can make business processes in Konveksi A&N more effective. This web-based system will be built using a system development methodology in the form of a prototyping method that uses the Hypertext Preprocessor (PHP) programming language. System modeling was developed using UML 2.5, use case diagrams, activity diagrams, and class diagrams. The final result of this study is a website-shaped information system that can document all data contained in the Konveksi A&N such as the existence of a master data that contains all the data of convection, production data, and also transaction data to facilitate the owner in checking back all activities that are occurs in Konveksi A&N. There are also some reports about the transaction that can display information related to data contained in the system.

The Defense POW/MIA Accounting Agency (DPAA) continues to diligently locate, recover, and identify over 81,000 missing US service members from past conflicts. To fulfill this important mission, massive amounts of information must be integrated from historical records, genealogy records, anthropological data, archaeological data, odontology data, and DNA. Previously a machine learning record-linkage application was developed to integrate DNA Family Reference Samples (FRS) data systems with the DPAA’s master data. This application was shown to link large record systems with a high level of accuracy and precision. Here this work is extended to further optimize the blocking strategy used during record linkage as well as the record match alpha-level threshold for the Bayesian Classifier. Optimization of the blocking strategy was able to improve application run-time per record by 20%. After record-match alpha-level optimization, the application was found to link 89.6% of the record out-group to DPAA master data at an accuracy of 99.6%. The improved run-time efficiency and match rate of the record-linkage pipeline will greatly benefit not only the DPAA’s FRS import process but also the linking of other big data sources supporting the DPAA mission.

MarketPlace of livestock is engaged in the business of selling and purchasing which broadly has the activities of selling and purchasing animals. Usually have several system Admins who work and control an application system by handling also the development of businessmen in the application, including making reports and managing master data. In this study business process observations were made from observations of free-range applications which have become a unique marketplace specifically for livestock. The business process that is happening right now, that an application provides a place for various pet shops to pour their sales business activities in accordance with the dimensions of the animals they sell, on the other hand also receives intensive care services for animal development for farmers. It takes a long time for a common marketplace application to get a service maintenance partner, and time brackets along with maintenance service wages that are proportional to the value of feed prices, an increase in the amount of food, and season conditions for a sales activity there is no precise analysis. Based on the needs of this matter, a design of a new market place application design is needed which is equivalent to a market place in general, which only has the ability to sell and purchase. In the analysis phase of modeling using Business Process Modeling Notation (BPMN), while the design stage uses Unified Modeling Language (UML). The design of this application will be a guideline in implementing software in future research.

Organizations in the business of Information Services deal with very large volumes of data which is collected from a variety of proprietary, as well as, public sources in multiple languages with different formats, naming conventions, and context. Mapping such data into enterprise master data for reporting and prediction is an effort-intensive, time-consuming process which is prone to errors. Machines cannot match these sources and map to master data accurately. Enterprises are eager to automate the human intensive tasks of data harmonization so that their resources can focus on finding the insights to drive the business. We undertook one such automation initiative for a global Market Research Major (MRM) and achieved a significant level of success leveraging Artificial Intelligence (AI) techniques. The Automated Data Harmonization (ADH) solution has been a multi-step approach of Dictionary Matching, Fuzzy Text Similarity, and different Machine Learning techniques. It has been implemented on the Big Data stack for better performance and scalability. In order to streamline the overall business process, runtime rules and workflow has been implemented. The Proof of Concept has yielded an overall F-Score within the range of 82–93% depending on the variation of the dataset. The deployed version is continuing to deliver high accuracy and gained adoption as a core micro-service across the organization. The Business as Usual (BAU) cycle time has been reduced by 80% (from 14 days to 3 days). While the solution is unique and tailored to meet a set of specific business requirements, it can be extended for media metadata standardization across multiple devices, author name and citation resolution in scholarly journals, leads resolution in multi-channel marketing and ad campaigns etc.

In the era of industry 4.0, digitalization is a very significant step to adopt in a company. Every worldly renowned industry, oil and gas included, needs to assimilate this term into its business process. Reserves and resources report depicts an oil and gas company future cash flow, wealth, performance, as well as growth. Therefore, creating and managing this report need meticulous attention. In order to comply with demand in industry 4.0 and simplify reserves and resources reporting process, reserves and resources database and web-based application need to be developed. First task to carry in establishing database is to create and input master data. Companies’ asset is classified into four common entities: asset, area, structure, and layer – as the smallest entity. Layer contains fluid type and oil and gas specific attributes. After master data is established, a web-based application to handle data transaction and reporting is developed. In order to maintain data security, access to this application is restricted into four roles: data entry user, data approval user, administrator, and management. Every role has its own privilege, for example data entry user can create, edit, and view data, while management can only view it. Other employees that need to access this application are based on management approval and restricted into view privilege under their asset location. This application has already been tested in two reserves and resources reporting window, and underwent bug-fixing and improvement process. Implementing this application in oil and gas company’s business process gives several benefits: minimizing human error, establishing a single data source, and cutting time consumption in creating reserves and resources report. This application is the solution in creating and managing the report, as well as displaying data in graphical view to help management makes corporate decision.

Productivity in the construction industry (both houses and infrastructure) has not been improving as expected, while other industries have been able to improve their productivity significantly. The appropriate use of building information modelling (BIM) technologies brings several benefits and advantages to construction projects. The main challenges of project efficiency emerge in the form of numerous requests for information during the construction project, which are considered to be waste in the processes. This highlights the need for a practical process model to plan the information flow for BIM-based projects. The main aim of this study is to propose a model to plan the flow of project information among primary stakeholders especially in infrastructure projects. Our main findings are firstly, the foundation for data management starts from defining unified one data for the product and the for the process. Unified data means one single repository of data – all stakeholders use the same unified data. It is also essential that data responsibilities and ownership are defined. Secondly, we found that the biggest challenges are that the data needs are not planned beforehand, resistance to change, difficulty receiving existing data and data must be modified before use. As a whole, it seems sometimes that the technology on data transfer is more important that what has been transferred and why. Finally our construction, the life cycle model for data flow originates from one data to all stakeholders, single data repository must be updated along the life- cycle of the object covering also the operations and maintenance, where the data has to be updated through the whole life-cycle. This new approach is intended to enable the early involvement of maintenance stakeholders in designing product data for a project lifecycle perspective. The model helps to change the current information flow and gain the benefits that a BIM-based process can offer. This study is based on case studies and is qualitative in nature and naturally needs more validation.

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

Master data management (MDM) is a method of maintaining, integrating, and harmonizing master data to ensure consistent system information. The primary function of MDM is to control master data to keep it consistent, accurate, current, relevant, and contextual to meet different business needs across applications and divisions. MDM also affects data governance, which is related to establishing organizational actors’ roles, functions, and responsibilities in maintaining data quality. Poor management of master data can lead to inaccurate and incomplete data, leading to lousy stakeholder decision-making. This article is a literature review that aims to determine how MDM improves the data quality and data governance and assess the success of MDM implementation. The review results show that MDM can overcome data quality problems through the MDM process caused by data originating from various scattered sources. MDM encourages organizations to improve data management by adjusting the roles and responsibilities of business actors and information technology (IT) staff documented through data governance. Assessment of the success of MDM implementation can be carried out by organizations to improve data quality and data governance by following the existing framework.

Implementation of Master Data Management (MDM) in an organization aims to help the process of consolidating and integrating various sources of master data into a solitary truth source. MDM is also able to help to solve the complexity of data that occurs in the synchronization, consolidation and cleaning process of data from redundancy, and to increase the value of operational efficiency through the standardized content which represents business-based process. The goal of this study is to assess the level of MDM maturity at Secretariat of Presidential Advisory Council during the COVID-19 pandemic era. The assessment was done using Master Data Management Maturity Model (MD3M) by Spruit-Pietzka. The master data management maturity level in the organization was assessed using a questionnaire filled out by SMEs in a group discussion. The result of the assessment showed that MDM maturity level at Secretariat of Presidential Advisory Council is 1. There are 61.29% (38 of total 62) implemented capabilities. This means that organization already has awareness in the management of master data. The organization can improve its MDM maturity to a higher level by implementing the missing capabilities.

The objective of this study is to analyze the potential of company data assets for data-driven, fact-based decision-making in product portfolio management (PPM). Data assets are categorized from th...

Purpose The purpose of this paper is to analyse current challenges and to articulate the preconditions for data-driven, fact-based product portfolio management (PPM) based on commercial and technical product structures, critical business processes, corporate business IT and company data assets. Here, data assets were classified from a PPM perspective in terms of (product/customer/supplier) master data, transaction data and Internet of Things data. The study also addresses the supporting role of corporate-level data governance. Design/methodology/approach The study combines a literature review and qualitative analysis of empirical data collected from eight international companies of varying size. Findings Companies’ current inability to analyse products effectively based on existing data is surprising. The present findings identify a number of preconditions for data-driven, fact-based PPM, including mutual understanding of company products (to establish a consistent commercial and technical product structure), product classification as strategic, supportive or non-strategic (to link commercial and technical product structures with product strategy) and a holistic, corporate-level data model for adjusting the company’s business IT (to support product portfolio visualisation). Practical implications The findings provide a logical and empirical basis for fact-based, product-level analysis of product profitability and analysis of the product portfolio over the product life cycle, supporting a data-driven approach to the optimisation of commercial and technical product structure, business IT systems and company product strategy. As a virtual representation of reality, the company data model facilitates product visualisation. The findings are of great practical value, as they demonstrate the significance of corporate-level data assets, data governance and business-critical data for managing a company’s products and portfolio. Originality/value The study contributes to the existing literature by specifying the preconditions for data-driven, fact-based PPM as a basis for product-level analysis and decision making, emphasising the role of company data assets and clarifying the links between business processes, information systems and data assets for PPM.

In the process of manufacturing, a large amount of manufacturing data is produced by different departments and different domain. In order to realise data sharing and linkage among supply chains, master data management method has been used. Through master data management, the key data can be shared and distributed uniformly. However, since these cross-domain data form a data network through the association of master data, how to evaluate the effectiveness and rationality of this network becomes the major issue in the proposed method. In this paper, a model of the master data network is built based on the theory of set pair analysis. In order to verify the master data, an evaluation method for the network is proposed. Finally, a case was presented to validate this network model and evaluation method.

Master Data Management (MDM) is an approach for effective management of shared master data across organizations. In the public sector, MDM initiatives have been developed; however, the adoption among local government remains slow and there has been little interest in MDM adoption in extant research. Building on a Technology-Organization-Environment (TOE) framework, a conceptual model which highlights a set of potential determinants affecting the adoption of MDM by local government was developed. To validate the model, data were collected via survey from 224 responses from Malaysian local government department units. Using SEM-PLS, the study confirmed that data quality and data governance are two determinants of MDM adoption specific to the context of Malaysian local government, and four other determinants – complexity, top management support, technological competence, and citizen demand – are found to have significant effects on MDM adoption by local government. Surprisingly, three determinants – relative advantage, data security, and government policy – are found to have non-significant relationships to the adoption of MDM by local government. In addition, top management support is revealed to be a cornerstone of MDM technological competence in local government. The study contributes to the theoretical, contextual, and practical knowledge of MDM and IT adoption in the context of local government.

Industrial enterprises rely on prediction of market behavior, monitoring of performance measures, evaluation of production processes and other data analyses to support strategic and operational decisions. However, although an adequate data quality (DQ) is essential for any data analysis and several methodologies for DQ assessment exist, not all organizations consider DQ in decision-making processes. E.g., inaccurate and delayed data acquisition leads to imprecise master data and poor knowledge of machine utilization. While these aspects should influence production planning and control, current approaches to data evaluation are too complex to use them on a-day-to-day basis. In this paper, we propose a methodology that simplifies the execution of DQ evaluations and improves the understandability of its results. One of its main concerns is to make DQ assessment usable to small and medium-sized enterprises (SME). The approach takes selected, context related structured or semi-structured data as input and uses a set of generic test criteria applicable to different tasks and domains. It combines data and domain driven aspects and can be partly executed automated and without context specific domain knowledge. The results of the assessment can be summarized into quality dimensions and used for benchmarking. The methodology is validated using data from the enterprise resource planning (ERP) and manufacturing execution system (MES) of a sheet metal manufacturer covering a year of time. The particular application aims at calculating logistic key performance indicators. Based on these conditions, data requirements are defined and the available data is evaluated considering domain specific characteristics.

Relying on the Biomedical Big Data Center of West China Hospital, this paper makes an in-depth research on the construction method and application of breast cancer-specific database system based on full data lifecycle, including the establishment of data standards, data fusion and governance, multi-modal knowledge graph, data security sharing and value application of breast cancer-specific database. The research was developed by establishing the breast cancer master data and metadata standards, then collecting, mapping and governing the structured and unstructured clinical data, and parsing and processing the electronic medical records with NLP natural language processing method or other applicable methods, as well as constructing the breast cancer-specific database system to support the application of data in clinical practices, scientific research, and teaching in hospitals, giving full play to the value of medical big data of the Biomedical Big Data Center of West China Hospital.

Remote Application Programming Interfaces (APIs), as for instance offered in microservices architectures, are used in almost any distributed system today and are thus enablers for many digitalization efforts. It is hard to design such APIs so that they are easy and effective to use; maintaining their runtime qualities while preserving backward compatibility is equally challenging. Finding well suited granularities in terms of the architectural capabilities of endpoints and the read-write semantics of their operations are particularly important design concerns. Existing pattern languages have dealt with local APIs in object-oriented programming, with remote objects, with queue-based messaging and with service-oriented computing platforms. However, patterns or equivalent guidances for the architectural design of API endpoints, operations and their request and response message structures are still missing. In this paper, we extend our microservice API pattern language (MAP) and introduce endpoint role and operation responsibility patterns, namely Processing Resource, Computation Function, State Creation Operation, Retrieval Operation, and State Transition Operation. Known uses and examples of the patterns are drawn from public Web APIs, as well as application development and system integration projects the authors have been involved in.

As the part of the fourth industrial revolution, the way to perform maintenance has been significantly influenced by digital solutions. Predictive maintenance is one of the key approaches in that context. The idea of increasing equipment availability and reducing maintenance costs at the same time has led to a strong interest from industry. Depending on use cases, predictive maintenance shows significant benefits, such as an increase in machine uptime, reduction in maintenance hours, and material used, as well as more stable machine outputs, reduction of lead times, and forced outages. Experience shows that the implementation at scale is not as easy as it first seems. There are a number of prerequisites: focusing on the right equipment, gathering a sufficient amount of data with high quality (failure, operations, maintenance and master data), and building, testing, validating, and continuously improving a predictive model, for instance. Ideally, there is a comprehensive maintenance system in place to ensure a solid base for the implementation of predictive maintenance. Certain elements like machine data collection and analytics, work process and standards, and criticality analysis need to be evaluated, and in many cases even optimized, to drive predictive maintenance. Only by considering those requirements predictive maintenance can show the full potential.

Numerous organizations are needing to establish data governance processes, standards, and methodologies and have been able to do this with on-premises SQL Server tools such as Master Data Services. However, there has been a major gap in the Azure space for such data governance products. Previously, Microsoft has attempted to bring data governance to Azure through hosting an MDS database on an Azure managed instance or through Azure Data Catalog, which hasn’t really been a full-fledged data governance product. Azure Purview is a cloud-based data governance product that is used to centrally manage data governance across your data estate, spanning both cloud and on-premises environments.

With the advent of big data in supply chain information systems (SCIS), data compliance and consistency are becoming vital. Today, SC stakeholders need to pay more attention to data governance, which requires changing traditional management methods. These can be achieved by mastering a single repository through what is usually named master data management (MDM). However, accomplishing this objective is particularly challenging in the complex logistics networks of supply chains (SC). The volatile nature of the logistics flows that increase exponentially because of the facilitation of exchanges' interoperability in the information systems. In this chapter, the authors propose an MDM-based framework for the supply chain information systems as an enabler for strong collaboration and compliance. For proof of concept, a case study of a French hypermarket is examined through benchmarking scenarios. The outcomes of the case validate our approach as a hands-on solution when applied correctly. Finally, the chapter discusses the key findings and the limitations of our framework.

PPATK established the APUPPT Training Center to increase the knowledge and competence of PPATK stakeholders and human resources through education and training in Anti Money Laundering (AML) and the Prevention of Terrorism Funding. It is able to develop and improve service quality, manage education and training through standardization of competent institutions and conduct training on certification of expertise and competence in AML and Terrorism Financing Prevention. In order to fulfill the needs of the Education and Training Center, this study design information system using the user-centered design (UCD) method that will be used internally by Education and Training Center employees called the Internal Information System of the Education and Training Center APUPPT (SINPU-PPT). SINPUT-PPT has the main goal of automating business processes and reducing the use of office stationery. The design identifies system requirements using the Unified Modeling Language (UML), designs the user interface using a Graphical User Interface (GUI) and designs the database. The results of this research is an information system design. The information system have 9 main menu tabs Home, Room Scheduling, Class Settings, Access Rights and Users, Master Data, Catering Arrangements, Training Program, Adding Letters and Receiving Letters. The results of this design can be used as a structured reference for coding programming and testing the development of the SINPU-PPT information system.

Data governance is a system integrating data management, improvement of data quality and data application and the whole process is a closed-loop procedure that is mutually associated, mutually promoted and improved, which serves as a solution to problems ignorance of data, uncontrollable of data and invalid data. With the development and application of big data, data governance plays an important role in improving utilization of enterprise data, enhancing enterprise performance and competitiveness. Based on the literature on corporate data governance, this paper analyzes the status quo, hot topics and research frontiers of corporate data governance at home and abroad based on the knowledge map. It is found that research objects of corporate data governance mainly focus on multinational enterprises, family enterprises, high-tech enterprises and smart cities. The research is conducted with focuses on data warehouse, data governance framework, methods, models and risk management. The future research of enterprise data governance is mainly on determining the scope of master data objects and metadata management.

Open Government Data (OGD) is one of the government initiatives to gain public trust. It involves the citizens using or reuse the data so that they can make benefits from it. To implement OGD, the government shall implement several principles; there are standard data, metadata, interoperability, and reference code or master data. However, current problems and technology need to be found for a lesson learned in the next implementation. A systematic literature review was used as a methodology to find the information. And the results are 19 problems encountered and divided into seven categories: the OGD portal, engagement with the public, data quality, data utilization, organization data privacy, and data interoperability. For technology, there are seven technologies has found regarding OGD: UTAT model and trust factor, Legal open data framework towards data governance, linked open data, RST API GeoServer, Open data maturity model, Research model of post-adoption in OGD, Blockchain, and Semantic Government Vocabulary (SGoV). Findings in this research are several fundamental problems in managing portal for OGD, like updating the data and checking for the features in the portal, and the assessment toward OGD might be about corruption.

Background: In developing countries, despite large public companies’ reliance on master data for decision-making, there is scant evidence to demonstrate their effective use of transactional data in decision-making because of its volatility and complexity. For the state-owned enterprise (SOE) studied, the complexity of generating high-quality transactional data manifests in relationships between customer call transactional data related to an electricity supply problem (captured by call centre agents, i.e. data creators) and technician-generated feedback (i.e. data consumers). Objectives: To establish the quality of customer calls transactional data captured using source system measurements. To compare this data set with field technicians’ downstream system transactions that indicated incorrect transactional data. Method: The study compared historical customer calls transactional data (i.e. source system data) with field technician-generated feedback captured on work orders (i.e. receiving system) in a power generation SOE, to ascertain transactional data quality generated and whether field technicians responded to authentic customer calls exclusively to mitigate operational expenses. Results: Mean values of customer call transactional data quality from the source system and technician-generated feedback on work orders varied by 1.26%, indicating that data quality measurements at the source system closely resembled data quality experiences of data consumers. The SOE’s transactional data quality from the source system was 80.05% and that of historical data set from evaluating feedback was 81.31% – percentages that exceeded average data quality measurements in literature. Conclusion: Using a feedback control system (FCS) to integrate feedback generated by data consumers to data creators presents an opportunity to increase data quality to higher levels than its current norm.

With the development of technology and application of industrial internet of things, a large amount of data is generated in the research and development (R&D) processes in manufacturing domain, including manufacturing procedures, enterprise management, and product transactions. However, these data usually maintained in different departments, which result in information isolation and data with relations cannot be synchronized. This issue leads to the waste of storage space for redundant data and human resources for coordinating essential information. Aiming these problems, we proposed a cloud-based data management platform architecture to collect and maintain the data from isolated domains and distributed departments. A graph database is employed to store the data emphasizing the relations between entities and Master Data Management is deployed to link the entities cross standalone databases. The efficiency of inspecting, managing and updating information across databases shall be improved by the features of the proposed platform.

We introduced in Chapter 8, “AI and Governance,” data governance and the use of AI capabilities making data governance smarter. A related capability used by many enterprises is master data management (MDM). Depending on the industry, vertical customer, person, organization, product, supplier, patient, employee, citizen, and asset are typical examples of master data entities. MDM is used to deliver a trusted 360° view of master data which helps many critical operational processes such as customer service, cross- and up-sell, consistent customer experience in a multichannel architecture, or streamlined new product introduction.

Evaluating of word embeddings hyper-parameters for master data quality support task is conducted in this work. We introduce the structure and management of master data. We also describe a method of training the embeddings model for elements and methods for estimating the resulting vectors. Using a corpus of training and validation data sets – 264 thousand records, we have conducted an experiment on models with a different set of parameters and get results. Our vectors give good results in mapping and classification problems on specific industry texts in comparison with standard approaches.

Master data management (MDM) can provide an integrated and unified view of key business entities to offer better support in business processes. Due to the very nature of master data-based applications, it is possible to use data with the highest possible level of quality. MDM can help ensure that some common concerns, like duplicates or inconsistencies, are prevented by sharing a ‘single version of the truth’ throughout the organisation, and, in some cases, allowing collaborative updates to the master data repository. Therefore, assuring the reliability of master data-based applications, would improve the organisation efficiency. This type of application should implement a set of functional requirements covering the basic operation of MDM principles. We propose a solution based on the evaluation and certification of ‘functional suitability’ of MDM applications. As part of our proposal, we inferred a set of functional requirements from parts 100 to 140 of ISO 8000. This set will be used as a reference in the required matching to compute values for each one of the metrics, properties, subcharacteristics and ultimately, functional suitability following a bottom-up procedure. Finally, the paper also describes the application of the evaluation procedure of an existing master data-based application.

In the online accounting system based on a website that is very sophisticated and growing, complete facilities are certainly needed to support the smoothness and ease of accountants to apply to a company's finance. as in most other list facilities in the online accounting data master system is very influential in the ease of application consists of several features such as managing tax, warehouse list, changing product units, and terms and method of payment of the transaction itself. as an example of this tax regulating feature, it is very important, because it serves to direct and display a list of the types of taxes that the company uses for sales transactions to customers or purchases to company suppliers. then for the warehouse list is also important with other features in the master data facility that is to monitor warehouses or can be referred to as the branch of the company that you noted in your company's online accounting. To change product units can make it easier to describe the number of products available in the company, such as dozen samples, packages, boards, fruits, and pcs. the terms and method of payment in this menu function to create new and display the payment methods used by your company to your suppliers when making transactions. So, with the facilities in the master data can facilitate and provide work efficiency in shortening the flow and process on the features available in the master data facility. Keywords​: ​Master Data, Register Warehouse, Change Products, and Payment Method​.

Since the 1970s, computer supported data processing has been implemented in the laboratory and laboratory information systems (LIS) are being developed. In the following years, the programs were expanded and new laboratory requirements were inserted to the LIS. In the last few years, the requirements have grown more and more. The current tasks of the LIS are not only the management of laboratory requirements but also management of processes, data security and data transfer and they have become very important. Therefore, the current monolithic architecture of LIS has reached its limits. New methodologies like service oriented architecture, e.g. microservices, should be implemented. Thereby different specialized manufacturers provide software for one or a few tasks. These tasks can be more easily actualized like in the new field of agile software development. This new concept has been designed to provide updates and customer requirements according to its new organization structure in program development in a short time. For efficient data transfer, new interfaces and a standardization of master data like logical observation identifier names and codes (LOINC®) are advisable. With the growing data transfer, data security plays an increasingly important role. New concepts like blockchain programming (e.g. Medrec) are currently tested in (laboratory) medicine. To get an overview of the requirements of the own LIS, an Ishikawa diagram should be created. The main points of an Ishikawa diagram are shown and discussed. Based on the today-collected data, expert systems will be developed. For this kind of data mining, a structured data exchange is necessary.

In designing an academic information system in the wider scope, the first thing to be built is an information system that can process master data. Examples of such master data is student data, course data, and lecturer data. This student data becomes very important because student data will become a reference or data source for other information system incorporated in an academic information system as a whole which is found in Islamic University As-syafiiyah. To build an academic information system that capable of processing master data student required an adequate framework. Codeigniter is a very popular framework as it is very light and has very complete documentation making it easy to use. Codeigniter also uses the MVC approach (Model, View, Controller) in the concept of application development so it will be easier if the development of this information system is growing or transferable in the development process. Based on the above, it is necessary to develop an academic information system with master data subsystem of students at Islamic University As-syafiiyah which can manage all student data with Codeigniter framework.

The information system of online teaching services with geo-location determination is a system that brings together and arranging meetings between private tutors and people looking for private tutors through Android application supported with geo-location. People can go looking for tutors of related subject for specific time of meeting and location, and then the system will search tutors with matched time of availability within nearest location radius. In addition, students can organize or plan a learning meeting session with the teacher through the application. This information system is a client-server based where the built application consists of Android applications running on the client environment and web applications running on the server environment. Mobile/client applications are used by end-users to search and organize learning session. Web applications serves as web services and interfaces for administrator to manage master data, view user and transaction data, print reports, and respond to complaints from end-users. The programming language used in building this information system is the Java programming language for mobile applications and PHP for web applications and web services. And using MySQL as the database.

In a nuclear power plant, classification is a key tool to manage equipment and materials. There could be different classifications corresponding to the different lifecycle phases and the challenge is to achieve and maintain interoperability, continuity, traceability between them. Therefore, it is important to define the equipment and materials classification based on standardized information models in order to effectively share and integrate lifecycle data of equipment and materials. To accomplish this, a method for standardized exchange of plant equipment and materials data based on ISO 15926 methodology in nuclear power plants is suggested. In the proposed method, we use an information model to expand the equipment and materials classification used for the operation and maintenance phase of a nuclear power plant. This is based on the ISO 15926 standard after analyzing the master data from the Shin Kori unit 1 and 2 of Korea Hydro & Nuclear Power Co., Ltd. (KHNP). Furthermore, as a data exchange tool in the proposed method, this study develops a method to exchange equipment and materials specification data by using iRINGTools.

One of the most critical issues to be faced when building integration solutions in the field of integrated automation of business-processes of an enterprise is the problem of managing the so-called master data. Master data management is a set of processes and tools for the ongoing definition and management of company core data (including reference data). You can come across another name - reference data management [1]. Master data is data with the most important information for running a business: about customers, products, services, personnel, technology, materials, and so on. They are relatively rarely changed and are not transactional [2]. The purpose of master data management is to ensure that there are no repetitive, incomplete, inconsistent data in various areas of the organization’s activities. An example of poor basic data management is the work of a bank with a client who already uses a loan product, but still receives offers to take such a loan. The reason for the misbehavior is the lack of current customer data in the customer service department. The basic data management approach envisages such processes as data collection, accumulation, data cleansing, their comparison, consolidation, quality control and data distribution in the organization, ensuring their subsequent consistency and control of use in various operational and analytical applications.

HighlightsThe graph-oriented approach for standard visibility event/master data is proposed.The approach is better than the event-oriented approach as follows.The approach provides a unified view for both event and master data.The approach alleviates recursive and redundant trace queries.The approach supports resource-level access control as well as event-level one. EPC Information Service (EPCIS) is a de-facto standard for visibility event/master data in supply chains, and the standard is widely known as a key solution to achieve object traceability. Despite its expressive power, this information service is prone to make application developers write queries redundantly and recursively, especially in distributed environments; additionally, it is hard for business partners to protect sensitive business contexts in each object level due to its event-oriented persistence approach. Therefore, we propose a graph-oriented persistence approach for the visibility data to achieve efficient and privacy-enhanced object traceability based on unified and linked EPCIS events.

ZusammenfassungEntscheidungen im Gesundheitswesen sollten sich auf verlässliche und aktuelle Daten stützen. Da solche Daten besonders aus dem ambulanten Sektor kaum vorhanden und/oder nur schwer und zeitverzögert zugänglich sind, soll das neu eingerichtete BeoNet-Register (BNR) die medizinische Versorgungsrealität und -qualität des ambulanten Sektors mithilfe von Routinedaten aus der elektronischen Patientenakte (eP) abbilden. Diese „real-world“-Daten sollen mit Primärerhebungen verknüpft eine breite Datenbasis zur Beantwortung von Fragen der Praxis, der Versorgungsforschung und Ökonomie bilden. Interessierte Praxisinhaber verpflichten sich zu einer längerfristigen Teilnahme an dem 2-phasigen Projekt (Pilot- und Roll-Out-Phase). Informationen der eP der Praxen werden pseudonymisiert, verschlüsselt und über standardisierte Schnittstellen zeitnah, vollständig und longitudinal in die Datenbank (DB) des BNR übertragen, ohne den Praxisbetrieb zu stören. Dafür wird die Behandlungsdatentransfer-Schnittstelle (BDT) als gemeinsamer Nenner unterschiedlicher Arztinformationssysteme genutzt. Die kompilierten Variablen beinhalten Informationen zur Praxis, Stammdaten des Patienten und medizinische Parameter inklusive Angaben zu Diagnose, Therapie und Überweisungen. Ergänzt werden sie durch zusätzliche erhobene ‚patient-reported outcomes‘. Datenerhebung und Datenschutz unterliegen strengen Anforderungen, die im Rahmen des Datenschutzkonzeptes und des Ethikantrages festgelegt und für alle Forscher bindend sind. Retrospektive Auswertungen seit der Einführung der eP sowie Analysen des aktuellen Erkrankungsspektrums des hausärztlichen, pneumologischen und pädiatrischen ambulanten Sektors sind möglich. Derzeit befinden sich 98 497 Patienten-IDs aus Hannover, München und Heidelberg nebst zugehörigen Informationen in der BNR- DB. Die BNR-Daten sind umfangreicher und detaillierter als die Abrechnungsdaten der Krankenkassen. (Semi-) automatisierte Daten- Prüfroutinen und regelmäßige Feedback-Berichte sowie Datenanalysen sind implementiert und werden kontinuierlich erweitert. Während der Pilotphase wurden 2 Anwendungsbeispiele des BNR erarbeitet. Hier wird das COPD-Projekt vorgestellt, dass die Inanspruchnahme von medizinischen Leistungen in Verbindung mit der Lebensqualität Betroffener analysiert. Die Roll-Out-Phase des Projektes startet 2016. Die Datenbasis des BNR für retro- und prospektive Querschnitts- und Längsschnittstudien wird Deutschlandweit und fortlaufend ausgeweitet.

Majelis Pendidikan Dasar dan Menengah Pimpinan Daerah Kabupaten Magelang needs information about the development of the school. The necessary data include data on human resources, asset data and data of their students. At the moment the data is obtained manually by looking to the schools. With the development of information technology is now expected to have a school information system that can provide information quickly and accurately. Data on each school if put into one large server or big data can be used to analyze the data. The purpose of this research is to implement the interconnection of distributed database system using the socket API. It is used to provide master data of all schools under Persyarikatan Muhammadiyah. To support the analysis of the data interconnection of distributed databases, socket API technology is used. With a system like this then the communications database can be connected so that the school's data will be sent directly through the socket API and the data will automatically be processed by the webservice in the server middleware and will be utilized in the next process.

The Master Data Management (MDM) empowers government agencies to consolidate and integrate multiple master data sources to a single source of truth. With MDM, the master data from different agencies that are valuable across agencies, applications and services will be identified and managed in a central repository as a high quality enterprise master data. However, the MDM establishment usually hindered by a data policy where most of the government agencies are reluctant to share their master data due to widespread privacy concerns. Hence, this study aims to define master data and its privacy classification in each government agencies by using a qualitative and quantitative data analysis approach. It involves participative case studies from seven (7) Malaysia’s local government agencies. The study identifies 36 sets of master data which generally grouped into three domains which are; (1) customers’ profile, (2) services and products, and (3) service providers’ profile. From these master datasets, 20 datasets (56%) are classified as open data. The result indicates that the government agency has a high potential to share these open master data to the centralized MDM platform with no worry of the privacy issues. This study presents a significant contribution to the MDM research area by clarifying master data definition and its privacy classification in government agencies.

Labeling decisions for core labeling and/or local labeling capture the outcome of all discussions on the product statements that are necessary to ensure safe and effective handling of pharmaceutical products, with a special focus on the decision concerning known, suspected, or hypothetical risks. Such decisions may determine if a topic is to be included in the label or provide a rationale for exclusion from the label. The need to provide special advice to users and the type of advice (eg, contraindications, precautions) are subject to labeling decisions as well. While the problem is well known to pharmaceutical companies, and technical solutions such as XML-based authoring/coding systems try to offer support from the information technology sector to handle the business problem, the current identification of medicinal products (IDMP) requirements raised by health authorities worldwide have put a new focus on the problem. This article will elaborate on the basic business problem and its requirements with respect to a solution.

The rapid development in the field of information-communication technologies, which has been recorded in recent years, it has caused an increase in the volume of data in companies last year about 40 to 50% [1]. By analysing large amounts of data, it is possible to get information that is important for the enterprise and on the basis of which it is possible to improve the decision-making process for managers. The main problems in the management and decision-making of enterprises is constantly growing amount of data generated within the undertaking and its surroundings. These data reach the volumes and structures, which is not possible from the time and cost to manage through the current management information systems. The fastest increasing volumes of data are unstructured data, which may contain data with significant information value, for the purposes of decision making in the enterprise. In the light of the principle of the processing of data in the existing MIS, i.e. the processing of structured data, such as data capture, utility companies have to transform and analyse. The question of how to process and integrate data of different types of technology, solves the Big Data. This technology allows to handle different kinds of data, from a variety of data sources, in a very short amount of time (in milliseconds).Â

Safety stock planning in ERP in general is dependent upon the planner having the experience to simulate planning scenarios. This paper focuses on the development of a mechanism to calculate adequate safety stocks in accordance with required service levels while enabling efficient configuration of the ERP safety stock parameters. The proposed mechanism could be of great benefit to industrial firms as it offers the ability to classify demand patterns, proposes replenishment strategies that are consistent with the demand profile, calculates key parameters and identifies the changes required to the ERP master data. The associated real world application is able to identify potential to save approximately £1.2 M in stock reductions and, more importantly, allows targeted actions to be implemented at material level. These results demonstrated that the proposed mechanism can be considered as a valuable new development for manufacturing industry to gain the competitive advantage.

Processes in manufacturing and logistics are characterized by a high frequency of changes and fluctuations, caused by the high number of participants in logistic processes and the variety of goods handled and services offered. This dynamic behavior particularly requires well documented processes, but at the same time it also complicates process documentation. Manually documented processes can, e.g., miss details of alternative process branches, and the continuing change in logistic processes renders the documentation quickly outdated. A possible solution approach is to automate the documentation of processes. This automated documentation, if based on transaction and master data from the IT systems connected to the logistic process, is called Process Mining. Being a subfield of Data Mining, Process Mining extracts sequences of activities from event logs in databases. Logistics has the opportunity to greatly benefit from the application of Process Mining, because the identification and tracking of goods in the supply chain involves many IT systems. However, the IT landscape in logistics is heterogeneous, because the data are scattered among different specialized systems for various purposes (e.g., warehousing, transportation planning, and billing) of different companies. Due to this lack of standardization, the data cannot simply be analyzed by a predefined routine. Therefore, additional information beside the stored data should be taken into consideration. Machine Learning offers the possibility to classify single items in large data sets and to categorize these items with regard to the context they are in. Adding context awareness to unstructured event data in logistics has the potential to improve the results of Process Mining. Our research investigates how to apply context awareness based on Machine Learning in Process Mining for logistic processes and demonstrates its performance in a logistic scenario.

Modern aircraft are more integrated with advanced systems functionalities, which result in ever-increasing aircraft complexity, further development difficulties and development delays. These system complexities are mostly in the form of system interactions that make it difficult to understand the overall system characteristics. At the early stages of final assembly line (FAL) design, one of the most important objectives is to arrange the installation and test tasks from components to sub-systems and systems in the proper sequence to meet the designed functions and prevent hazards from the integration process. Improper sequencing of the final assembly process will cause rework, time delays, cost and potential safety risk in development. In the field of final assembly line design, previous research has mostly focused on assembly line balancing or supply chain design based on structural parts assembly. However, these approaches do not consider the early final assembly line definition or test allocation for system functions. In this paper, the research proposes a method based on a systems engineering view and integrated computer aided design (CAD) to help better understand system interactions and generate viable final assembly process sequencing. This research aims to develop a concept of unified master data for final assembly design, which contains 3D geometrical CAD, system functions and interaction characteristics. The paper will present the methodology framework, key concepts and associated industrial software packages for implementation. The paper concludes with further discussion of an initial case study.

Every citizen has the same rights to obtain the education, including for children with disabilities. However, from the current researchers there are several problems faced with the implementation of Extraordinary School (SLB) education in Central Java Province, which is the limitation of human resources policy, budget and infrastructure facilities. This research aims to observe and conduct analysis of the policy reform of Sekolah Luar Biasa (Extraordinary School) of Education management in Central Java Province and supporting factors and inhibitors of SLB education management policy conducted by the government of Central Java Province. The research method used by researchers is qualitative, with data collection using triangulation techniques. The results showed that the policy reform of SLB Education management in Central Java Province in the field of apparatus resources still need to be addressed because still many who have not been literate with technology. However, it does not affect them in the service given to the community. These recommendations given improving the quality and quantity are human resources apparatus through the technology and training. The addition of personnel, development of infrastructure adapted to disabled, master data collection and budget adjustments. Preparation of Standard Operational Procedure (SOP) sustained to the LAW No 8 in 2016 about the disabled. The creation of governor regulation that is specifically governs the disability. Key words: Policy Reformation,Disability,Standard Operational Procedure,Education Management,Extraordinary School.

Master data is required as a reference to perform data processing. The primary purpose of Master Data Management is to ensure that the master data reflects the current state of the business. However, many companies do not have a data management strategy, especially large companies which have large amounts of data. Master Data Management Maturity Model (MD3M) can be used to conduct an assessment of the maturity of the MDM in a company. The purpose of this research is to measure the maturity of the existing master data management at PT XYZ using Master Data Maturity Management Model (MD3M). This research used MD3M model by Spruit and Pietzka which has five key topics and 13 focus areas. A questionnaire is used to identify the level of master data management maturity. The questionnaire contains two sets of questions. The first set of questions tests the influential factors, and the second set of questions is about the capabilities to be assessed. The Subject Matter Experts (SMEs) who has a position as manager and Settlement Service Department and Reporting Department staff will answer the questionnaire given. The questionnaire will be mapped into a matrix to identify the maturity level. Measurement conducted in this study showed that the level of maturity at PT XYZ is 0, even though the findings in this study found that 54 out of 65 capabilities have been implemented that is equal to 83%. This is because the maturity level in the key topic data quality is still 0 (zero). By improving the capabilities that have not been implemented, the organization will achieve a higher level of maturity.

Based on the network marketing system of cloud computing is that the entire e-commerce trading platform and external database connected to the database connection terminal users can master data and timely update, build a collaboration between the seller and the buyer, and master the rapid transfer of relevant information and products to speed up the electronic commerce and circulation system. Maximize the whole e-commerce links fluency and benefits. E-commerce marketing is a kind of marketing on the Internet, which is a process of completing a series of marketing links with the help of the Internet to achieve marketing goals. Network marketing system uses the characteristics of cloud service platform technology to achieve cloud-based services on demand. At the same time, enterprises can set up their own network marketing application system without independent investment, and can utilize various services provided by cloud computing manufacturers and build application systems and databases with their own enterprise characteristics.

Geospatial data is important in supporting census and survey in Statistics Indonesia. Some problems exist about it are inaccurate maps, the existence of incomplete data attribute, and there are many versions exist in silo systems. They can impact the result of census or survey data quality. Improvement applied to overcome problems is managing geospatial data by adopting geodatabase. To improve the application in the future, it needs to be evaluated. By the reason, this research aims to measure the maturity level of master data management of geospatial data and arrange strategy to improve master data management of geospatial data in the future. This research used Master Data Management Maturity Model to measure the level of maturity and mapped the result to reference and master data management activity of Data Management Body of Knowledge. The result is Statistics Indonesia has not achieved any level yet for geospatial master data management and must concern to apply five activities for improvement in the future namely are to understand reference and master data integration needs, to identify reference and master data sources and contributors, to define and maintain the data integration architecture, to define and maintain match rules, and to establish golden record.

Data is an important asset that used as a key for making decisions for the company’s operations and business needs. The data is not only taken from one database source but from various databases that exist to be a single entity. However, obstacles arise when the process is carried out, which is the existence of duplication data with different formats, but the data is same. Problems that can be reduced or even eliminated by implementing good data governance. One process that exists in data governance is Reference and Master Data. This process can be the foundation for making a master data management tools using the open source platform. This research focuses on analyzing existing master data management architectures and designing new architectures. So, design of master data management tools architecture for open source systems that can be applied at PT XYZ.