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Background and objectives: Documenting patient data in psoriasis clinical practice can improve care, but standardized and transparent documentation is rare. The current project aimed to develop a data set for the documentation of psoriasis in daily practice. Material and methods: In four online Delphi rounds and one in-person meeting, 27 psoriasis experts allocated variables to a standard, an optimal and an optional data set. Most of the questions were standardized. Open questions were included to allow for the provision of reasons and to enlarge the data sets. Furthermore, in the in-person meeting we considered a) patients' attitudes and b) dermatologists' information on the current usage and acceptability in Germany. Results: The consensus approach resulted in a data set with 69 variables. The standard data set includes 20, the optimal data set 31 and the optional data set 18 variables. In summary, the data set can mainly be grouped into master data, general status and medical history data, medical history of psoriasis, status of psoriasis, diagnostics and comorbidity, therapies and patient-reported outcomes. Conclusions: The consensus recommendation of a standard, an optimal and an optional data set for routine care of psoriasis intends to be a decision-making aid and an orientation for both daily practice and further development of documentation systems.

Semantik web merupakan teknologi yang mampu memahami konteks seperti halnya manusia memahami konteks tersebut. Teknologi semantik dapat dimanfaatkan dalam berbagai bidang, salah satunya adalah klasifikasi topik skripsi mahasiwa. Banyak skripsi membahas topik serupa, tapi dengan peristilahan berbeda. Hal tersebut seringkali menimbulkan kesulitan dalam menentukan apakah suatu topik sudah terlalu sering dibahas. Oleh sebab itu, penelitian ini bertujuan untuk mengklasifikasikan dan memetakan bidang minat skripsi mahasiswa Program Studi S1 Teknik Informatika (TI) Unpad. Langkah-langkah yang dilakukan dalam penelitian ini adalah mengumpulkan data, menentukan entitas, implementasi ontologi, dan klasifikasi data menjadi beberapa kelas berdasarkan bidang minat di Program Studi TI Unpad (Sistem Informasi dan Multimedia, Kecerdasan Buatan dan Robotika, Jaringan Komputer, dan Metode Numerik). Langkah akhirnya adalah proses training dan testing data menggunakan algoritma k-nearest neighbour (KNN). Klasifikasi aktual data menunjukkan bahwa mayoritas skripsi di Prodi TI Unpad adalah tentang Sistem Informasi dan Multimedia, dengan presentase 50.23%. Sedangkan, klasifikasi prediktif data dengan algoritma KNN menunjukkan persentase 47.88%. Hasil yang berbeda tersebut diperoleh karena Confusion Matrix menunjukkan nilai sebagai berikut: AUC (0.711), CA (0.545), F1(0.578), Precision (0.669), Recall (0.545).

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.

The need for transportation for the community, especially in Tangerang Regency, which is used for holidays, tours, study tours, or pilgrimage. is a promising business opportunity for transportation companies.Because the need for transportation is important for the smooth running of its activities. PT. Sumber Jaya Trans Tangerang, which is involved in passenger transportation services, is one of the companies engaged in this field. In its operation still uses a manual system in renting buses. Many obstacles occur in this bus rental, such as business processes that have not been computerized, and data storage that is less effective and efficient. The research method in the analysis of the system used is PIECES which consists of Performance, Information, Economy, Control, Efficiency and Service. The system design uses object-oriented analysis method with Unified modeling Language (UML), the programming language used is PHP and the database used is MySQL. This system is web-based and uses the PHP 7 programming language, Web Design with CSS Bootstrap, Laravel Framework and MySQL for database design, while supporting software used in making programs is Visual Studio Code. In this research, a system has features such as master data, transactions, bus schedules, destinations, calendars, transaction reports, road warrant reports and activity logs. With this bus rental system, the bus rental process that is running will be more effective and efficient.

We present a data cleaning project that utilizes real vendor master data of a large public university in the United States. Our main objective when developing this case was to identify the areas where students need guidance in order to apply a problem-solving approach to the project. This includes initial analysis of the data and the task at hand, planning for cleaning and testing activities, executing this plan, and communicating the results in a written report. We provide a dataset with 29K records of vendor master data and a subset of the same data with 800 records. The assignment has two parts—the planning and the actual cleaning, each with its own deliverable. It can be used in many different courses and completed with almost any data analytics software. We provide suggested solutions and detailed solution notes for Excel and for Alteryx Designer.

Production tends to become decentralized to provide more flexibility and robustness. Hence planning and control is also shifting to decentralized solutions to cope with a changing product mix. We present an intensive simulation study of different types of production and analyze the influence of product structure, product complexity and multiple use of materials. We show for which type of production self-organizing planning and control performs best. For this, we give a brief introduction to our self-organizing production and outline how we generate a wide variety of production master data that are freely customizable in terms of their statistical parameters, like product complexity, multiple use of materials and degree of organization, to reflect different types of productions. The results of our simulation study show the advantages of our self-organizing production.

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.

Background: Manufacturing companies nowadays have to cope with ever-increasing speed and complexity to manage their global supply chain. Information flow manage the supply chain needs to be more accurate and real-time than ever before. Item master data management is no exception as well. This research demonstrates the potential benefits of applying robotic process automation (RPA) technology in master data management based on two companies in the manufacturing industry. Methods: The method approached by the research was a qualitative method, utilizing interpretation of data extracted from literature and semi-constructed interviews. Results: The article proposes a framework for RPA implementation in the master data managing process. Automation with software robots can greatly benefit the organization, namely: lower processing time, reduce human errors, lower operations cost, improved compliance level and higher data accuracy. Conclusions: With the objective to examine the benefits of RPA in master data management, the researchers have investigated 2 companies in the manufacturing industry to understand how they have benefited from RPA for item master data management processes. The result showed that RPA is affirmed to bring about many benefits for the organizations through structured automation. The researchers have proposed a framework for implementing RPA to capture item master data based on the change management model and the framework is separated in 3 phases involving important tasks in each phase. Future researches might use this study as a stepping stone for further studies in cognitive RPA, utilizing cognitive technology in combination with RPA technology with the mean to achieve a higher level of automation.

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.

Master data (MD) is operational data of the organization which requires minimal updates. Master data is a very crucial element of the organization and is handled using the master data management (MDM). MDM is a process, which is used to enhance the quality of data and process flow in an organization. MDM is used to control the quality of data and creates restrictions on data usage. Master data management helps to maintain the data integrity and provides a single copy of master data. Many organizations are using MDM for business purposes; but still, master data is unable to handle the poor data in the organization. This poor data creates a negative impact on the organization. Due to cost constrain, many small and medium size (SMEs) enterprises are unable to adopt the MDM process. In this paper, we first explain the different aspects of master data, and then we highlight the major studies performed in the field of MDM. Through this study, we provided four major observations in context to the research trends and future research.

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 authors discuss the use of files being accumulated by library www-serves (log-files) for calculating altmetrics. The log-files comprise information on all queries therefore they can be used for calculating statistical indicators of online user activity index in compliance with the federal standard GOST 7.0.20-2014. The authors also analyze the provisions of the standard as referred to online user services control. The COUNTER format is the international format assigning the list and form for presenting statistical data on using digital resources. The current version 5, the COUNTER Code and the algorithm of master data (log-files) processing are discussed. The authors conclude that Russian libraries have not introduced the unified method for counting the requests to their digital resources so far. Meaningful combination of GOST requirements, COUNTER fundamental indicators and calculation of indicators obtained through log-files analysis enable to analyze the relevance of library digital resources to various user categories.

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.

The airport is one of the entry gates of a country, in the airport there are many work divisions that support and facilitate all existing activities, one of which is a cleaning service that has a large enough function that is to improve cleanliness and comfort in the airport area, cleaning services that exist when This is managed by the company and works with companies that focus on cleaning service in each terminal. The placement of the cleaning service area is still manual and takes a lot of time and involves several parts in the process, and inhibits in terms of time in the process of cleaning the area at the airport so that it can reduce the quality of services that have an inconvenience, to overcome the problem. which is currently running to find out the components of the problem that exist. The results to be achieved in this study is the application of a system that facilitates the process, namely by making an information system placement of web-based cleaning service work areas that is used as a reference in the system development process later. The analytical method used in this study uses the PIECES method, to assist in the process of analyzing the system that has been running. The Programming Language used in building this system uses PHP, MySQL as a database to store master data, transactions and users.

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.

Inventory information system is a system to determine stock inventory in the warehouse of a company. Inventory information system has been widely used or developed in a company with a wide range of technologies and systems. Problems at UD. Miasa is not the availability of an information system for controlling inventory of stock items available in the warehouse. a system of inventory information would be very helpful UD. Miasa in running the business. Therefore, in this study will be to design and build an information system that supplies desktop based on inventory information in clear and detailed. The system built can also perform archiving of data such as data items item master data, supplier master data, master customer data, incoming goods, goods out, inventory taking, as well as forecasting for the purchase of goods in the next month. The end result of this research is the creation of an information system invetory built using Visual Studio 2010 application and uses the MySQL database system.

Regional Water Company (PDAM) Gianyar Regency is one of the regionally owned companies in Gianyar Regency. Gianyar PDAM is responsible for the availability of clean water for consumers in the Gianyar region and its surroundings. In a water distribution system, the PDAM monitors water pressure in the pipeline so that water is guaranteed to flow to the customer. During this time the system of recording water pressure on the manometer installed in each particular zone is done manually using report paper. The difficulty caused is the slow monitoring of water pressure reported by the recording officer to the distribution officer, so that the water distribution process becomes stagnant which ultimately harms the customers and the PDAM itself. This study aims to create a system that can record water pressure which makes it easier for the distribution head to get information quickly. With the research phase which includes data collection techniques, system analysis, and system design to maximize research and implement systems and tests on each system, it can be concluded that the system that has been made can run well and as expected. The functional features generated in this study are the dashboard graph of the average water pressure per month, processing master employee data, processing village master data, processing master manometer data, processing schedule data, manometer monitoring by officers, manometer monitoring by admin and manometer monitoring report .

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.

The use of information and communication technologies facilitates energy management (EM) at both district and building levels, but also generates a considerable amount of data. To gain insights into such data, it is essential to resolve the cross-domain data interoperability problem and determine an approach to exchange performance information and insightful data among various stakeholders. This paper developed an EM-KPI (key performance indicator) ontology to exchange key performance information and data for districts and buildings. The ontology contains two components: namely KPIs and EM master data; these, respectively, represent the multi-level performance information for energy performance tracking and the key data for data exploitation. Through a demonstration, a sample linked dataset generated using the data correlation predefined in the ontology is presented. The linked data analysis proves the feasibility of the ontology for exchanging data among different stakeholders and for exploring insights in relation to performance improvement. European Commission - Seventh Framework Programme (FP7) China Scholarship Council Guangzhou Science and Technology Innovation Talent Project UCD Energy Institute 24 month embargo - AC

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.

Jiaji Logistics is one of the biggest logistics enterprises in our country, but the survey found that there are still many problems in it, which limits the development of Jiaji logistics. One of the important factors is the location of the distribution center. In order to overcome this difficulty, the location of Jiaji logistics is used scientifically, and it can also help the development of logistics industry in China. First of all, in this paper, by analyzing distribution center location of the relevant literature both at home and abroad, according to oneself the circumstance of master data, choose the Baumov Model for the theme as a solution. Then, the factors affecting the distribution center of Jiaji logistics are selected, and the optimal solution is obtained by the successive approximation method. The final result will be the distribution center capacity, location, volume allocation and so on. The improved model of the Baumov Model was a great help to the location of the Jiaji logistics, which reduced the cost of the Jiaji logistics.

This paper describes a taxonomy which allows to assess and compare different implementations of master data objects. A systematic breakdown of core entities provides a framework to tell apart four subdividing categories of master data objects: independent and dependent objects, relational objects, and reference objects that serve to attribute information. This supports the preparation of data migrations from one system to another.

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.

Master data management is an essential task for organizations and even more critical when collaborations are pursued. Using centralized platforms to manage master data across business partners is straightforward but also entails risks. Besides the dependency on intermediaries, data sovereignty is limited and a single point of failure persists. With new decentralization trends and the uprising blockchain technology, there is potential for optimized and sovereign master data management across entities. We conduct a design science study to assess blockchain technology’s suitability to store and share master data in supply chains. The developed artifact was quantitatively evaluated, focusing on costs and transaction time to further contribute insights to blockchain technology’s economic suitability. The Ethereum-based application was implemented in evan.network and Ropsten test networks. The results substantiate the previous theoretical statements in the literature with reliable numerical data, which indicate that permissioned blockchain networks are more scalable and low-cost than permissionless networks. We also highlight further research opportunities.

Retail companies are looking for ways to support or automate the data entry process for product master data which is currently often a time-consuming and cost-intensive manual process. The basis which many attributes and business processes in master data management depend on is the classification of articles to a certain product category. In this paper we propose a machine learning approach to classify articles according to the Global Product Classification schema by their product name, brand name and other attributes such as the product weight and dimensions. One of the challenges in our data set is posed by the product names containing a significant amount of abbreviations, for which we implement several preprocessing strategies. Additionally, the data set suffers from class imbalance and missing values that must be considered. Different classification algorithms, data imputation methods and feature combination strategies are evaluated. We show that automatic classification can be performed successfully based on the partly abbreviated product names despite the challenges mentioned. A simple Support Vector Machine model shows to outperform more sophisticated models and the brand names. The product dimensions and other additional attributes did not increase prediction quality.

Master data refers to the data that represents the core business of the organization, shared among different applications, departments, and organizations and most valued as the important asset to the organization. Despite the outward benefit of master data mainly in decision making and organization performance, the quality of master data is at risk. This is due to the critical challenges in managing master data quality the organization may expose. Hence the primary aim of this study is to identify factors influencing master data quality from the lens of total quality management while adopting the systematic literature review method. The study proposed 19 factors that inhibit the quality of master data namely data governance, information system, data quality policy and standard, data quality assessment, integration, continuous improvement, teamwork, data quality vision and strategy, understanding of the systems and data quality, data architecture management, personnel competency, top management support, business driver, legislation, information security management, training, change management, customer focus, and data supplier management that can be categorized to five components which are organizational, managerial, stakeholder, technological, and external. Another important finding is the identification of the differences for factors influencing master data compared to other data domain which are business driver, organizational structure, organizational culture, performance evaluation and rewards, evaluate cost/benefit tradeoffs, physical environment, risk management, storage management, usage of data, internal control, input control, staff participation, middle management's commitment, the role of data quality and data quality manager, audit, and personnel relation. It is expected that the findings of this study will contribute to a deeper understanding of the factors that will lead to an improved master data quality.

One of the major factors that lead to a restaurant’s success is its location. This paper proposes a simple technique which uses K-Means Clustering, an unsupervised machine learning algorithm, to rank and predict the neighborhoods in a city to open new restaurant branches. Most important attributes like user rating, frequent visits and venue category are consider while clustering the locations. With the help of ‘beautiful soup’ package, Mapsofindia.com is scraped to identify the geographical locations of the city. Main components of master data are Postcode, Neighborhood name, Latitude and Longitude information of the city. After cleaning the data and clustering the locations, each and every cluster is carefully observed and it is found that Cluster number two has neighborhoods with ‘Indian Restaurant’ as most frequently visited venue. Geographical details of the locations are retrieved using geolocator python package and foursquare API is efficiently harnessed in identifying the place people love to visit in a specified location. This paper discusses how to predict the location for opening a new restaurant. The same methodology can be applied to predict a location for opening any consumer-based business like Pharmacy, Ice Cream shops, Bakery’s, boutique etc.

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.

Master data sets are an important asset for organizations and their quality must be high to ensure organizational success. At the same time, data migrations are complex projects and they often result in impaired data sets of lower quality. In particular, data quality issues that involve multiple attributes are difficult to identify and can only be resolved with manual data quality checks. In this paper, we are investigating a real-world migration of material master data. Our goal is to ensure data quality by mining the target data set for data quality rules. In a data migration, incoming data sets must comply with these rules to be migrated. For generating data quality rules, we used a SVM for rules at a schema level and Association Rule Learning for rules at the instance level. We found that both methods produce valuable rules and are suitable for ensuring quality in data migrations. As an ensemble, the two methods are adequate to manage common real-world data characteristics such as sparsity or mixed values.

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.

Creating work orders and scheduling work orders are two vital processes in Dynamics 365 Field Service. In order to create and schedule work orders in different ways, you need to configure the system with multiple settings and master data as prerequisites.

Master data is the blood of industry. Due to the complicated environment of materials flow - materials are frequently exchanged from one place to another, collecting or cleaning data from such environment becomes a challenging task. Unlike the common NLP tasks which already have massive labeled data, labeled data only share a small proportion compared to thoes unlabeled dirty data in the task of cleaning industry master data. Workers who take response in recording data are inclined to make the work done as fast as possible, this may cause the data records vary in thousands ways: abbreviation of material's name, lack of basic properties, wrong measurement unit or number, etc. This paper introduces some practical methods to handle the data cleaning problem. In data preprocessing and alignment, traditional NLP technics such as trie [1], Aho-Corasick(AC) [2] and wordnet [3] can take advantage of hidden statistical informations; we use tree search to help solving data correcting problem which can construct all possible combinations of data records; finally lattice-LSTM [4] are applied in splitting and reconstucting data properties.

Master data management is a process to integrate several data sources into one for good, consistent, and evenly standardized data. Several companies have been successful in creating a good quality data source for their data. However, some companies yet to have this system. A government agency in Indonesia finds difficulties in managing their data source. The problem lies in data duplication, lack of control on data reference, and the lack of application to monitor all data sources. This paper is carried out to fix the MDM monitoring dashboard that has been created in advance. The monitoring dashboard is created with the pureshare method. This paper designed a monitoring dashboard that can monitor the process and quality of master data. The benefit of this research is to get good quality and consistent master data, which can be used in all organizations, and its authenticity can be guaranteed.

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.

The value stream method is widely used in the manufacturing industry to analyze and redesign value streams. However, with the increasing complexity of modern production systems, conducting a value stream analysis (VSA) and extracting reliable information for an accurate value stream design (VSD) becomes a challenging task for practitioners. Utilizing data from production-related IT systems offers the potential to support the value stream method with target-oriented analyses. Process mining (PM) supports the VSA by deriving process flows from production data as well as by analyzing process performances. Focused analyses of master data and transactional data enable reliable VSD activities without having to assume an oversimplified current state. This paper provides a framework for a continuously integrated data assistance within the value stream method, presenting a team structure, best practice procedures, and requirements for the application of the data assisted value stream method supported by examples from industry projects.

Master data construction is at the core of the enterprise information strategy, and it is the collection place of data to ensure the consistency and uniqueness of the target system data. In this paper, the identification indicators are determined from the characteristics of master data. The indicator weight is based on the business importance, so as to construct the master data identification score system. According to the data survey and census results, identify the master data and clarify the scope of master data attributes, and finally build the business owner master data model, which lay a good foundation for later data governance.

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.

There are three types of user in the Electronic Veterinary Management (EVM) : employee; veterinarian; and manager. The employee role is the weakest authority and the manager is the strongest. Employees can manage the master data in the system that are the pet’s owner, pet data, treatment fee, and the queue system. Veterinarians can modify the treatment data. The final users, the manager can manipulate the user authority data and access the revenue of the clinic. The architecture of this system is model, view, and controller by servlet, JSP and Java language, MySQL is the database management system and iReport is the report generating tool. The average EVM time for getting pet’s data is 25.14 seconds and a standard deviation value is 10.02. While as the average time of the original process is 37.09 seconds, s.d. is 23.24. Therefore EVM is faster accessing than the original pet record procedure.

Extensible Markup Language (XML) is emerging as the primary standard for representing and exchanging data, with more than 60% of the total; XML considered the most dominant document type over the web; nevertheless, their quality is not as expected. XML integrity constraint especially XFD plays an important role in keeping the XML dataset as consistent as possible, but their ability to solve data quality issues is still intangible. The main reason is that old-fashioned data dependencies were basically introduced to maintain the consistency of the schema rather than that of the data. The purpose of this study is to introduce a method for discovering pattern tableaus for XML conditional dependencies to be used for enhancing XML document consistency as a part of data quality improvement phases. The notations of the conditional dependencies as new rules are designed mainly for improving data instance and extended traditional XML dependencies by enforcing pattern tableaus of semantically related constants. Subsequent to this, a set of minimal approximate conditional dependencies (XCFD, XCIND) is discovered and learned from the XML tree using a set of mining algorithms. The discovered patterns can be used as a Master data in order to detect inconsistencies that don’t respect the majority of the dataset.

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.

Статья посвящена разработке методики управления мастер-данными при внедрении учетной системы. В статье рассматривается необходимость обособления единой среды управления мастер-данными, в которой связи работ и документов реализуются на инструментальном уровне, а технологии работы с данными унифицируются, что позволяет повысить эффективность процессов управления данными, сократить количество передач управления и ответственности между функциональными направлениями путем максимального сосредоточения функций этого процесса в «одних руках», а именно в руках группы НСИ, которая имеет детальный план работ, собственный бюджет и систему маршрутизации для всех функциональных направлений.The article is devoted to the development of master data management techniques. The article discusses the need to isolate a single master data management environment in which work and document communications are implemented at the instrumental level, and data processing technologies are unified, which allows to increase the efficiency of data management processes, reduce the number of control transfers and responsibilities between functional areas by maximizing concentration The functions of this process are in the same hands, namely, in the hands of the NSI group, which has a detailed work plan, its own budget and a routing system for all functional areas.