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Data Analytics for the Smart Grid
Published in Stuart Borlase, Smart Grids, 2018
Greg Robinson, Jim Horstman, Mirrasoul J. Mousavi, Gowri Rajappan
Master Data Management (MDM): Gartner defines MDM as “a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts” [1]. With master data managed and provided as a service, the data will not get corrupted as they traverse the enterprise. MDM is key to supporting the single version of the truth architectural goal. System of record data and data relationships are needed to navigate through the various domains on anything beyond a small scale. With MDM, the Foundational Data Store can consume the data and then establish shared dimensions of all the data. MDM functionality can be achieved in different ways: providing a registry for information about an object and cross-references for disparate object identifiers, consolidating data from multiple sources and integrating into a single repository for replication to other destination systems, federating by having a single virtual view of master data from one or more sources, and data propagation, which is the process of copying master data from one system to another. However it is constructed, the foundation of the MDM solution will be the master data model, and this model will be complicated to build and maintain if it is not based on a common data model.
Managing the Project Life Cycle
Published in Bert Brijs, Business Analysis for Business Intelligence, 2016
Master data: From the first iteration and onwards master data should be your guide on the path to an upgradeable BI system. Master data indicate immediately what the organization is interested in as it takes special care of these objects throughout all source systems. You either take all of them into the scope of your solution or, if this is too much of a burden for the project’s budget and time constraints, foresee graceful update possibilities for future developments.
Other Techniques Essential for Modern Reliability Management: I
Published in Edgar Bradley, Reliability Engineering, 2016
These are data held by an organisation that describe the entities that are both independent and fundamental for an enterprise; data that it needs to reference in order to perform its transactions. Master data include information on customers, suppliers, materials, services, assets, locations and employees. There is an international standard for master data, ISO 8000, which has the title, Data Quality.
Data-driven Begins with DATA; Potential of Data Assets
Published in Journal of Computer Information Systems, 2022
Hannu Hannila, Risto Silvola, Janne Harkonen, Harri Haapasalo
All company transactions in business IT solutions rely on master data that relate to products, customers, or suppliers, setting high-quality goals for consistent master data throughout the entire product lifecycle.11–14 Nevertheless, the reality often means inconsistencies in data definitions, data formats and values causing negative impacts and inefficiencies in organizations.14–17 According to Walker and Moran18 “new business opportunities will be missed without an expanded focus to connect datasets to master data – in order to realise the 360-degree view”. Based on Porter and Heppelman19, a whole new customer relationship mind-set is needed with smart and connected products, which gain a wide range of customer data and insights from product usage for sophisticated market segmentation as well as product and service tailoring and thereby for alternative pricing models. The extant literature focuses much on technology and less on how to turn data assets to a competitive advantage.
Digitalisation of a company decision-making system: a concept for data-driven and fact-based product portfolio management
Published in Journal of Decision Systems, 2022
Hannu Hannila, Seppo Kuula, Janne Harkonen, Harri Haapasalo
Data governance and a holistic, corporate-level data model has a central role in this concept to connect business processes via master data and to ensure trustworthy data sources over business IT silos. A corporate-level data model pays attention to all data assets, starting from master data, supplemented by transactional data and IoT data. These assets together provide a 360-degree view of the company’s data assets and simultaneous reporting and analytics capabilities through all enterprise solutions containing relevant data for fact-based PPM analysis according to company PPM performance management targets and KPIs.