Intelligent Data Hub – Taking MDM to the Next Level. MDM solutions have been instrumental in solving core data quality issues in a traditional way, focusing primarily on simple master data entities such as customer or product. Organizations now face new challenges with broader and deeper data requirements to succeed in their digital transformation. Download this Semarchy white paper here.
The relation between CX and MDM. Customer experience (CX) and Master Data Management (MDM) must go hand in hand. Both of these themes involves multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. To be successful within customer experience in the digital era you need classic master data outcomes as a 360-degree view of customers as well as complete and consistent product information. Watch this Riversand webinar here.
The Intelligent Data Hub: MDM and Beyond. Why the traditional MDM concept must be extended by encompassing more data sources than classic master data, adding more capabilities such as data discovery, collaborative data governance and measuring results with data dashboards and how an Intelligent Data Hub can help achieve this mission and will underpin your digital transformation. Watch this Semarchy recording online here.
The State of Product Information Management 2020. An overview of why PIM solutions are implemented in more and more organizations, which capabilities a 2020 PIM solution needs to cover, where the market is heading and who the PIM vendors in the market are and how they affect your purchase of PIM. Download this white paper here.
Data Quality – What, Why, How, 10 Best Practices & More! As data is becoming a core part of every business operation the quality of the data that is gathered, stored and consumed during business processes will determine the success achieved in doing business today and tomorrow. Read this Profisee article here.
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Data Parsing, Matching and De-duplication. Data parsing, standardization, matching, and de-duplication are the cornerstones of successful Master Data Management (MDM). They are also critical parts of successful data quality programs, and are key steps in building data warehouses as well as any data integration and consolidation initiatives. You could say that today few organizations can function effectively without implementing data parsing and matching processes often in many data domains. Learn about this eLearningcurve course here.
Data Monetization. Data monetization is about harvesting direct financial results from having access to data that is stored, maintained, categorized and made accessible in an optimal manner. Traditionally data management & analytics has contributed indirectly to financial outcome by aiming at keeping data fit for purpose in the various business processes that produced value to the business. Today the best performers are using data much more directly to create new services and business models. Watch this Information Builders webinar here.