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The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Consumers have grown more and more immune to ads that aren’t targeted directly at them.
The main market driver generating demand for knowledge graphs is that B2B clients are on the lookout for intelligent knowledge management solutions that work the same way as the solutions Apple, Amazon, Google and Microsoft provide to their B2C users. The third challenge is how to combine data management with analytics.
This is especially beneficial when teams need to increase data product velocity with trust and dataquality, reduce communication costs, and help data solutions align with business objectives. In most enterprises, data is needed and produced by many business units but owned and trusted by no one.
Master data management (MDM), on the other hand, is focused on ensuring dataquality and consistency across different systems and applications. MDM creates a single, verified source of data that can be used throughout an organization, and enforce policies and standards to maintain dataquality.
– We see most, if not all, of data management being augmented with ML. Much as the analytics world shifted to augmented analytics, the same is happening in data management. You can find research published on the infusion of ML in dataquality, and also data catalogs, data discovery, and data integration.
The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.
My experiences as architecture principal in B2C-connected vehicle integration for the automotive OEM industry focused on unlocking and enabling the access and flow of data from vehicles globally and at scale. The real risk of making impactful business decisions with questionable data lineage and quality was obvious.
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