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Master Data Management("MDM") is a technology-enabled discipline in which business and Information Technology ("IT") work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.
As a result of business unit and product line segmentation, the same business entity (such as Customer, Supplier, Product) will be serviced by different product lines; redundant data will be entered about the business entity in order to process the transaction. The redundancy of business entity data is compounded in the front- to back-office life cycle, where the authoritative single source for the party, account and product data is needed but is often once again redundantly entered or augmented.
A typical example is the scenario of a bank at which a customer has taken out a mortgage and the bank begins to send mortgage solicitations to that customer, ignoring the fact that the person already has a mortgage account relationship with the bank. This happens because the customer information used by the marketing section within the bank lacks integration with the customer information used by the customer services section of the bank. Thus the two groups remain unaware that an existing customer is also considered a sales lead. The process of record linkage is used to associate different records that correspond to the same entity, in this case the same person.
Data enrichment is the process of improving the accuracy and reliability of your raw lead and customer data by adding new and supplemental information and by verifying the information against third-party sources.
Customer data can originate from a number of sources. It can be obtained directly from leads themselves—for example, by having them fill out a form in order to download a white paper or request a product demo or requesting a meeting with a salesperson. It can be obtained from data tracking software that tracks user engagement on your own properties (such as Google Analytics, Mixpanel, Appsflyer, etc.).
Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting.
Data classification is the process of analyzing structured or unstructured data and organizing it into categories based on file type, contents, and other metadata.
A taxonomy is a "subject map" to an organization’s content. A taxonomy reflects the organization’s purpose or industry, the functions and responsibilities of the persons or groups who need to access the content, and the purposes/reasons for accessing the content.