Data mart – Data marts provide a long-range view of data within a given subject area, such as sales or finance. Data marts provide the same benefits of a data warehouse, but with limited scope and size
Data masking – Data masking does not just replace sensitive data with blanks. It creates characteristically intact, but inauthentic, replicas of personally identifiable data or other highly sensitive data in order to uphold the complexity and unique characteristics of data. In this way, tests performed on properly masked data will yield the same results as they would on the authentic dataset.
Data mining – Unlike data analytics, in which discovery goals are often not known or well defined at the outset, data mining efforts are usually driven by a specific absence of information that can’t be satisfied through standard data queries or reports. Data mining yields information from which predictive models can be derived and then tested, leading to a greater understanding of the marketplace.
Data obfuscation – Data obfuscation is often used interchangeably with data masking. Data obfuscation scrambles data to anonymize it
Data Preparation – The goal of data preparation is the same as other data hygiene processes: to ensure that data is consistent and of high quality. Inconsistent, low quality data can contribute to incorrect or misleading business intelligence. It can create errors and make analytics and data mining slow and unreliable. By preparing data for analysis up front, organizations can be sure they are maximizing the intelligence potential of that information.
Data Quality – Quality data is useful data. To be of high quality, data must be consistent and unambiguous. Data quality issues are often the result of database merges or systems/cloud integration processes in which data fields that should be compatible are not due to schema or format inconsistencies. Data that is not high quality can undergo data cleansing to raise its quality.