Category: Data Modeling

Technology - Privacy Data Masking Techniques

Technology – Privacy Data Masking Techniques

Data masking techniques are a way of creating an alternate version of data that cannot be easily identified or reverse engineered. This alternative version will have the same format across many databases and preserve usability. Masking techniques are generally applicable to non-production environments that do not need actual data. Data masking is a way to meet GDPR requirements and offers many organizations a competitive advantage. It also makes data useless for cyber-attackers while preserving its usability. Data Pseudonymization The European Commission, data protection authorities, and industry should support state-of-the-art data pseudonymization techniques. Specifically, the European Commission should support research and

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Technology – The Benefits of Data Tagging and Classification

Data tagging and classification help organizations understand the value of their data and determine if it is a risk. This helps them comply with GDPR, industry-specific regulations, and privacy policies. As a result, they can easily identify the level of access that users have to data, as well as the sensitivity of the data. The following are the benefits of data tagging and classification: They improve data management and discoverability, and they help modern enterprises better manage their data. Data tagging and classification are critical for information discovery and e-discovery. They help businesses reduce the cost of storing and retrieving

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Technology – Understanding Data Model Entities

Data Modeling is an established technique of comprehensively documenting an application or software system with the aid of symbols and diagrams. It is an abstract methodology of organizing the numerous data elements and thoroughly highlighting how these elements relate to each other. Representing the data requirements and elements of a database gra phically is called an Entity Relationship Diagram, or ERD. What is an Entity? Entities are one of the three essential components of ERDs and represent the tables of the database. An entity is something that depicts only one information concept. For instance, order and customer, although related, are

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Technology – Why Business Intelligence (BI) needs a Semantic Data Model

A semantic data model is a method of organizing and representing corporate data that reflects the meaning and relationships among data items. This method of organizing data helps end users access data autonomously using familiar business terms such as revenue, product, or customer via the BI (business intelligence) and other analytics tools. The use of a semantic model offers a consolidated, unified view of data across the business allowing end-users to obtain valuable insights quickly from large, complex, and diverse data sets. What is the purpose of semantic data modeling in BI and data virtualization? A semantic data model sits

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Why Use Materialized Views?

If we consider Materialized Views (MV) in their simplest form, as a point in time stored query result, then materialized views serve two primary purposes Performance Optimization and Semantics Simplification. Performance Optimization There are several ways in which materialized views can improve performance: Reduce Database Workloads: materialized views can reduce database workloads by pre-assembling frequently used queries and, thereby, eliminating the repetitive execution of joins, aggregations, and filtering. Facilitate Database Optimizers: in some databases can be partitioning and indexing which are considered by database optimizers. Also, some databases, in which more than one materialized view has been applied to a

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