Big Data and Data Science
Objectives: Since the incorporation of Big Data within data warehousing and business intelligence environments, Data Science techniques are being used to provide a 'windshield' view of the future of the organisation. Predictive, real-time or model-based, capabilities using different types of...
Data Quality
Objectives: An effective data management involves a series of complex and interrelated processes that enable an organisation to use its data to achieve strategic objectives. Data management includes the ability to design data for applications, store and access it securely,...
Metadata Management
Objectives: Metadata include information about technical and business processes, rules and constraints on the data and on logical and physical data structures. They describe the data itself, the concepts that the data represents and the connections betweeen the data and...
Data Warehousing and Business Intelligence
Objectives: The concept of Data Warehouse dates back to the 1980s when technology enabled organisations to integrate data from a variety of sources into a common data model. The integrated data could provide information on operating processes and create new...
Reference und Master Data
Objectives: In any organisation, certain data are needed in all business areas, processes and systems. If this data is shared and all business units can access the same costumer lists, geographic location codes, business units lists, delivery options, parts lists,...
Document and Content Management
Objectives: Document and Content Management entails controlling the capture, storage, access and use of data and information stored outside relational databases. Its task is to preserve the integrity of documents and other unstructured or semi-structured information, and at the same...
Data Integration and Interoperability
Obejectives: Data Integration & Interoperability (DII) describes processes related to the movement and consolidation of data within and between data stores, applications and organisations. Integration consolidates data into coherent, physical or virtual forms; Interoperability of data is the ability of...
Data Security
Objectives: Data Security encompasses planning, developmnet and implementation of data security policies and procedures. Specifications of data security differ according to the industry and nation, but in any case the aim of data security practices is unchanged: protecting data assets...
Data Storage and Operations
Objectives: Data Storage and Operations involve the design, implementation and support of stored data to maximaze its value troughout its life cycle, from the creation/acquisition to the removal. Data Storage and Operations include two secondary activities: database support and database's...
Data Modeling and Design
Objectives: Data Modeling is the process of discovering, analisying and studying data requirements and the subsequent fundamental component of data management. The modeling process requires organisations to discover and document how data combine. Data models describe data assets and enable...
Data Architecture
Objectives: The term Architecture refers to the art and science of construction. More generally, architecture refers to an organised arrangement of components meant for optimising the function, performance, feasibility, costs and aesthetics of an overall structure or system. Data Architecture...
Data Governance
Objectives: Data Governance (DG) is defined as the exercise of authority and control (planning, monitoring and implementation) in the management of data assets. All organisations make decisions about data regardless of whether they have a formal Data Governance function. Those...