Enterprise Data Management

Fortira holds capabilities with Enterprise Data Management (EDM), Enterprise Information Management (EIM), Enterprise Data Warehouse (EDW), Enterprise Data Modeling (EDM), Enterprise Data SecurityEnterprise Data Strategy, Data Governance and Data Stewardship, Data Science, Machine Learning, Artificial Intelligence (AI) and all the relevant Business Processes, Disciplines and Practices used to manage the Information created from an Organization’s Data as an Enterprise Asset  and supports process of delivering, monitoring and managing security across all data objects and repositories within an organization.

Enterprise Data Management (EDM)

The ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. EDM is focused on the creation of accurate, consistent and transparent content. EDM emphasizes data precision, granularity and meaning and is concerned with how the content is integrated into business applications as well as how it is passed along from one business process to another.

Enterprise Information Management (EIM)

The set of business processes, disciplines and practices used to manage the information created from an organization’s data as an enterprise asset. EIM functions ensure that high quality information is available, protected, controlled and effectively leveraged to meet the knowledge needs of all enterprise stakeholders, in support of the enterprise mission

In computing, a data warehouse (DW or DWH), also known as an Enterprise Data Warehouse (EDW), is a system used for reporting and data analysis. DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data and are used for creating analytical reports for knowledge workers throughout the enterprise.

Enterprise Data Modeling

Enterprise Data Modeling (EDM) is the practice of creating a graphical model of the data used by an enterprise or company. Typical outputs of this activity include and Enterprise Data Model consisting of Entity Relationship Diagrams (ERD), XML Schemas (XSD), and an enterprise wide data dictionary. Producing such a model allows for a business to get a ‘helicopter’ view of their enterprise.

Data governance

Data governance is a control that ensures that the data entry by an operations team member or by an automated process meets precise standards, such as a business rule, a data definition and data integrity constraints in the data model.

Enterprise Data Strategy is the comprehensive vision and actionable foundation for an organization’s ability to harness data-related or data-dependent capability.

Enterprise Data Security

The process of delivering, monitoring and managing security across all data objects and repositories within an organization.

The terms Data Governance and Data Stewardship

are sometimes used interchangeably, but there is actually a difference. Data Governance brings together cross-functional teams to make interdependent rules or to resolve issues or to provide services to data stakeholders. These cross-functional teams – Data Stewards and/or Data Governors – generally come from the Business side of operations. They set policy that IT and Data groups will follow as they establish their architectures, implement their own best practices, and address requirements. Data Governance can be considered the overall process of making this work. 

Data Stewardship is concerned with taking care of data assets that do not belong to the stewards themselves. Data Stewards represent the concerns of others. Some may represent the needs of the entire organization. Others may be tasked with representing a smaller constituency: a business unit, department, or even a set of data themselves.An accountability-focused definition of Data Stewardship is “the set of activities that ensure data-related work is performed according to policies and practices as established through governance.”

Data science

is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics, similar to Knowledge Discovery in Databases (KDD).

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”.

Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior.

Any entity that is comprised of data. For example, a database is a data asset that is comprised of data records. A data asset may be a system or application output file, database, document, or Web page. A data asset also includes a service that may be provided to access data from an application.