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Our Data glossary Management

This glossary aims to contribute to the clarification of key notions and concepts commonly used in Data Management. It is based on common and documented sources designed to harmonize Data Management knowledge and practices. You are convinced of the standardization need in the field and would like to contribute ? Please, send us a note !

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Data Enablement

Data Enablement is not (despite the temptation) a substitute for Data Governance and Data Management which remain essential components of the process. It’s an outcome-focused approach with a seemingly simple objective : ensure that the right data is provided to the right resource at the right time. And this, in conformance with regulatory standards and the rules of Ethics. Data Enablement is the action of providing to people and resources in organizations the necessary means to use data in an efficient, informed and responsible way at an enterprise-wide level.

From people’s perspective, Data Enablement consists in creating and reinforcing Data skills and know-how within the teams through training and other practical support means. It is also about creating a Data culture to raise awareness of Data stakes and associated practices, to facilitate collaboration between teams and to ensure the effective operationalisation of Data strategies. Successfully aligning teams at different levels of the organization is a critical success factor and helps prevent that "culture eats strategy for breakfast" as Peter Drucker put it.

In terms of technology, the approach places particular emphasis on the adoption of the tools by the teams. Various studies show that the success of technology projects is closely linked to the level of robustness and user-friendliness of the tools.

Finally, in terms of methodology, Data Enablement is not a project but a discipline that allows for a profound and lasting transformation of the company's data management practices. Unlike a project that has a beginning and an end, it must be considered as a continuous activity that must benefit from the ongoing engagement of individual contributors as well as executive-level sponsorship.

Data Enablement plays an even more important role in the context of the digital enterprise and the increasing use of data by technical resources such as Artificial Intelligence (AI). A few examples include in that regard the automation of business critical operations, predictions and automated decision making that may be sensitive in several aspects.

More on this : Dataversity, IDC, IBM

Data Governance

Data Governance is defined as the exercice of authority and control (planing, monitoring and enforcement) over the management of data assets.

The Data Governance function guides all other data management functions with the following key focus areas on Data :
- Strategy,
- Policy,
- Stewardship & Ownership,
- Culture Change,
- Principles & Ethics,
- Data Valuation,
- Data Maturity Assessment,
- Data Classification.

Sources : DAMA DMBok 2

Data Intelligence

IDC defines Data intelligence as intelligence about Data (not - like Analytics - from Data).

Data intelligence leverages business, technical, relational and operational metadata to provide transparency of data profiles, classification, quality, location, lineage and context; Enabling people, processes and technology with trustworthy and reliable data.

Sources : IDC.

Data Management

Data Management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycles.

Data management activities are wide-ranging. They include everything from the ability to make consistent decisions about how to get strategic value from data to the technical deployment and performance of databases.

The different disciplines of Data Management can be grouped as follows :
- Data Governance,
- Foundational activities :
- Data Protection (Privacy, Security, Risk Management),
- Metadata Management,
- Data Quality Management;
- Lifecycle management activities,
- Plan & Design : Architecture, Modeling, Design,
- Enable & Maintain : Big Data Storage, Data Warehousing, Master Data Management, Data Storage & Operations, Reference Data Management, Data Integration & Interoperability;
- Use & Enhance : Data Science, Data Visualization, Data Monetization, Predictive Analytics, Master Data Usage, Business Intelligence, Document & Content Management

Sources : DAMA DMBok 2