What does ADM mean in UNCLASSIFIED

Analytic Data Models are framework models that provide support to companies in making data-driven decisions. They help define the structure and content of data elements across an organization, enabling users to categorize, analyze and understand data quickly and efficiently. ADMs provide a centralized repository of information with detailed descriptions of each data element and its related business context. This enables organizations to better organize data for reporting, analysis, forecasting, decision-making and collaboration.

An Analytic Data Model helps organizations better manage their data assets by providing visibility into existing data sources, their relevance within the organization's business processes, and how those sources may be used or leveraged. Additionally, it facilitates easier access to the right information for business users while maintaining security protocols associated with sensitive or proprietary data. By providing a single source of truth and curating organizational knowledge about data elements in one place, it can significantly reduce time spent on developing new reports or fact-checking old ones.

An Analytic Data Model helps ensure that decision makers have access to accurate and relevant information when making decisions. It allows users to filter out irrelevant or unnecessary information by providing clear definitions for each data element so that only the relevant pieces are included in any analysis. Additionally, because all necessary information is organized into one central repository, users can quickly access insights from multiple areas within an organization which helps them make more informed decisions without having to manually collate disparate sources.

Regulatory requirements often mandate organizations maintain a record of certain pieces of information as evidence in case there is a need for audit or investigation at any point in time. An Analytics Data Model provides a secure environment where this documentation can be stored along with associated metadata such as date created, user who created it etc., making it easier for companies to respond swiftly when required by law enforcement agencies or auditing authorities. In addition to aiding compliance efforts, it also improves risk management within an organization as there is improved transparency into what kind of information is being stored where.

The most common use cases for Analytical Data Models include providing guidance on report design; automating responses to inquiries like what type of customer performed a certain transaction; retrieving historical trends based on similar criteria; predicting future trends based on past performance; segmenting consumers according to predefined criteria like age demographics; and understanding customer preferences by tracking their online behavior using real-time analytics tools like machine learning algorithms.

Business analysts, statisticians and other analytics professionals are primarily responsible for developing and maintaining an Analytical Data Model in accordance with the specific needs of their organization’s stakeholders – whether they are C-level executives or competitive intelligence experts – so they can identify solutions more effectively. Additionally, IT teams use Analytics Data Models during software development projects to ensure developers have easy access to all relevant data they need throughout the application life cycle.