Omni
Data Management
Data management refers to the processes, policies, and practices involved in acquiring, organizing, storing, securing, maintaining, and utilizing data throughout its lifecycle. This includes activities such as data collection, storage, retrieval, processing, analysis, backup, recovery, and disposal. Effective data management aims to ensure data accuracy, consistency, integrity, security, and accessibility while supporting organizational objectives such as decision-making, compliance with regulations, and business continuity. It is usually an indispensable basis for analysis .
Close terminology
Data Governance – Refers to the framework of policies, processes, and controls implemented to ensure that data assets are managed effectively, securely, and in compliance with regulatory requirements and organizational standards.
Data Quality Management – Involves activities aimed at maintaining and improving the quality of data, ensuring that it is accurate, complete, consistent, and relevant for its intended use.
Data Security – Encompasses measures and protocols implemented to protect data from unauthorized access, disclosure, alteration, or destruction, thereby safeguarding sensitive information and maintaining confidentiality, integrity, and availability.
Data Integration – Involves the process of combining data from different sources or formats to create a unified and coherent view, enabling seamless data access, analysis, and decision-making across an organization.
Master Data Management (MDM) – Focuses on creating and managing a single, accurate, and authoritative source of key organizational data, such as customer, product, or supplier information, to ensure consistency and reliability across systems and applications.
Data Warehousing – Involves the creation and management of centralized repositories or databases specifically designed for storing and analyzing large volumes of structured and/or unstructured data, typically to support reporting, analytics, and business intelligence initiatives.
Data Privacy – Addresses the protection of individuals' personal information and the legal and ethical considerations surrounding its collection, use, and disclosure, in compliance with privacy regulations and standards such as EU’s GDPR (General Data Protection Regulation).
Actuality
Data can be collected from various sources, often in its original, unprocessed form. Raw data serves as the foundation for analysis, containing the actual bits of information, observations or measurements.
Granularity
Amount of detail that is applied or included in description of data.
Validation
Well controlled quality of the data input helps in avoiding, or at least reduce, many problems at later stages of processing and maintaining data.
Preprocessing
Data preprocessing includes steps like: handling missing values , dealing with outliers , and standardizing formats . Clean and free of flaws data is essential prerequisite for starting data analysis.
Data Definitions
Features or variables of items in the dataset, including their units of measurement, types and metadata, relationships, and significance. Understanding data features, including their relevance and availability, which is supported by domain knowledge , crucial for accurate interpretation and contextual analysis. In technical sense, data structures can be re-defined by applying normalization (reducing redundancy) or denormalization (increasing performance by introducing redundancy).
Information Architecture
Format, granulation, grouping and relations between data - weather they are structured, semi-structured, or unstructured - depend mainly on needs of the owner (organisation) and use cases applied. The structure will influence the choice of appropriate analysis methods, tools, as well as its time and costs.
Recovery
Retrieving or restoring data that has been lost, accidentally deleted, corrupted, or made inaccessible due to various factors such as hardware failures, software issues, human errors, malware attacks, or natural disasters. Data recovery typically involves using specialized software tools, techniques, or services to recover lost or damaged data from storage devices such as hard drives, solid-state drives (SSDs), USB drives, memory cards, or backup systems. The goal of data recovery is to recover as much of the lost or inaccessible data as possible, thereby minimizing the impact of data loss and restoring normal operations.
Protection
Data by its nature require protection – starting from its authenticity, through its integrity, to appropriate usage. Additionally, particular types of data can be protected by law, as confidential or personal data.
INs and OUTs (section under development)
coming in
going out
Controls to review
regulation, documentation, reports