Data management comprises all disciplines related to handling data as a valuable resource.
The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems." However, during this period, random access processing was not competitively fast, so those suggesting "process management" was more important than "data management" used batch processing time as their primary argument. As application software evolved into real-time, interactive usage, it became obvious that both management processes were important. If the data was not well defined, the data would be mis-used in applications. If the process wasn't well defined, it was impossible to meet user needs.
Topics in data management include:
Data governance
Data asset
Data governance
Data trustee
Data custodian or guardian
Data steward
Data subject
Data ethics
Data architecture
Data architecture
Dataflows
Data modeling and design
Database and storage management
Data maintenance
Database administration
Database management system
Business continuity planning
Hierarchical storage management
Data subsetting
Data security
Data access
Data erasure
Data privacy
Data security
Reference and master data
Data integration
Master data management
Reference data
Data Integration and inter-operability
Data movement (ETL, ELT)
Data interoperability
Documents and content
Document management system
Records management
Data warehousing and business intelligence and Analytics
Business intelligence
Data analysis and data mining
Data warehouse and data mart
Data analytics
Metadata
Metadata management
Metadata
Metadata discovery
Metadata publishing
Metadata registry
Data quality
Data discovery
Data cleansing
Data integrity
Data enrichment
Data quality assurance
Secondary data
In modern management usage, the term data is increasingly replaced by information or even knowledge in a non-technical context.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
The terms schema matching and mapping are often used interchangeably for a database process. For this article, we differentiate the two as follows: schema matching is the process of identifying that two objects are semantically related (scope of this article) while mapping refers to the transformations between the objects. For example, in the two schemas DB1.Student (Name, SSN, Level, Major, Marks) and DB2.Grad-Student (Name, ID, Major, Grades); possible matches would be: DB1.Student ≈ DB2.Grad-Student; DB1.
Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data. Data analysts typically spend the majority of their time in the process of data wrangling compared to the actual analysis of the data.
Master data management (MDM) is a technology-enabled discipline in which business and information technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets. Organisations, or groups of organisations, may establish the need for master data management when they hold more than one copy of data about a business entity. Holding more than one copy of this master data inherently means that there is an inefficiency in maintaining a "single version of the truth" across all copies.
This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat
This hands-on course teaches the tools & methods used by data scientists, from researching solutions to scaling up
prototypes to Spark clusters. It exposes the students to the entire data science pipe
The course introduces modern methods to acquire, clean, and analyze large quantities of financial data efficiently. The second part expands on how to apply these techniques and robust statistics to fi
Enterprises collect data in large volumes and leverage them to drive numerous concurrent decisions and business processes. Their teams deploy multiple applications that often operate concurrently on the same data and infrastructure but have widely differen ...
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Knowledge representation and reasoning (KRR, KR&R, KR2) is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build.
A relational database is a (most commonly digital) database based on the relational model of data, as proposed by E. F. Codd in 1970. A system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems are equipped with the option of using SQL (Structured Query Language) for querying and updating the database. The term "relational database" was first defined by E. F. Codd at IBM in 1970. Codd introduced the term in his research paper "A Relational Model of Data for Large Shared Data Banks".
Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...
As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...