Departement Of Artificial Intelligence (AI)
Subject : DATABASE
Data :
Definition :
“ Data is a collection of raw facts and figures . ”
Data can be defined as a collection of facts, measurements, or observations that can be used for reference, analysis, or calculation. In its raw form, data may appear as numbers, characters, or any other data type that can be processed by a computer. It is often raw and unprocessed information that requires analysis to give it meaning.
Data serves as the foundation for information and knowledge; when data is processed, organized, structured, or presented in a given context so as to make it useful, it is referred to as information. This transformation can involve analysis, interpretation, or conversion into a more readable format (such as charts or reports).
Data refers to qualitative or quantitative attributes of a variable or set of variables. Data is typically the result of measurements and can be visualized using graphs or images. Data is often collected through observations or measurements, and can be analyzed using statistical methods. In the context of computing and data analysis, data can be structured or unstructured.
Types of data :
Structured Data
Structured data is highly organized and easily understandable by machine language. It resides in fixed fields within a record or file, typically in databases, spreadsheets, or other forms of data tables.
· Examples: This includes data in relational databases, spreadsheets where specific information is stored in predetermined columns and rows, and anything else that can be neatly fitted into fields in a database table like IDs, dates, or phone numbers.
· Storage: Commonly stored in SQL databases, it's easy to enter, query, and analyze using standard database tools and simple queries.
· Analysis: Because of its organization, structured data is the easiest to work with using standard data analysis tools, including statistical software and machine learning algorithms.
Unstructured Data
Unstructured data doesn't have a predefined model or schema. It is typically text-heavy, but may contain data such as dates, numbers, and facts as well.
· Examples: This includes books, journals, multimedia files (like audio and video), social media posts, satellite imagery, and surveillance imagery.
· Storage: It is often stored in non-relational databases like NoSQL databases, data lakes, or object storage.
· Analysis: Analyzing unstructured data is complex and may require advanced processing techniques like natural language processing (NLP), image recognition, and complex algorithms.
Semi-structured Data
Semi-structured data does not reside in a relational database but has some organizational properties that make it easier to analyze, with tags or other markers to separate semantic elements and enforce hierarchies of records and fields.
· Examples: Email is a classic example of semi-structured data, as it contains structured data (like the email address, subject, and date) but also unstructured data (the body of the email). Other examples include XML, JSON, and YAML files.
· Storage: Can be stored in NoSQL databases or specialized data stores like data lakes, which can accommodate complex structures or nested data.
· Analysis: While it may not fit neatly into a relational database, semi-structured data can still be queried using special tools and languages like XQuery for XML or SQL extensions for JSON.
Hybrid Data
Additionally, there's a concept of "Hybrid Data," which may
contain both structured and unstructured elements. This is not a formal category but rather an acknowledgment of the complexity of real-world data:
· Examples: A corporate database with a standard schema that also contains email fields, comments, or transaction descriptions. These fields may include free-form text that is not structured.
Importance of data :
Data is the lifeblood of modern decision-making. By providing objective evidence, it empowers individuals and organizations to make informed decisions, solve problems effectively, and identify new opportunities. In the business realm, data about customer preferences and market trends is critical for shaping strategies and operations. In healthcare, patient data is crucial for improving treatments and outcomes. Policymakers rely on data to craft regulations that reflect societal needs and to measure the impact of their initiatives. In science, data drives discovery, enabling researchers to validate theories and contribute to human knowledge. Furthermore, the rise of big data analytics has transformed data into a key asset for predictive insights, allowing for better planning and resource management across various sectors. The digital age's growth has only deepened the importance of data, making it an invaluable asset for driving progress and innovation in virtually every facet of society.
The importance of data in the modern world cannot be overstated. It serves as a cornerstone for decision-making across various fields, and its roles are multifaceted. Here's a closer look at the significance of data:
ü Decision-Making
ü Problem-Solving
ü Innovation
ü Performance Improvement
ü Personalization
ü Risk Management
ü Policy Making
ü Scientific Research
ü Economic Analysis
ü Health Outcomes
ü Predictive Analytics
ü Education
ü Resource Management
ü Social Changes
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