Database

An electronic database is a collection of data used for specific purposes, stored and organized in ways that enhance rapid search and retrieval by computer. If it is a database-management system, information is retrievable in response to questions and keywords. A database will respond to metadata that is either structural or descriptive, describing other data or information about data that makes searching a database quicker. A database is like a file broken down into records containing information about the subject matter collected in the database. Information in the database is sorted by keywords; data are grouped and arranged and selected from fields to produce statistics, manage data, and create reports. Governments, businesses, banks and financial investment firms, hospitals, and charities are among the plethora of organizations collecting data and building databases to better manage their security, sales, customer service, outreach, and money collection from target markets.

Background

A database is organized to provide information about people, places, uses, and actions. A database is generally organized into one of five types: flat, hierarchical, network, relational, and object-oriented. A flat database is the simplest format for easy and rapid access to information. A hierarchical database has data in larger categories with subcategories for detailed data retrieval with links among records at various levels. Network databases have multiple links and indicators to various records. In cases where links do not yield the data, relations are probed among records; thus, the relational database. Their development was concomitant to technological advancements in processors, computer memory, storage, and networking. Size, type, report capabilities, and overall performance were only limited by the technology because database architecture had little trouble keeping up.

Big data collection and user companies like Facebook, Groupon, Google, and others spirited the rapid growth of the database. Facebook used a relational organization but moved on to build its own graph database in order to more effectively and efficiently store, expand, and deliver on the relationships among people, places, and things, i.e., social networking. Changing database architectures made it possible for Facebook to serve one billion users around the world and Twitter to store and deliver 250 million tweets every day. Universities maintain among the largest databases, archiving journals, books, texts, and related materials valuable and available to researchers worldwide. Crime fighting and homeland security are among the fastest-growing databases used by the government and subcontractors.

A fascinating case study reported in 1993 revealed the early use of database information built by a business for targeted marketing under a US government patent. It is a method and system for target marketing infrequent shoppers from a database built from bank check codes collected from a store’s customer accounts relating to customer shopping habits. A check reader builds a database from the circuitry and terminal data collection on all transactions. It developed prior credit verification systems for check verifications. The database not only became a marketing networking system based on prior shopping history data collection systems but also enabled stores to better handle risk management to check verification.

Databases Today

In the twenty-first century, individual government agencies and other institutions build databases according to their needs. Hospitals and insurance companies build databases from electronic medical records for billing, epidemiological studies, management efficiency, savings on labor and supplies, target marketing, and other needs. Scientists use database information to tell them about all kinds of environmental changes and alterations to plant life, the universe, and the earth. Many of these are free on the internet for law reviews, medicine, games, interactive social media, and even for evaluating marriage prospects.

Security of the database is the overriding concern of today’s IT specialists. Hacking has cost millions of people their privacy; companies millions of dollars in upgrading their technology fast and furiously; and banks, governments, and insurance companies billions of dollars in illegal database break-ins and stolen cash. The Bank of Bangladesh experienced a cyber break-in in 2016, losing $81 million. In April 2016, the press reported that an unprecedented database leak—over eleven million files—were stolen from the world’s fourth-biggest law firm specializing in offshore (tax evasion) accounts. These became known in the media as the Panama Papers, and their revelation forced the prime minister of Iceland to resign and sent political shockwaves through governments elsewhere—all from a hack or leak in the firm’s database.

Security remained a major concern for database administrators in subsequent years, as the 2020s continued to see hacking incidents, breaches, and other security failures of record scale. For example, in 2023, the Indian Council of Medical Research, one of the oldest and most prestigious biomedical research bodies in the world, was hacked; this hack resulted in the compromise of more than 815 million records, including many with confidential personal information, making it one of the worst data breaches in history up to that point.

Database development and management have entered a third phase of life, according to Michael Stonebraker, MIT’s leading database researcher. Above all, Stonebraker believes the future is with data scientists developing predictive models for businesses. They will replace business analysts who can only tell what was done in the past—what sold, when, where, and what payments were made. Databases are their playgrounds, and if they understand statistics and data mining, their employers will prosper.

Graph databases, another business model, are expected by some to improve data volume at a faster pace among data. Graph databases move at the speed of business, adding to a database’s existing structure. And graph databases are agile, evolving with changing business requirements.

By the mid-2020s, artificial intelligence (AI) had become central to database optimization, and major platforms like JPMorgan Chase used AI for fraud prevention in transactional databases, among other uses. Additionally, cloud-native databases, designed to run in cloud environments across multiple servers and regions, had become standard. Emerging technologies included quantum databases, which promised exponentially faster speeds in processing complex datasets, especially in the finance and pharmaceutical fields.

Bibliography

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