Data Breaches

A data breach, also known as a data leak or data spill, is an event that includes the illegal inspection, access or retrievial of data by a person, an application or otherwise a service. It is a form of security breach that is intended to steal or broadcast the data to an unsafe or illicit site.

Data Capture

Data capture, or electronic data capture, is the process of extracting information from a document and converting it into data readable by a computer.

Data Enrichment

Data enrichment is defined as the merging of third-party data from an external authoritative source with an existing database of first-party customer data. Brands do this to enhance the data they already possess so they can make more informed decisions with a larger pool of higher quality data.

Data Mining

Data mining is the process of investigating concealed configurations of data rendering at different viewpoints for classifying valuable data, which is gathered and collected in standard zones, such as data warehouses, for effective investigation, data mining systems, assisting the corporate decision-making process plus further data needs in order to finally reduce costs and raise revenue.

Data Points

A data point is defined as a distinct component of data. In a broad common sense, every single detail is considered as a data point. In an arithmetical or systematic framework, a data point is typically imitative in terms of size or investigation and can also be exemplified in an arithmetic and/or detailed manner.

Data Protection Act

The Data Protection Act (DPA) is a United Kingdom law passed in 1988. It was established to manage how individual or consumer data could be used by any organizations or government organizations. It protects the public and also provide some instructions on how to use the data people's data.

Data Provider

The term data provider is used to describe the process of retrieving data from relational data sources in non-real time applications. The data provider manages the data at each stage by mapping the logical column definitions in the application view to physical table columns in the customer database.

Data Science

What is Data Science? 

  • Data Science is a multidisciplinary field that combines techniques from various domains, including statistics, computer science, machine learning, and domain-specific knowledge, to extract valuable insights and knowledge from data. It involves collecting, cleaning, analyzing, and interpreting data to make data-driven decisions, solve complex problems, and discover patterns, trends, and correlations. It also encompasses the development of predictive models and algorithms to support decision-making and automation.

How Data Science is Different from Computer Science:

  • Data Science and Computer Science are related fields but serve different purposes:
    • Data Science focuses on extracting valuable insights and knowledge from data, solving real-world problems through data analysis, and employing techniques like statistical analysis, machine learning, data visualization, and domain expertise. It finds applications in various domains, such as finance, healthcare, and marketing, where data-driven decision-making is crucial.In contrast, Computer Science is a broader field primarily concerned with algorithms, data structures, software development, and computer systems. Its goal is to design and construct software solutions and computing systems, covering areas like programming, algorithm design, computer architecture, and software engineering.

The Benefits of Data Science

  1. In a survey of 1,200 professionals conducted by the ACFE, 85% of respondents agreed that data analysis was essential for detecting and preventing fraud, and 80% agreed that data analysis was essential for investigating and analyzing fraud incidents
  2. Organizations that use advanced analytics for fraud detection reported a reduction in losses, and 82% reported a decrease in the time it takes to detect fraud
  3. Organizations that use proactive data monitoring can reduce their fraud losses by an average of 54% and detect scams in half the time

 

Exploring Tools and Technologies for Data Science Solutions 

Data Science solutions encompass various tools, techniques, and methodologies for working with data, including:

    • Data Collection: Gathering data from various sources, such as databases, APIs, web scraping, and sensors.
    • Data Cleaning and Preprocessing: Handling missing data, outliers, and formatting issues to prepare data for analysis.
    • Machine Learning and Statistical Modeling: Using algorithms and libraries to build predictive models.
    • Data Visualization: Creating visual representations of data for effective communication.
    • Deployment and Automation: Methods for deploying machine learning models into production systems and automating data pipelines.

Fraud.net is a cutting-edge fraud prevention platform that harnesses the power of data science and machine learning to combat fraudulent activities across diverse industries. To witness the effectiveness of Fraud.net’s data-driven solutions in action, you can explore more about this on our official website or get in touch with our sales team to arrange a demo. 

Data Security Standard

The Payment Card Industry Data Security Standard (PCI DSS) is a widely recognized set of rules and policies proposed to improve the security of cash, debit and credit card transactions and also to protect credit cardholders, to prevent the mismanagement of their private data. The PCI DSS was formed in association with four major credit-card companies: Visa, MasterCard, Discover and American Express in 2004.

Data Set

Data set is an assortment of data. Usually a data set match up to the subjects of a distinct database table, or otherwise a particular arithmetical data matrix, where each single column of the table indicates a specific variable, and each row match up to a set of affiliates of the query data set.