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Related Concept Videos

Dark Triad and Person Perception01:29

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Person perception is influenced by both external behaviors and the observer’s internal characteristics, including personality traits. Individuals with dark personality traits, comprising psychopathy, Machiavellianism, and narcissism — collectively known as the dark triad – exhibit manipulative and exploitative tendencies in social contexts. These traits affect how they perceive others and how they are perceived.The Role of Dark Personality Traits in Person PerceptionBlack et...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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The Dark Data Quandary.

Daniel J Grimm1

  • 1Georgetown University Law Center.

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Summary
This summary is machine-generated.

Organizations store vast amounts of "dark data" they cannot analyze, creating risks. Understanding this unmanaged data is crucial for legal and business decision-making to avoid hidden liabilities and flawed evidence.

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Area of Science:

  • Computer Science
  • Information Science
  • Data Governance

Background:

  • The digital universe is expanding rapidly, outpacing Big Data analytics capabilities.
  • Decreasing costs of cloud storage enable mass data retention, creating a gap between stored and analyzable data.
  • Organizations accumulate vast amounts of

Purpose of the Study:

  • To highlight the growing problem of "dark data"—data that is stored but not analyzed or understood.
  • To examine the risks associated with dark data for organizations and the judicial system.
  • To advocate for increased awareness and management of dark data.

Main Methods:

  • Conceptual analysis of Big Data trends and challenges.
  • Examination of the implications of unmanaged data for organizational risk and legal proceedings.
  • Review of current data governance and privacy landscapes.

Main Results:

  • Dark data constitutes the majority of the digital universe.
  • Organizations face invisible risks due to unknown data content, impacting compliance with privacy and governance laws.
  • Dark data can obscure critical information in legal evidence and introduce bias.

Conclusions:

  • Effective management and understanding of dark data are essential for mitigating organizational risks.
  • Awareness of dark data is crucial for ensuring fair and accurate outcomes in legal settings.
  • Addressing the dark data challenge is vital to realizing the full potential of Big Data analytics.