Data Collection and Management
Expert-defined terms from the Professional Certificate in AI-Driven Program Evaluation course at London School of International Business. Free to read, free to share, paired with a globally recognised certification pathway.
Data Collection and Management #
Data Collection and Management
Data collection and management are crucial components of any evaluation process,… #
This glossary aims to provide a comprehensive list of terms related to data collection and management to enhance understanding in the Professional Certificate in AI-Driven Program Evaluation course.
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Data Collection
Data collection refers to the process of gathering information or data points fo… #
It involves systematically collecting relevant data to address research questions or evaluate the effectiveness of a program. Data collection methods can vary and may include surveys, interviews, observations, and document reviews.
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Data Management
Data management involves the organization, storage, and maintenance of collected… #
Effective data management practices are essential for maintaining the integrity of the data throughout the evaluation process.
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Data Sources
Data sources are the origins of data used in an evaluation #
These sources can include primary sources such as surveys, interviews, and observations, as well as secondary sources such as existing databases, reports, and documents. Understanding and selecting appropriate data sources is critical for conducting a comprehensive evaluation.
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Data Quality
Data quality refers to the accuracy, reliability, and completeness of collected… #
High-quality data is essential for producing valid and reliable evaluation results. Data quality can be assessed through various methods, including data validation and data cleaning.
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Data Validation
Data validation is the process of ensuring that collected data is accurate, cons… #
This process involves checking for errors, inconsistencies, and missing values in the data set. Data validation techniques can include range checks, consistency checks, and validation rules.
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Data Governance
Data governance refers to the overall management of data within an organization… #
It involves establishing policies, procedures, and responsibilities for ensuring the quality, security, and privacy of data. Effective data governance helps maintain data integrity and compliance with regulations.
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Data Security
Data security involves protecting data from unauthorized access, disclosure, alt… #
Ensuring data security is essential for maintaining the confidentiality and integrity of sensitive information. Data security measures can include encryption, access controls, and regular security audits.
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Data Storage
Data storage refers to the physical or digital location where data is stored for… #
Proper data storage practices are essential for ensuring data accessibility, reliability, and security. Common data storage methods include databases, cloud storage, and external hard drives.
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Primary Data
Primary data refers to data collected directly from original sources through met… #
Primary data is specific to the evaluation at hand and is often used to address unique research questions or objectives.
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Secondary Data
Secondary data refers to data that has been collected by someone else for a diff… #
Secondary data sources can include existing databases, reports, and documents. Integrating secondary data with primary data can provide a more comprehensive evaluation.
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Data Triangulation
Data triangulation is the process of using multiple data sources, methods, or re… #
By triangulating data from different sources, researchers can reduce bias and increase the reliability of their conclusions.
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Data Integrity
Data integrity refers to the accuracy and consistency of data over its entire li… #
Maintaining data integrity involves ensuring that data is not altered or corrupted during collection, storage, or analysis. Data integrity checks are essential for detecting and preventing data errors.
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Data Accuracy
Data accuracy refers to the correctness and precision of data values in relation… #
Accurate data is free from errors, inconsistencies, or biases that could affect its reliability. Ensuring data accuracy is crucial for generating valid evaluation results.
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Data Completeness
Data completeness refers to the extent to which all required data points are pre… #
Incomplete data can lead to biased or unreliable evaluation results. Data completeness checks are essential for identifying missing values and ensuring the integrity of the data.
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Data Cleaning
Data cleaning is the process of identifying and correcting errors, inconsistenci… #
Cleaning data involves standardizing formats, removing duplicates, and imputing missing values to ensure data quality. Data cleaning is a crucial step before data analysis.
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Data Verification
Data verification is the process of confirming the accuracy and reliability of c… #
Verification methods can include double-entry verification, third-party validation, and cross-referencing data with external sources. Data verification helps ensure data quality.
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Data Auditing
Data auditing involves reviewing and evaluating data management processes to ens… #
Auditing data practices helps identify potential risks, errors, or inefficiencies in data collection and management. Data audits are essential for maintaining data integrity.
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Data Stewardship
Data stewardship refers to the responsibility and accountability for managing an… #
Data stewards are individuals or teams tasked with ensuring the quality, security, and privacy of data assets. Effective data stewardship practices are essential for data governance.
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Data Policies
Data policies are formal guidelines or rules that govern the collection, storage… #
Data policies outline best practices, procedures, and responsibilities for maintaining data integrity, security, and compliance. Adhering to data policies is essential for effective data management.
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Data Standards
Data standards are established norms, formats, and protocols for collecting, sto… #
Adhering to data standards ensures consistency, interoperability, and quality across different data sources and systems. Common data standards include data formats, coding schemes, and metadata definitions.
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Data Privacy
Data privacy refers to the protection of individuals' personal information and t… #
Ensuring data privacy involves implementing policies, procedures, and technologies to safeguard sensitive data from unauthorized access or disclosure.
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Data Confidentiality
Data confidentiality is the assurance that sensitive or proprietary data is kept… #
Maintaining data confidentiality helps prevent unauthorized disclosure, misuse, or theft of confidential information. Data confidentiality is critical for protecting privacy rights.
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Data Breaches
Data breaches occur when sensitive or confidential data is accessed, disclosed,… #
Data breaches can result in financial loss, reputational damage, and legal consequences for organizations. Preventing data breaches requires robust data security measures and incident response protocols.
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Data Backup
Data backup is the process of making duplicate copies of data to protect against… #
Backup copies are stored in separate locations or on different media to ensure data can be restored in the event of hardware failure, human error, or cyberattacks. Data backup is essential for data recovery.
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Data Retention
Data retention refers to the policies and practices for storing and preserving d… #
Organizations establish data retention schedules based on legal requirements, business needs, and data usage. Proper data retention ensures compliance with regulations and efficient data management.
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Data Archiving
Data archiving is the process of moving inactive or historical data to long #
term storage for preservation and future access. Archiving data helps reduce storage costs, improve performance, and comply with data retention requirements. Data archiving strategies include data compression, encryption, and indexing.
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Mixed Methods
Mixed methods refer to research designs that combine quantitative and qualitativ… #
By integrating multiple methods, researchers can gain a more comprehensive understanding of complex phenomena and enhance the validity of their findings. Mixed methods are often used in program evaluation.
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Data Integration
Data integration involves combining data from different sources or formats to cr… #
Integrated data sets provide a holistic perspective on complex relationships and patterns that may not be apparent when analyzing data in isolation. Data integration is essential for comprehensive evaluations.
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Data Compliance
Data compliance refers to the adherence to legal regulations, industry standards… #
Ensuring data compliance helps protect individuals' privacy, maintain data security, and avoid potential penalties or liabilities. Data compliance is essential for ethical data management.
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Cybersecurity
Cybersecurity is the practice of protecting computer systems, networks, and data… #
Cybersecurity measures include firewalls, encryption, antivirus software, and security protocols to safeguard sensitive information and maintain the integrity of digital assets.
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Data Protection
Data protection involves safeguarding data assets from loss, theft, or unauthori… #
Data protection strategies include encryption, access controls, data backup, and employee training to mitigate risks and ensure data confidentiality and integrity.
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Disaster Recovery
Disaster recovery is the process of restoring data and IT systems after a disrup… #
Disaster recovery plans include backup strategies, data recovery procedures, and contingency measures to minimize downtime and data loss in emergencies.
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Data Disposal
Data disposal refers to the secure and responsible removal of data that is no lo… #
Proper data disposal practices prevent unauthorized access, data breaches, and compliance violations. Data disposal methods include data shredding, degaussing, and secure erasure.
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Business Continuity
Business continuity is the ability of an organization to maintain essential oper… #
Business continuity planning involves risk assessments, contingency plans, and recovery strategies to ensure the resilience of business processes and data systems in emergencies.
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Risk Management
Risk management is the process of identifying, assessing, and mitigating risks t… #
Effective risk management involves developing strategies, controls, and monitoring mechanisms to minimize potential threats and capitalize on opportunities.
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Compliance
Compliance refers to adhering to legal regulations, industry standards, and orga… #
Compliance requirements vary by industry and jurisdiction and may include data protection laws, financial regulations, and quality standards. Compliance monitoring is essential for risk management.
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Information Security
Information security is the practice of protecting data assets, systems, and net… #
Information security measures include access controls, encryption, security policies, and awareness training to mitigate risks and maintain the confidentiality and integrity of information.
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Evaluation Design
Evaluation design refers to the overall plan or framework for conducting an eval… #
A well-designed evaluation ensures that the data collected is relevant, reliable, and valid for answering the evaluation questions and objectives.
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Evaluation Reporting
Evaluation reporting involves communicating evaluation findings, conclusions, an… #
Evaluation reports may include data visualizations, narratives, and summaries to convey the key insights and implications of the evaluation results. Effective reporting is essential for informing decision-making.
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Stakeholder Engagement
Stakeholder engagement is the process of involving individuals, groups, or organ… #
Engaging stakeholders through consultations, feedback sessions, and collaboration helps ensure that evaluation findings are relevant, credible, and actionable. Stakeholder engagement is essential for promoting transparency and accountability.
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Communication
Communication is the exchange of information, ideas, and feedback between indivi… #
Effective communication in evaluation involves clear, timely, and targeted interactions with stakeholders to ensure understanding, engagement, and collaboration throughout the evaluation process.
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Data Visualization
Data visualization is the presentation of data in visual formats, such as charts… #
Data visualization enhances understanding, interpretation, and communication of complex data sets, making it easier for stakeholders to grasp key insights and make informed decisions.
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Reporting Templates
Reporting templates are pre #
designed formats or structures for organizing and presenting evaluation findings, conclusions, and recommendations. Using reporting templates helps standardize reporting processes, ensure consistency, and facilitate the creation of professional and visually appealing reports. Reporting templates can include sections for data analysis, key findings, and action plans.
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Action Plans
Action plans are detailed strategies, tasks, and timelines for implementing reco… #
Action plans outline specific actions, responsibilities, and resources needed to achieve desired outcomes and improve program performance. Monitoring and evaluating action plans are essential for tracking progress and ensuring accountability.
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Implementation
Implementation refers to the process of putting into action or carrying out plan… #
Effective implementation involves coordinating activities, engaging stakeholders, and monitoring progress to ensure that intended outcomes are achieved. Monitoring and evaluation are essential for assessing implementation success.
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Monitoring and Evaluation
Monitoring and evaluation are systematic processes for tracking progress, assess… #
Monitoring involves ongoing data collection and analysis to gauge implementation fidelity and progress. Evaluation involves assessing the effectiveness, impact, and sustainability of programs to inform decision-making and continuous improvement.
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Continuous Improvement
Continuous improvement is an ongoing process of enhancing programs, processes, o… #
Continuous improvement involves identifying areas for growth, setting goals, and implementing changes based on data-driven insights and feedback. Continuous improvement fosters innovation and excellence.
48. Data #
driven Decision-making
Data #
driven decision-making is the practice of using data analysis, evidence, and insights to inform strategic, operational, or policy decisions. Data-driven decision-making empowers organizations to make informed choices, optimize resources, and achieve desired outcomes based on objective information and performance metrics. Data literacy and analytical skills are essential for effective data-driven decision-making.
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Performance Metrics
Performance metrics are quantitative or qualitative measures used to assess the… #
Performance metrics help track progress, identify trends, and evaluate outcomes against predefined goals or benchmarks. Selecting relevant and actionable performance metrics is essential for measuring success and driving improvement.
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Key Performance Indicators
Key performance indicators (KPIs) are specific, measurable, and actionable metri… #
KPIs are used to monitor progress, evaluate outcomes, and inform decision-making based on predefined targets or objectives. Aligning KPIs with strategic goals is essential for measuring success and driving performance improvement.
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Decision Support
Decision support involves providing data, analysis, and tools to assist decision #
makers in making informed and evidence-based choices. Decision support systems help