"Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making."
The use of data and analytics to inform decision-making and improve organizational effectiveness.
Data analysis: Techniques and methods for processing, cleaning, and analyzing data to generate insights and make informed decisions.
Predictive modeling: Machine learning algorithms and statistical modeling techniques used to forecast outcomes and identify patterns and trends in data.
Descriptive statistics: Methods for summarizing and describing data using measures of central tendency, variability, and distribution.
Business intelligence: Tools and technologies used to collect, store, and analyze data from multiple sources, with the aim of improving business performance.
Data visualization: Techniques and tools for creating visual representations of data to help communicate insights and patterns in a more intuitive way.
Data warehousing: The process of collecting, sorting, and storing large amounts of data from various sources into a single, centralized repository for easy access and analysis.
Data governance: Policies, procedures, and standards for managing data, ensuring accuracy, completeness, and security, as well as compliance with regulatory requirements.
Data ethics: The ethical considerations surrounding the collection, storage, and use of data, including issues related to privacy, confidentiality, and bias.
Data storytelling: The art of presenting data in a compelling and engaging manner to communicate complex ideas and insights to diverse audiences.
Text analytics: Techniques for analyzing and deriving insights from unstructured text data, such as social media posts, customer feedback, and online reviews.
Data mining: The process of discovering patterns and relationships in large datasets using statistical and machine learning techniques.
Dashboards and scorecards: Tools for displaying key performance indicators (KPIs) and other relevant metrics in a visual format, enabling stakeholders to monitor performance and make data-driven decisions.
Data quality management: The process of ensuring that data is accurate, complete, and consistent across different systems and platforms.
Data integration: The process of combining data from different sources and formats into a single, unified view for analysis and reporting.
Data architecture: The overall design and structure of data systems, including data sources, storage, and retrieval mechanisms, as well as data processing and transformation workflows.
Maturity models: Frameworks for assessing an organization's data and analytics capabilities and identifying areas for improvement and development.
Descriptive analytics: Descriptive analytics involves analyzing past data to understand what has happened in the organization, often through the use of charts, graphs, and tables.
Predictive analytics: Predictive analytics involves analyzing current data to make predictions about future trends and events, such as sales or employee turnover.
Prescriptive analytics: Prescriptive analytics involves using data to make recommendations or decisions about what actions an organization should take to achieve specific goals.
Diagnostic analytics: Diagnostic analytics involves analyzing data to understand why a particular event or outcome occurred. This can help organizations identify areas for improvement and make data-driven decisions.
Business intelligence (BI): Business intelligence involves using data to support business decision-making, often through the use of data visualization tools and dashboards.
Big data analytics: Big data analytics involves analyzing large volumes of data to uncover patterns and insights that may not be apparent with smaller data sets.
Data mining: Data mining involves using statistical techniques to find patterns or relationships in data sets, often used to discover new insights or identify hidden trends.
Machine learning: Machine learning involves using algorithms to analyze data and learn from it, allowing organizations to make predictions or automate processes.
Text analytics: Text analytics involves analyzing unstructured data such as emails, social media posts, or customer feedback to uncover insights.
Social media analytics: Social media analytics involves monitoring and analyzing social media data such as likes, comments, and shares to better understand customer behavior and sentiment.
"In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively."
"Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains."
"Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes."
"Business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information."
"In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA)."
"EDA focuses on discovering new features in the data."
"CDA focuses on confirming or falsifying existing hypotheses."
"Predictive analytics focuses on the application of statistical models for predictive forecasting or classification."
"Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources."
"Data integration is a precursor to data analysis."
"All of the above are varieties of data analysis."
"Data analysis is closely linked to data visualization."
"Data analysis plays a role in making decisions more scientific and helping businesses operate more effectively."
"Data mining focuses on statistical modeling and knowledge discovery for predictive purposes."
"Business intelligence focuses mainly on business information."
"Data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA)."
"EDA focuses on discovering new features in the data."
"CDA focuses on confirming or falsifying existing hypotheses."
"Text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources."