Descriptive Analytics 편집하기

'''Descriptive Analytics''' is a type of data analysis that focuses on summarizing historical data to identify patterns, trends, and insights. It is the foundation of data-driven decision-making, helping organizations understand what has happened in the past by providing a clear view of data in its historical context.
==Key Concepts==
*'''Historical Analysis:''' Descriptive analytics analyzes past events to provide a summary of what has occurred.
*'''Data Aggregation:''' Collecting and organizing raw data into a summarized and structured format.
*'''Data Visualization:''' Representing data through charts, graphs, and dashboards to make insights more accessible.
*'''Business Reporting:''' Generating periodic reports that summarize business performance and key metrics.
==Techniques Used in Descriptive Analytics==
Descriptive analytics employs a variety of techniques, including:
*'''Data Aggregation:'''
**Summing, averaging, or counting data points to provide an overview.
*'''Data Mining:'''
**Extracting patterns from large datasets using statistical methods.
*'''Data Visualization:'''
**Using visual tools such as bar charts, line graphs, and heatmaps to present insights.
*'''Statistical Measures:'''
**Applying measures like mean, median, mode, standard deviation, and variance to understand data distributions.
==Examples of Descriptive Analytics==
Descriptive analytics is widely used across various industries:
{| class="wikitable"
!Industry!!Example
|-
|'''Retail'''||Analyzing monthly sales data to identify best-selling products.
|-
|'''Healthcare'''||Monitoring patient records to track disease incidence rates over time.
|-
|'''Finance'''||Summarizing quarterly financial performance to assess profitability trends.
|-
|'''E-commerce'''||Evaluating website traffic and user behavior metrics to optimize marketing campaigns.
|}
==Tools for Descriptive Analytics==
Several tools and platforms are commonly used for descriptive analytics:
*'''Spreadsheets:''' Microsoft Excel, Google Sheets.
*'''Business Intelligence Tools:''' Tableau, Power BI, Looker.
*'''Statistical Software:''' R, SAS, SPSS.
*'''Data Warehousing Platforms:''' Snowflake, Amazon Redshift, Google BigQuery.
==Advantages==
*'''Clear Insights:''' Provides a clear and concise summary of historical data.
*'''Informed Decision-Making:''' Helps stakeholders understand past performance to guide future actions.
*'''Broad Applicability:''' Can be applied across industries and functional areas.
*'''Ease of Use:''' Relies on straightforward data analysis and visualization techniques.
==Limitations==
*'''Backward-Looking:''' Focuses only on past events without predicting future outcomes.
*'''Limited Predictive Power:''' Cannot provide insights into why events happened or what might happen next.
*'''Dependence on Data Quality:''' Accurate insights depend on the quality and completeness of historical data.
==Applications==
Descriptive analytics is widely used in:
*'''Business Operations:''' Tracking KPIs and operational metrics.
*'''Marketing:''' Analyzing campaign performance and customer behavior.
*'''Healthcare:''' Summarizing patient outcomes and hospital efficiency.
*'''Supply Chain Management:''' Monitoring inventory levels and shipment statuses.
==Comparison with Other Types of Analytics==
{| class="wikitable"
!Type!!Focus!!Example
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|'''Descriptive Analytics'''||What happened?||Monthly sales report showing trends.
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|'''Diagnostic Analytics'''||Why did it happen?||Root cause analysis of a sales decline.
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|'''Predictive Analytics'''||What will happen?||Forecasting future sales based on trends.
|-
|'''Prescriptive Analytics'''||What should we do?||Recommendations for inventory optimization.
|}
==See Also==
*[[Diagnostic Analytics]]
*[[Predictive Analytics]]
*[[Prescriptive Analytics]]
*[[Business Intelligence]]
*[[Data Visualization]]
*[[Key Performance Indicators (KPIs)]]
[[Category:Data Science]]