What is Descriptive Analytics?
At its core, Descriptive Analytics is the practice of interpreting historical data to understand changes over time and identify trends. It offers a comprehensive view of past data and analyses it to extract valuable insights.
This branch of business analytics forms the base of all advanced analytical disciplines such as predictive analytics and prescriptive analytics, as it sets the stage for drawing insights and informing decisions.
Functionality and Features
Descriptive Analytics employs a variety of techniques for data interpretation. This includes data aggregation and data mining methods to provide an understanding of business performance by interpreting historical data.
Benefits and Use Cases
Descriptive Analytics is fundamental to businesses for numerous reasons. The first is its ability to provide a clear understanding of past behaviours and how they might influence future outcomes. It also helps organizations uncover patterns that can lead to better strategic decision-making.
Challenges and Limitations
Although effective, Descriptive Analytics does have limitations. Primarily, while it provides insight into what has occurred, it doesn't offer explanations as to why a particular event happened nor predict future outcomes.
Integration with Data Lakehouse
Descriptive Analytics plays a crucial role in a data lakehouse setup. Data lakehouses can store vast amounts of raw data. Descriptive Analytics can then be used to process and understand that data, thereby facilitating better business decision-making.
Security Aspects
Like any data-related process, Descriptive Analytics requires strong security measures. Ensuring data privacy, maintaining data integrity, and protecting against data breaches are essential aspects of this kind of analysis.
Performance
Descriptive Analytics influences performance by steering the tactical actions taken by management. By understanding past performance, businesses can take actions to improve future performance.
FAQs
What is Descriptive Analytics? Descriptive Analytics involves processing historical data to understand past behaviour and trends.
What techniques does Descriptive Analytics use? It primarily uses data aggregation and data mining methods.
What are the limitations of Descriptive Analytics? It only provides insights into what has happened, not why it happened, or what will happen in the future.
Why is Descriptive Analytics important in a data lakehouse setup? It processes and interprets vast amounts of raw data stored in a lakehouse, thereby aiding decision-making.
Glossary
Data Lakehouse: A hybrid data architecture that combines the best features of data warehouses and data lakes.
Data Aggregation: The process of collecting and summarizing data in a condensed form.
Data Mining: The practice of examining large databases to generate new information.
Data Breach: An incident where confidential data is accessed or stolen without authorization.
Data Privacy: The aspect of data protection that deals with the proper handling of data - consent, notice, and regulatory obligations.
