Descriptive Analytics - Advantages and Disadvantages

Descriptive analytics is a process to drive insights from data; it is also known as a data decision-making method which identifies patterns and trends. Descriptive Analytics is beneficial in different aspects but also has limitations like oversimplification, data quality, and its reactive nature rather than being proactive in addressing future challenges.

Some of the key advantages of descriptive analytics are as follows −

• Significant for Historical Data − Descriptive analytics enables businesses to obtain insights from past business events, patterns, and trends. It is significant in the historical context of decision-making.
• Simplicity and Accessibility − Descriptive analytics is easy to apply, this phenomenon making it accessible to multiple users, including those who have limited technical expertise.
• Trend analysis and pattern recognition − It reveals patterns and trends in data, enabling businesses to identify opportunities and risks. and areas for improvement.
• Easy to understand and interpret − It includes data summarization and visualization which makes it amongst users, enabling them to understand and interpret data.
• Data-driven decision-making − By analysing and summarizing data, companies can make informed decisions. It gives significant insights by summarizing large volumes of datasets, allowing organizations to better analyse historical data, trends, and pattern recognition. It allows decision-makers to make more informed decisions by providing a clear view of historical data; It assists organisations with strategic planning and operational efficiencies.
• Versatility across Industries − It is versatile; hence it applies to different industries like healthcare, banking, retail, and manufacturing, where business performance can be measured using historical data.
• Cost-Effective Approach − The descriptive analytics approach is less expensive than predictive or prescriptive analytics since it does not require complex algorithms or large-scale processing resources.
• Supports performance evaluation − It makes it easier to evaluate business performance, track progress using key performance indicators (KPIs) metrics and create benchmarks.
• Foundation for Further Analysis − It lays the groundwork for more advanced analytics, such as predictive or prescriptive analytics, by preparing and organizing data.

Disadvantages or Limitations of Descriptive Analytics

Disadvantages or Limitations of Descriptive Analytics are as follows −

• Limited in terms of insights It primarily summarizes historical data and may not provide detailed insights into the underlying causes or future projections.
• Limited Scope Descriptive analytics answers "what happened" not why it happened or what may happen in the future. It lacks the predictive abilities to find insights.
• Lack of context It may show data without providing adequate context or explanations, necessitating additional analysis and interpretation to derive useful results.
• Lack of Causality It does not provide the reasons for data trends or patterns, rendering it ineffective for identifying underlying issues or driving change.
• Reactive rather than proactive It works with historical data; it provides insights into past events but may not forecast future challenges or changes.
• Over-Reliance on Historical Data It works on historical data; it may fail to account for changes in market conditions, customer behaviour, or other dynamic factors that could influence future outcomes.
• Reliance on quality data Accurate and high-quality data is critical, and if the underlying data is inadequate, wrong, or biased, the conclusions drawn may be faulty or misleading.
• Potential oversimplification Summarizing complicated data into simplified metrics and visuals may oversimplify the underlying information, resulting in the loss of essential nuances.
• Misinterpretation There is a risk of misinterpreting the data, leading to incorrect conclusions and decisions.
• Data Quality Dependency The accuracy and utility of descriptive analytics are strongly reliant on the quality of the underlying dataset. Poor data quality can result in incorrect insights.