- Business Analytics - Home
- Business Analytics Basics
- Business Analytics - What It Is?
- Business Analytics - History and Evolution
- Business Analytics - Key Concepts and Terminologies
- Business Analytics - Types of Data
- Business Analytics - Data Collection Methods
- Different Tools used for Data Cleaning
- Business Analytics - Data Cleaning Process
- Different Sources of Data for Data Analysis
- Business Analytics - Data Cleaning
- Business Analytics - Data Quality
- Descriptive Analytics
- Descriptive Analytics - Introduction
- How Does Descriptive Analytics Work?
- Descriptive Analytics - Challenges and Future in Data Analysis
- Descriptive Analytics Process
- Descriptive Analytics - Advantages and Disadvantages
- Descriptive Analytics - Applications
- Descriptive Analytics - Tools
- Descriptive Analytics - Data Visualization
- Descriptive Analytics - Importance of Data Visualization
- Descriptive Analytics - Data Visualization Techniques
- Descriptive Analytics - Data Visualization Tools
- Predictive Analytics
- Predictive Analytics - Introduction
- Statistical Methods & Machine Learning Techniques
- Prescriptive Analytics
- Prescriptive Analytics - Introduction
- Prescriptive Analytics - Optimization Techniques
Descriptive Analytics - Challenges and Future in Data Analysis
Challenges to Descriptive Analytics
Descriptive analytics is a type of analytics which summarizes historical data to get answers like what has happened. Like any other tool and method, descriptive analytics also has challenges. These are as follows −
1. Provides insight into what is happening not why
Descriptive Analytics examines the relationship between variables. It simply describes what is happening not why it happened or what could happen in future. Overall, Descriptive Analytics is backward-looking, which limits its utility in dynamic environments; it does not offer predictive or prescriptive analytics. Descriptive Analytics is based on historical data, so it is unsuitable to work with real-time data to find insights.
2. Poor Data Quality
If the data size is large then this approach does not produce accurate results. To get accurate results, the data must be thoroughly cleansed, and the quality of the data must be ensured to get informative results. Poor data quality may consist −
- Data Inconsistency − Data gathered from multiple sources may have different formats, structure which affects processing, accuracy, and integration challenges.
- Missing Data − Incomplete data sets might be biased with outcomes, making it harder to make valid conclusions.
- Data Cleaning − Cleaning and preparing data for analysis can be time-consuming and error-prone.
3. Challenges with Large Size Data
Big Data Challenges: Handling and processing large amounts of data necessitates extensive computational resources and efficient algorithms. With large data, finding insights can be difficult, and there is a risk of focusing on less important trends.
4. Data Interpretation
Analysts may knowingly add bias by focusing on specific data trends while overlooking others, resulting in skewed conclusions. Descriptive analytics provides insights into "what happened," but without context, understanding "why" can be challenging.
5. Complexity of Data Visualizations
Data visualizations can confound stakeholders rather than clarify findings, particularly if they are poorly constructed. Poorly drawn charts or graphs may mislead data, leading to incorrect interpretations.
6. Security and Privacy Concerns
Handling sensitive or personal data necessitates strict adherence to privacy standards, which can limit access to important data. Protecting data from breaches or unauthorized access is crucial, especially when working with large datasets.
7. Cost and Resource Constraints
Implementing and maintaining descriptive analytics tools and infrastructure can be costly, especially for smaller companies. A lack of skilled data analysts can impede an organization's capacity to properly use descriptive analytics.
Addressing these challenges needs strong data management standards, technological advancement, and effective communication of findings to decision-makers.
Descriptive Analytics in Future Data Analysis
Businesses are rapidly becoming data-driven, leveraging descriptive analytics results to optimize or improve business processes from sales and financials to supply chain management. Descriptive analytics is a fundamental technique used by businesses to extract meaningful insights from historical data. It is a technique for monitoring trends and performance. However, it is a simple method to do the same. To find effective outcomes, companies must combine descriptive analytics with predictive, diagnostic, and prescriptive analysis to gain more depth insights, accurate predictions, and ways to enhance outcomes. The future of data analytics is expected to shift from predictive analytics to prescriptive analytics.
The ideal application of data analytics depicts what has happened while effectively predicting what will happen next. Consider the example of a GPS navigation system. Descriptive analytics analyse prior delivery routes, timings, and fuel consumption. It does not provide how to improve speed, or how to save gasoline. In today's time, Organizations are more inclined towards predictive analytics to do this. Prescriptive analytics can help assess multiple travel routes and recommend the optimal option to use.
Descriptive analytics will continue to play an important part in future data analysis, but it will change in a different way to meet the demands of complex and dynamic data environments.
The following points describe how descriptive analytics will be the part of future of data analytics −
1. Advanced Analytics Integration
Descriptive analytics will increasingly be combined with predictive and prescriptive analytics to create a more complete picture of data. Organizations can make more informed decisions by combining "what happened," "what could happen," and "what should be done." As technology progresses, the capacity to analyse data in real-time will improve descriptive analytics, allowing companies to respond faster to changes and trends.
2. Enhanced Data Visualization Tools
Future descriptive analytics will most likely include more user-friendly and interactive dashboards that allow users to drill down into data, Customization, and explore insights dynamically. Artificial intelligence (AI) will allow for more sophisticated visualizations that automatically identify major trends, outliers, and patterns, making data interpretation easier for users.
3. Data Quality Management
Advances in AI and machine learning will result in more automated data cleaning processes, lowering the time and effort needed to prepare data for analysis. Enhanced data lineage tracking technologies will help to ensure data integrity, making it easier to identify and address data quality issues.
4. Data Democratization
Descriptive analytics methods will become more accessible to non-technical users, allowing employees within a company to study data without requiring specialized skills. Simplified interfaces and natural language processing (NLP) will enable users to query data and generate reports without having to grasp complex data structures.
5. Ethical and Responsible Analytics
As awareness of ethical challenges in data analysis rises, descriptive analytics will place more emphasis on identifying and eliminating bias to provide accurate insights. Future descriptive analytics will most likely incorporate elements that provide transparency into how analyses are carried out, ensuring that consumers can trust the insights generated.
6. Integration with IoT and Edge Computing
The growth of Internet of Things (IoT) devices generates large amounts of data, which may be analysed descriptively to monitor and optimize operations in real-time. Descriptive analytics will be done more frequently at the edge where data is generated, lowering latency and enabling faster decision-making in time-sensitive applications.
7. Scalable and Versatile
Cloud computing will make descriptive analytics more scalable and versatile, allowing businesses to manage larger datasets and complex studies. To manage the expanding volume of data, future descriptive analytics will rely on parallel processing and distributed computing frameworks, resulting in faster and more efficient analysis.
8. Data Privacy and Security
There will be more emphasis on privacy to analyse data while protecting individual privacy. Secure data analysis environments will become more popular, allowing enterprises to do descriptive analytics on sensitive data while maintaining confidentiality.
9. Integration with Decision-Making Processes
Descriptive analytics will become more integrated with AI-powered decision support systems that deliver actionable insights and suggestions based on previous data. Organizations will employ descriptive analytics to undertake scenario analysis, allowing them to better comprehend the prospective repercussions of certain decisions based on historical data trends.
10. Customization and Personalization
Descriptive analytics technologies deliver more personalized insights based on user preferences, roles, and goals, making data more relevant and useful. As users interact with descriptive analytics tools, they will learn and adapt, providing more relevant data and insights over time.
In the future, descriptive analytics will become more integrated, automated, and user-friendly, playing an important part in the larger landscape of data analysis while adapting to new technological and ethical problems.