Today, most organizations focus on data for making business decisions, and for good reason. However, data alone is not the goal. Numbers and facts are meaningless unless you have valuable information that leads to more appropriate action. Analytics solutions provide a convenient way to use business data. However, the number of market decisions it makes can be daunting – and many seem to cover different categories of descriptive analysis overview. How does the organization make sense of all of this? Start by understanding the different types of analysis, including descriptive, diagnostic, predictive, and prescriptive analysis.
For learning analysis, it is a reflective analysis of student data and aims to provide an overview of historical behavior and patterns of effectiveness in online learning environments.
For example, in an online discussion forum, descriptive analysis can determine how many students participate in a discussion or how many times students post to a discussion forum.
Here in this blog we are going to learn more about descriptive analytics.
What is descriptive analytics?
Descriptive analysis is defined as the interpretation of historical data to better understand changes in a company. Descriptive analysis describes the use of various historical data for comparison.
The most commonly reported financial indicators are the result of descriptive analysis for example, change in price for the year, sales growth for the month, number of users or total customer sales. All of these measures describe what happened in a company during a certain period of time.
Using full data sets in this type of analysis aids comparisons and provides an accurate picture of what is going on.
This type of analysis allows users to gain useful information about areas of strength and weakness, while also identifying patterns and relationships that would not otherwise be visible. Descriptive analysis technique are broadly divided into five categories:
- Government Business Metrics: Determine which indicators are important in evaluating performance against business objectives.
- Identify the data you need: For accurate measurements using KPIs
- Data extraction and preparation: Includes deduplication, transformation and cleaning
- Data analysis: key indicators are calculated and compared against business objectives to evaluate performance against historical results.
- Presentation of Data: Business intelligence tools enable users to present data visually in a way that analysts who do not contain data can understand it.
Some types of descriptive analysis include, but are not limited to:
- Aggregation of data
- Receiving data
- Data requests
How does descriptive analysis overview work?
Data aggregation and data mining are two techniques used in descriptive analysis to find historical data. Data is pre-collected and sorted based on data aggregation to make data sets more manageable than analyzers.
Data mining describes the next step in analysis and involves searching for data to identify patterns and significance. The identified model is analyzed to find out how students interact with the learning content and with the learning environment.
How is descriptive analysis used?
Descriptive analysis is mainly used to extract and present information in a visual and intuitive format. The company determines the KPIs and relevant metrics to measure before identifying the key data to obtain.
The analysis system then pulls data from a variety of sources including shadow storage, databases, and logging systems. After the data is extracted, it must be cleaned and modified for analysis. Preparing data for analysis is the most time consuming process that data analysts have to perform.
Once the data is ready for analysis, you can use descriptive analysis to find relationships and patterns in the main areas of the metric. The techniques used in this process include regression analysis, emphasis, and aggregate statistics.
Key indicators are calculated and compared with business objectives. The newfound trend is then compared to the previous trend. The data is then packaged into graphs, charts and other intuitive formats for easy reading.
Descriptive Analytics Tools
- Average, median and mode calculator
- Dispersion and standard deviation calculator
- Coefficient of variation calculator
- Percentile calculator
- Quartile distance calculator
- Aggregate dispersion calculator
- Asymmetry and courtesy calculator
- Sum of squares calculator
- Easy histogram creation
- Frequency allocation calculator
- Simple manufacturer of frequency ranges
- Simple graphic maker
- Histogram
Advantages of descriptive analysis
Here are some of the advantages of using this information:
- Report your return on investment (ROI) quickly and easily by showing how performance is meeting your business or target.
- Identify performance gaps and problems early – before they become problems.
- Identify specific students who need additional support no matter how many students or staff there are.
- Identify successful learners to offer positive feedback or additional resources.
- Analyze the value and impact of course design and training resources
The role of Descriptive analysis in banking
Banking analysis or data mining applications in banking improve bank efficiency by improving the way banks segment, handle, acquire, and retain customers. In addition, improvements in risk management, customer understanding and fraud allow the bank to maintain and grow a profitable customer base.
Applying data retrieval and predictive analytics to get the latest insights and scalable projections can help banks gain insights covering all types of customer behavior, including channel transactions, account opening and closing, defaults, fraud, and customer exit.
This helps banks be efficient by overcoming the myriad of challenges they face. With the ever-increasing demand for analytics, which has managed to produce more sophisticated and accurate results, more and more banks today have a suite of analytics.
While core reporting continues to be a relevant factor in banks, advanced forecasting and prescribing analytics are now starting to generate meaningful insights.
- Fraud Analysis
- Risk Analytics
- Loan amount prediction/classification
- Customer Analytics
- Customer Insights
Conclusion:
Descriptive analytics has changed the way companies work. The ability to take large amounts of data and break it down into recognizable trends opens up a lot of possibilities. In addition, analysis can present complex information in a visual and intuitive format.
Investing in analytics provides companies with a better understanding of how they work, and advanced analytical techniques provide stakeholders with context for important information.