- 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 Process
The descriptive analytics process is a set of sequential steps; every step plays a key role in performing the descriptive analytics process. These are as follows −
1. Data collection
The descriptive analytics process starts with data collection; in this step, an analyst or expert collects data from relevant sources. This could be databases, spreadsheets, surveys, or related data repositories. The collected data should be comprehensive and include the subject being analysed.
The data collection phase is critical in descriptive analytics because if the collected data is correct then it ensures accuracy and meaningful results. It also ensures that what you want to analyse and the specific questions you aim to answer determines the data type and its scope. In line with this, Data collection plays a key role in −
- Define Analysis Objectives
- Identify Data Sources
- Data Collection Methods
- Data Gathering
- Ensure Data Quality
- Data Integration
- Data Storage
2. Data Cleaning and Preparation
Data cleansing and preparation ensures accurate and reliable analysis. This step includes handling missing values, data inconsistency, duplicates, and outliers. Data cleaning can be done using the following mechanisms −
- Delete records − Delete records with missing values (if they are minimal).
- Imputing missing values − Impute missing values with mean, median, and mode.
- Handling of outliers − Management of outliers (values that are different from data) by removing them.
- Handling of Inconsistent Data − The data which is in different formats or typographical errors in categorical data.
- Dealing with Duplicates − Identify and delete duplicate records to avoid skewed analysis.
Data Preparation
Data preparation includes data transformation; it is a process of transforming data into a standard form to analyse. It transforms variables if necessary to normalize or scale numerical data. It creates new features that might help to make analysis simple or easier or used in data modelling. It can be done using the following mechanisms −
- Normalization − Data normalization includes scaling numerical data to ensure that different features contribute equally to the analysis.
- Encoding Categorical Variables − Convert categorical data into numerical ones.
- Feature Engineering − Developing new characteristics based on existing data that may prove more useful for analysis.
- Data Aggregation − Summarizing data to a higher level.
Data Integration
It is a process to combine data into a single unit collected from different sources. Data Integration merges or joins datasets. This process addresses data inconsistencies raised during combining data.
Data Reduction
It is a process to reduce data. Data reduction can be done using the dimensional reduction technique. It can reduce the number of variables in a dataset using methods like Principal Component Analysis (PCA).
This phase ensures high data quality, and data reliability for analysis. Data preparation includes data transformation and keeps it in a consistent form.
3. Data Exploration
Data exploration in descriptive analytics entails examining and visualizing datasets to identify patterns, correlations, and insights. It is a primary phase in the analytics process that allows analysts to grasp the data's underlying structure and features before moving on to more complex analyses.
In this step, data analysts examine the data to better comprehend its qualities and find patterns or trends. This can be accomplished using different strategies including summary statistics, data visualization, and exploratory data analysis. Statistics standards for data analysis like mean, median, mode, and standard deviation are given data's central tendencies and dispersion.
It uses charts and graphs to visualize data. Some of the common visualization techniques are histograms, box plots, scatter plots, and bar charts; these help to depict the data's distribution and relationships, making it easier to discover data patterns or data anomalies; and relationships between variables using correlation matrices, pair plots, or heat maps.
4. Segmentation
Data segmentation in descriptive analytics is a process of breaking down a large volume dataset into smaller parts that can be manageable, and useful. This segmentation can be done using demographics, geographic location, periods, or product category variables. Segmenting the data allows for a more focused analysis and uncovers insights specific to each segment. This is frequently used to find patterns, trends, or insights.
Data segmentation divides a dataset into relevant groups depending on specific criteria. Segmentation simplifies analysis and delivers more specific insights, leading to better decision-making. For example, segmenting customer data by age group can reveal information about customer preferences and purchasing behaviour.
5. Key performance indicators (KPIs)
In descriptive analytics, Key Performance Indicators (KPIs) are metrics used to evaluate the performance of a process or activity by examining historical and current data. Descriptive analytics aims to understand what happened by summarizing and evaluating historical data.
Descriptive analytics seeks to summarize data to provide key insights. This entails computing measurements such as averages, totals, percentages, or ratios that are relevant to the subject under consideration. Key performance indicators (KPIs) are precise measures used to assess the effectiveness of a business process, product, or service. KPIs provide relevant data and serve as standards for measuring progress or performance against specified goals or objectives.
KPIs provide insights into past performance and help identify trends, strengths, and areas for improvement within a business or process.
6. Historical trend analysis
Historical trend analysis in descriptive analytics entails looking at data across time to identify patterns, trends, and changes. This method is frequently utilized in a variety of industries, including business, finance, healthcare, and social sciences, to make educated judgments and predictions using historical data.
Descriptive analytics examines historical patterns to determine how variables or measurements have evolved. This identifies patterns, seasonality, and long-term trends. For example, evaluating sales data over the years can indicate seasonal sales peaks or uncover trends in specific product categories. Historical trend analysis identifies trends that might improve decision-making, estimate future performance, and find development opportunities.
Historical trend analysis in descriptive analytics is an effective tool for comprehending the past and making sound predictions. Organizations can obtain important insights into strategic decision-making by carefully evaluating historical data patterns.
7. Data reporting and visualization
The primary objective is to summarize and present the key results from the data. Descriptive analytics seeks to comprehend what has occurred based on past data. Descriptive analytics frequently uses data visualization techniques such as line charts, bar graphs, and heat maps to depict data trends over time. This makes it easier to recognize patterns and anomalies. Data insights and discoveries obtained through the descriptive analytics method must be properly presented. This is often accomplished using reports or visual dashboards. Reports describe the analysis and findings, which may include summary data, infographics, and narrative descriptions. Reporting and visualization facilitate effective communication while also assisting stakeholders to interpret and derive data insights.
These integrate reporting with visualization, giving users to quick overview of the data before drilling down into it as needed. They frequently update in real-time and can be adjusted according to user roles or preferences. Using language, graphics, and statistics to convey a story; technique helps to contextualize the data, making it more approachable and practical.
Tools like Tableau, Power BI, R (ggplot2), Python (Matplotlib, Seaborn), and D3.js are commonly used for creating visualizations.
8. Continuous monitoring and iteration
Descriptive analytics is an ongoing effort. Data monitoring and regular updates are necessary to find patterns and trends. As new data becomes available, the analysis must be updated to reflect the most current facts. Continuous monitoring enables the ongoing assessment, evaluation, and adaptation of methods concerning new data insights.