Descriptive Analytics - Challenges and Future in Data Analysis

Descriptive analytics is a type of data analytics that focuses on describing and evaluating historical data to better understand what happened in the past. It entails different tools and techniques to evaluate raw data and convert it into useful information.

The working of descriptive analytics starts with metrics; the organization create a set of metrics first that measures business performance against business goals.

Data for descriptive analytics is collected using two basic techniques: data aggregation and data mining. Data aggregation is a technique used by organisations to collect and organize data into standard forms of data sets. The data obtained is examined using a variety of tools and methodologies, including summary statistics and pattern tracking. Analysts use these to evaluate data and identify patterns, which in turn affect performance.

For example, in a multi-national company, a digital meeting is organised; descriptive analytics can determine how many members were actively present during the discussion, their participation level, and how many posts were made during the discussion. Another example would be to report financial information such as year-over-year pricing changes, monthly sales growth (or drop) figures, and revenue. This information is based on what has happened within a specific business period.

Descriptive Analytics Process

The descriptive Analytics Process involves some set of steps, these are as −

1. Data Collection

In this step, users collect data sets from different sources like databases, data warehouses and spreadsheets. This data can include both structured data (numerical values and categorical variables) and unstructured data (text or graphics).

2. Data Preparation

This step includes data cleaning and processing to ensure accuracy and data consistency. Data preparation works on missing values, removing duplicates, and transforming the data into a standard form that can be used for analysis. This process applies once data is loaded into a data repository system.

3. Exploratory Data Analysis

Use exploratory data analysis approaches to better comprehend the dataset. This includes analysing statistical values, data distributions, and visualizations to detect trends, outliers, and relationships in the data.

4. Data Summarization

Descriptive statistics are used to summarize the dataset; it includes mean, median, mode, standard deviation, and percentile. These statistics provide a quick summary of the dataset's key tendencies and dispersion.

5. Data Visualization

It presents data in visual forms to easily understand. Visual representations of data include standard dashboards, charts, and graphs and Visualizations help identify trends, patterns, and anomalies more intuitively.

6. Data Interpretation

Interpret the summary data and graphics to draw relevant conclusions and observations about historical events and patterns. This analysis allows stakeholders to comprehend the implications of the data and make informed decisions based on the results.

Algorithms Used for Descriptive Analytics

Several algorithms are commonly used for descriptive analytics, such as −

1. Clustering algorithms

Clustering methods such as k-means and hierarchical clustering are used to group data points together based on their qualities. Clustering is used to divide data into meaningful groupings and find underlying trends.

2. Association rules

Apriori and FP-Growth are examples of association rule mining algorithms that are used to uncover interesting correlations and associations between variables or items in a collection. This is very valuable for market basket analysis and recommendation systems.

3. Time series analysis

Time series methods, such as autoregressive integrated moving averages (ARIMA) and exponential smoothing models, are used to examine data collected at regular intervals. These algorithms aid in identifying patterns, trends, and seasonality in time-dependent data.

4. Text mining and natural language processing (NLP)

Text mining and natural language processing algorithms are used to evaluate unstructured text data such as customer reviews, social media posts, and survey results. Text data can be analyzed using techniques like sentiment analysis, topic modelling, and named entity recognition to derive important information.

5. Decision trees

Decision tree techniques like ID3, C4.5, and CART are used to build hierarchical structures that express decision rules based on input data. Decision trees are effective for classifying and identifying key elements in data.

6. Geographic information systems (GIS)

GIS algorithms are used to analyze and display spatial data. These algorithms aid in the mapping of data to physical locations, spatial analysis, and the identification of location-specific patterns or trends.

7. Regression analysis

Regression techniques like linear regression, logistic regression, and polynomial regression are used to model the connection between dependent and independent variables. Regression analysis is used to understand the impact of one or more variables on a result of interest.

8. Data mining techniques

To find odd or noteworthy patterns in data, descriptive analytics uses a variety of data mining techniques, including anomaly identification, pattern recognition, and outlier analysis.

It is important to note that the employment of various algorithms is determined by the type of the data and the analysis's objectives.

Descriptive Analytics Process: How Descriptive Analytics Works?