Descriptive analytics

As the foundational layer of data studies, descriptive analytics involves delving into information pools to understand past and current events. It is conducted to know “What happened and what is happening” to make customer-centric and profitable business plans. Enterprises can use descriptive analytics to visualize and summarize data trends, getting the context required to evaluate their markets and identify the potential areas of threats and opportunities.

Companies using descriptive analytics can create reports and dashboards to get a clear overview of their business performance. A gist of historical and recent data helps decision-makers determine patterns and outliers that are valuable inputs for future actions.

Diagnostic analytics

While descriptive analytics records events, diagnostic analytics explains why specific trends occurred, uncovering the root cause of issues and helping organizations take corrective measures.

Diagnostic analytics includes advanced statistical techniques, hypothesis testing, and data mining to determine relationships across variables. It can help realize why sales declined or orders were not fulfilled in time in a particular region, allowing an organization to adjust its marketing strategies or supply chain management accordingly. Power BI and Tableau are two commonly used data visualization tools to represent data graphically using diagnostic analytics.

Predictive analytics

Predictive analytics harnesses historical data to build models and forecast future events. While predicting the future is impossible, predictive analytics helps identify what might be vital and drive proactive decision-making.

Companies can use tools such as machine learning algorithms and statistical models such as decision trees, random forests, and logistic regression to predict demand, churn, and even detect fraud. Additionally, they can elevate their marketing, sales, and internal operations while allocating resources based on predictive analytics insights.

Prescriptive analytics

Prescriptive analytics recommend actions for specific outcomes. Businesses can use this AI-assisted technique to make informed decisions aligning with their marketing, inventory management, and supply chain management goals. Predictive analytics also improves resource allocation, enabling users to enhance contingency planning.

Prescriptive analytics also help evaluate multiple scenarios and suggest the best course of action, especially for long-term planning decisions. It can also simulate the impact of supply shortages or transportation disruptions to provide strategies to minimize the effects of such risks.

Text analytics

Text analytics or text mining or natural language processing (NLP) analyzes unstructured textual data in documents, emails, social media posts, and customer reviews to provide actionable insights through sentiment analysis, topic modeling, and named entity recognition to understand customer expectations better.

Spatial analytics

It deals with location-specific data and is therefore essential for companies whose operations – retail, hospitality, logistics, urban planning – and revenue are impacted by location. Geographic Information Systems (GIS) are commonly used for spatial analytics, enabling organizations to identify key customer regions and new markets, optimize supply chain management, improve delivery routes, and manage disasters.

Cluster analytics

Clustering or segmentation analysis categorizes data points with shared characteristics to reveal hidden patterns and relationships within large datasets. Businesses leverage it to understand customer segments and market trends and identify inefficiencies. They can customize sales strategies, optimize products and services, and execute data-driven decisions, boosting overall performance.