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Data analysis can be grouped into five main categories:
1.Descriptive analysis
2.Diagnostic analysis
3. Predictive analysis
4. Prescriptive analysis
5. Cognitive analysis
1.Descriptive Analytics :
Descriptive analysis is a sort of data research that aids in describing, demonstrating, or helpfully summarizing data points so those patterns may develop that satisfy all of the conditions of the data.
Business intelligence and data analysis rely heavily on descriptive analytics.
The main goal of descriptive analytics is to summarise, describe, and understand data patterns, trends, and distributions. It provides a historical view of data and helps organisations to answer questions such as “What happened?” or “What was the trend?”
Key benefits:
Summarisation of Data: Descriptive analytics summarises large and complex data sets into easy-to-understand information.
Understanding Data Patterns: Descriptive analytics helps organisations to understand the patterns and trends in their data.
Identifying Anomalies: Descriptive analytics can help organisations to identify unusual patterns and anomalies in their data.
Historical View: Descriptive analytics provides a historical view of data, which can help businesses to understand how their business has evolved.
Examples of Descriptive Analytics in Business :
Sales and revenue analysis: You can use descriptive analytics to see what months or days had the highest sales and adjust your marketing strategy accordingly.
Customer behaviour analysis: Descriptive analytics can give insight into customer behaviour, such as which products they buy the most, how frequently they purchase, and which promotions they respond to best.
Market share analysis: You can see how your brand stacks up against your competitors by analysing market share data.
Inventory Analysis: Manufacturers and retailers can use descriptive analytics to track inventory levels, identify trends in demand, and optimise their supply chain.
2. Diagnostic Analytics :
Diagnostic analytics is a data analysis focused on finding the root cause of a particular problem or issue. In other words, it’s all about answering the question, “Why did this happen?”
Diagnostic analytics is often used in combination with descriptive analytics to provide a comprehensive understanding of a situation or issue. Descriptive analytics summarises what has happened, while diagnostic analytics helps us understand the underlying causes of that behaviour.
Key benefits:
Uncovers the root cause of a problem – This approach involves analysing data to identify patterns, trends, and anomalies that can explain why a problem or issue is occurring.
Can be used across the business – Diagnostic analytics can be applied to many issues, including troubleshooting, optimisation, fraud detection, and root cause analysis
Examples of Diagnostic Analytics in Business :
Cause-and-Effect Analysis: Root cause analysis is an essential application of diagnostic analytics in business. Big companies like Amazon leverage vast amounts of customer purchase history, browsing behaviour, and shipping data to identify operational issues and find the root cause of problems.
Supply Chain Analytics: Organisations use diagnostic analytics to identify the root causes of supply chain issues, such as delayed shipments, inventory shortages, and quality problems.
Optimization: Companies use diagnostic analytics to identify areas for improvement and optimize their processes. For example, a manufacturer might analyse production data to determine why they are encountering bottlenecks in the production line and find ways to improve efficiency.
Fraud detection: Using diagnostic analytics, companies can identify patterns and anomalies in their data that might indicate fraud, such as unusual spending patterns or suspicious transaction activity
3. Predictive Analytics
Predictive analytics is a type of data analytics that uses advanced statistical algorithms, machine learning, and other techniques to predict future events or outcomes. It aims to help organisations make proactive decisions and to provide insights into potential risks and opportunities.
Key benefits:
It uses statistical algorithms and machine-learning techniques
Predictive modelling for forecasting and estimating future outcomes
Identification of patterns and trends in data
Proactive decision-making for organisations
Predictive analysis for risk assessment and opportunity identification
Examples of Predictive Analytics in Action:
Retail Industry: Retail companies use predictive analytics to analyse customer data and forecast future sales. This information is used to optimize inventory levels and improve supply chain management.
Banking and Financial Services: Banks and other financial institutions use predictive analytics to detect fraud, assess credit risk, and identify potential investment opportunities.
Healthcare Industry: Healthcare organisations use predictive analytics to forecast future demand for medical services, identify at-risk patients, and improve patient outcomes through personalized care plans.
Manufacturing: Manufacturers use predictive analytics to predict when equipment is likely to fail, allowing them to schedule maintenance and prevent unplanned downtime.
4. Prescriptive Analytics
Prescriptive analytics is a type of analytics that takes predictive analytics one step further by providing recommendations and suggestions for action based on the predictions made.
It combines predictive analytics with optimisation algorithms, decision science, and rule-based systems to help organisations make informed decisions and take proactive measures to optimise outcomes.
Prescriptive analytics answers the question, “What should we do?” by analysing available data and recommending a course of action to achieve desired results.
Key benefits:
Decision optimisation: Prescriptive analytics uses mathematical algorithms and optimisation techniques to find the best possible solutions for a given set of conditions and constraints.
Predictive modelling: Predictive models use statistical algorithms, machine learning, and other advanced techniques to predict future events or outcomes.
Real-time analysis: Prescriptive analytics operates in real-time, providing real-time recommendations and updated suggestions as new data becomes available.
Dynamic visualisations: Prescriptive analytics uses interactive, dynamic visualisations to communicate the insights and recommendations generated from the data.
Integration with other analytics types: Prescriptive analytics integrates with other analytics, such as descriptive, diagnostic, and predictive analytics, to provide a complete picture of the data and support informed decision-making.
Examples of Prescriptive Analytics in Business :
Supply Chain Optimization: Prescriptive analytics in supply chain management helps companies make informed decisions on inventory levels, production schedules, and transportation routes.
Fraud Detection: By analysing historical data, prescriptive analytics can help financial institutions identify patterns and anomalies in transactions that may indicate fraud.
Customer Segmentation: Prescriptive analytics helps companies better understand their customers by segmenting them based on demographic, geographic, or behavioural characteristics.
5. Cognitive Analytics
Cognitive analytics is an advanced type of data analytics that utilises artificial intelligence (AI) and machine learning (ML) algorithms to process and analyse vast amounts of unstructured data.
Cognitive analytics aims to help organisations extract insights and make predictions from complex and unstructured data sets that may be difficult to process using traditional methods.
Key Features:
Advanced Natural Language Processing (NLP): Cognitive analytics uses NLP to extract insights from large amounts of unstructured data, such as customer reviews and social media posts.
Deep Learning Capabilities: Cognitive analytics uses deep learning algorithms to analyse data and predict future trends and patterns.
Automated Insights: Cognitive analytics automates the insights extraction process, reducing the time and effort required to analyse large amounts of data.
Predictive Capabilities: Cognitive analytics uses algorithms to predict future events and outcomes.
Interoperability: Cognitive analytics integrates with other tools and platforms, allowing organisations to easily combine insights from multiple sources and get a more comprehensive view of their data.
Examples of Cognitive Analytics in Business :
Healthcare: Cognitive analytics can analyse medical images, such as x-rays or MRIs, to help diagnose diseases or identify potential health risks.
Customer service: Cognitive analytics can analyse customer feedback and sentiment, such as comments on social media or support tickets, to identify trends and patterns that can help improve customer experiences.
Retail: Cognitive analytics can analyse customer purchasing patterns, such as the items they buy and when they buy them, to help retailers optimise their inventory and improve the customer experience.
Market Research: Cognitive analytics can analyse consumer data to gain insights into consumer behaviour and preferences.
Identifying Anomalies: Descriptive analytics can help organisations to identify unusual patterns and anomalies in their data.
Historical View: Descriptive analytics provides a historical view of data, which can help businesses to understand how their business has evolved.