• Based on manual review and simpler statistical techniques
• usually use predefined models and formulas

• It allows you to process huge amounts of data at high speed and accuracy.
• Uses machine learning and deep learning algorithms to identify hidden relationships in data and generate insights that a human analyst might miss.
• learns from data and independently improves performance over time
• can predict future sales trends by analyzing seasonal patterns, customer behavior, and external factors

• Statistical methods: Statistical methods are focused on testing hypotheses and describing relationships between variable
• Machine learning approaches: They allow computers to automatically learn from data and improve their performance through experience.

Descriptive analytics

• The study of past events.
• Goal is to answer the question: "What happened?"
• It is used to analyze historical data and display key performance indicators (KPIs).
• This type of analytics does not predict the future, but rather provides a clear overview of past performance, which helps management understand the current state of the business.  
• Examples include sales reports, trend charts, and monthly revenue reports. 
• Tools like Excel, Power BI, and Tableau are often used at this stage.

Diagnostic Analytics

• Goes a step further and tries to answer the question: "Why did this happen?"
• Here, more advanced methods such as correlation analysis, drill-down approaches and statistical models are used to find the causes of certain events.
• The goal is to understand the causes, not just the symptoms, thus laying the foundation for better decisions in the future. 
• Example: sales fall in a particular month, diagnostic analytics tries to find out the reasons — a change in market conditions, reduced demand, or a failed campaign?

Predictive Analytics

• Predictive analytics answers the question, "What is likely to happen?"
• This type of analytics uses statistical models, machine learning algorithms, and historical data to predict future trends or outcomes.
• AI and machine learning enable continuous improvement of predictions as models learn from new data and automatically adapt to changes. 
• Examples include predicting sales, demand, market changes, or customer behavior.

Predictive Analytics

• It represents the most advanced stage and answers the question: "What should we do?"
• It combines predictive models with optimization algorithms to suggest concrete actions to achieve the best possible outcome. 
• For example, an AI system can recommend optimal product pricing, advertising strategies, or resource allocation. 
• Prescriptive analytics not only predicts the future, but actively guides decisions, making business more efficient, faster, and competitive.

Time Series analysis

• The study of data that has been collected over a period of time, with the aim of identifying patterns, trends, and seasonal changes. 
• It allows you to understand how certain variables change over time — for example, product sales by month, energy demand, or market price fluctuations.
• Models such as ARIMA (AutoRegressive Integrated Moving Average), Exponential Smoothing or LSTM neural networks in more advanced AI systems.
• Time series analysis requires high-quality and sufficiently long datasets, as irregularities or flawed data can lead to inaccurate predictions.

Predictive modelling

• Uses statistical and AI methods to build models that predict future outcomes based on existing data. 
• The process begins with data collection and cleaning, after which the data is divided into training and test sets. 
• The model is trained on historical data using machine learning algorithms such as linear regression, Decision Trees, Random Forests, XGBoost or neural networks. 
• After that, the accuracy of the model is evaluated and used to make predictions on new data. 
• Predictive models often use multiple variables at once, allowing for a deeper understanding of the relationship between factors.

Scenario modelling and simulations

• Advanced techniques that allow the analysis of possible future outcomes based on various combinations of variables and assumptions. 
• For example, a company can model three sales scenarios: optimistic (high market growth), real (stable business) and pessimistic (decline in demand). 
• Each scenario is tested using simulations, often through methods such as Monte Carlo simulation, which generate thousands of possible combinations of variables to estimate a range of possible outcomes.
•  The advantage of this approach is that it allows managers to better understand risks and uncertainties, and to prepare strategies for different circumstances in advance. 
•  Scenario modeling and simulations are increasingly being linked to AI tools that automatically generate and test thousands of scenarios in a very short amount of time. In this way, organizations get a broader and more accurate picture of future opportunities, which enables smarter and more resilient business decisions to be made.

Detecting market patterns and emerging opportunities

• Artificial intelligence (AI) allows organizations to analyze vast amounts of data and uncover hidden patterns in the market that human analysts might miss.
• By using advanced machine learning algorithms and time series analysis, AI can identify changes in demand, seasonal trends, and new market opportunities.
• For example, AI systems analyze historical sales data, competitor prices, and social signals to detect growing products or services before they become mainstream.
• This type of analytics allows businesses to react quickly to changes and adjust their strategies — whether it's launching a new product, changing pricing policies, or entering a new market. 
• AI thus becomes a crucial tool for predicting trends and making strategic decisions based on data rather than intuition

Competitive analysis using NLP and web scraping

• AI uses natural language processing (NLP) and web scraping technologies to collect and analyze competitor data from various online sources — websites, news, social media, and reviews. 
• NLP allows computers to "understand" textual data, identify tone, key terms, and sentiment, while web scraping automatically collects the latest information about competitors' prices, promotions, and market activities.
• AI can even visualize this data through interactive dashboards, allowing for faster decision-making on positioning strategy, marketing, and innovation. In this way, AI significantly increases the competitiveness and agility of the business.

Social Media Analytics for Consumer Behaviour Insights

• Social networks are an inexhaustible source of consumer data. 
• AI analytics uses Natural Language Processing (NLP) and sentiment analysis to process comments, reviews, and posts, in order to understand customer behavior and attitudes. 
• For example, machine learning models can recognize positive, neutral, and negative emotions related to a particular brand or product.
• This analysis allows businesses to react quickly to changes in market perception, identify customer needs, and tailor marketing campaigns in real-time. 
• AI can identify trends in consumer interests, predict which topics will become popular, and help personalize offers. 

AI for Data Analysis

AI in this sphere is not only used to process information, but also to discover deeper insights that can improve business processes, optimize resources, and predict future trends.

Prediction models

Models that can process vast amounts of data from a variety of sources — from sales records and customer behavior, to market trends and social media

AI for Business Prediction & Trends

The use of algorithms and computer models that can automatically learn from data, recognize patterns, and make       decisions or predictions without direct human intervention.

Industry Application

AI in data analytics is used today in a number of fields, from finance and marketing to medicine and industry, enabling more informed decisions to be made based on evidence and data.