Data & Advanced Analytics

What is Data Analytics?

In a nutshell, it means analytics like correlations, hidden patterns and other meaningful insights that are derived from the act of putting together and then examining large volumes of data.

Okay, but can u elaborate please?

Imagine you own a company that has been in operations for the last few years. Besides certain industry-specific solutions, you might have a core system or an ERP or separated components like financial and accounting system, customer relationship management (CRM) system, procurement system, human resource management system, several in-house developed systems, some competitive benchmarking data in Excel Sheets etc. You want to use data to help you in making business decisions but you realise that it is too difficult or near impossible. The systems do not talk to each other. Some are installed in servers within your premise. Some are hosted in datacentres while others reside on clouds. This is where big data becomes extremely useful. Big Data technology allows you to process large volumes of data that can come in any shape, form or size. The data has to undergo a series of transformation and cleansing process before they can be consumed by downstream analytical tools. The end result of which would be beautiful reports, charts, graphs and dashboards. With that said, visualisation and reporting is just the tip of the iceberg. Big Data would help you discover and uncover facts that you never know exist like hidden patterns, habits, trends and much more. You can also choose to add on machine learning and artificial intelligence to help you create predictive and decision engines that help suggest actions and decisions to bring your business to the next level. There are multiple routes from which you can choose to achieve your intended objectives but in order to understand which route to choose, you must first understand the components that make up the big data solution.

"Data is the new oil. It's valuable but if unrefined it cannot be used”

- CLIVE HUMBY, British Mathematician and Data Science Entrepreneur

Data is the New Oil

"Data is the new oil". "It's valuable, but if unrefined it cannot really be used". The phrase was originally coined by Clive Humby, a British mathematician and data science entrepreneur. The phrase was immortalized over the last decade as it was echoed by Gartner, The Economist, Forbes and in pretty much every article that speaks about the importance of data. From marketing and finance to logistics and product, decision-making of most large private corporations today are driven by data at all levels.

While data warehouses and data lakes may share some similar features and use cases, there are fundamental differences between the two in terms of design characteristics, data management philosophies and ideal use conditions. To understand which works best for you, we need to first understand the difference between the two.

Data Ingestion

Data ingestion is the process of collecting data from various data sources e.g. data lakes, databases, sensors, IoT devices, SaaS Applications, social media and even manual Excel files and putting them into a targeted data repository most often a data warehouse. Knowing the type, size and complexity of data would eventually influence your decision on what to do next and how to do it. A very crucial decision to be made at this stage is whether to choose real-time data ingestion or batch data ingestion

Data Lake

A data lake is a centralized data repository system where data from a variety of sources whether structured, semi-structured or unstructured are stored as-is in their raw format usually accomplished through an ELT (Extract-Load-Transform) process. Data lakes help eliminate data silos as it can act as single landing zone for data from multiple sources. Data lakes ingest all data types in their source format without the need for structures and pre-defined data schemas. Data is aggregated and transformed only at the point of query. This data model is known as schema-on-read.

Diagram: Big Data Steps/Process

In an ELT process, data is extracted from data sources and loaded into the data lake in its raw form. The data is only transformed at the point of query.

Schema-on-Read Data Model does not require clear understanding of use cases. Compute resources are consumed at the point of query making it more cost-effective.

Data Warehouse

A data warehouse is a data management system that provides business intelligence for structured operational data. Data warehouses use predefined data schema to ingest structured data usually accomplished through an ETL (Extract-Transform-Load) process. The data source must fit into a predefined structure before it can enter the warehouse. This is also known as schema-on-write data model. The data is later connected to downstream analytical tools for Business Intelligence (BI) initiatives.

In an ETL process, data must first be extracted onto the staging layer of the data warehouse. The data is then cleaned and transformed into Dimensional Models and finally loaded onto a Data Mart (or Data Cube) for reporting, dashboarding and visualizations.

Schema-on-Write Data Model requires clear understanding of use cases as well as more time and compute resources.

When to Use Data Lake and When to Use Data Warehouse

There is no hard and fast rule, but the below can be used as a quick guide:

Use Data Warehouse when you:

have smaller data volumes that does not change too fast
need periodic reports and dashboards like daily sales, weekly financial report or monthly performance reports
deal with structured data
have full clarity of what you want to accomplish (Data can only be loaded to the data warehouse after the use for it has been defined)
want fast and easy access to multiple operational business users

Use Data Lake when you:

have larger data volumes that changes rapidly
need real-time/near real-time data that tells you what happened one minute or five minutes ago
deal with structured, semi-structured and unstructured data in its raw form
do not have full clarity of use case (Data is loaded as-is in its raw form without any transformation)
want fast and easy access to a few Data Scientists and Power Users

If you think of a data warehouse as a store of bottled water – cleansed and packaged and structured for easy consumption, the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake and various users of the lake can come to examine, dive in, or take samples - James Dixon, Founder of Pentaho

So, Data Lake or Data Warehouse?

Data Warehouse is the better option for companies that have clearly defined use cases and operational business users who need periodic reports, dashboards and visualizations. However, data warehouse can become very expensive to maintain as the volume of data increases. Data Lake is the perfect solution for a centralized repository to eliminate data silos. It is fast to deploy and perfect for large data volumes. Since data is only transformed at the point of query, only data scientists and superpower users can consume the data. Having said that, this does not mean that data from data lake cannot be consumed by analytical software, business intelligence, and others. To harness the technological potential of both the data warehouse and the data lake, large corporations can choose to build a data lake and then have the data loaded into data warehouses and data marts.

Data Mart: Purpose-Built Analytics for Business Functions

Once data has been centralised and structured in a data warehouse, the next step is often to create data marts — specialised subsets of the warehouse designed to serve specific business units, departments, or use cases.

While a data warehouse offers a single source of truth across the organisation, its structure can be complex and performance-intensive. Data marts simplify this by creating focused datasets that are tailored to the needs of marketing, finance, operations, or other teams. This allows users to query only the data relevant to their function, resulting in faster performance and easier insights.

Facts and Dimensions
Data marts are typically organised using a star schema or snowflake schema structure, comprising:

Fact tables – These hold quantitative data or metrics (e.g., sales amount, revenue, number of transactions). Facts are usually time-stamped and indexed for efficient aggregation.
Dimension tables – These hold descriptive attributes related to the facts (e.g., customer name, product category, region, date). Dimensions provide context and filtering capabilities for analysis.

By structuring data marts with clear facts and dimensions, organisations enable fast, business-friendly analytics that support slicing and dicing data across various angles.

How Data Moves:

ETL/ELT pipelines extract relevant data from the data warehouse.
Business rules, calculations, and aggregations are applied to produce fact and dimension tables.
These tables are organised into subject-specific data marts, either as logical views or separate databases/tables optimised for specific use cases.

Example Tools:

Snowflake – Logical or physical data marts with rich support for data sharing
Amazon Redshift – Purpose-built for performance-tuned marts
Google BigQuery BI Engine – Accelerated analytics with data mart modeling
Alibaba Cloud MaxCompute – Distributed data warehousing and mart design
Azure Synapse Analytics – SQL-based mart creation for enterprise use
Talend Data Fabric – ETL/ELT pipelines to model and populate data marts
Qlik Compose for Data Warehouses – Automates the creation of fact and dimension models

Data Visualisation & Dashboards: Turning Data into Decisions

With data now organised in focused data marts, it can be consumed by downstream visualisation and business intelligence (BI) tools. These tools connect to the data marts (or directly to the warehouse, depending on architecture) to deliver interactive dashboards, reports, and analytics to end users.

These platforms help stakeholders:

Monitor KPIs in real-time
Track business trends
Perform self-service analytics without writing code

How Data Flows:

Visualisation tools connect via ODBC/JDBC connectors, APIs, or native integrations.
Data is queried live (or periodically cached) from the data marts.
Dashboards and charts are generated and updated dynamically based on underlying data.

Example Tools:

Tableau – Renowned for advanced visual analytics and intuitive dashboards
Microsoft Power BI – Tight integration with enterprise ecosystems (Azure, Excel, etc.)
Qlik Sense – Associative data model with fast in-memory analytics
MicroStrategy – Enterprise-grade BI platform with embedded analytics, mobile BI, and federated access
Alibaba Quick BI – A robust tool for dashboarding and data exploration on Alibaba Cloud
Talend Data Preparation – For pre-visualisation data wrangling and transformation

These tools support both operational dashboards (for real-time monitoring) and strategic dashboards (for high-level executive insights), enabling decisions from the frontlines to the boardroom.

From Upstream to Downstream

Data doesn't just stop at the warehouse. A well-architected data pipeline ensures that:

The data warehouse acts as the central repository
Data marts, structured with clear facts and dimensions, provide domain-specific, performance-optimised access
Analytics and BI tools empower users to explore and act on insights

This end-to-end flow transforms raw data into actionable intelligence, enabling better, faster, and more informed decision-making across your organisation.

Sample Dashboard 1

Sample Dashboard 2

Sample Dashboard 3

Sample Dashboard 4

Data Analytics Journey

Many different types of data analytics can be used to examine raw data, big data, or statistics to uncover valuable insights that businesses can use for decision-making. There are primarily four main types of data analytics which offer distinctive perspective, insights, patterns, correlations, and trends. They are descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics.

Descriptive analytics is a form of analytics that examines data or content to answer the question, “What happened?”

Diagram: Advanced Analytics

Descriptive Analytics

Descriptive analytics focuses on summarizing historical data to provide insights into what has happened in the past. It answers the question “What happened?” and lays the foundation for further evaluation by establishing a baseline understanding of the data’s historical context. Some of the typical use cases involve:

Reporting on sales performance.
Visualizing website traffic over time.
Demand trends.
Summarizing customer feedback.
Customer segmentation.
Financial reporting.

Scenario Example
A retail company uses descriptive analytics to create weekly reports on sales performance across different product categories, helping managers track trends and make informed decisions on inventory management.

Advantages

Provides a snapshot of historical data.
Simplifies data interpretation making it easier to understand
Helps identify anomalies in data
Forms the foundation for more advanced data analytics

Disadvantages

Limited in providing actionable insights for the future.
May oversimplify complex scenarios as it is highly dependent on the quality of the data sample; unsuitable or biased samples may distort conclusions

The below are some of the most common Descriptive Analytics Techniques:

Data Aggregation - Summarizes data by grouping and combining information based on certain criteria. An example of use is aggregating sales data by month to analyze monthly performance.
Data Summarization - Provides a concise overview of key metrics such as mean, median, mode, and standard deviation. An example of use is summarizing employee performance ratings for a given period.
Data Visualization - Represents data visually using charts, graphs, and dashboards for better understanding. An example of use is creating a bar chart to visualize sales performance across different product categories.
Histograms - Displays the distribution of a dataset by dividing it into bins and representing the frequency of values in each bin. An example of use is analyzing the distribution of customer ages in a given dataset.
Pie Charts - Represents the proportion of each category in a dataset as a slice of a pie. An example of use is showing the market share of different product lines.
Frequency Distribution - Lists the number of occurrences of each unique value in a dataset. An example of use is creating a frequency distribution table for customer purchase amounts.
Heatmaps - Displays the intensity of data values with color gradients. An example of use is analyzing website traffic patterns over different hours of the day.
Time-Series Analysis - Examines data points collected over time to identify trends, seasonality, and recurring patterns. An example of use is plotting monthly revenue over a year to identify seasonal trends.
Percentiles and Quartiles - Divides a dataset into percentile ranges to understand the distribution of values. An example of use is analyzing salary distributions by calculating quartiles.
Central Tendency Measures - Describes the center of a dataset using metrics like mean, median, and mode. An example of use is calculating the average sales price for a specific product.
Cross-Tabulation (Contingency Tables) - Summarizes data by creating a table that shows the relationship between two categorical variables. An example of use is analyzing the correlation between product satisfaction and customer demographics.
Summary Reports - Provides a comprehensive summary of key metrics and performance indicators. An example of use is generating a monthly report summarizing sales, expenses, and profit.

When applying descriptive analytics, it is important to choose the most appropriate techniques based on the nature of the data and the specific objectives of the analysis. Data cleaning and preparation are also crucial steps to ensure accurate and meaningful results.

Some of the most commonly used tools for Descriptive Analytics are Excel Spreadsheets and Business Intelligence/Visualization Tools like Qlik Sense, Tableau and Power BI.

How to Get Started with Descriptive Analytics
To get started with descriptive analytics, one shall first collect relevant data, learn data visualization tools and techniques as well as understand basic statistics for summarizing data.

Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?”

Diagnostic Analytics

Diagnostic analytics uncovers relationships between variables to understand factors contributing to specific outcomes by examining and scrutinizing historical data. It involves root cause analysis and often follows descriptive analytics. It aims to answer the question, “Why did it happen?”. Some of the typical use cases involve:

Investigating reasons for a sudden drop in sales.
Analyzing Employee turnover.
Identifying factors leading to customer churn.
Analyzing the root cause of production issues.
Fraud detection.

Scenario Example
An e-commerce platform uses diagnostic analytics to investigate a sudden increase in customer complaints, discovering that a recent website update caused usability issues, leading to a swift resolution.

Advantages

Helps in problem-solving and identifying issues.
Provides insights into the "why" behind specific outcomes.
Early detection of anomalies/trends/patterns reduces the likelihood of issues escalating into more significant challenges.
Supports informed decision-making and development of targeted solutions based on a deeper understanding of events.

Disadvantages

Requires deep domain knowledge to interpret results accurately.
Requires in-depth analysis and expertise for accurate interpretation.
Can be resource and time-intensive, particularly when dealing with large datasets or complex algorithms thus making it less practical for frequent use.
Mainly focused on understanding historical patterns rather than predicting future ones.

The below are some of the most common Diagnostic Analytics Techniques:

Root Cause Analysis - Investigates the primary factor or factors that contribute to a particular outcome or issue. An example of use is identifying why a manufacturing process is yielding defective products.
Drill-Down Analysis - Examines data at a more granular level to get a detailed view of specific components contributing to a broader trend. An example of use is investigating which specific products are causing a decline in overall sales.
Correlation Analysis - Identifies statistical relationships between variables, helping to understand how changes in one variable may impact another. An example of use is determining if there is a correlation between advertising spending and sales revenue.
Pareto Analysis - Applies the 80/20 rule to identify the most significant factors contributing to a problem or trend. An example of use is identifying the key products or customers that contribute most to revenue, expenses or issues.
Comparative Analysis - Compares different sets of data to identify patterns, differences, or anomalies. An example of use is comparing the performance of different regions or teams to understand variations in sales or productivity.
Regression Analysis - Examines the relationship between a dependent variable and one or more independent variables, helping to understand how changes in one variable affect another. An example of use is analyzing how changes in marketing spending impact overall revenue.
Time-Series Analysis - Examines data points collected over time to identify trends, seasonality, and recurring patterns. An example of use is understanding the monthly variations in website traffic or sales.
Fishbone Diagrams (Ishikawa or Cause-and-Effect Diagrams) - Visual representation that helps identify and categorize potential causes of a specific problem. An example of use is investigating the root causes of delays in product delivery.
Scatter Plots - Graphically represents the relationship between two variables to identify potential correlations. An example of use is analyzing the relationship between employee training hours and performance metrics.
Hypothesis Testing - Formulates and tests hypotheses to determine if there is a statistically significant relationship between variables. An example of use is testing whether changes in pricing strategy have a significant impact on customer satisfaction.

When performing diagnostic analytics, it is essential to combine these techniques judiciously, considering the nature of the data and the specific objectives of the analysis. Domain knowledge and a thorough understanding of the business context are crucial for effective diagnostic analytics.

Some of the most commonly used tools for Diagnostic Analytics are statistical analysis software, data drilling and data mining software, and software for anomaly detection.

How to Get Started with Diagnostic Analytics
To get started with diagnostic analytics, one shall need to acquire skills in SQL for data querying as well as learn Python/R etc. for statistical analysis for identifying patterns as the diagnostic analytics process entails defining the problem, gathering relevant data, formulating hypotheses, evaluating hypotheses, interpreting results, recommending solutions, implementing changes, and tracking outcomes.

Predictive analytics is a form of advanced analytics that examines data or content to answer the question, “What is likely to happen?”

Predictive Analytics

Predictive analytics uses statistical algorithms, machine learning (ML) and data mining to analyze historical data to make predictions about future outcomes. It aims to answer the question “What is likely to happen?” and it empowers organizations to find potential opportunities, assess risks, and make proactive and informed decisions. Some of the typical use cases involve:

Forecasting future sales and demand based on historical data.
Risk assessment.
Predicting equipment failures in manufacturing.
Anticipating customer preferences for personalized recommendations.
Predicting consumer trends.
Predicting employee attrition.
Healthcare risk classification.

Scenario Example
An energy company utilizes predictive analytics to forecast future energy consumption patterns, enabling them to optimize energy production and distribution, reducing costs and improving efficiency.

Advantages

Enables proactive and informed decision-making based on data-driven forecasts and predictions.
Streamlines planning through anticipating future demands and trends.
Improves strategies by targeting specific demographics and personalizing campaigns for better customer engagement.
Mitigates risks by predicting potential challenges.

Disadvantages

Requires high-quality data and may be sensitive to outliers.
Heavily relies on the quality of input data; inaccurate, incomplete, or biased data can lead to unreliable predictions and skewed outcomes.
Handling sensitive or personal data in predictive analytics raises privacy concerns and requires compliance with data protection regulations to avoid legal and ethical issues.
Predictive models might lose their precision over time due to shifts in market conditions and changes in consumer behaviors; regular updates are necessary to ensure accuracy.
Developing and maintaining predictive models can be resource-intensive and time consuming, requiring skilled professionals, computational power, and ongoing updates to remain effective.

The below are some of the most common Predictive Analytics Techniques:

Linear Regression - Establishes a linear relationship between a dependent variable and one or more independent variables. An example of use is predicting sales based on factors like advertising expenditure and seasonality.
Logistic Regression - Used when the dependent variable is binary, predicting the probability of an event occurring. An example of use is predicting customer churn (yes/no) based on various customer attributes.
Decision Trees - Creates a tree-like model to make decisions based on input features. An example of use is predicting whether a customer will purchase a product based on demographic and purchase history.
Random Forest - Ensemble learning method that builds multiple decision trees and combines their predictions. An example of use is predicting stock prices based on various economic indicators.
Gradient Boosting - Builds a series of weak models and combines them to create a strong predictive model. An example of use is predicting customer satisfaction based on feedback and historical data.
Time Series Analysis - Analyzes data points collected over time to identify patterns, trends, and seasonality. An example of use is forecasting future sales based on historical sales data.
ARIMA (AutoRegressive Integrated Moving Average) - A time series forecasting method that combines autoregression and moving averages. An example of use is predicting future demand for a product based on historical sales data.
K-Nearest Neighbors (KNN) - Classifies data points based on the majority class among their nearest neighbors. An example of use is predicting customer preferences based on similarities with other customers.
Support Vector Machines (SVM) - Finds a hyperplane that best separates data into different classes. An example of use is predicting whether an email is spam or not based on various features.
Neural Networks - Deep learning models composed of interconnected nodes that mimic the structure of the human brain. An example of use is predicting customer behavior using a neural network with multiple hidden layers.
Clustering Analysis - Groups similar data points into clusters based on certain features. An example of use is predicting market segments and customer behavior through clustering analysis.
Ensemble Methods - Combines multiple models to improve predictive performance. An example of use is using an ensemble of models, such as bagging or stacking, to predict customer churn.
Naive Bayes - A probabilistic classifier based on Bayes' theorem, often used for classification problems. An example of use is predicting whether an email is spam or not based on the probability of certain words.
Association Rule Mining - Identifies relationships and patterns within large datasets. An example of use is predicting product recommendations based on the association between different items in a shopping cart.

When applying predictive analytics, it is essential to select the most suitable technique based on the specific characteristics of the data and the goals of the prediction task. Additionally, careful validation and evaluation of the predictive models are crucial to ensure their accuracy and reliability.

Some of the most commonly used tools for Predictive Analytics include ML libraries, data mining tools, data visualization tools, BI software, statistical modeling tools, and predictive analytics software. One of the famous no-code tool is Altair RapidMiner.

How to Get Started with Predictive Analytics
To get started with predictive analytics, one shall need to learn programming languages like Python or R as well as explore machine learning algorithms and techniques as predictive analytics is an ongoing process that requires defining goals, data collection and cleaning, selecting prediction techniques, training and validating models, model deployment, making predictions, performance assessment, and continuous improvement.

Prescriptive analytics is a form of advanced analytics that examines data or content to answer the question, “What should we do?”

Prescriptive Analytics

Prescriptive analytics provides recommendations on what actions to take to optimize outcomes. It combines insights from descriptive, diagnostic, and predictive analytics. It is an advanced form of analytics that offers recommendations to achieve a specific result. It uses data, mathematical algorithms, and business rules to suggest the best course of action that your organization should take to refine decision-making processes. It aims to answer the question “What should we do?” by giving actionable recommendations. Some of the typical use cases involve:

Optimizing supply chain routes for efficiency.
Recommending personalized marketing strategies.
Prescribing actions for fraud detection and prevention.
Provide recommendations for dynamic pricing strategies.
Provide recommendations for personalized healthcare treatment plan.

Scenario Example
A logistics company employs prescriptive analytics to optimize delivery routes based on real-time traffic data, reducing delivery times and fuel costs while improving overall service quality.

Advantages

Guides decision-making by recommending actionable optimal actions.
Incorporates real-time data for dynamic decision support.
Improves resource efficiency and reduces costs by recommending efficient resource allocation.
Increases revenue by suggesting best pricing strategies and targeted marketing approaches.
Enhances strategic planning with the ability to adapt strategies to changing conditions.

Disadvantages

Requires advanced analytics capabilities.
Significantly dependent on accurate and comprehensive data; if the input data is incomplete, inaccurate, or outdated, it can lead to flawed recommendations.
Implementing recommended actions can be very complex.
Raises ethical concerns related to privacy, particularly in areas like personalized marketing.
Deployment includes investment in technology, data infrastructure, and skilled personnel, which can have substantial costs.
Requires continuous monitoring and maintenance to guarantee relevance and accuracy.

The below are some of the most common Prescriptive Analytics Techniques:

Optimization Algorithms - Mathematical algorithms designed to find the best solution among a set of feasible options. An example of use is optimizing supply chain routes, production schedules, or resource allocation.
Simulation Modeling - Creates a model to simulate real-world scenarios and understand the potential outcomes of different decisions. An example of use is simulating the impact of changes in manufacturing processes on production efficiency.
Game Theory - Analyzes interactions between different entities to identify optimal strategies and outcomes. An example of use is determining pricing strategies in a competitive market.
Prescriptive Machine Learning - Combines predictive analytics and optimization techniques to recommend actions that maximize or minimize a specific objective. An example of use is recommending pricing adjustments to maximize revenue based on predicted customer behavior.
Decision Support Systems - Computer-based tools that aid decision-making by providing insights, analysis, and recommended actions. An example of use is supporting executives in making strategic decisions, such as market entry strategies.
Expert Systems - Rule-based systems that mimic the decision-making process of a human expert in a particular domain. An example of use is providing expert advice in fields like healthcare for treatment recommendations.
Prescriptive Analytics Software Platforms - Integrated platforms that leverage various techniques for optimization and decision support. An example of use is using dedicated software to optimize inventory levels in a retail supply chain.
Goal-Seeking Models - Seeks the optimal input values to achieve a desired outcome. An example of use is adjusting marketing budgets to maximize customer acquisition within a given cost constraint.
Constraint Programming - Solves problems subject to constraints, ensuring that decisions comply with predefined rules and limitations. An example of use is developing schedules that adhere to workforce constraints and operational requirements.
Prescriptive Analytics Dashboards - Interactive dashboards that provide real-time insights and recommended actions based on ongoing data analysis. An example of use is monitoring key performance indicators and receiving recommendations for process improvements.
Heuristic Approaches - Rule-of-thumb methods that provide practical solutions, especially when optimization is challenging. An example of use is using heuristic algorithms for vehicle routing in delivery logistics.
A/B Testing and Multivariate Testing - Experimentation techniques that compare different versions of a product or process to determine the most effective one. An example of use is optimizing website design or marketing campaigns through controlled experiments.
Monte Carlo simulation - A sub-type of simulation that involves generating repeated random samples to model the probability distribution of various outcomes and obtain numerical results. An example of use is analyzing the potential outcomes and uncertainties associated with a construction project schedules and costs.

Prescriptive analytics often involves a combination of these techniques to provide comprehensive and actionable recommendations. It requires a deep understanding of business objectives, constraints, and the ability to interpret and act on the insights provided by the models and tools.

Some of the most commonly used tools for Prescriptive Analytics include ML libraries, data mining tools, data visualization tools, BI software, statistical modeling tools, and predictive analytics software.

Tools used for prescriptive analytics include optimization software, simulation tools, neural networks, AI/ML, prescriptive analytics platforms, advanced analytics platforms and BI tools.

How to Get Started with Prescriptive Analytics
To get started with predictive analytics, one must first build a solid foundation in descriptive, diagnostic, and predictive analytics and then learn optimization techniques and algorithms since prescriptive analytics revolves around defining objectives, collecting and integrating relevant data, preparing data, selecting prescriptive analytics tools, building models, implementing recommendations, monitoring and assessing performance, and constantly enhancing predictive analytics models.