Data is often referred to as the new oil. For good reason.
In our data-driven world, understanding and interpreting data has become a necessity for businesses seeking to make informed decisions. This process, known as data analysis, has several levels - each offering unique insights that can drive strategic decision-making. In this article, we will delve into the seven levels of data analysis: Descriptive, Diagnostic, Predictive, Prescriptive, Causal, Exploratory, and Big Data analysis.
We will also explore how Artificial Intelligence (AI) fits into these levels, the tools used, job roles, and practical applications in various industries.Understanding the relationship between the levels of data analysis and data governance can help businesses maximize the value they derive from their data while minimizing risks.
1. Descriptive Analysis: What Happened?
Descriptive analysis, the most basic level of data analysis, involves summarizing raw data to make it understandable. The central question here is, "What happened?" Tools such as Microsoft Excel, Tableau, and Power BI are commonly used for generating visualizations and reports.
Job roles: Data Analysts, Business Intelligence Specialists.
Skills required: Basic understanding of statistics, data visualization techniques.
Industry application: A retail business analyzing its past sales performance.
2. Diagnostic Analysis: Why Did It Happen?
Diagnostic analysis goes a step further to understand why certain events occurred. It seeks to answer the question, "Why did it happen?" Tools like SQL for database querying and statistical software like SPSS or SAS are typically used at this stage.
Job roles: Data Scientists, Statisticians.
Skills required: Advanced statistical methods, problem-solving skills.
Industry application: An e-commerce company investigating the cause of a sudden drop in website traffic.
3. Predictive Analysis: What Will Happen?
Predictive analysis uses historical data to predict future events. The question here is, "What will happen?" Tools like R, Python, and predictive modeling software like RapidMiner or KNIME are commonly used.
Job roles: Data Scientists, Machine Learning Engineers.
Skills required: Advanced statistics, programming, machine learning.
Industry application: An insurance company predicting the likelihood of a claim being fraudulent.
4. Prescriptive Analysis: What Should We Do?
Prescriptive analysis provides recommendations on what actions to take based on the data. It answers the question, "What should we do?" Tools such as Python, R, and optimization tools like FICO Xpress and IBM ILOG CPLEX are used at this level.
Job roles: Decision Analysts, Operations Research Analysts.
Skills required: Advanced analytics, decision-making skills.
Industry application: A logistics company optimizing delivery routes and schedules.
5. Causal Analysis: What Causes What?
Causal analysis identifies the cause-and-effect relationships between different variables. The question here is, "What causes what?" Tools such as R and Python, along with packages for causal inference, are commonly used.
Job roles: Data Scientists, Statisticians.
Skills required: Understanding of statistical methods, causal inference techniques.
Industry application: A pharmaceutical company studying the effect of a drug on patient recovery rates.
6. Exploratory Analysis: What Are We Not Seeing?
Exploratory analysis is used to discover patterns, relationships, or anomalies that weren't previously known. It seeks to answer, "What are we not seeing?" Tools like Python, R, and Tableau are often used for this purpose.
Job roles: Data Analysts, Data Scientists.
Skills required: Data mining, data visualization, problem-solving skills.
Industry application: A marketing firm identifying new customer segments.
7. Big Data Analysis: How Can We Handle Massive Amounts of Data?
Big Data analysis involves processing vast amounts of data that can't be handled using traditional methods. It requires specialized tools like Hadoop, Spark, and Hive.
Job roles: Big Data Analysts, Big Data Engineers.
Skills required: Understanding of big data technologies, programming.
Industry application: A social media platform analyzing user behavior from millions of posts.
The Role of AI in Data Analysis
AI, specifically machine learning, plays a vital role in many levels of data analysis. It can automate tasks, improve accuracy, and provide deeper insights. In predictive analysis, machine learning models are trained on historical data to forecast future events. In prescriptive analysis, AI can simulate various scenarios and recommend optimal actions. Popular AI tools used by data analysts include TensorFlow, PyTorch, and Scikit-learn.
Understanding these levels of data analysis is crucial for any organization looking to leverage its data effectively. Each level offers unique insights that can drive informed decision-making. With the advent of AI, these processes can be automated and enhanced, leading to more accurate predictions and optimized strategies.
Your Future in Data Awaits
No matter where you stand today, the world of data has a place for you. With the right guidance, education, and determination, you can ascend the data career ladder and make a lasting impact.
Act Now: Contemplating a career in data? Let the Lacroix Institute be your guiding star. Reach out today, and our advisors will help you get started or explore your options. The future is data-driven; let's embark on this journey together.
