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Mar 26, 2024 · Big data analytics refers to the process of examining large, diverse datasets—structured, semi-structured, and unstructured—to extract meaningful information. It involves using advanced analytical techniques, algorithms, and tools to identify trends, predict outcomes, and generate actionable insights. Big data is characterized by the 4Vs ...
- What is data analysis and why is it important? Data analysis is, put simply, the process of discovering useful information by evaluating data. This is done through a process of inspecting, cleaning, transforming, and modeling data using analytical and statistical tools, which we will explore in detail further along in this article.
- What is the difference between quantitative and qualitative data? How you analyze your data depends on the type of data you’re dealing with—quantitative or qualitative.
- Data analysis techniques. Now we’re familiar with some of the different types of data, let’s focus on the topic at hand: different methods for analyzing data.
- The data analysis process. In order to gain meaningful insights from data, data analysts will perform a rigorous step-by-step process. We go over this in detail in our step by step guide to the data analysis process—but, to briefly summarize, the data analysis process generally consists of the following phases
Techniques. Data Aggregation: Gathering data from different sources and putting it together to see the big picture. Data Mining: Digging deep into data to find important patterns or information. Data Visualization: Making different types of graphs, charts, and maps to help people see what the data says. Example.
- Diagnostic analytics. Diagnostic analytics is one of the more advanced types of big data analytics that you can use to investigate data and content. Through this type of analytics, you use the insight gained to answer the question, “Why did it happen?”.
- Descriptive analytics. Descriptive analytics is one of the most common forms of analytics that companies use to stay updated on current trends and the company’s operational performances.
- Prescriptive analytics. Prescriptive analytics takes the results from descriptive and predictive analysis and finds solutions for optimizing business practices through various simulations and techniques.
- Predictive analytics. As the name suggests, this type of data analytics is all about making predictions about future outcomes based on insight from data.
The main difference between big data analytics and traditional data analytics is the type of data handled and the tools used to analyze it. Traditional analytics deals with structured data, typically stored in relational databases. This type of database helps ensure that data is well-organized and easy for a computer to understand.
Big data refers to the vast volumes of data generated every second from various sources such as social media, business transactions, sensors, and more. This data can be structured, semi-structured, or unstructured. The primary characteristics of big data are often described by the three Vs: Volume, Velocity, and Variety.
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Jan 2, 2024 · Understanding the different types of big data analytics, what each is used for, and their strengths and weaknesses is critical in harnessing the full potential of data-driven decision-making. Knowing the distinction between various types of data analytics allows you to fully leverage the array of data analytics tools and techniques.
