Types of Data Analytics

Summary

Jonathan Wild introduces the different types of data analytics, each serving a unique purpose in business decision-making. Descriptive analytics helps understand past data, diagnostic analytics identifies causes for certain events, predictive analytics forecasts future outcomes, and prescriptive analytics advises on the best actions to take. The video also highlights cognitive analytics, an advanced AI-driven approach for businesses looking to stay on the cutting edge. Together, these analytics tools empower businesses to optimize their operations and make data-driven decisions.

Takeaways

• 😀 Descriptive Analytics focuses on analyzing past and present data to understand what has happened or is currently happening in a business.
• 😀 Descriptive Analytics is often used for reporting purposes, like creating a sales report for the previous month.
• 😀 Diagnostic Analytics aims to answer 'why' something is happening by identifying the root causes of specific events or trends.
• 😀 An example of Diagnostic Analytics is analyzing why returns are higher at a particular store location.
• 😀 Predictive Analytics looks forward and uses past data to forecast future outcomes, like predicting sales for the next quarter.
• 😀 Prescriptive Analytics provides recommendations on what actions to take based on predictions, helping businesses make informed decisions.
• 😀 Companies use Prescriptive Analytics to test different decision outcomes, such as determining the best location for a new store to maximize sales.
• 😀 Descriptive and Diagnostic Analytics tend to be simpler and focus on past data, offering less complexity but valuable insights.
• 😀 Predictive and Prescriptive Analytics focus on the future, are more complex, and provide higher value to businesses, but they require advanced models and statistical methods.
• 😀 Cognitive Analytics is the cutting-edge form of analytics, using artificial intelligence (AI), deep learning, and machine learning to handle complex tasks like analyzing call center data for pattern recognition.
• 😀 Cognitive Analytics represents the future of business intelligence, helping companies make data-driven decisions through advanced technologies.

Q & A

What is the primary purpose of data analytics?

The primary purpose of data analytics is to analyze data to discover important trends, patterns, and insights that can help businesses make informed decisions.

What is descriptive analytics and when is it used?

Descriptive analytics is used to understand what has happened and what is currently happening in a business. It is commonly used for reporting and turning raw data into easy-to-read formats, such as sales reports.

How does diagnostic analytics differ from descriptive analytics?

Diagnostic analytics goes a step further by answering why something happened. While descriptive analytics reports what happened, diagnostic analytics investigates the root causes of events, such as understanding why returns increased at a particular store.

What role does predictive analytics play in business?

Predictive analytics helps businesses forecast future outcomes by analyzing past data and statistical models. For example, companies use predictive analytics to project future sales, customer behavior, or market trends.

Can you explain how prescriptive analytics supports decision-making?

Prescriptive analytics helps businesses determine the best course of action based on predictive insights. It suggests what a company should do next by analyzing different potential outcomes. For example, it could help a business choose the best location for a new store.

What is the difference between predictive and prescriptive analytics?

Predictive analytics forecasts what is likely to happen in the future, whereas prescriptive analytics suggests the best actions to take based on those predictions. Predictive analytics is about forecasting, and prescriptive analytics is about guiding decisions.

Why are predictive and prescriptive analytics considered more complex than descriptive and diagnostic analytics?

Predictive and prescriptive analytics are more complex because they involve advanced statistical models, forecasting, and decision-making algorithms that require deeper data analysis and computational resources, whereas descriptive and diagnostic analytics are more focused on summarizing and investigating past events.

What is cognitive analytics, and how is it different from traditional analytics?

Cognitive analytics uses advanced technologies like AI, deep learning, and machine learning to analyze large, complex datasets. It goes beyond traditional analytics by automating data analysis and decision-making, making it more efficient and capable of identifying patterns that humans might miss.

Can you provide an example of cognitive analytics in business?

An example of cognitive analytics in business is using AI to analyze thousands of customer service call logs to detect patterns or issues, enabling businesses to improve customer support and make data-driven decisions.

How do businesses benefit from investing in cognitive analytics?

Businesses benefit from cognitive analytics by being able to process large and complex datasets faster and more accurately. It allows them to uncover hidden insights, automate decision-making, and stay ahead of the competition by using cutting-edge technologies like AI and machine learning.