In today’s data-driven world, the ability to gather, process, and analyze data is critical for making informed decisions. Data analytics provides organizations with the tools to gain insights and drive value from the vast amounts of data they collect. There are four types of data analytics – descriptive, diagnostic, predictive, and prescriptive – each with its own unique approach and purpose.
What happened in the past?
This type of analytics deals with summarizing and describing historical data to gain insights and understanding. It provides answers to questions like what happened, when did it happen, how often did it happen, and where did it happen.
Example: Let’s say a company wants to analyze its website traffic over the past year. They can use descriptive analytics to summarize and visualize the data, such as the number of visits, page views, bounce rate, and average time on site. This type of analysis helps them understand what happened and how their website performed over time.
This type of analytics deals with examining past data to identify the reasons why certain events occurred. It provides answers to questions like why did it happen, what were the contributing factors, and what can we learn from it?
Example: If the company notices a sudden drop in website traffic, it can use diagnostic analytics to identify the possible causes. They can examine different metrics, such as the sources of traffic, keywords used in search queries, and content engagement. This type of analysis helps them answer the question of why it happened and what specific factors contributed to the drop in traffic.
What awaits us in the future?
This type of analytics deals with analyzing current and historical data to make predictions about future events. It uses statistical algorithms and machine learning techniques to identify patterns and trends in the data and make predictions about future outcomes.
Example: The company can use predictive analytics to forecast future website traffic based on historical data and other relevant factors. They can use machine learning algorithms to identify patterns and predict future outcomes, such as the expected number of visits or the likelihood of a visitor converting into a customer. This type of analysis helps them anticipate future trends and make data-driven decisions to optimize their website performance.
This type of analytics deals with using data and insights to recommend the best course of action for a given situation. It uses advanced techniques like optimization and simulation to evaluate different options and recommend the best possible solution.
Example: Let’s say the company wants to increase its website traffic and improve its conversion rate. They can use prescriptive analytics to identify the best course of action. They can analyze different scenarios and evaluate the potential outcomes of each, such as optimizing the website content, increasing ad spend, or implementing a referral program. This type of analysis helps them recommend the best action to achieve their desired outcomes.