Companies increasingly work with big data to improve performance and dominate markets. These processes are so important that companies devote whole departments just to managing the data that they have. To help your company grow, here is what you need to know about the types of big data analytics.
What is Big Data Analytics?
Big data analytics refers to the process of collecting, organizing, and analyzing massive data sets. Companies collect more data than ever before, and that data is usually compiled into massive databases waiting to be analyzed. Traditional methods of analyzing that data could take a very long time. Instead, companies rely on modern big data analytics and tools to cut down processing time and provide value right away.
This process involves the use of advanced data analytics algorithms, machine learning techniques, and predictive analytics tools to identify patterns, trends, and correlations in the data. With these insights, organizations can make better, data-driven decisions about their next moves in a market.
What Are the Four Types of Big Data Analytics?
There are four main types of big data analytics techniques that organizations use to derive meaningful insights from their data.
Descriptive Analytics
The first type is descriptive analytics, which summarizes and provides insights into past events and current scenarios. Essentially, it lets you understand specific situations based on what the data shows.
Descriptive analytics focuses on providing an objective summary of what has happened in the past or what is currently happening. That way, your business decisions are made with a much better understanding of current conditions.
Descriptive analytics answers questions such as:
- What happened?
- How much has changed?
- What is the current state of affairs?
- What are the trends and patterns in the data?
For example, a retail store analyzes its sales data from the past year to understand which products were selling the most and the least. By summarizing the sales data, the store owner can make informed decisions about which products to stock or discontinue in the future.
They may also use the data to identify patterns or trends in consumer behavior, such as increased sales during certain seasons or for specific products. This is a very common way to use big data, as it can drive business decisions to invest in specific things that are likely to show returns. In this case, it will help the shop owner decide which products to offer and improve overall sales performance.
Diagnostic Analytics
The second type is diagnostic analytics which uses data mining and machine learning techniques to understand why particular events occurred in the past. By analyzing historical data, it identifies patterns and provides insights into why things happened in the way they did.
Diagnostic analytics can be used for various purposes, such as identifying the reasons behind a decrease in sales, understanding customer preferences, and identifying factors contributing to a high employee turnover rate. This type of analytics focuses on discovering the root cause of a particular problem and can help organizations make more informed decisions about how to address it.
Implementing diagnostic analytics requires a significant amount of historical data, and organizations must have robust data management systems in place to ensure data accuracy and completeness. Machine learning algorithms are then used to analyze the data and identify trends and patterns that can help explain the reasons behind certain events.
Overall, diagnostic analytics provides a deeper understanding of past events and can help organizations develop more effective strategies for the future. By understanding the factors that contribute to specific outcomes, organizations can make data-driven decisions that enable them to optimize their operations and improve their overall performance.
Predictive Analytics
The third type of analytics is predictive analytics, which helps identify future trends by using data mining, machine learning, and statistical modeling techniques. This type of analysis is crucial in determining which outcomes of future events are more likely to occur based on historical data.
This type of analysis is often used in healthcare, finance, and marketing industries to evaluate the effectiveness of previous strategies and identify opportunities for improvement. Diagnostic analytics can also help businesses understand the root causes of problems and make informed decisions about future actions.
For example, a company that experiences a decrease in sales may use diagnostic analytics to identify the reasons behind the decline and create a new marketing strategy to address the issue. Overall, diagnostic analytics helps organizations use past data to drive future success.
Prescriptive Analytics
The fourth type is prescriptive analytics, which goes beyond predictive analytics and provides actionable insights on what course of action to take based on data analysis results. Essentially, it helps organizations to assess multiple outcome scenarios and make informed decisions by presenting a range of possible actions to take based on data insights.
Prescriptive analytics uses optimization techniques to recommend the best possible solution based on the goals set by the organization. It uses machine learning algorithms to evaluate possible outcomes and assign probabilities to future events. This helps organizations make data-driven decisions and take actions that can positively impact their business outcomes.
For instance, a retailer using prescriptive analytics can analyze customer data to determine which discounts or promotions are likely to be effective in increasing sales or encouraging repeat purchases. The retailer can then decide on the most effective course of action to take, such as offering a discount on a specific product or providing targeted offers to a particular group of customers, based on the data insights.
Overall, prescriptive analytics helps organizations to be more proactive and strategic in their decision-making processes by providing actionable insights that can lead to better outcomes and improved business performance.
Putting Big Data To Use
Understanding and putting big data to use is a difficult process. Fortunately, you can get help from KitelyTech, which specializes in helping businesses adopt better technologies. Call us at (800) 274-2908 to discuss your potential uses for big data analytics.
