There’s no doubt that data analytics is important for businesses—it helps them make better decisions, understand their customers, and improve their products and services. By providing businesses with real-time insights and accurate information, data analytics can help businesses gain—and maintain—a competitive edge in the marketplace.
Before you employ data analytics in your business, it’s important to understand the different types of analytics processes and their respective benefits. Keep reading to learn more about the different types of data analytics and when to use them
Understanding Data Analytics
So, what is data analytics? Put simply, data analytics is the process of gathering data, manipulating it into a usable format, and then analyzing it to find trends or patterns. Businesses can use analytics to extract valuable insights into Social media posts and email correspondence are examples of unstructured data.
There are different types of data analytics processes; the one you choose will depend on your specific needs and goals.
Descriptive analytics is a branch of analytics that deals with describing what has happened in the past. It usually employs historical data to create reports and dashboards that show how a business is performing. This can include data such as customer demographics, purchasing habits, and website traffic. By understanding how customers behave and identifying trends, businesses can make better decisions about what actions to take in the future.
One of the biggest benefits of descriptive analytics is that it can help businesses understand what is happening in real-time. Descriptive analytics can also help businesses understand how different changes may impact their business. For example, if a business were to change its prices, descriptive analytics can illustrate how this would impact sales.
What was the average sale amount last month? How many customers visited my store today? Which products are selling the best? What was our profit margin last quarter?
Businesses can utilize descriptive analytics to determine the best way to allocate their resources and how to improve their operations.
Predictive analytics is a field of data analytics that uses statistics and machine learning to make predictions about future events. There are many different applications for prescriptive analytics. Retailers can use it to recommend what products to stock and how to price them. Airlines can use it to recommend when to offer promotions and what routes to fly. Hospitals can use it to recommend what treatments to prescribe for patients.
The use of this analytics process has exploded in recent years due to the availability of big data. With so much data available, companies can use predictive analytics to figure out how to best target their customers and determine ways they can improve the customer service experience. For example, if a company knows that a customer is likely to leave, it can use predictive analytics to develop a strategy to prevent that from happening.
Some of the most common uses of predictive analytics include:
- Predicting customer behavior
- Predicting product demand
- Predicting financial trends
- Predicting election outcomes
- Predicting stock prices
There are three main types of predictive analytics: Supervised learning, unsupervised learning, and reinforcement learning.It involves using algorithms to find patterns in data and then using those patterns to make predictions about new data.It involves using algorithms to learn how best to achieve the desired outcome by trial and error.
Prescriptive analytics is a type of analytics that uses machine learning and artificial intelligence to provide recommendations for what actions should be taken to achieve specific goals. It goes beyond descriptive and predictive analytics by not just telling you what has happened or what might happen in the future, but also by telling you what you should do to achieve a desired outcome.
Prescriptive analytics works by using data from a variety of sources, including historical data, transactional data, and data from sensors and other devices. The algorithms then utilize this information to create models that can predict the likely outcomes of various actions.
One of the benefits of prescriptive analytics is that it can help businesses to identify and take advantage of opportunities that they might not have been aware of otherwise. It can also help businesses to mitigate risks and make better decisions.
All three of these types of data analytics are important for businesses. Descriptive analytics can help businesses understand their customers and identify trends. Predictive analytics can help businesses predict what customers will do in the future and shape their marketing campaigns accordingly. And prescriptive analytics can lead to better decision-making based on predictions about what will happen in the future.