Introduction
What is data analytics? It’s a question we’ve all asked at some point. You’ve probably seen the words “data analytics” thrown around in company meetings, but you’re still not quite sure what it means and if it’s relevant to your business. Perhaps even more importantly, why should I care about data analytics? In this post, we’ll look at what data analytics is and give you some examples of how companies use it to make better decisions about their products and services.
Data analytics is a broad term that refers to any type of analysis performed on data.
Data analytics is a broad term that refers to any type of analysis performed on data. It can refer to any number of different types of analyses and approaches, but it’s not limited to one specific type of analysis.
Data analytics can be used in many different ways, such as:
- Predictive modeling and machine learning algorithms (e.g., neural networks) — these are used to predict future outcomes based on historical data. For example, you could use this approach to predict what products will sell best at your store based on past sales trends or which customers are likely to leave for another company if they don’t get the service they want from you right away!
- Descriptive statistics — these include measures like mean, median and mode; percentiles; standard deviation; quartiles etc.. These allow us not only understand what our numbers mean but also compare them against other sets of numbers so we know whether they are higher/lower than average or have some other interesting characteristic.”
There are three main types of data analytics applications: descriptive, predictive, and prescriptive.
There are three main types of data analytics applications: descriptive, predictive and prescriptive.
Descriptive analytics is used to analyze current conditions and trends. For example, you may want to know how many customers have visited your website in the past month or what products they were interested in purchasing. Predictive analytics helps you predict future behavior based on historical patterns (e.g., if we offer the same product at a discount during Black Friday sales next year, we can expect increased sales). Prescriptive analytics identifies patterns in business processes to make recommendations for future outcomes (e.g., if we change our pricing structure based on seasonality and customer demand).
Descriptive analytics is used to analyze current conditions and trends, while predictive analytics is used to predict future behavior based on historical patterns.
Descriptive analytics: This is the kind of data analysis that helps you understand what’s happening now. For example, if your company wants to know how many people are buying its products in a given month, descriptive analytics will tell you exactly who those customers are and how much they’re spending.
Predictive analytics: Predictive analysis uses historical data to predict future outcomes based on current conditions. This can include things like estimating sales based on past sales figures or predicting customer service issues based on customer complaints received over time (or even before they happen).
Prescriptive analytics: Prescriptive analytics takes predictive modeling one step further by helping companies decide what actions should be taken based on their insights into customers’ behavior patterns or other factors affecting business performance–and then automate those actions through software systems so employees don’t have to manually do everything themselves!
Prescriptive analytics identifies patterns in business processes to make recommendations for future outcomes.
Prescriptive analytics is a subset of predictive analytics. It’s used to recommend actions based on historical data, but unlike predictive analytics (which looks at past performance), prescriptive analytics looks at future outcomes and makes recommendations for how to achieve them.
Prescriptive analysis can be applied in many industries or business processes: healthcare providers could use it to determine which treatments will be most effective; marketing departments could use it to predict customer behavior; retailers could use it to optimize inventory management and pricing strategies.
Data analytics helps you understand your customers better and leads you to more profitable decisions.
Data analytics is a tool for understanding your customers. It helps you make better decisions about how to serve them and find new ways to do so. For example, if you have a store that sells clothing, data analytics can tell you which kinds of clothing are most popular with your customers, who the most frequent shoppers are, and where they live–all valuable insights into how best to serve them.
Another way data analytics helps businesses is by helping them understand their competition better so they know what sets each business apart and what challenges they face in terms of attracting new customers or retaining current ones.
Conclusion
Data analytics can be a powerful tool. It can help you understand your customers better and lead you to more profitable decisions. But it’s also important to remember that data analytics is just one piece of the puzzle when it comes to making smart business decisions. You still need good data and analysis skills, as well as an understanding of what questions you want answered by this analysis before starting any project.
