Data analytics is an umbrella term for a number of different ways that data can be analyzed. Data analytics can occur in the cloud or on-premises, and can use methods from statistics, data mining, predictive modeling and machine learning.
Data analytics tools may include spreadsheets, databases, statistical programs and online dashboards. These tools can provide insights about customers, competitors and business performance. They can also help businesses make more strategic decisions by showing trends and relationships between factors, such as sales versus weather patterns.
Data analytics often starts with descriptive analysis to provide insights into what happened, why it happened and how to fix it. Predictive analytics looks at what might happen next, while prescriptive analytics recommends specific actions to take or decisions to make based on predictions.
Data analytics has many applications. In general, a business analyst will take a large amount of data and use it to identify trends or make predictions about the future. Many other functions of a business rely on data analysis to make decisions- in fact, almost all of them will. In addition to business intelligence, the function that focuses purely on data analytics to make decisions for the business and understand it further, engineering, sales, marketing, and product will all take insights for analyzing data to make better informed decisions.
One specific type of data analytics is known as web analytics, which involves aggregating data about who visits a website and what they do when they get there.
There are several different types of data analytics strategies including:
In the context of business, data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics (DA) is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists to verify or disprove scientific models, theories and hypotheses.
Data analytics can be considered a subset of business intelligence (BI), which refers to a broad set of applications and methodologies for collecting, storing, analyzing and providing access to data to help enterprise users make better business decisions. In addition, BI can include the presentation layer that provides end users with tools such as dashboards, scorecards, reports and alerts when important changes occur within an organization's business environment.
Data analytics is a diverse field with a wide range of applications across various industries. In finance, algorithmic trading firms like Citadel employ data analytics to make split-second decisions in the stock market, utilizing historical data and real-time market feeds to optimize their trading strategies. In the healthcare sector, organizations like IBM Watson Health use data analytics to analyze patient records and medical research, aiding in the discovery of personalized treatment options and disease prevention. Retail giants like Amazon leverage data analytics to provide personalized product recommendations based on user browsing and purchase history, enhancing the customer shopping experience. These examples highlight how data analytics empowers organizations to extract valuable insights from data, drive efficiency, and make data-driven decisions in fields as diverse as finance, healthcare, and e-commerce.