Analytics

Analytics is the systematic computational analysis of data or statistics. It helps people find, understand, and share important patterns in information. This process is part of a larger field called data science and involves using data patterns to make better decisions.

People and organizations use analytics to study business data, helping them describe, predict, and improve how well they are doing. There are different types of analytics, such as descriptive, diagnostic, predictive analytics, prescriptive analytics, and cognitive analytics. Analytics can be applied in many areas, including marketing, management, finance, online systems, information security, and software services.

Because analytics often needs to handle large amounts of data, known as big data, it uses the latest methods from computer science, statistics, and mathematics. In fact, spending on big data and business analytics solutions was estimated to reach $215.7 billion in 2021, and the market for analytic platforms software grew by $25.5 billion in 2020, according to International Data Corporation and Gartner.

Analytics vs analysis

Data analysis looks at past information to understand what happened. It is part of a bigger area called data analytics. Analytics uses many steps of data analysis to figure out why things happened and what might happen next. This helps leaders make better choices for their groups or companies.

Analytics is a field that uses many different skills. It needs good computer knowledge, math, statistics, and ways to describe data. It also uses special methods to predict what might happen in the future. These methods include things like neural networks, decision trees, and other types of modeling to understand patterns in data. It also uses ways to find groups and relationships in data without being told what to look for.

Applications

Marketing optimization

Marketing groups use analytics to check how well their campaigns work and to help decide where to spend money and who to target. They look at things like who their customers are, what they buy, and what they think. This helps them understand customers better and share their messages more effectively.

Marketing analytics uses different kinds of information to help companies decide about brands and making money. It includes making predictions, testing ideas, using machines to help, and sharing sales information right away. Companies use this to make smart choices and improve their results.

People analytics

People analytics studies how people behave at work to help improve how companies are run. It can also be called workforce analytics or HR analytics. This helps companies decide who to hire, who to reward, and what jobs to give people. For example, it can help figure out why employees leave a company during tough times.

Portfolio analytics

Businesses use analytics to look at groups of loans or accounts. Banks have many accounts, some risky and some safe. They need to balance making money with the chance that people might not pay back loans. Analytics helps decide who to lend to and how much to charge for loans.

Risk analytics

Banks use models to guess how likely a person is to miss a payment. They use this to decide if someone is a good risk for a loan. Companies also use risk analysis to spot fake transactions, like when someone suddenly spends a lot of money on a credit card.

Digital analytics

Digital analytics collects and studies data from online activities. This includes tracking what people search for and how they click on ads. Many companies use this to improve their online marketing and see how well their efforts are working.

Security analytics

Security analytics uses technology to watch for events that might be dangerous to a company's computer systems. Tools help spot unusual activities that could be threats.

Software analytics

Main article: Software analytics

Software analytics collects information about how people use and create software.

Challenges

Businesses today deal with huge amounts of changing information, called big data. This makes it hard to find useful patterns. Another challenge is analyzing information that doesn’t fit into regular tables, like emails or documents. This type of information is called unstructured data.

These challenges lead to new ideas in how we handle information. For example, some systems now use many computers working together to process information faster. Analytics is also used in education to help understand how students are doing, but it can be tricky for teachers to use these tools correctly. Some tools are designed to be easier to understand for educators.

Risks for people include unfair treatment based on things like gender or skin colour, such as through different prices or statistical discrimination.