What is Data Analytics?
Types of Data Analytics
- Descriptive analytics helps answer the question “What happened?” For a business, this can be used to describe outcomes to stakeholders. By developing key performance indicators (KPIs), descriptive analysis strategies can help track successes or failures. For example, metrics such as return on investment (ROI) are often used. Specialized metrics can also be developed to track performance specific to an industry. This process requires the collection of relevant data, data processing, analysis and visualization. Together, this can provide essential insight into past performance.
- Diagnostic analytics helps answer questions about why things happened. These techniques supplement basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the causes behind trends and outcomes. Key performance indicators are further investigated to discover why they improved or worsened. This analysis could look like the following steps:
- Identify anomalies in the data. These may be unexpected changes in a metric or a particular market.
- Collect data related to these anomalies.
- Implement statistical techniques to find relationships and trends that explain the anomalies.
- Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to reoccur or change. Predictive analytical tools and techniques include a variety of statistical and machine learning techniques, such as neural networks, decision trees and regression.
- Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made, even in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large datasets.
What is the Role of Data Analytics?
Why is Data Analytics Important?
Data Analytics FAQ
Data analytics helps individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.
There are various types of data analysis including descriptive, diagnostic, prescriptive and predictive analytics. Each type is used for specific purposes depending on the question a data analyst is trying to answer. For example, a data analyst would use diagnostic analytics to figure out why something happened.
There are various tools used in data analysis. Some data analysts use business intelligence software, such as Tableau. Others may use programming languages such as SQL or Python, which have various statistical and visualization libraries.
There is not much career data specific to data analysts, but they fall within the same category as data scientists. According to the U.S. Bureau of Labor Statistics, the projected growth for data scientists is 35% between 2022 and 2032. The same data shows that data scientists earned a median salary of $103,500 in 2022. However, salary compensation for data analysts varies depending on where they work and what industry they work in.