Data analytics is the process of analyzing raw data to find trends and answer questions. It has a broad scope across the field. This process includes many different techniques and goals that can shift from industry to industry.

The data analytics process has components that can help a variety of initiatives. By combining these components, a successful data analytics initiative can help answer business questions related to historical trends, future predictions and decision making.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends and summarizing data. Another part of data analytics is advanced analytics. This process takes advantage of advanced tools to extract data, such as machine learning and deep learning, to make predictions and discover trends.

The availability of machine learning tools, massive data sets and more affordable computing power helped enable the use of these techniques and others in many industries. Big data analytics assists businesses in drawing meaningful conclusions from complex and varied data sources.

Data analytics is a broad field. There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and place in the data analysis process. These are also the primary data analytics applications in business.

These types of data analytics provide the insight that businesses need to make effective and efficient decisions. Used in combination, they provide a well-rounded understanding of a company’s needs and opportunities.

Data analysts work at the intersection of information technology, statistics and business. They combine these fields in order to help businesses and organizations succeed. A major goal for data analysts is to increase efficiency and improve performance by discovering patterns in data.

The work of a data analyst involves working with data throughout the data analysis pipeline. The primary steps in the data analytics process are data mining, data management, statistical analysis and data presentation. The balance of these steps depend on the data being used and the goal of the analysis.

Data mining is an important step for many data analytics tasks. This involves extracting data from unstructured data sources. These may include written text, large complex databases or raw sensor data. The key components in this step are to extract, transform and load data (often called ETL). ETL converts raw data into a useful and manageable format and prepares data for storage and analysis.

Data management or data warehousing is another key aspect of a data analyst’s job. Data warehousing involves designing and implementing databases that allow easy access to clean data. This step generally involves creating and managing SQL databases. Non-relational and NoSQL databases are also common.

Statistical analysis allows analysts to find insights within data. Both statistics and machine learning techniques are used to analyze data and create statistical models that reveal trends. These models can then be applied to new data to make predictions and inform decision making. Statistical programming languages such as R or Python (with pandas) are essential to this process. In addition, open-source libraries and packages such as TensorFlow enable advanced analysis.

The final step in many data analytics processes is data presentation. This step prepares insights to be shared with stakeholders. Data visualization is an important tool in this step. Compelling visualizations can help tell the story of data, which may help executives and managers understand the importance of these insights.

The applications of data analytics are broad. For example, analyzing big data can optimize efficiency in many different industries. Improving performance enables businesses to succeed in an increasingly competitive world.

Data analytics has an important role in the banking and finance industries, where it is used to predict market trends and assess risk. Credit scores are an example of data analytics that affects consumers. These scores use many data points to determine lending risk. Data analytics is also used to detect and prevent fraud to improve efficiency and reduce risk for financial institutions.

The use of data analytics goes beyond maximizing profits and ROI. Data analytics can provide critical information for healthcare (health informatics), crime prevention and environmental protection. For example, researchers are using machine learning to protect wildlife.

The use of data analytics in healthcare is already widespread. Predicting patient outcomes, efficiently allocating funding and improving diagnostic techniques are just a few examples of how data analytics is revolutionizing healthcare. The pharmaceutical industry is also using machine learning. For instance, drug discovery is a complex task with many variables that machine learning can make easier. Pharmaceutical companies also use data analytics to understand the market for drugs and predict their sales.

The internet of things (IoT) is a concept often used alongside machine learning. Together, these devices provide a great opportunity for data analytics. IoT devices contain sensors that collect meaningful data points for their operation. Devices like the Nest thermostat track movement and temperature to regulate heating and cooling. Smart devices like this can use data to learn from and predict your behavior. This will provide advanced home automation that can adapt to the way you live.

The applications of data analytics are seemingly endless. More and more data is being collected every day—this presents new opportunities to apply data analytics to more parts of business, science and everyday life.

If you are interested in data analytics and considering earning a degree, you may check our lists of online master’s in data science programs and online master’s in business analytics programs.

Last updated October 2023