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What is data analytics? Transforming data into better decisions
What is data analytics?
Data analytics focuses on gleaning insights from data. It comprises the processes, tools, and techniques of data analysis and management, and its chief aim is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise to shape business processes and improve decision-making and business results.
Data analytics draws from a range of disciplines, including computer programming, mathematics, and statistics, to perform analysis on data in an effort to describe, predict, and improve performance. So to ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more.
What is AI data analytics?
AI data analytics is a rapidly growing specialty within data analytics that applies AI to support, automate, and simplify data analysis. It leverages ML, natural language processing, and data mining, along with foundational models and chat assistance for predictive analytics, sentiment analysis, and AI-enhanced business intelligence. AI tools can be used for data collection and data preparation, while ML models can be trained to extract insights and patterns.
The four types of data analytics
Analytics breaks down broadly into four types: descriptive analytics attempts to describe what has transpired at a particular time; diagnostic analytics assesses why something has happened; predictive analytics ascertains the likelihood of something happening in the future; and prescriptive analytics provides recommended actions to take to achieve a desired outcome.
To explore these more specifically, descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. Business analytics is the purview of business intelligence (BI). Diagnostic analytics uses data, often generated via descriptive analytics, to discover the factors or reasons for past performance. Predictive analytics applies techniques such as statistical modeling, forecasting, and ML to the output of descriptive and diagnostic analytics to make predictions about future outcomes. Predictive analytics is often considered a type of advanced analytics, and frequently depends on ML and/or deep learning. And prescriptive analytics is another type of advanced analytics that involves the application of testing and other techniques to recommend specific solutions that will deliver outcomes. In business, predictive analytics uses ML, business rules, and algorithms.
Data analytics methods and techniques
Data analysts use a number of methods and techniques to analyze data. According to Emily Stevens, managing editor at CareerFoundry, seven of the most popular include:
- Regression analysis: A set of statistical processes used to estimate the relationships between variables to determine how changes to one or more might affect another, like how social media spending might affect sales.
- Monte Carlo simulation: A mathematical technique, frequently used for risk analysis, that relies on repeated random sampling to determine the probability of various outcomes of an event that can’t otherwise be readily predicted due to degrees of uncertainty in its inputs.
- Factor analysis: A statistical method for taking a massive data set and reducing it to a smaller, more manageable one to uncover hidden patterns, like when analyzing customer loyalty.
- Cohort analysis: A form of analysis in which a dataset is broken into groups that share common characteristics, or cohorts, for analysis like understanding customer segments.
- Cluster analysis: A statistical method in which items are classified and organized into clusters in an effort to reveal structures in data. Insurance firms might use cluster analysis to investigate why certain locations are associated with particular insurance claims, for instance.
- Time series analysis: A statistical technique in which data in set time periods or intervals is analyzed to identify trends over time, such as weekly sales numbers or quarterly sales forecasting.
- Sentiment analysis: A technique that uses natural language processing, text analysis, computational linguistics, and other tools to understand sentiments expressed in data, such as how customers feel about a brand or product based on responses in customer forums. While the previous six methods seek to analyze quantitative or measurable data, sentiment analysis seeks to interpret and classify qualitative data by organizing it all into themes.
Data analytics tools
Data analysts use a range of tools to aid them surface insights from data. Some of the most popular include:
- Apache Spark: An open source data science platform to process big data and create cluster computing engines.
- AskEnola AI: A conversational analytics tool for business users.
- Data analysis with ChatGPT: OpenAI’s chatbot can generate code to perform data analysis, transformation, and visualization tasks using Python.
- dbt: An open source analytics engineering tool for data analysts and engineers.
- Domo Analytics: A BI SaaS platform to gather and transform data.
- Excel: Microsoft’s spreadsheet software for mathematical analysis and tabular reporting.
- Julius AI: An AI assistant to analyze spreadsheets and databases.
- Knime: A free and open source data cleaning and analysis tool for data mining.
- Looker: Google’s data analytics and BI platform.
- MySQL: An open source relational database management system to store application data used in data mining.
- Observable: A data analysis platform with AI tools for exploratory data analysis and data visualization.
- Orange: A data mining tool ideal for smaller projects.
- Power BI: Microsoft’s data visualization and analysis tool to create and distribute reports and dashboards.
- Python: An open source programming language popular among data scientists to extract, summarize, and visualize data.
- Qlik: A suite of tools to explore data and create data visualizations.
- R: An open source data analytics tool for statistical analysis and graphical modeling.
- RapidMiner: A data science platform that includes a visual workflow designer.
- SAS: An analytics platform for business intelligence and data mining.
- Sisense: A popular self-service BI platform.
- Tableau: Data analysis software from Salesforce to create data dashboards and visualizations.
Data analytics vs. data science
Data analytics is a component of data science used to understand what an organization’s data looks like. Generally, the output of data analytics are reports and visualizations. Data science takes the output of analytics to study and solve problems. The difference between data analytics and data science is often about timescale. Data analytics describes the current or historical state of reality, whereas data science uses that data to predict and/or understand the future.
Data analytics vs. data analysis
While the terms data analytics and data analysis are frequently used interchangeably, data analysis is a subset of data analytics concerned with examining, cleansing, transforming, and modeling data to derive conclusions. Data analytics includes the tools and techniques used to perform data analysis.
Data analytics vs. business analytics
Business analytics is another subset of data analytics. It uses data analytics techniques, including data mining, statistical analysis, and predictive modeling, to drive better business decisions. Gartner defines business analytics as solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.
Data analytics examples
Organizations across all industries leverage data analytics to improve operations, increase revenue, and facilitate digital transformations. Here are three examples:
UPS transforms air cargo operations with data, AI: UPS’s Gateway Technology Automation Platform (GTAP) uses AI and digital asset tracking to reduce costs, improve on-time performance, and enhance operational safety at its Worldport air hub.
NFL leverages AI and predictive analytics to reduce injuries: The NFL’s Digital Athlete platform leverages AI and ML to run millions of simulations of in-game scenarios, using video and player tracking data to identify the highest risk of injury during plays, and develop individualized injury prevention courses.
Fresenius Medical Care anticipates complications with predictive analytics: Fresenius Medical Care, which specializes in providing kidney dialysis services, is pioneering the use of a combination of near real-time IoT data and clinical data to predict when kidney dialysis patients might suffer a potentially life-threatening complication called intradialytic hypotension (IDH).
Data analytics salaries
According to data from PayScale, the average annual salary for a data analyst is $70,384, with a reported range from $51,000 to $95,000. Salary data on similar positions include:
| JOB TITLE | SALARY RANGE | AVERAGE SALARY |
| Analytics manager | $79,000 to $140,000 | $110,581 |
| Business analyst, IT | $58,000 to $114,000 | $80,610 |
| Data scientist | $73,000 to $145,000 | $103,441 |
| Quantitative analyst | $74,000 to $161,000 | $109,421 |
| Senior business analyst | $72,000 to $127,000 | $95,484 |
| Statistician | $61,000 to $139,000 | $97,082 |
PayScale also identifies cities where data analysts earn salaries that are higher than the national average. These include San Francisco (24.2%), Seattle (10.2%), and New York (9.5%).
