What is data analytics? Transforming data into better decisions

What is data analytics?

Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools, and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means to analyze and shape business processes, and to 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. 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 are the four types of data analytics?

Analytics breaks down broadly into four types: descriptive analytics, which attempts to describe what has transpired at a particular time; diagnostic analytics, which assesses why something has happened; predictive analytics, which ascertains the likelihood of something happening in the future; and prescriptive analytics, which 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. In business analytics, this 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 machine learning (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 a type of advanced analytics that involves the application of testing and other techniques to recommend specific solutions that will deliver desired 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:

1. 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 — for example, how social media spending might affect sales.
2. 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.
3. Factor analysis: A statistical method for taking a massive data set and reducing it to a smaller, more manageable one to uncover hidden patterns — for example, for analyzing customer loyalty.
4. Cohort analysis: A form of analysis in which a dataset is broken into groups that share common characteristics, or cohorts, for analysis — for example, to understand customer segments.
5. Cluster analysis: A statistical method in which items are classified and organized into groups called 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.
6. 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.
7. 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 data (data that can be measured), 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 
• Domo Analytics: A BI SaaS platform to gather and transform data  
• Excel: Microsoft’s spreadsheet software for mathematical analysis and tabular reporting 
• Klipfolio: A cloud-based web application for self-service BI and reporting 
• Looker: Google’s data analytics and BI platform 
• 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 
• QuickSight: An analytics service from Amazon designed to integrate with cloud data sources 
• R: An open-source data analytics tool for statistical analysis and graphical modeling 
• RapidMiner: A data science platform that includes a visual workflow designer 
• SAP Analytics Cloud: A cloud-based analytics and planning solution 
• 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
• Talend: An ETL tool used by data engineers, data architects, analysts, and developers 
• Zoho Analytics: A self-service BI and data analytics platform 

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:

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).

UPS delivers resilience, flexibility with predictive analytics: Multinational shipping company UPS has created the Harmonized Enterprise Analytics Tool (HEAT) to help it capture and analyze customer data, operational data, and planning data to track the real-time status of every package as it moves across its network. The tool helps it keep track of the roughly 21 million packages it delivers every day.

Predictive analytics helps Owens Corning develop turbine blades: Manufacturer Owens Corning, with the help of its analytics center of excellence, has used predictive analytics to streamline the process of testing the binders used in the creation of glass fabrics for wind turbine blades. Analytics has helped the company reduce the testing time for any given new material from 10 days to about two hours.

Data analytics salaries

According to data from PayScale, the average salary for a data analyst is $66,310 per year, with a reported salary range of $48,000 to $91,000. Salary data on similar positions includes:

JOB TITLE	SALARY RANGE	AVERAGE SALARY
Analytics manager	$74,000 to $136,000	$104,540
Business analyst	$50,000 to $88,000	$66,898
Business analyst, IT	$54,000 to $104,000	$73,893
Data analyst	$48,000 to $91,000	$66,310
Market research analyst	$44,000 to $80,000	$59,103
Operations research analyst	$51,000 to $120,000	$82,833
Quantitative analyst	$65,000 to $142,000	$92,089
Senior business analyst	$67,000 to $121,000	$89,595
Statistician	$59,000 to $126,000	$86,349

PayScale also identifies cities where data analysts earn salaries that are higher than the national average. These include San Francisco (30.8%), New York (10.7%), and Washington (10%).