Data analytics technologies are any data science tool or software used to manipulate and analyze a dataset. While some data analytics technologies use complex algorithms and artificial intelligence to draw conclusions about a dataset, more traditional tools rely on statistical analysis and mathematical calculations to return more descriptive analytics. 

In addition, many data analytics technologies can be described as big data technologies which focus on a more holistic approach to data collection and analysis. These big data technologies create frameworks or platforms for the analysis of data within a complex system of tools and techniques that are used to better understand data correlations. Data analytics technologies encompass many different tools, depending on the types of data analysis needed.

Types of Data Analytics Technologies and Tools 

There are several common types of data analytics technologies depending on the industry that you are in and the type of data that you are working on. Generally, data analytics technologies can be categorized as statistical analysis tools, business intelligence tools, database management tools, and machine learning tools. While these categories do not include all of the tools available to data scientists, the following list offers a general introduction to data analytics technologies and their uses within the industry. 

Statistical Analysis Tools

Data analytics technologies that rely on statistical analysis are the most traditional tools within the data science industry, and they are also the most common tools used for finance and investing, as well as academic research. From beginner-friendly spreadsheet software, which is useful for returning descriptive analytics, to more advanced software, which focuses on prescriptive analytics, statistical analysis tools have many uses across fields. As the name suggests, statistical analysis tools rely on mathematical functions and formulas in order to learn more about a dataset. But, these tools can also require the use of statistics-friendly programming languages like R. And, while many statistical analysis tools only require the use of calculations and theories to analyze data, many of these tools now include features that automate the data analysis process. In addition, many of these tools can transform data analytics into visualizations, such as charts and graphs, as well as organize and clean data.

Examples of Statistical Analysis Tools:Microsoft Excel, IBM SPSS, and SAS (Statistical Analytics Software)

Business Intelligence Tools

Business intelligence (BI) tools have also gained considerable popularity within the data science industry because these tools are more versatile than most traditional data analytics technologies. And, while the name might suggest otherwise, business intelligence tools are not just used in business and finance, but also for any team or individual that requires some form of comparative data analysis—the process of analyzing different parts of a single dataset or comparing multiple datasets at the same time or in the same space. So, many business intelligence tools can be used to not only analyze a dataset but also to create reports and dashboards which compare several sheets and workbooks full of data. Many data scientists utilize BI tools when they have a larger collection of data, but also when they need to make decisions by weighing the costs, benefits, and risks of various scenarios and potential outcomes.

Examples of Business Intelligence Tools:Tableau, Microsoft Power BI, and IBM Cognos Analytics

Database Management Tools

Prior to analyzing data, data scientists must collect and store that data, which is generally when database management tools are used. Database management tools primarily include software for managing and manipulating data, as well as storing that data in a secure database management system. However, database management tools have features that go beyond data storage and include tools for exploratory and diagnostic data analysis. In addition, most database management tools are categorized based on data type, with some tools relying on SQL databases and others utilizing NoSQL databases. But, regardless of the designation, database management tools allow data scientists to search and query data, as well as pinpoint any early data analytics issues within a dataset, such as missing values or other errors.

Examples of Database Management Tools:Microsoft SQL Server, MySQL, and MongoDB

Machine Learning Tools

In contrast to more traditional data analytics technologies, machine learning tools rely on automation, artificial intelligence, and the deployment of machine learning models to analyze a dataset. Similar to business intelligence tools, many machine learning tools can be used for predictive and prescriptive analytics which forecast potential future occurrences or outcomes based on past data. And, while some of these tools are more beginner-friendly and require little to no experience with programming languages, many machine learning tools require some knowledge of coding with languages like Python. These tools also tend to combine multiple aspects of working with data, from the process of collection to visualization. With that being said, machine learning tools are widely considered to be the future of data science and analytics.

Examples of Machine Learning Tools: Microsoft Azure, IBM Watson, and Amazon Web Services (AWS)

Interested in learning more about Data Analytics Technologies?

Many of Noble Desktop’s Data Science Classes focus on teaching students and professionals how to use data analytics tools and technologies. For example, the Data Analytics Technologies Bootcamp takes a business analytics approach to Microsoft Excel, SQL, and Tableau. By combining these tools, data scientists can learn how to generate useful insights for working on prescriptive analytics and database design, as well as developing data visualizations with digital dashboards. 

The Data Analytics Certificate program also includes instruction in how to use data analytics tools with a focus on business intelligence and information technology. This dynamic certificate program pairs knowledge of tools such as Excel, SQL, and Tableau, with training in the fundamentals of the Python programming language. In addition to these live online data science classes and certificate programs, you can also find in-person data analytics classes at a location near you. Any data scientist or analyst can learn more about data analytics tools and technologies through Noble Desktop!