What are the best data analytics tools for data analysts? What are the tools every data analyst or data scientist must have in 2022? This article gives you an insight into the best data analytics tools. Similar post – Best database certifications for database administrators in 2022 and also read on how to become a successful and skilled data scientist.
Data analysis is a core practice of modern businesses. Choosing the right data analytics tool is challenging, as no tool fits every need. Similar post – Best Machine Learning Frameworks(ML) for Experts in 2022.
To be able to perform data analysis at the highest level possible, analysts and data professionals will use tools and software that will ensure the best results in several tasks from executing algorithms, preparing data, generating predictions, automate processes, to standard tasks such as visualizing and reporting on the data. Similar post – Best machine learning course online and certifications for beginners.
Best data analytics tools for data analysts in 2022
What are the best data analytics tools for data analysts in 2022? Below we list the top 24 data analytics tools for data analysts to work with.
1. Microsoft Power BI
Microsoft Power BI is a top business intelligence platform with support for dozens of data sources. It allows users to create and share reports, visualizations, and dashboards. Users can combine a group of dashboards and reports into a Power BI app for simple distribution. Power BI also allows users to build automated machine learning models and integrates with Azure Machine Learning.
2. SAP BusinessObjects
SAP BusinessObjects provides a suite of business intelligence applications for data discovery, analysis, and reporting. The tools are aimed at less technical business users, but they’re also capable of performing complex analysis. BusinessObjects integrates with Microsoft Office products, allowing business analysts to quickly go back and forth between applications such as Excel and BusinessObjects reports. It also allows for self-service predictive analytics.
Sisense is a data analytics platform aimed at helping both technical developers and business analysts process and visualizes all of their business data. It boasts a large collection of drag-and-drop tools and provides interactive dashboards for collaboration. A unique aspect of the Sisense platform is its custom In-Chip technology, which optimizes computation to utilize CPU caching rather than slower RAM. For some workflows, this can lead to 10–100x faster computation.
4. TIBCO Spotfire
TIBCO Spotfire is a data analytics platform that provides natural language search and AI-powered data insights. It’s a comprehensive visualization tool that can publish reports to both mobile and desktop applications. Spotfire also provides point-and-click tools for building predictive analytics models.
Thoughtspot is an analytics platform that allows users to explore data from various types of sources through reports and natural language searches. Its AI system, SpotIQ, finds insights automatically to help users uncover patterns they didn’t know to look for. The platform also allows users to automatically join tables from different data sources to help break down data silos.
Qlik provides a self-service data analytics and business intelligence platform that supports both cloud and on-premises deployment. The tool boasts strong support for data exploration and discovery by technical and non-technical users alike. Qlik supports many types of charts that users can customize with both embedded SQL and drag-and-drop modules.
7. SAS Business Intelligence
SAS Business Intelligence provides a suite of applications for self-service analytics. It has many built-in collaboration features, such as the ability to push reports to mobile applications. While SAS Business Intelligence is a comprehensive and flexible platform, it can be more expensive than some of its competitors. Larger enterprises may find it worth the price due to its versatility.
Tableau is a data visualization and analytics platform that allows users to create reports and share them across desktop and mobile platforms, within a browser, or embedded in an application. It can run on the cloud or on-premises. Much of the Tableau platform runs on top of its core query language, VizQL. This translates drag-and-drop dashboard and visualization components into efficient back-end queries and minimizes the need for end-user performance optimizations. However, Tableau lacks support for advanced SQL queries.
9. Google Data Studio
Google Data Studio is a free dashboarding and data visualization tool that automatically integrates with most other Google applications, such as Google Analytics, Google Ads, and Google BigQuery. Thanks to its integration with other Google services, Data Studio is great for those who need to analyze their Google data. For instance, marketers can build dashboards for their Google Ads and Analytics data to better understand customer conversion and retention. Data Studio can work with data from a variety of other sources as well, provided that the data is first replicated to BigQuery using a data pipeline like Stitch.
Redash is a lightweight and cost-effective tool for querying data sources and building visualizations. The code is open source, and an affordable hosted version is available for organizations that want to get started fast. The core of Redash is the query editor, which provides a simple interface for writing queries, exploring schemas, and managing integrations. Query results are cached within Redash and users can schedule updates to run automatically.
11. Periscope Data
Periscope Data — now owned by Sisense — is a business intelligence platform that supports integrations for a variety of popular data warehouses and databases. Technical analysts can transform data using SQL, Python, or R, and less technical users can easily create and share dashboards. Periscope Data also boasts a number of security certifications, such as HIPAA-HITECH.
Metabase is a free, open-source analytics and business intelligence tool. This tool allows users to “ask questions” about data, which is a way for non-technical users to use a point-and-click interface for query construction. This works well for simple filtering and aggregations; more technical users can go straight to raw SQL for more complex analysis. Metabase also has the ability to push analytics results to external systems like Slack.
13. Jupyter Notebook
Jupyter Notebook is a free, open-source web application that can be run in a browser or on desktop platforms after installation using the Anaconda platform or Python’s package manager, pip. It allows developers to create reports with data and visualizations from live code. The system supports more than 40 programming languages. Jupyter Notebook — formerly IPython Notebook — was originally programmed using Python, and allows developers to make use of the wide range of Python packages for analytics and visualizations. The tool has a wide developer community using other languages as well.
14. IBM Cognos
IBM Cognos is a business intelligence platform that features built-in AI tools to reveal insights hidden in data and explain them in plain English. Cognos also has automated data preparation tools to automatically cleanse and aggregate data sources, which allows for quickly integrating and experimenting with data sources for analysis.
Chartio is a self-service business intelligence system that integrates with various data warehouses and allows for easy import of files such as spreadsheets. It has a unique visual representation of SQL that allows for point-and-click construction of queries, which lets business analysts who aren’t familiar with SQL syntax modify and experiment with queries without having to dig into the language.
Mode is an analytics platform focused on giving data scientists an easy and iterative environment. It provides an interactive SQL editor and notebook environment for analysis, along with visualization and collaboration tools for less technical users. The mode has a unique data engine called Helix that streams data from external databases and stores it in memory to allow for fast and interactive analysis. It supports in-memory analysis of up to 10GB of data.
KNIME — short for the Konstanz Information Miner — is a free, open-source data analytics platform that supports data integration, processing, visualization, and reporting. It plugs in machine learning and data mining libraries with minimal or no programming requirements. KNIME is great for data scientists who need to integrate and process data for machine learning and other statistical models but don’t necessarily have strong programming skills. The graphical interface allows for point-and-click analysis and modelling.
Looker is a cloud-based business intelligence and data analytics platform. It features automatic data model generation that scans data schemas and infers relationships between tables and data sources. Data engineers can modify the generated models through a built-in code editor.
RapidMiner provides all the technology users need to integrate, clean, and transform data before they run predictive analytics and statistical models. Users can perform nearly all of this through a simple graphical interface. It can also be extended using R and Python scripts, and numerous third-party plugins are available through the company’s marketplace. However, the product is heavily optimized for its graphical interface so that analysts can prepare data and run models on their own.
Domo provides more than 1,000 built-in integrations — called connectors — that allow users to transfer data to and from on-premises and cloud external systems. It also supports building custom apps that integrate with the platform, which allows developers to extend the system with immediate access to the connectors and visualization tools. Domo comes as a single platform that includes a data warehouse and ETL software, so businesses that already have their own data warehouse and data pipeline set up may want to look elsewhere.
21. Oracle Analytics Cloud
Oracle Analytics Cloud is a suite of cloud business intelligence and analytics applications. It’s focused on helping large enterprises transition their legacy systems to a modern cloud platform. Users can take advantage of its wide range of analytics features to do everything from producing simple visualizations to using machine learning algorithms to obtain insights from data.
R is an open-source programming language and computing environment with a focus on statistics and graphical data visualization. The language features numerous graphical tools and over 15,000 open source packages available, including many for loading, manipulating, modelling, and visualizing data. The environment allows technical analysts with programming skills to build almost any type of data analysis, but users without those programming skills should look elsewhere.
Python is an open-source, high-level programming language that’s often used by technical analysts and data scientists. It now boasts more worldwide developers than Java and has more than 200,000 available packages. Python can handle many different analyses on its own and can integrate with third-party packages for machine learning and data visualization. Popular data visualization packages include Matplotlib, Plotly, and Seaborn. Python is also used as a programming interface to other analytics systems.
Microsoft Excel is the most common tool used for manipulating spreadsheets and building analyses. With decades of development behind it, Excel can support almost any standard analytics workflow and is extendable through its native programming language, Visual Basic. Excel is suitable for simple analysis, but it is not suited for analyzing big data — it has a limit of around 1 million rows — and it does not have good support for collaboration or versioning. Enterprises should consider more modern cloud-based analytics platforms for large and collaborative analyses.
How to Choose the Right Data Analytics Tool For Your Business?
There are many data analytics tools in the market and it is quite difficult for businesses to choose the right one for themselves.
When a company takes their business requirements into consideration, they have to get some extensive research done beforehand, where they would gather knowledge about data analytics tools and how to use them in the correct manner.
In business, experienced data analysts and data scientists are the ones who take care of all the data-related work.
When it comes to code creation, especially with SQL, there are some data analytics tools that provide an enticing experience, compared to other tools that are more connected to the click-and-point review best suited for interns.
A company should always work with data analytics software that offers the best support for visualizations that are relevant specifically to their business targets. For instance, think about the ability of data analytics software for data modeling.
Now, there are some data analytics tools that support a more semantic and syntactic layer or can model data themselves.
If a company decides not to use one of these data analytics tools, they will have to use SQL or data analytics tools, such as the Data Build Tool or DBT, in order to model the data before the analysis.
The requirement for data analytics tools is unmatched, and there will never be too much data to process. In order to do the processing efficiently and successfully, there will always be an abundance of data analytics tools.