10 Tools Used in Data Analytics You Need to Know in 2022
Most businesses generate tons of unstructured data in their day-to-day operations. Reporting software and data analytics tools allow you to transform such datasets into easy-to-read insights that support better problem-solving and business decisions.
Data analytics services involve using analytic technologies that allow businesses to tap into their data’s hidden value. However, it’s essential to understand the type of data you need to visualize to choose the most suitable analytics tool that helps you meet your business needs. Below you’ll find an overview of the 10 leading tools used in data analytics.
Top Data Analytics Tools
This business analytics product by Microsoft offers interactive data visualizations created through the tool’s self-service intelligence capabilities. The user need not perform technical programming to create reports and dashboards alone.
Power BI provides a diverse range of products, each offering specialized functionalities to meet various data analytics services needs. It also offers robust combination solutions like Azure + Power BI and Office 365 + Power BI that assist users in data analysis as well as data connection and data protection across various Office platforms.
Businesses automatically gather loads of data across various divisions, from applications, websites, and other digital platforms. Splunk can help you collate, study, and visualize this data to obtain actionable insights. It can help predict and prevent problems through monitoring experience.
The tool gives your data analytics consulting team clear visibility into the database to ensure effective detection and diagnosis of issues. Through Splunk, you can also inspect and conceptualize business processes and streamline your complete security stack.
Python is open-source software that performs code-based programming and development and works on object-focused scripts. This programming language is readable, easy to code, and simple to learn, especially for beginners. The tool is composed of machine learning libraries and can efficiently work in integration with other programming software like SQL, JSON, and MongoDB. Click here if you are looking to hire Python developers and software development experts.
Tableau is a highly comprehensive data analytics tool that allows the user to create maps, dashboards, and visualizations from any database size. Tableau Public (the free version) allows non-experts to experiment with their data and develop visualizations of varying complexity levels.
You will need data analytics companies’ powerful databases and coding expertise to wield Tableau’s full potential. Also, it can work with different sources and databases like MS Excel and corporate data warehouses.
Built by Eclipse, Talend makes for a perfect ETL tool for data integration, with the capability of managing the entire ETL process and delivering easy-to-access, compliant, and readable data for everyone.
The Talend products are developed to provide on-demand clean and complete data without compromising data quality. It also facilitates cloud API services, Big Data integration, and data preparation. The tool also offers Stitch Data Loader and Data Catalog.
Coming from Qlik’s suite of data integration, data analytics, and programming platforms, QlikView offers data analytics and business intelligence functionalities. You’ll need a hybrid cloud environment to deploy Qlik to access to analytics and integration processes, like the automation of data warehouses.
Qlik allows data analytics companies to integrate, analyze, and transform multiple data sets into clean dashboards and explore augmented analytics to facilitate business decision-making.
As Google’s data analytics platform, Looker enables organizations to analyze and integrate data sourced from multiple databases to transform them into interactive dashboards and reports. The tool also allows you to create custom applications and work with workflows.
Apache Spark is a preferred open-source data science platform that simplifies structured and unstructured data processing. Since it is a big data analytics solution, the software can process heavy datasets that are often too complex to work with for other databases.
Apache Spark’s powerful processing engine can facilitate the creation of pipelines, machine learning, cluster management, and deep learning. With its machine learning library (MLib), users can access algorithms for collaborative filtering, regression, classification, etc.
Being Amazon’s Power BI and analytics software, QuickSight is a cloud service that can work in integration with cloud data sources like SaaS, AWS, Excel, etc. QuickSight’s objective is to let you explore your database and create simple reports and visual interpretations.
You can leverage QuickSight’s advanced capabilities for machine learning. Similar to Power BI, the tool lets its users share reports and data analysis and supports collaborative analysis of data.
Analyze, explore, and visualize data network graphs in MS Office Excel through NodeXL. Its Pro version has additional features, like providing quick access to network data streams across social media platforms, powerful report generation, advanced network metrics, and text and sentiment analysis.
Plus, its content analysis feature lets you search your datasets for top-performing content and content types, allowing you to share the right content with the right audience at the right time.
Putting It Together
Data analysis tools are indispensable in a business environment where you want to draw actionable insights from crucial customer data. These tools can help you trace trends and patterns to help you make smart business decisions.
Whether you aim to perform basic or advanced analysis of any type and volume of data, you can take your pick from various data analysis tools available on the market today. Plus, the no-code machine learning software has made advanced data analysis much easier than ever, enabling businesses to make the most out of unstructured data.
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