Data analysis is a key component for operating a successful business in today's tech-savvy world. When analyzing data sets, however, every business has its own needs. While some companies employ data scientists to work with complex big data, others have fewer and less complicated data sources that even non-technical users can navigate. Your specific needs will influence the type of tool your company chooses for data management:

Table of Contents

  1. Xplenty
  2. Zoho Analytics
  3. Tableau Public
  4. RapidMiner
  5. KNIME
  6. Domo
  7. Python
  8. Looker
  9. Sisense
  10. Microsoft Power BI Desktop
  11. Conclusion

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1. Xplenty

Xplenty offers a unique approach to data integration. As a cloud-based ETL, Xplenty simplifies creating ETL pipelines with no-code or low-code options that accommodate all levels of tech experience. Scalable and elastic, the platform can handle everything from deployments to security within a user-friendly interface. Essentially, Xplenty takes over the complicated data pipeline architecting process and the underlying operational resources involved, so that the data scientists can focus on analyzing the data.

2. Zoho Analyst

A self-service analytics tool, Zoho Analytics merges business intelligence with data management and data analytics in an intuitive software system that can process cloud and on-premises data. The user interface boasts many features, and employees of all skill levels can create data visualizations and predictive analytics, and then share them company-wide to ensure collaborative decision-making.

3. Tableau Public

Tableau Public is a predictive data analytic software that helps everyone see the value of their data—even those who are using data analysis software for the very first time. One of the biggest perks offered by Tableau is its speed for analyzing large data sets. As far as BI tools go, the speeds are faster than most of its competitors, and it can easily create statistical analysis from sources like Excel spreadsheets without the need to build complicated machine learning algorithms.

4. RapidMiner

Well-loved by data scientists and data analysts, RapidMiner is a visual data analytics tool. Through the use of predictive artificial intelligence, full automation is possible, making this one of the lowest-maintenance business intelligence tools for tasks such as data visualization, data mining, and data modeling. Features include drag-and-drop capabilities, a large algorithm library, pre-built templates, and recommendations to guide beginners. Because these recommendations act as tutorials, learning how to use the data analytics tool is easy. RapidMiner is equally useful for employees who have API knowledge and Microsoft SQL training as it is for those who do not.

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5. KNIME

KNIME (or Konstanz Information Miner) is a free, open-source data analytics platform. However, unlike other open-source big data platforms, KNIME does not require its users to know how to code. Instead, it makes use of a user-friendly interface that features drag-and-drop metrics, predictive technologies, and connectors that allow data integration from sources like Oracle, Apache Spark, Microsoft SQL, Microsoft Excel, and Google Docs. This is a good option for employees who are not familiar with coding or if pricing is a concern.

6. Domo

Domo is a BI (Business Intelligence) and data analytics system in a singular, predictive platform. With its simplistic ETL, everyone in the organization, regardless of whether they have had prior SQL training or knowledge, can embrace large data sets. This process uses natural language and drag-and-drop capabilities for data mining and building custom data visualizations. Because of Domo's reliance on the Cloud, it is best for organizations in which all departments use cloud storage and less effective for hybrid environments.

7. Python

One of the most well-known data analytics tools, Python is open-source software that is free to use and easily customizable. Its data analytics library, known as Pandas, supports a plethora of file formats, including Excel spreadsheets. Using this data, the Python library enables advanced analytical operations to take place in real-time. Some of the most popular tasks include data visualization, indexing, and merging. The platform is most suitable for data scientists and those who are familiar with programming languages like Python.

8. Looker

After a recent merge with Google Data Cloud, the Looker platform upgraded some features of the former system. For starters, Looker makes use of a single platform that all departments can use. This eliminates confusion from one department to the next and allows for more efficient collaboration throughout entire organizations. One of the most intriguing features of Looker is its ability to support a variety of technology stacks, including popular public hosting options like Talend, AWS, GCP, and hybrids. Likewise, Looker supports over 50 SQL languages to fuel multi-cloud data visualization and improve overall functionality.

9. Sisense

Sisense is a data analytics software system powered by Artificial Intelligence, and it services data sets stored both on the cloud and directly on-premises. The unique platform is suitable for code-first and code-free approaches to ensure that all users, regardless of their familiarity with Java, data analytics, APIs, or algorithms, can harness the power and insight that comes with big data analysis. Along with end-to-end governance, Sisense gives users the ability to infuse analytics into any application, workflow, or product.

10. Microsoft Power BI Desktop

Microsoft Power BI Desktop is a data analysis tool that facilitates company-wide business intelligence. Thanks to its intuitive interface, casual users can create and share reports, while those with more technological experience can build machine learning models to automate mundane tasks. They can also integrate Power BI with existing Azure machine learning models. This streamlined approach to data analysis tools helps companies by identifying hidden insights with measurable metrics in up-to-the-minute data sets.

Conclusion

When you use the right data analytics tool, you can enhance collaboration between departments, increase profits and gather valuable insight so you make the right business decisions. The right tool, however, will largely depend on the skill set of your employees and the architecture of your company.

How Xplenty Can Help Improve Business Intelligence

Integrate Your Data Today!

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If you're looking for a data analytics tool that has a user-friendly interface and can process large data sets quickly and accurately, schedule a call to discuss our 14-day pilot of Xplenty today.