49,378 Companies
- United States: 10,750 Companies
- North America: 13,215 Companies
- EMEA: 19,041 Companies
- United Kingdom: 3,391 Companies
- APAC: 9,272 Companies
- Australia and New Zealand: 1,719 Companies
(MSPs, CRM Vendors, Resellers, ISVs, CRM Software Companies) in our database across the globe
What is Data Analysis?
In order to identify trends and make inferences about the information they contain, data sets are examined using data analytics (DA), a process. More often than not, specialised hardware and software are used to perform data analytics. In order to help businesses, make better business decisions, data analytics technologies and techniques are extensively used in the commercial sector. In order to support or refute scientific models, theories, and hypotheses, scientists and researchers also use analytics tools. Data analytics is a term that broadly covers a range of applications, including basic business intelligence (BI), reporting, online analytical processing (OLAP), and various types of advanced analytics. In that regard, it resembles business analytics, another term that serves as a catch-all for various methods of data analysis.
1. Rapidminer
A data science platform called RapidMiner assists businesses in creating predictive machine learning models from data. You must have a technical background because it is intended for data analytics teams who wish to take on difficult jobs and handle enormous amounts of data. You can choose from a variety of options, such as TurboPrep, which enables you to clean and prepare your data, AutoModel, which offers several techniques to build machine learning models, and DataStudio, which enables you to create a visual workflow and study your data, depending on your needs.
2. Tableau
Data analysts use Tableau, a business intelligence tool, to view, examine, and comprehend their data. Tableau offers quick analytics and can investigate a variety of data sources, including databases, spreadsheets, Hadoop data, and cloud services. Due to its robust GUI, it is simple to use. It requires less work to create excellent interactive dashboards. With Tableau, a market leader, you can deal with real data without expending too much time on data manipulation. It has updated its services to give users access to smart dashboards, data exploration, convenience of use, quick analytics, and other improvements.
3. Power BI
Microsoft's Power BI is yet another potent corporate analytics tool. You may connect to numerous data sources, visualise your data, and distribute the results around your company. Your data can come to life with Power BI's live dashboards and reports. Microsoft Excel and other data analytics tools can be connected with Power BI. It provides options like Office 365 Plus Power BI and Azure + Power BI. This can be very beneficial in enabling users to link data, protect data across many office systems, and undertake data analysis.
4. Knime
An open-source, cost-free platform for data analytics, reporting, and integration is called KNIME, or Konstanz Information Miner. It is designed for analytics on a GUI workflow and aids in data collection as well as the creation of models required for production and deployment. The following free online courses might help you learn more about KNIME. KNIME offers two software options. Specifically, the KNIME server and analytics software. With the first, you can create workflows, reusable components, and clean up your data.
5. Airtable
An easy-to-use cloud collaboration application called Airtable is described as "part spreadsheet, part database." Like other conventional spreadsheet applications, it offers data analysis and data visualisation features, but it also has a strong database at the back end. You can quickly organise, track, and find data in a database by using "views." Additionally, through an API, developers may integrate Airtable with different programmes.
6. SAS
A popular statistical software package for data management and predictive analysis is called SAS. SAS is a proprietary programme for which businesses must pay a fee. For students to learn and use SAS, a free university version has been released. The graphical user interface is straightforward. It is therefore simple to learn. However, using the tool effectively requires some proficiency in SAS programming. The SAS DATA phase, which is used to produce, import, modify, combine, or calculate data, aids inefficient data handling and manipulation.
7. Looker
Looker will support you in data management, analytics, visualisation, and business intelligence. It is a platform built on the cloud. Looker offers drag-and-drop functionality for items, role assignment, and mapping features for user-friendliness. It offers precise graphs and tables so you can readily view the data in further detail. It aids in the development of small applications. Look ML is a language that can be used for this. Learning this language is simple.
8. ClicData
ClicData is an all-encompassing business intelligence platform with numerous features for data connectivity, data transformation, automation, and visualization. The operating systems and devices that ClicData supports are all in the cloud. The drag-and-drop interface makes it simple to connect, combine data from various sources, and create dashboards within a day. Additionally, to full-service BI with in-app support and professional services, they provide self-service BI with online resources.
9. Qlik
Data analytics, data visualisation, and self-service business intelligence are all capabilities of QlikView. By offering capabilities like Data Integration, Data Literacy, and Data Analytics, it attempts to expedite the value that data can provide to businesses. It provides consumers with a range of items, some of which are trial versions that may be tried out for free for 30 days. Over 50000 clients worldwide depend on QlikView. It has several features for ad hoc inquiries and enables speedy decision-making. The quantity of data that it can store is unlimited, and it responds instantly.
10. Dataddo
Dataddo is a no-coding, cloud-based ETL platform that priorities flexibility. With a large selection of connectors and the freedom to select your own metrics and attributes, Dataddo makes it quick and easy to build stable data pipelines. You don't need to add any components to your architecture that you weren't already using or alter your fundamental workflows because Dataddo seamlessly integrates into your current data stack. Instead of wasting time learning how to use yet another platform, Dataddo's user-friendly interface and quick setup allow you to concentrate on integrating your data.
Data Analysis FAQs
In order to identify trends and make inferences about the information they contain, data sets are examined using data analytics (DA), a process. More often than not, specialised hardware and software are used to perform data analytics. In order to help businesses, make better business decisions, data analytics technologies and techniques are extensively used in the commercial sector. In order to support or refute scientific models, theories, and hypotheses, scientists and researchers also use analytics tools.
- Defining the question.
- Collecting the data.
- Cleaning the data.
- Analyzing the data.
- Sharing your results.
- Embracing failure.
- Summary.
Techniques for qualitative and quantitative data analysis are the two main ways for analyzing data.