Data Analysis is the technique by which raw data is transformed into useful statistics, insights, and explanations to make Data-driven business decisions. Data Analysis has become the cornerstone of modern business operations. It is a daunting task to choose the best Data analytics tool since no tool fits every need. Let’s look at the key factors for choosing between the Data analytics tools and then explore some of the most popular Data analytics tools available in the market today.
Data analysis is one of the most significant attributes of data science. Data analysis is very important for business strategies, key business decisions, product development, expansion, and much more. Data analysis can create maximum viable insights based on collected data. Data analysis turns the data into meaningful information. Analysis normally determines the normalcy, unusualness, trendiness of the data.
SAS
SAS continues to be one of the widely used data analytics tools in the industry. Some flexibility on pricing from the SAS Institute has helped its cause. SAS continues to be a robust, versatile, and easy to learn the tool. SAS has added tons of new modules. Some of the specialized modules that have been added in the recent past are – SAS analytics for IoT, SAS Anti-money Laundering, and SAS Analytics Pro for Midsize Business.
ClicData
ClicData is an end-to-end business intelligence platform with extensive data connectivity, data transformation, automation, and visualization features. ClicData is 100% cloud-based and works on all operating systems and devices.
Within a day, you can easily connect, blend data from various sources and build dashboards with their drag-and-drop interface. They offer self-service BI with online resources as well as full-service BI with in-app support and expert services.
ClicData offers a free trial and four plans that will suit mid-sized and enterprise companies.
Tableau
Tableau is among the most easy-to-learn Data analytics tools that perform an effective job of slicing and dicing your data and creating great visualizations and dashboards. Tableau can create better visualizations than Excel and can most definitely handle much more data than Excel can. If you want interactivity in your plots, then Tableau is surely the way to go.
Excel
Excel is, of course, the most widely used data analytics software in the world. Whether you are an expert in R or Tableau, you will still use Excel for the grunt work. Non-analytics professionals will usually not have access to tools like SAS or R on their systems. But everyone has Excel. Excel becomes vital when the analytics team interfaces with the business steam.
Power BI
Power BI is a Microsoft product used for business analytics. Named as a leader for the 13th consecutive year in the Gartner 2020 Magic Quadrant, it provides interactive visualizations with self-service business intelligence capabilities, where end users can create dashboards and reports by themselves, without having to depend on anybody.
Products:
Power BI provides the following products:
- Power BI Desktop
- Power BI Pro
- Power BI Premium
- Power BI Mobile
- Power BI Embedded
- Power BI Report Server
All these products differ by the functionalities offered by them. Few of them are free for a certain period of time and then you have to take the licensed versions
Multinational organizations such as Adobe, Heathrow, Worldsmart, GE Healthcare are using Power BI to achieve powerful results from their data.
Power BI has recently come up with solutions such as Azure + Power BI and Office 365 + Power BI to help the users analyze the data, connect the data and protect the data across various Office platforms.
SQL Programming Language
Structured query language (SQL) is the standard language created to communicate with databases and is particularly useful when handling structured data. Used to search, add, update, and delete data, among others, SQL makes it easy to organize structured data.
Most structured data that exists, is stored in SQL, so programs written for the language can easily unlock data for powerful results.
KNIME
Konstanz Information Miner or most commonly known as KNIME is a free and open-source data analytics, reporting, and integration platform built for analytics on a GUI-based workflow.
KNIME provides the following two software:
- KNIME Analytics Platform – Is an open-source and used to clean & gather data, make reusable components accessible to everyone, and create Data Science workflows.
- KNIME Server – Is a platform used by enterprises for the deployment of Data Science workflows, team collaboration, management, and automation.
Companies such as Siemens, Novartis, Deutsche Telekom, Continental use KNime to make sense of their data and leverage meaningful insights.
You do not need prior programming knowledge to use KNIME and derive insights. You can work all the way from gathering data and creating models to deployment and production.
QlikView
Qlikview and Tableau are essentially vying for the top spot amongst the data visualization giants. Qlikview is supposed to be slightly faster than Tableau and gives experienced users a bit more flexibility. Tableau has a more intuitive GUI and is easier to learn.
Splunk
Splunk is more popular than some of the more known Data analytics tools like Cloudera and Hortonworks. It started as a ‘Google for log files’, which means its primary use was to process machine log files data. It has now become much more than that. Splunk has great visualization options, and a web interface makes it easy to use.
R
R at a glance:
- Type of tool: Programming language.
- Availability: Open-source.
- Mostly used for: Statistical analysis and data mining.
- Pros: Platform independent, highly compatible, lots of packages.
- Cons: Slower, less secure, and more complex to learn than Python.
R, like Python, is a popular open-source programming language. It is commonly used to create statistical/data analysis software. R’s syntax is more complex than Python and the learning curve is steeper. However, it was built specifically to deal with heavy statistical computing tasks and is very popular for data visualization. A bit like Python, R also has a network of freely available code, called CRAN (the Comprehensive R Archive Network), which offers 10,000+ packages. It integrates well with other languages and systems (including big data software) and can call on code from languages like C, C++, and FORTRAN. On the downside, it has poor memory management, and while there is a good community of users to call on for help, R has no dedicated support team. But there is an excellent R-specific integrated development environment (IDE) called RStudio, which is always a bonus!
MonkeyLearn
MonkeyLearn is a no-code machine learning platform that provides a full suite of advanced data analysis tools to analyze unstructured data, in real-time and around the clock.
You can set up MonkeyLearn to automatically analyze large collections of data right away using pre-trained models, or you can create your own customized text analysis models in a simple point-and-click interface.
MonkeyLearn’s suite of data analysis tools allows you to classify data by topic, sentiment, intent, and more, or extract relevant information, like names, locations, and keywords. Native integration and a robust API make it easy to connect the tools and apps you already use to MonkeyLearn’s machine learning tools.
To make it really simple to discover insights in your text data, MonkeyLearn Studio provides an in-app data visualization tool, so you can go from data analysis to data visualization all in one place.
Check out available plans and pricing or schedule a free demo with one of our experts.
RapidMiner
RapidMiner is a data science platform that helps companies build predictive machine learning models from data. It’s aimed at data analytics teams that want to tackle challenging tasks and handle large amounts of data, so you’ll need a technical background.
Depending on your needs, you can opt for different solutions, including TurboPrep, which allows you to clean and prepare your data; AutoModel, which provides different algorithms to build machine learning models; and DataStudio, to create a visual workflow and explore your data.
Conclusion
Data analysis is a process by which we scours and distills data to extract the key takeaways. It’s also a process that involves testing and measuring data so as to determine ‘true value’, and compare results.