Data analysis is the process of turning raw data into useful statistics, information, and explanations to drive data-driven business decisions.
Data analysis is now a priority for businesses, and choosing the right data analysis tool is key to turning troves of data into usable information.
There are many tools out there that can take your data analysis to the next level. Below, find the best tools available today to help you analyze your data.
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 allow 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.
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.
There’s a free trial available for some of these products.
KNIME is a free, open-source platform to create data science workflows. It has an intuitive drag and drop interface that allows you to import data from different sources, build advanced machine learning solutions, and visualize data.
Like most open platforms, it’s constantly being updated and has an active community of contributors. KNIME allows users to visually create flows making it simple for even non-programmers.
Talend offers a suite of cloud apps for data integration. It’s designed to help businesses collect all their data in a single platform so that teams can access the right data when they need it.
The platform has a series of in-built machine learning components, which allow users to analyze data without the need to code. It uses classification, clustering, recommendation, and regression algorithms.
Talend offers a free open-source version and various commercial alternatives.
Microsoft Excel can be used to filter, organize, and visualize quantitative data, making it the perfect tool for performing simple data analysis. You can use a wide range of formulas, filters, and create pivot tables, charts and graphs, to synthesize the data you’ve gathered. But there’s a limit to the amount of data that Excel can handle, so you may need more powerful tools if you’d like to analyze data at scale.
Explore common functions and formulas for data analysis in Excel.
Airtable is a user-friendly cloud collaboration tool defined as “part spreadsheet, part database”. It provides data analysis and data visualization functions (like other traditional spreadsheet tools) but with a powerful database on the backend. By using “views”, you can easily interact with the database to manage, track, and find data. Plus, developers can connect Airtable with other apps through an API.
There’s a free plan available with the basic features for you to get started.
Business intelligence tools, like Microsoft Power BI, are extremely important in the data analysis process because they make it easy for businesses to spot trends, patterns, and insights across large sets of data.
Microsoft Power BI allows users to import data from hundreds of sources, and drag and drop elements, to create real-time dashboards and reports. Equipped with AI, an Excel integration, and pre-built and custom data connectors, you can gain valuable insights and easily share them with the rest of your team.
Pricing options for self-service BI or a premium service for advanced analytics.
Tableau is a powerful analytics and data visualization platform that allows you to connect all your data and create compelling reports and interactive dashboards that update in real-time. It’s easy to use, supports large amounts of data, and can be run on-premise or in the cloud.
There’s a free trial available and different plans for individual users and organizations.
Free and open-source programming languages are a great option if you’re building your own data analysis tools. R is widely used for exploratory data analysis, statistical computing, and data visualization. At first, it was mainly used by researchers and academics but has now branched out into the business world. Learning R is relatively easy, even if you don’t have a programming background.
Python is one of the most in-demand programming languages today and it’s considered the preferred language for machine learning. It stands out for being very flexible, allowing you to build solutions for various use cases. Plus, it’s fairly straightforward to learn and write.
Choosing the right data analytics tool can be a challenge. First, you need to understand your business – what are the problems you’re trying to solve and which data is available to analyze? Can your team code, or will you need to use integrations? Which data analysis method will you need to follow?
You’ll also need to check if tools are in line with your data security and governance policies. And, finally, you’ll need to consider costs.
If you're having trouble detecting which tool is the right for you, let’s dive into how to choose the right data analysis tool.
Before choosing a data analysis tool, you need to decide the type of data you want to analyze. Is it quantitative or qualitative data?
Quantitative data, often numerical data stored in spreadsheets and databases, is easy to transform into visual insights using Excel and BI tools like Tableau. But making sense of qualitative data such as open-ended survey responses, emails, and social media conversations, often calls for AI data analysis software.
Do you have a team of data scientists and engineers who understand SQL, R and Python? These programming languages are often used in data management tools and are easily understood by those who have a background in coding.
However, if your team is far from technical, you might prefer to opt for no-code data analysis tools.
You could hire a team of data scientists if you want to use more complex data analysis tools, but the high costs might just sway you in favor of no-code data analysis tools.
Finally, price and time-to-implement data analysis tools will also influence your decision. When comparing data analysis tools, you might want to check whether you can pay for what you use or if there’s a fixed monthly fee?
You’ll also need to consider the costs of infrastructure. Will you need to invest in a data warehouse or cloud-based data storage, and data pipelines? Again, you'll need to think about your current team structure and if you have the budget to hire a team of experts if you opt for more complex data analysis tools.
Finally, how long does it take to implement data analysis tools? Do you need a solution that’s up and running in 4 weeks or 4 months? If you’re building your own data analysis tools, you’ll need to factor in the costs of unproductive waiting time.
Once you’ve answered all the above questions, you’ll have a better understanding of which tools are right for you.
Data analysis tools help companies draw insights from customer data, and uncover trends and patterns to make better business decisions.
There are a wide number of online data analysis tools you can make use of, whether you want to perform basic or more advanced data analysis.
Thanks to no-code machine learning software, advanced data analysis is now easier than ever, allowing businesses to reap the benefits from huge amounts of unstructured data.
August 31st, 2020