Data has become vital for businesses. When analyzed correctly, it can help businesses improve customer experiences, streamline processes, and give them a competitive advantage.
By 2025, nearly 25% of the world’s data will be real-time information, compared to just 15% in 2017. This means businesses will need to have the right data infrastructures in place to turn troves of real-time data into usable information.
Data analysis is now a priority for businesses, and choosing the right tools is key to smart decision-making and meeting the growing demand of your customers.
However, not all data is equal.
While it takes little effort to manage quantitative data, stored in spreadsheets and databases, making sense of unstructured data ‒ such as emails, social media posts, and customer support tickets ‒ is often complex.
Fortunately, with machine learning and business intelligence (BI) tools, analyzing qualitative data is more accessible than ever. Visit MonkeyLearn, a no-code platform, to learn how you can instantly connect machine learning and BI tools to the apps you already use.
With so many options available, choosing the right tools to analyze your business data can be overwhelming. So, to help you out here’s a rundown of the top data analysis tools on the market.
Data analysis tools fall into these categories:
Machine learning has revolutionized the data analysis landscape, enabling businesses to automatically analyze large collections of data in real-time and around the clock – saving them time and resources.
All industries, from retail and hospitality to medicine and finance, are using machine learning to automatically sort incoming data, extract important information from documents, emails, reviews, etc., and gain granular insights to make data-based decisions.
When it comes to implementing machine learning tools, companies can choose to build or buy machine learning software. If building, you’ll need a team of specialized data scientists, coders, and engineers, and infrastructure to maintain your software. All this, combined with the time it takes to build complex software, can end up being hugely expensive.
SaaS solutions, on the other hand, simplify the process, by providing ready-to-use tools (which require no training) that can be implemented with little to no-code in a few weeks. Easily scaled up or down to fit your data requirements.
Below, are some of the best machine learning tools available:
MonkeyLearn is an easy-to-use machine learning platform that provides a full suite of text analysis tools. You can start analyzing qualitative data right away with pre-trained models or customize your own in a simple point-and-click interface.
Classify data by topic, sentiment, intent, and more, or extract relevant information, like names, locations, and keywords. Then, connect text analysis models to the tools or apps you already use via native integrations MonkeyLearn’s robust API.
Discover MonkeyLearn Studio, where you can choose from custom-built templates that help you get even more granular insights from your data. Then, use the in-app data visualization tool, making it easy to spot patterns and trends.
RapidMiner is a data science platform that helps companies build predictive machine learning models from data. It’s aimed at 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.
Spreadsheet tools, like Excel and Google Sheets, are widely used by businesses to organize, sort, and analyze quantitative data. However, they are not the best choice when it comes to handling large amounts of data or performing advanced data analysis.
However, quantitative data analysis is always a good starting point in the data analysis process. And these tools are great for collecting quantitative data.
Microsoft Excel is used to filter, organize, and visualize quantitative data, making it the perfect tool for performing simple data analysis. 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.
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 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.
By creating powerful visualizations, such as reports, dashboards, and graphics, you can showcase the results of your data analysis in a very simple way. Here are two of the top business intelligence tools you can find:
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 prebuilt and custom data connectors, you can share 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.
Programming languages can be useful if you’re opting to build your own data analysis tools, or self serve. The most widely used programming languages in data science are R and Python, both of which are free and open-source.
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.
Data analysis tools help companies draw insights from data, and uncover trends and patterns to make better decisions.
As you can see, there are a wide number of data analysis tools you can make use of, whether you want to perform basic or more advanced data analysis.
Thanks to machine learning, advanced data analysis is now easier than ever, allowing businesses to reap the benefits from huge amounts of unstructured data.
August 31st, 2020