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.
Discover AI tools to analyze text data.
Data analysis tools are software and programs that collect and analyze data about a business, its customers, and its competition in order to improve processes and help uncover insights to make data-driven decisions.
Take a look at some of the best data analytics tools available. There’s something for every business here.
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.
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.
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.
With both cloud and on-premises deployment, Qlik offers helpful tools for those with expansive technical backgrounds or users that are not even fully computer literate. QlikView offers in-memory data processing for super fast results and the visualization of color-coded data relationships makes the results and insights easy to understand.
The SAS Business Intelligence platform focuses on visualizations that can be easily understood and shared simply across an organization for insights with a clear path to implementing change, in order to streamline processes and improve customer satisfaction.
SAS BI aims to help clients answer specific questions, like “Where do my customers come from?” and “Where are most accidents occurring?”
Looker integrates with existing tools to introduce new, highly-focused data that can show previously unseen data relationships to help teams make more informed decisions.
Customizable programs and applications ensure that models are designed specifically for individual clients. And many of their “embedded analytics solutions” come pre-designed for industries like retail, healthcare, and more.
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.
Choosing the right data analytics tool can be a challenge. If you are struggling to select the tool from the list above, answer these questions and you’ll be well on your way to finding the best data analytics tool to fit your needs:
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 data analysis tools in 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.
COVID-19 has changed everything and international research firms, like Gartner, recommend that companies leverage new AI and machine learning techniques, to extract new customer feedback and data from internal systems, and all over the internet, rather than solely relying on historical data.
SaaS data analysis tools, like MonkeyLearn, for example, use powerful machine learning techniques to (1) gather, (2) analyze, and (3) visualize data results. And instituting techniques, like a customer feedback loop will ensure that you’re constantly gathering and analyzing customer data to improve products and services and, ultimately, your bottom line.
If you receive a few hundred data points a month, you might be able to handle this data without advanced tools that automate data collection and analysis processes. However, if your data runs into the thousands, you’ll want to implement AI tools to avoid wasting time on manual and tedious tasks.
Analyzing huge amounts of text data by hand, for example, is not only repetitive but it’s likely to lead to poor results. Why? Because humans are subjective. If you need to hire many hands to sort and tag your data, each human tagger will label your data points differently.
AI tools are trained using one set of criteria, and are often customizable, so you can ensure that your data delivers accurate and actionable insights.
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