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.
However, 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 the types of data you want to analyze. Then you’ll need to assess each tool for available data integrations, data security and governance, costs, complexity, and more.
Let’s dive into how to choose the right data analysis tool.
With so many options available, choosing the right tools to analyze your business data can be overwhelming. So we recommend asking yourself the following questions:
First, you need to know what type of data analysis you want to perform: quantitive or qualitative. 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 unstructured data often calls for more advanced data analysis software, equipped with machine learning.
Do you have a team of data scientists and engineers who understand complex programming languages like SQL, R and Python,which are often used in data management tools?
Or is your team less technical? If you don't have a team of data scientists and engineers, you might prefer to opt for no-code data analysis tools instead of hiring a team of experts. They're easy to connect to business intelligence (BI) tools through point and click integrations and provide ready-made solutions.
Price and time-to-implement will also influence your decision. Can you pay for what you use or is there 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 for each day the build takes.
Once you’ve answered all the above questions, you’ll have a better understanding of which tools are right for you.
With a growing number of no-code machine learning tools that easily connect to business intelligence (BI) tools, analyzing qualitative data is more accessible than ever.
Learn more about both quantitative and qualitative data analysis tools and which ones are right for your business.
There are four types of data analysis tools, and we’ve listed some providers below each group. However, you’ll probably end up combining tools from each of the following categories (depending on what type of data you’re analyzing):
Machine learning has revolutionized the unstructured 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 tools available for real-time data analysis:
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 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.
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 and analyzing 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.
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 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:
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 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 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