Choosing a Data Analysis Tool: 4 Things to Consider
Find a data analysis tool that meets your business needs and works across teams.
Not every business is able to access and harness the data they need to make decisions. According to an Oxford Economics survey, only 38% of businesses say they have all the data to support analytics-based decision-making.
If you’re struggling to analyze and collect data, it’s likely time to add data analysis tools to your tech stack. These tools are designed to collect data, interpret it, and draw out meaningful insights. They help you organize data, democratize its access across multiple teams, and remove delays between asking a question and receiving an answer.
Selecting a data analysis tool is a big decision—one that can have strategic consequences for your organization for years. A tool that doesn’t fit your needs may cause confusion and keep your team members from uncovering valuable data insights. On the other hand, tools that fit your business needs can help you surface insights about your customers and products and guide your decision-making.
Choose the right data analysis tool by answering these four questions about your company’s data needs.
1. How Does Your Company Approach Data?
Before you begin exploring options, you need to have a good grasp of your data needs. This includes understanding:
- What does your business require from data analysis tools?
- What problem(s) are you trying to solve?
- Where and how is data currently stored?
- How does your company approach data quality and trustworthiness?
Knowing what you require from a data analysis tool will help you formulate a list of requirements from a vendor. Knowing where your data is stored will answer the question, “Can this tool work with my data in its current state?”
You may discover your data isn’t well-equipped to answer business questions—even with adding a data analysis tool. Low-quality or inaccessible data can limit what you are able to achieve with data analysis tools.
If you don’t have effective data governance in place, you need to make some decisions about moving forward. Gartner research found that organizations believe poor quality data to be responsible for an average of $15 million per year in losses. As it is often said: garbage in, garbage out.
Your new data analysis tool will only be as effective as your data’s integrity and accessibility. Clean up the data before you implement a new data analysis tool to get the best start.
2. Who Will Be Using the Data Analysis Tool?
A variety of employees at your company—C-suite, product managers, developers, marketing, and more—rely on data analysis to make decisions for their department. With that in mind, look for a data analysis tool that can meet the needs of all departments and can integrate with their data sources.
Ask each department to assign a representative to be part of an inter-department analysis team. This group will represent all the people who will use the tool and who should have buy-in during the selection process. They can share examples of business questions they would try to answer through a data analysis tool. Your product team may want customer cohort analysis and user journey mapping features, while your marketing team may want insights on engagement. Once your teams have listed their requirements, ensure the tool can meet those needs.
Game, trivia, quiz and learning platform Kahoot! Is one such team that saw large returns from making data analysis accessible to everyone across the organization.
At Kahoot!, product managers, engineers, marketers and customer support specialists use Amplitude for data analysis. As Kahoot!’s Head of Data Martí Colominas wrote:
“Everyone talks about big data, machine learning, AI, etc., but as the industry matures, we can’t forget about the basics. People preach about the importance of data governance, but at the same time, organizations still suffer from that classic bottleneck where all requests go through the data analysts. A large part of our success in how we’ve scaled with Amplitude is that we put a lot of time into creating self-service analytics that allow people across the company to use product data for analysis by themselves. Today, our core team comprises around 170 employees. Of that, about 150 have an Amplitude account, with 100 of those being monthly active users… It’s quickly become imperative that everyone in the organization can access and use that data with Amplitude in order to understand customer behaviors and drive growth, loyalty, and adoption throughout their verticals.”
3. What Skills Are Needed to Use the Tool?
Some companies may have sophisticated data science teams that can handle complex SQL queries and sophisticated tools. But you don’t need skilled data professionals to make a data analysis tool work at your company.
Fill in the gaps with training. Say a tool requires SQL skills your team does not currently have. Consider how you will teach these skills or if you will need to hire someone with the right skills.
A better option is a tool that is easy to use and democratizes access to data. Data democracy means all teams have access to the data analysis tool because you don’t need highly technical skills to use it.
If you don’t provide training or find an easy-to-use option, your data analysis tool may create bottlenecks. Non-technical team members will have to wait for someone with the right skills to answer their questions with the tool. Chances are, the data scientists won’t have the same depth of knowledge about the topic as the team that submitted the question.
Empower all departments with an accessible data analysis tool, so everyone can effectively gather answers and draw data points together.
4. How Important Is Data Visualization?
Every data analysis tool is able to interpret data. How it presents its findings varies from platform to platform.
Visualization is a crucial feature. If a data analysis tool doesn’t give outputs in easy-to-understand ways, your organization won’t likely take full advantage of the tool. Team members will feel less confident presenting data and making data-driven decisions if the tool presents it in convoluted charts. Your C-suite may be reluctant to invest in a tool if reporting is confusing.
The more emphasis you place on visualization, the more you should expect your data analysis tool to present results in a meaningful way. KPIs, metrics, and other business impacts should be accessible, shareable, and customizable.
Make the Right Data Analysis Tool Selection
The more robust your data analysis tools are, the better you’ll be able to use data for your business needs. Some of the hardest-to-answer questions can have their answers surfaced through data. This can lead you to “what comes next?” for your company.
Amplitude’s Digital Optimization System is a unified solution that brings together product analytics and digital personalization. Amplitude Analytics captures the data needed for teams to understand and optimize every customer experience. Get started today.
Rachel Hall
Former Campaign Operations Manager, Amplitude
Rachel Hall is a former campaign operations manager at Amplitude, where she manages global campaigns centered around thought leadership, coaching, and education in the product analytics space. Previously, she worked at Palo Alto Networks and graduated from U.C. Berkeley.
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