Data is fodder for decision-making. According to the NewVantage partners’ survey, in 2023, 91,9% of the Fortune 100 companies obtained measurable results from investments in data analysis.
Data drives leadership, helps optimize business processes, and provides solid ground underfoot. Visual data provided by data visualization software works best. There are plenty of off-the-shelf solutions in the market; however, you may want to provide your business with custom data visualization software – perfectly tailored to your processes and strategy. Alternatively, you may want to build a SaaS tool to help other companies become leaders.
For both cases, you need the knowledge of data visualization software development to succeed.
Custom Data Visualization Software’s Advantages
First of all, let’s take a glance at the landscape of existing solutions. Then, we’ll find out what the custom software’s benefits are over the ready-to-use one. Our ultimate goal is to help you decide whether you really need the individually-oriented solution, and what steps you need to take to build it.
The Most Popular Data Visualization Platforms and the Practices They Provide
Data manipulation is a process that has different levels of performance complexity. One day, you may need to draw a simple chart to share it with your colleagues. The other day, you’ll need to prepare a comprehensive report for stakeholders. Finally, you may want to build functionality for a data visualization platform. All mentioned tasks require different technical approaches. Consequently, the market of data visualization solutions offers a large variety of ready-made alternatives.
In the article focused on data visualization software development, it makes sense to talk about the most user-friendly and popular tools that don’t require coding skills, can be used in web browsers, and are offered for moderate price. These platforms are labeled as GUI-based (Graphical User Interface). These tools have interfaces that allow you to deal with data and create its visual representations through a graphical interface by pressing buttons, using drop-down menus, drag-and-drop, and other features. Their opposite is advanced software for data analytics and developers that uses command-line or text-based inputs.
Here are examples of the most commonly employed tools used by managers, content creators, data analysts, and other professionals who need data visualization and analysis in their working routine:
- Tools for visualization. Their goal is to present data in a visually appealing, easily digestible way. Some can make simple calculations, such as summarizing or multiplying columns. In particular, ChartBlocks can handle different data visualization types and allows the creation of custom charts that can be posted, imported, shared, or embedded into websites. Datawrapper is a magic wand for content creators and journalists since it empowers users with features for creating insightful, engaging, and elegant visuals and embedding them into news websites.
- Business intelligence tools. This type of solution provides more advanced opportunities for not only visualizing data but analyzing big data. Two of the most popular platforms, Microsoft Power BI and Tableau, empower managers and data analytics with all the features needed for data analysis, visualization, and reporting. These tools’ dashboards are the mighty utilities for the search for insights. Both platforms use ML and AI technology to provide advanced analysis and make predictions based on historical data.
Unlike described below no-coding platforms, such tools as D3.js, Ember Charts, Python or R’s libraries (matplotlib, ggplot2, leaflet) require specific knowledge and are used by data scientists for advanced data analysis and by developers whose task is to build a data visualization software.
The Sense of Custom Data Visualization Tool Development
It may seem that the number of technical opportunities provided by off-the-shelf tools is more than sufficient. Then, why does one may need a custom data visualization program?
Let’s consider an example. Imagine that you’re the owner of an e-commerce business specializing in custom-made, eco-friendly furniture. You want to create a dashboard for your business performance assessment. Apart from measuring the standard metrics that characterize sales, logistics, and other business processes, you need to evaluate the sustainability index of every piece of furniture. Since you position your online store as eco-friendly, it’s important for your clients that your furniture is environmentally friendly. You expect each unit’s sustainability index to be reflected in your online store.
To measure the sustainability index, you should include in your calculations the following metrics:
- the material from which each unit of furniture is manufactured;
- the level of production energy efficiency;
- the carbon footprint of transportation and logistics.
In addition, you want to see the map that mirrors the trends in demand for your products worldwide. Finally, you’d like your visualization software to be able to predict the demand for your furniture in different locations depending on the local consumer preferences and attitudes toward ecological concepts.
Chances are low that you’ll find all necessary features in an off-the-shelf tool that provides the connections with all sources of data needed, has all necessary features for visualization, and can deal with predictive analytics. For such a case, building a data visualization software perfectly tailored to your business needs makes more sense. To succeed, consider cooperation with a reliable partner who knows how to build a data visualization tool that is perfectly customized and is eager to provide you with expertise.
How to Build a Data Visualization and Analysis Tool in Three Basic Steps
To succeed in building information visualization software, you should start with market research, planning the project, and creating the project documentation. Proceed with making a minimal viable product (MVP), your tool’s version with only the basic features needed to validate the idea of your product. Then, develop and scale up your platform when your tool’s value for the target audience is approved.
Step 1. Discovery Phase
You can’t set in motion relying on your eagerness to build a successful product, your intuition, and enthusiasm shared by a couple of your colleagues. You need a solid knowledge of what you’re required to do, as well as what you can expect from your investments.
Discovery is a phase that ensures that you and your team have a clear vision of goals, milestones, resources needed (including budgets), and possible pitfalls on your path from the custom data visualization tool idea to the launch of the grown-up product.
The discovery phase is a stage on which you get the answers to the following questions, to name a few:
- What are my project’s strategic goals?
- Who are my customers, and what do they expect? How can my product meet their expectations?
- Who are my competitors, and what are their strengths and weaknesses?
- Is my idea of developing data visualization software feasible?
- What are the risks associated with my project?
The discovery stage includes a number of “events” – collaborative actions aiming to perform the market research, provide the set of assessments (technical and financial), and outline the project’s plan and budget. Discovery phase involves plenty of participants, including stakeholders, technical experts, external consultants, and user representatives.
The discovery stage, as a part of the development process, includes its own steps. They are as follows:
- market research;
- requirement gathering;
- feasibility study;
- wireframing;
- prototyping;
- technical specification development;
- budgeting;
- roadmapping.
The output of this stage is a package of documentation and recommendations. More importantly, discovery results in the creation of “palpable” products, such as wireframes (the project’s visual structure and layout) and a clickable prototype (an interactive model of a tool that simulates the user interface and user experience).
All the work during the discovery phase can be done by a development team if you hire programmers at the earliest stages of project development. However, you may consider the discovery phase a self-sufficient service and outsource it to a development team before deciding on implementation. Such an approach helps avoid investing in an idea that is less likely to succeed. It’s the clear advantage of the discovery phase: you can get all essential information to start without extra effort and extra budget.
Step 2. MVP development
Before you immerse into the idea of building the best data visualization tool ever, it’s worth starting with the safer strategy – making an MVP.
Remember that an MVP isn’t just a simple or shortened version of the “real” software. MVP is still a self-sufficient product. It brings value to users: empowers them with opportunities to solve problems, enhance productivity, and gain insights into different topics. However, advanced features in an MVP are absent; they will be developed as a part of the full-fledged product’s functionality as soon as the product’s viability and feasibility are approved.
MVP is a way to find out if an idea meets market requirements and if it has the potential for profitability that the stakeholders expect. It takes less time and money to develop an MVP. That’s the reason why building this type of product is recommended as the initial step in web based data visualization software development.
Let’s look at the example of an MVP for data analytics tool with data visualization features. The functionality of such a product may include the following opportunities:
- Data import and integration. An ability to import data from common databases.
- Basic visualization types. Include bar charts, line graphs, pie charts, and histograms.
- Dashboard creation. A user-friendly interface for creating customizable dashboards.
- Real-time data processing. The ability to process and display data in real-time.
- Filtering and data segmentation. Filter data by different parameters like time periods, product categories, store locations, etc.
- Basic analytical tools. Include tools for basic analysis, such as calculating total sales, average transaction value, or sales comparisons over different periods.
- Export functionality. The ability to export charts and reports for use in presentations or for further analysis in other tools.
- Data security features. Ensure user information and data integrity protection.
After an MVP launch, you’ll gather feedback from end users, stakeholders, business analytics, and a sales team. The positive feedback is a foundation for making a decision on further project development.
Step 3. Building a Full-fledged Tool
How can you develop data visualization software that is more advanced than your MVP? Let’s proceed with our example of a data visualization tool we mentioned earlier. As far as you’ve decided to build a well-rounded software product, you may consider adding the following features to your tool:
- Advanced data visualization options. Introduce more complex and diverse types of visualizations, like heat maps, scatter plots, tree maps, and advanced interactive charts.
- Customizable dashboard features. Allow more customization options in dashboards, like the ability to create templates, drag-and-drop functionalities, and advanced layout options.
- Data integration capabilities. Expand the range of data sources that can be integrated, including more third-party APIs, social media analytics, and CRM systems.
- Collaboration tools. Add features that facilitate collaboration, such as shared dashboards, real-time data sharing, and comment sections.
- Enhanced interactivity in reports. Allow users to interact with the reports, like drilling down into charts for more details, or dynamically changing parameters to see different scenarios.
- Advanced filtering and data manipulation. Provide more sophisticated data filtering options, including custom query building and data manipulation functionalities.
- Enhanced security features. Provide advanced encryption, user authentication, role-based data access control, and compliance with data protection regulations.
The number of features you may want to add to your tool for data analysis and visualization varies impressively and depends on users’ feedback, marketing goals, and available resources (budget, team size, and expertise).
The next stage in the software development life cycle is scaling it up. It means enhancing the tool to handle increased usage, adding more features, expanding its capabilities, or reaching out to a broader audience. This can involve various activities like improving infrastructure to handle more users, optimizing performance, increasing marketing efforts, or expanding into new markets or user segments.
Keep in mind that software product development is an ongoing process. You need to adjust a product to users’ needs constantly to keep up with their expectations that, in turn, change fast since the market offers new opportunities every day.
Conclusion
To build a data visualization software that stands out, it’s crucial to align it closely with user needs and market demands. Starting with a solid discovery phase ensures a deep understanding of these needs and the competitive landscape. Building an MVP is a strategic approach to validate your idea with minimal risk, focusing on essential features.
The feedback obtained from the MVP launch is instrumental in guiding the development of a comprehensive, full-featured tool. Here, introducing advanced features becomes vital. Remember, the journey doesn’t end with development; it evolves into scaling and continuous adaptation to meet dynamic market needs and user expectations.
Overall, the secret to successfully creating a big data visualization software lies in meticulous planning, focusing on user requirements, and embracing a flexible development process that adapts to feedback and changing market trends.
