Today, many organizations are developing data-intensive applications such as interactive dashboards, infographics, personalized data visualizations, and graphs for users’ data entitlements. If your application needs to display a visualization of a bar chart or other simple data, it’s easy to use the charting framework to compose the visual and render the chart. However, the built-in analytics capabilities of the data visualization platform may provide a richer end-user experience and tools to support easier and faster expansion.
Incorporating analytics can be a powerful approach to enhancing your application when visualization experiments are important. For example, the product owner of an application may find that different user personas require their own dashboard, even if they start with a simple visual. The data visualization platform makes it much easier to develop, test, and iterate on these dashboards than coding visuals.
Another important advantage of using a data visualization platform is that data scientists and subject matter experts can participate in the application development process. Instead of software developers creating requirements for translating into code, visualizations are repeatedly improved by a group of people who are most familiar with business needs, data, and data visualization best practices.
Why Use Data Visualization Tools
Let’s look at some use cases for embedding data visualizations when rapid development and experimentation is required.
- Analysis can be incorporated into enterprise systems that contain data from several other data sources. An example is a sales manager dashboard displayed within a customer relationship management (CRM) application that contains financial data from an enterprise resource planning (ERP) system and prospective data from a marketing automation platform.
- For customer-oriented mobile and web applications, simple charts or graphs can facilitate user interaction. Consider a stock trading application that graphs stocks on an investor’s watchlist and highlights stocks that are close to low prices when it may be the right time to buy.
- Media organizations and other organizations that publish content may want to pursue it. Data journalism, Journalists write articles about data sets and visualization of one or more data, and Data and analysis are the foundation of the story..
- Marketing infographicIncluding graphic design and data visualization, is built into websites and other marketing tools.
- For the company trying Data driven,this is Select a data visualization platform To develop analytics and incorporate them into enterprise or customer applications.
- Organizations that are already using data visualization tools may need to extend their visualizations with custom integrations and capabilities to work with or process data through workflows.
- The entire customer application can be a data visualization of data products and services. This approach is common in data, financial services, insurance, and e-commerce businesses where data is a product and analytics can be a differentiator. In such cases, you can use the data visualization platform to develop your product and leverage the flexibility of the platform to integrate it into another system to help your team innovate and support rapid expansion.
Incorporating analytics drives innovation
The difference in data visualization is that the required requirements, design, and functionality are likely to be very iterative. As more stakeholders and users learn more about data and useful insights, they may change the required experience, design, and functionality.
As a result, visualization libraries may be easy for developers to use, but they may not be the best development approach for embedding analyzes that require frequent iterations. Iterative design is especially true for journalism and marketing, where the goal is to allow users to design, develop, and publish data visualizations without the need for developer or engineer support.
Steps to embed analytics in your app
Review the following development considerations when considering incorporating analytics into your application:
- Who are the users? Also, what questions do users have to answer in the analysis? The best dashboards and data visuals answer specific questions and perform business functions, as well as reports on data.
- Is the app used on the web, mobile, or both? This requirement qualifies the screen dimensions, number of graphs, and amount of data considerations that developers must consider in their designs.
- How much data do you need to process and what are your performance requirements? To get a larger dataset and better performance Database materialized view, In-memory database, And aggregate data visualization may be required.
- what Data governance And security define a user’s data entitlements? Developers need to dimension these rules as use cases and create test scenarios to verify that their implementation complies with data governance. In addition, if you have important row-level and column-level data governance rules, you may need to change the visuals.
- Team should Develop standards and center of excellence for data visualization Guide chart types, color schemes, labels, style guides, and other rules that provide a consistent user experience.
- Data is subject to change, so it’s a good idea to create test automation with continuous integration and continuous delivery (CI / CD) pipeline data visualization, but production incidents. It can also be run as an application monitor to warn you.
These are some of the steps developers, data scientists, and agile teams need to include when incorporating analytics into their apps.
Want inspiration?Check the analysis Tableau Public, Microsoft Power BI Gallery, Sisense sample dashboard, When Qlik gallery for example. While many dashboards serve as stand-alone tools, they can provide greater business value when incorporated into customer and internal workflow applications.
Copyright © 2021 IDG Communications, Inc.
Reasons to incorporate analytics and data visualization into your app
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