What Is Big Data?
Big data refers to a piece of data collected by mobile applications or IoT tools that is so large, fast, and complex that it is impossible to analyze it using traditional methods. In general data analysis, the focus was usually on historical data research. The role big data in mobile app development has now become a hot topic because most people are now moving towards mobile applications.
However, Big Data analytics is a technology that processes large-scale, complex real-time data for accurate business information. Since Big Data is about real-time data analysis, speed is an important factor here. A few well-known companies that use Big Data are:
- Walmart – A data-mine used to provide product recommendations to its customers
- Netflix – Big Data was used to predict customer recommendations.
- British Airways – Big Data Used to Find Your Personal Offering.
- US Xpress – Leveraged Big Data to improve the production and management of efficient vessels.
But how can you apply Big Data to your next mobile app development project? Let’s find out.
How does Big Data contribute to mobile app development?
Let’s say you’re from Houston trying to figure out how to make a successful app; you should know that not all apps that go to the app market go up and down. One needs to make a strategy to ensure that mobile app development is done properly.
This is where Big Data comes into play. They can help mobile app developers like you by providing comprehensive information on all major aspects of mobile app development. Let’s take a look at – six ways that Big Data affects mobile app development.
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Understanding the Audience
Before anyone starts working with mobile app development company on a project, understanding your critical audience is important. Extensive data analysis can provide you with valuable information on how users from different domains and age groups interact with the mobile app. Using such analytics data, mobile app developers in Houston can better introduce customized mobile applications.
The best example of audience understanding would be Uber. Uber uses Big Data statistics to analyze real-time traffic conditions, cab availability, and travel time estimates before giving you estimated costs.
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UX Analysis
After analyzing customer needs, mobile app development requires a complete understanding of how users use apps. Big data reveals which part of the mobile app makes users spend most of their time. By using such information, you can make appropriate changes to the structure and improve the user experience.
In addition, after a thorough analysis of user behavior in various aspects of the mobile app, developers can incorporate new features selected in a way that your market audience likes.
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Final marketing
Big data analysis provides intelligent marketing tools and services such as Predictive Analysis. Allows organizations to predict the success of your application and recommend the necessary steps to increase brand reputation. In addition to the predicted analysis, Big Data provides data reduction services that allow mobile application developers to transfer data between multiple applications faster. This makes it easy to hold customized marketing campaigns on different channels at the same time.
Big Data Analysis also provides you with information about customer purchasing behavior and statistical data to help you make your mark on the market. Using Big Data can affect your advertising in the following ways:
- Provides better pricing decisions, content sharing, and projects that attract customers.
- Dealing with customer pain
- Provides a direct way to reach your intended audience
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Real-time data
The mobile apps market is constantly evolving according to the latest styles and customer preferences. To stay ahead of the race, one must be aware of these changes. Big Data Statistics can give you real-time information to make informed decisions about improving your sales transformation.
A good example of real-time data would be that of eligibility tracking apps. These apps monitor their users’ daily activity patterns like eating, sleeping, etc. For the mobile app to work properly, the app must continue to receive uninterrupted real-time data. The data is then analyzed, and the app notifies its users in the event of any abnormalities.
