3 Reasons Why Data Science is Important in Mobile App Development
With the massive volumes of data being produced today, data science has become an integral part of businesses in nearly every sector. And as it has grown popular over the years, companies have started using it to expand their business and boost customer satisfaction — through mobile app development.
In this article, we’ll learn about the importance of data science in developing mobile apps. But before we go there, let’s delve deeper into understanding the concept of data science and how it works.
What Exactly is Data Science?
Data science is the study of data to obtain meaningful business insights. It is a multidisciplinary method for analyzing massive volumes of data that integrates concepts and practices from the domains of mathematics, statistics, artificial intelligence, and computer engineering. The analysis helps data scientists uncover patterns and answer questions like what happened, why it happened, what will happen next, and what can be done with the results.
Oftentimes, the term data science is interchangeably used with data analytics by most people. However, the two are actually different in a way. Data science is the umbrella term for all facets of data processing. So, from collection to analysis (the primary goal of data analytics), modeling, and insights, data science has always been there. Most often, data scientists and data analysts work together to create peer-reviewed proposals and achieve common business goals. One of them is improving the quality of application development.
How Does Data Science Work in Mobile App Development?
More than data analysis, data scientists are expected to be knowledgeable about data transformations and machine learning (ML). These are needed because they are in charge of creating data processing pipelines, designing architecture, preparing application analytics, and developing machine learning solutions.
Once they finish developing ML models, they hand them off to the team of app developers. This group then integrates the models into a larger application and deploys them.
To understand it better, here’s how both teams work. The data scientist focuses on data ingestion, preparation, model development, and deployment. They’re also retraining and redeploying the model regularly for newly labeled data, data drift, user feedback, or changes in model inputs.
Meanwhile, app developers are focused on building, maintaining, and constantly updating the larger business application, of which the model is a component.
Both parties work hand-in-hand to achieve end-to-end performance, quality, and reliability objectives in the entire mobile app development. Now that we clearly understand how they work — let’s break down why data science is important in mobile app development.
Why is Data Science Important in Mobile App Development?
1. It gauges user expectations.
Before anything, understanding your target audience always comes first in business. Why? Because your success in developing apps or growing the company will always depend on the people. And frequently, they look for functionality and convenience on the mobile apps available on the market.
In light of this, businesses rely on data science to answer critical questions about user demographics, activities, and paths. These insights help you incorporate relevant and innovative ideas when developing a new mobile app. The data collected will be your foundation, from the design layout to the app’s overall functionality.
After all, developing mobile apps aims to increase your customer success and reduce churn. Therefore, it’s always essential to learn about their needs so you can design and develop apps of unmatched quality.
2. It uncovers hidden issues that might get overlooked.
Data science is your guy when it comes to uncovering problems that often get overlooked in the development process. Most of the time, the mobile app’s issues only get noticed after deploying the program on the market.
In light of this, just like how you practice Android mobile device management, you secure, monitor, and manage the app from the day it’s developed until it’s widely available on the market. And you can only be successful in that approach once you utilize data science.
You must know that big data analytics is a subset of Data science. Thus, it can detect patterns even in seemingly unstructured or unrelated data. These insights can help draw conclusions and other app lifecycle predictions.
3. It helps enhance the development process.
Even though analytics plays a significant role in mobile app development, it is not the only role of a data scientist. They must have programming skills as they will use SAS, R, Python, and more to enhance the models in the development process. They leverage predictive and statistical analysis and machine learning (ML).
ML is a technology that focuses on automating the extraction of insights to create knowledge models capable of carrying out specific tasks. Marketers often define it using AI (Artificial Intelligence) as it’s a more generic term.
These machine-learning solutions brought by data science enhance every operation in the app development process, from collecting data to predictive analysis, giving you better insights.
The Bottom Line
Data science is quite diverse, has a lot to offer, and is developing quickly. It greatly helps mobile app development by introducing the system to artificial intelligence, machine learning, and data-driven decision-making. Investing more in this technology will lead to the emergence of more advanced tools in the next few years.
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Originally posted on OpenDataScience.com
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