/ How a data app enhances your organization’s BI efforts
How a data app enhances your organization’s BI efforts
A data app is different from a traditional web or mobile app. It is a software application that processes and analyzes data in real-time using sophisticated algorithms.
Data apps are often used to support business intelligence, but they can also be used for a variety of other purposes. A data app can deliver quick answers to complex questions by combining raw information from the internet with your own internal systems and running analytics through large amounts of data. If you have not yet implemented a data app at your company, now is a good time to consider it. With bespoke data-driven applications, you can have a competitive advantage.
The term “data application” is used to differentiate from “business intelligence applications,” which mostly refer to the tools and platforms used for analyzing data. However, it also refers to custom-designed solutions or off-the-shelf products that have been configured by a team of developers specifically for your company’s needs.
What makes a data app so unique?
Data apps are unique in their ability to connect to multiple data sources, such as databases and spreadsheets, engage in data manipulation, and showcase data analytics.
For example, a data app could pull in customer contact information from a database, then pull in any purchase history the customer has had with your company from another database. Additionally, you could import data from a dataset that contains information on each sale price for products sold by your company across different locations.
Data apps are also helpful because they allow you to analyze large datasets quickly and efficiently. This can be done by combining multiple datasets into one single location or by using queries that enable you to analyze subsets of your original data set at once (for instance, if you only want results explicitly related to female customers).
Traditional apps vs. data apps
Unlike traditional web and mobile apps, data apps are created to analyze large-scale information quickly for reporting. While some traditional apps have this capability, there is also an emphasis on user-generative value like gaming, banking, social media, or other outward-facing benefits.
This is because data apps tend to be smaller and more streamlined than traditional apps. The primary purpose of a data app is to simplify any task that requires data-intensive operations. That means breaking down highly complex functions into easy-to-understand steps for both technical and non-technical users. This tool must allow for data import, transformation, summarization, visualization, and export.
Business intelligence requires data-driven insights. A data app is designed to answer this call by visually displaying valuable metrics that enhance decision-making. That means you are developing an app that is both exploratory and explanatory, all through a seamless user experience.
Data apps are expected to run on a variety of platforms, including desktops, laptops, and mobile devices. The overall performance of the data app will depend on the performance of the servers supporting the design. Most of the time, this will require cloud-based computing to ensure use anywhere with a network connection and architecture that can handle big data demands.
Data app benefits
Data apps are an excellent way to make your organization’s data more accessible. They’re easy to use, update, control, and deploy. Furthermore, they can be maintained and scaled easily when needed.
When you begin developing a new data app, you should take the following factors into consideration:
Real-time user personalization
The real-time personalization and recommendation capabilities of data apps are a great way to engage customers. For example, you can use data apps to recommend products based on the customer’s preferences and previous purchases.
More importantly, the data app must meet the needs of the user. With so many variations of data-driven insights available in the modern marketplace, the demand for more customized data apps is only likely to grow. Meeting that demand with personalized features that offer data manipulation and analysis in real-time elevates the app’s usefulness to BI applications.
You want a data app that provides predictive features for future insight. Data apps need to simplify data-intensive operations so you can answer very specific questions in less time than it would take to manually comb through the mountains of collected data.
That means a quality data app should provide artificial intelligence and machine learning benefits. This way, the app can improve and scale over time to accommodate the needs of the organization or user. You want an app that learns from daily use. Again, this is because the current market demands such accommodations in order to remain competitive.
The first thing to note about data apps is that they are interactive. When you implement a data app in your team, your team members have the ability to see their data and make changes in real-time. They can see how the rest of the team is doing and easily track progress.
The second thing to note is that a data app makes it easy for you to update information as needed. Your team will be able to manage their own data without having to wait for IT support when things don’t go as planned (which can happen often). And because most people today have some kind of device, there’s no need for someone else to be on staff at all times in order for them to access their metrics. This means fewer overhead costs associated with purchasing extra computers or smartphones.
Ease of updates
Another key benefit of data apps is that they are easy to update and control, especially if the app is hosted on the cloud. As the user base grows and more users want to access the same information, it’s easy to add more compute power without having to change the configuration of the physical servers.
This benefit is likely to be relative to the organization, as some startups with only 2 to 5 employees will not need as robust a solution as an enterprise-level business. However, having the capability to scale on demand is critical to getting the most out of your data app.
Most data apps have a flexible, easy-to-use interface that enables you to create custom reports and dashboards. If you want to update your data app, all you need is an internet connection and a few minutes of downtime. You don’t have to wait on IT or wait for someone else’s schedule to accommodate yours—you can manage it yourself.
Data apps are also self-serving because they are so easy for end users and business users alike to use. They don’t require specialized training or knowledge of programming languages. All they require is an understanding of basic functions (such as “sort” or “filter”) so as to understand the data.
Deploying a data app takes just a little bit longer than deploying a traditional report because you need some technical know-how on how to connect the app with your existing infrastructure (i.e., a database management system), but once the process is complete, it’s smooth sailing from there.
With an intuitive data app, employees will no longer need help navigating unfamiliar websites because now every user has access in one place where everything’s organized neatly. This removes the fuss over managing permissions issues, so everyone gets what they need quickly without things getting out of control.
Data apps are a powerful way to explore your data, and they are becoming an increasingly important part of the BI landscape. They have unique capabilities that make them ideal for handling complex analytics, real-time personalization and recommendation, predictive functions, and more.
The point is to use these highly focused apps to find answers to complex and specific questions quickly. The more the app can deliver on that promise, the more value it offers your organization. This is even more true if those answers are presented using modern visualization. If you are considering the development of a data app, be sure it fulfills these primary goals first, and then you can add enhancements later.
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