Modern data stack (MDS) has been the talk of the town for the past couple of years. An explosion of data toolings have emerged to redefine what business data can do. Though still in early development, the topology of MDS is getting increasingly clearer.
The entire top layer can essentially be defined as “Data applications”. They simplify data-intensive operations so users can gather rich insights or perform actions with their data. You may also have heard them referred to as “analytical apps”, “BI apps” or simply “data apps”. In this article, we’ll walk you through what a data app is and how it may be useful to unlock more value from your data.
While data apps can take on many forms, such as Uber’s driver tracking service or Amazon’s recommendation engine, the common thread running through modern data apps is their potential to transform a business's productivity.
Data apps provide value by institutionalizing workflows or helping users understand complex relationships in data through interactive visualizations.
Why do we need data apps?
Data apps are on the rise. The way organizations process data has shifted drastically over the years. As the modern data stack matures, we’re outgrowing old-school dashboards and reports. These old-school dashboards are often insufficient for analysis and lack the necessary context to make meaningful insights.
We’re moving away from dashboards and reports to real-time, interactive data apps. This not only helps companies become more data-driven but also relieves the burden that’s placed on BI and data teams to keep up with a company’s data demands.
Apps are often self-service, so end users can quickly and easily locate and analyze specific data without deep involvement from BI or data teams. Users of any skill level can effectively interact with data to impact day-to-day decisions and long-term strategies.
Business Intelligence Dashboard vs. Data Application
On the surface level, data apps and BI dashboards may seem to have a lot of similarities. After all, BI dashboards also draw from diverse datasets and serve analytical purposes. However, data apps can provide greater functionality and power to an organization.
Let’s take a look at some of the main differences between data apps and BI dashboards.
BI dashboards tend to show more static displays of information since they generally aren’t built to optimize speed and concurrency. While they can be sufficient for analyzing long-running historical data and presenting metrics, dashboards can become sluggish when it comes to analyzing real-time data.
Data apps are built with real-time data in mind. With real-time analytics, business owners will be able to keep up with and stay ahead of market trends. They’ll also be able to quickly address, and even anticipate, customer needs to increase customer satisfaction and employee productivity.
BI dashboards lack the ability to build sophisticated user interaction. While most dashboards can drill down on information or filter data, there aren’t many options for performing more complex user actions. This limits dashboards to analytical purposes. Once a discovery is made, users will have to leave the dashboard to act on their insights.
Data apps can perform any action that a BI dashboard can, plus more. Data apps offer writeback and integration capabilities with third-party systems, increasing functionality and allowing users to interact more directly with the data. For example, if we’re looking at an ad management app, you’ll not only be able to analyze ad data, but pause or change the budget of a campaign directly in the app. It’s also possible to run models and perform predictive analyses. This streamlines the process from analysis to action.
Exploratory vs. Explanatory
BI dashboards are great for presenting metrics to help with decision-making. However, users already need to have metrics in mind to report on in order for the dashboard to be useful. Therefore, dashboards mainly serve an exploratory purpose. Users can explore the data that’s presented, but there’s little additional information to supplement it.
On the other hand, data apps are both exploratory and explanatory. Data apps are focused on answering very specific questions, so they can narrow down on scope to provide additional value. Not only will users be able to explore their data, but they can also use their insights to communicate findings and inspire action.
Users of BI dashboards may encounter obstacles when trying to scale distribution. Their data infrastructure often struggles to support many users at a time well, causing slowdowns and frustration when using dashboards. Many tools also require you to have proprietary software to view a dashboard, making them difficult and expensive to share across an entire team, department, or organization.
Data apps can be widely distributed without affecting performance, so all users can extract the most value from them. There’s no need to download additional software since they run in a browser. Anyone with the right link and the appropriate permissions will be able to access the data app without running into performance issues.
BI dashboards serve mainly as data visualization and analytics tools. They look fairly similar across the board. A dashboard is generally made up of a collection of graphs, tables, etc. that help convey important business metrics and data points.
Since data apps are more customizable in content and interaction, they have more diverse use cases.
What can data apps be used for?
The types of data apps built will largely depend on a company’s industry and operational processes. Since data apps are highly customizable and scalable, it’s important to identify your business's specific needs and build an app that’s unique to your goals.
That being said, here are several common use cases for data apps
Anomaly detection: Anomaly detection is important for detecting security breaches or identifying fraud. Data apps can alert users to examine data in reaction to an event or anomaly. Teams can then go in to determine whether the anomaly is a potential threat. This is particularly helpful for use cases such as Application Performance Monitoring, Predictive Analytics, and Fraud Detection.
Real-time recommendations: Data apps can combine historical and real-time data to deliver instant recommendations to a user. For example, an e-commerce store can track a customer’s prior purchases as well as current site activity to provide the most relevant recommendations.
Real-time supply chain and logistics: When managing supply chain, it’s important to have transparency into inventory, orders, vehicles, people and equipment. Keeping within a delivery time is essential to keep customers happy. With real-time monitoring, logistics providers can see if there are any sudden delays caused by traffic, weather, etc. and make adjustments.
Alerting and notification: Data apps can be built to send users an alert or notification when an event is triggered. For example, customer support reps can receive alerts when a new request is submitted so they can act quickly to resolve the issue.
Workflow automation: Workflow automation is the replacement of the flow of manual, repetitive tasks with software that can automate the process. This helps streamline processes and enhance productivity. Data apps can help automate work across numerous applications to increase efficiency and minimize errors made.
Predictive modeling: Data apps can use machine learning and statistical techniques to extract information from your data to discover trends and predict future outcomes. This can be particularly useful for forecasting future profits, targeting potential customers, preventing malfunctions in manufacturing, and determining staffing needs.
Data apps are already everywhere. If you look around, you’ll notice how prevalent they are in your everyday life. From real-time social media updates to food delivery tracking, we’re already experiencing the power of data apps. In this next step of the digital transformation journey, businesses can harness the strength of data apps to bring teams better engagement and extract more value out of their data.
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