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People can usually interpret data more easily when they view it graphically. That’s why it’s often more straightforward to understand household spending trends when we can see it in a pie chart, for example. It’s also why coaches sketch out plays during team huddles — and how in televised football games, fans can better experience the action when commentators illustrate plays onscreen with video markers.
For businesses, data apps put the information that employees or customers need into their hands in a similar way, in a format that’s easy to consume. Data apps allow end users to work with all kinds of data — often in real time as it’s created — by making information accessible through visual reports and dashboards. Being able to visualize and manipulate fresh data supports business decision-making, drives efforts to automate data workflows and helps companies remain agile amid constant change.
Embracing data modernization and cloud data architectures can support your digital transformation initiatives while advancing data-driven business objectives. This guide provides an overview of data apps and their role in modern, data-driven strategies.
The term data applications is a combination of web applications and data visualization. Sometimes also called analytical applications — and even “two-way applications” because they have read-write capabilities — data apps allow users to visualize and manipulate all kinds of data to help track business performance and inform decisions.
Companies create data apps for employees and customers for a variety of uses, from loan eligibility calculators and product recommendation engines to dashboards compiling real-time sales data, maintenance updates on vital equipment or employee performance metrics.
Data apps are an evolution of technologies like Excel macros, which allowed end users to more visually manipulate data directly within a spreadsheet rather than only being able to work with it in columns and rows. But macros came with challenges, including being limited to static data, as well as offering primitive data security. Fortunately, data-handling approaches have advanced, and we’re no longer bound by those constrained, early capabilities.
Data apps can securely plug into multiple sources of business data, allowing end users to visualize data in a variety of ways to inform business decisions and help people do their jobs more effectively — no data science experience required. Data apps enable informed decision-making based on real-time metrics, including app usage data, sales tracking, customer sentiment and more.
Data apps come in many forms — from embedded apps to enterprise-grade platforms and client-facing tools that serve up external data as a service. Some data apps are used internally to help make business decisions, and others are designed for customers to guide decisions or manipulate their own data in the context of a service they’re receiving from you. Here’s a look at some common types of data apps.
Data apps are often embedded in other apps, making data visualization and dashboards accessible from within an app or website rather than making users rely on a standalone app. Dashboards, for example, are usually embedded in other apps to aggregate specified data and present it on one screen showing different but related data for a given time frame. For example, fitness trackers and smart watches pull metrics tracked by devices into dashboards so users can see their activity at a glance.
An enterprise data platform provides a system upon which companies can build, scale and deploy data applications to the right teams.
Service providers in a range of industries keep their customers in the know by providing data apps that aggregate external data for planning, forecasting and performance analysis. Investment firms use dashboards that compile historical market data, for example. Another use case is social media platforms providing advertisers with real-time analysis of online ad campaigns.
All data applications require a way to integrate and unify data from disparate or siloed sources and systems, a data storage component, data processing/transformation features and data visualization capabilities.
Data apps need access to all kinds of data that may reside in or be generated by disconnected systems inside or outside the enterprise. A data integration layer unifies and streamlines diverse data, making it available to data apps.
Data integration creates a single, accessible data environment, helping businesses break down internal silos. Data sharing fills a related but slightly different function, extending the accessibility created by data integration to external parties. This is often required for data apps built for external use, helping to uphold data privacy and security requirements.
Data apps must be able to pull data from wherever it’s stored, whether it’s a data warehouse, data lake or some other system. The operational data store (ODS) is a storage method gaining popularity because of its flexibility — particularly in the context of data apps. An ODS is a central database that aggregates data from multiple systems, providing a single destination for storing a variety of data.
ODS is well-suited for use with data apps because it is faster and more responsive than a traditional data warehouse. Unlike systems that use an extract, load and transform (ELT) or extract, transform, and load (ETL) process, an ODS ingests raw data from production systems in its original format, storing it as is. ODSes are designed for light-duty queries on small datasets because they only store the most recent operational data, making them suitable for strategic, real-time data queries and analysis — for example, determining how much product was sold in the past hour or pinpointing where in the world most of today’s online sales originated.
Raw data is typically processed and transformed in some way (often using ELT/ETL) before it’s served up for use in data apps. An ODS may make it possible to skip this step or use a different process, but generally there is some processing that needs to take place so a data app can interpret data correctly.
Data visualization capabilities and a user-friendly UI are key for data apps because their purpose is to democratize data by putting it into the hands of many teams across the enterprise, most of whom will not be data engineers.
Methods to secure data must keep pace with ever-evolving cyber threats. In every industry — but especially in highly regulated ones like finance and healthcare — you must be able to protect sensitive data at all times. Methods in most data apps include user authentication and access controls, data encryption and anonymization.
For data apps to present data in a format that’s easy to consume, they need a way to automate data pipelines, so tasks required to bring data from where it’s stored into a dashboard or interactive report happen seamlessly in the background.
Data apps bring together data from separate but related systems, add analytics and present it visually so users can consume it more easily. This speeds up and improves the quality of business decision-making for enterprises and ultimately improves the customer experience, too. Companies can also streamline operations using insights gained from internal dashboards and automated data pipelines. And data apps can be helpful in informing strategic planning by combining historical data with forward-looking analytics, improving business forecasting and scenario planning.
Data applications make it possible for people in different roles throughout an organization to integrate data into their daily work, solving problems or improving business processes based on real-word performance and other data. Let’s drill down on some examples.
Customer engagement apps consolidate information about how customers interact with your organization and give sales and marketing teams a view of the customer journey to identify areas for improvement. Key performance indicators (KPIs) tracked often include customer satisfaction, engagement data such as number of active users over time, churn rates to track retention and activity logs showing trends in customer behavior.
Mobile field apps collect data on how users interact with apps in real time based on preferences they set. These apps can also capture qualitative data via in-app surveys and mobile questionnaires along with quantitative data about what users do in an app and how long they stay. Companies use this data to identify and fix problems through updates.
Tracking the sales performance of certain products gives teams a clear picture of how well or poorly those products are selling by geography, time period and buyer persona. You can feed this data into predictive AI or other analytics tools to create sales forecasts, plan new product rollouts and ensure you have enough raw materials on hand to keep pace with market demand.
Speaking of market demand, supply chain management platforms can help you avoid product shortages by tracking raw material availability so you can more accurately forecast demand, adjust inventory levels and look for ways to streamline logistics or tap alternate suppliers.
Data apps provide diverse teams with the insights they need to run more efficiently, respond faster to customer demands and keep products stocked or flowing from manufacturing sites. Let’s review the ways data apps can drive these business insights.
Data apps can serve up data in real time to inform business decisions related to new product rollouts or updates, inventory and supply chain management and trends in customer sentiment.
Your marketing team may want to be able to track how customers use your products and interact with service agents. Presenting this data in dashboards and visualizations, which you can augment with external customer sentiment information from social media, paints a fuller picture of what customers want, so you can plan product rollouts and customized offers accordingly.
By combining data from many areas of the business, you can isolate bottlenecks and determine where to automate processes to enhance efficiency. Tracking routines let you monitor progress and more easily present results to management.
If your business is delivering digital products, data apps can track usage patterns and present them in a variety of visual formats — including heat maps and time graphs so you can dynamically adjust infrastructure and computing resources to ensure uninterrupted delivery during peak usage. You might even combine aggregate data with that of individual power users to offer those customers the option to pay more for better performance on demand.
Along with their many benefits, data apps also come with challenges, many of which mirror the hurdles organizations face when dealing with big data, including keeping data secure, integrating different types of information from diverse sources and managing large volumes of data efficiently.
Who can count the times a mobile app has asked if (and how) it can track their activity? Multiply that by thousands — or millions — of users to get an idea of the scope of responsibility companies have to protect customer data.
Data apps require raw data from many sources that aren’t always compatible with one another. Normalizing this information can be a challenge all on its own.
The systems that feed your data apps must be able to handle a literal flood of information. Meanwhile, your production servers must be able to simultaneously handle a large and changing number of simultaneous users.
The most successful data apps provide a consistent experience across different devices, such as web browser, mobile app and PC application. This presents a coding challenge for DevOps teams.
Some data applications put an array of data into the hands of the people who use it to do their jobs effectively, streamline operations, make better decisions and respond quickly to market changes. Others provide services to customers that complement existing products, create new revenue streams and strengthen customer loyalty.
The landscape of data-driven applications is changing rapidly, and businesses will need to use proven strategies and innovative technologies to keep up with that change and find success.
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