How To Create Data-Driven Design

Big data is now a routine part of everyday life, so it is little surprise that developers lean on it when building modern applications. Data-driven applications are attracting particular attention. Many in the industry see them as the next significant step in mobile development, though anyone who follows current data science trends will notice that similar solutions already exist. Fitness trackers and smartwatches are familiar examples of software built around continuous data collection.

Such products vary widely in quality, which gives developers a strong incentive to build something more engaging, distinctive and genuinely useful.

Why data-driven design matters

It is worth setting out why this approach tends to produce successful and popular applications. Several benefits stand out.

Understanding user behaviour. Design begins with collecting data on how people interact with an app, what they prefer and how they behave. This reveals which features they value and where they run into difficulty. With that understanding, decisions about usability and new functions become far easier to make.

Prioritising objectively. Access to usage data lets developers rank and build the features users actually need. It is about as targeted an approach as exists, so each new feature or update is more likely to resonate with the intended audience and lift engagement and satisfaction.

Retaining users. Churn is one of the persistent problems in app development: people download an app, use it briefly, then delete it or drift away. Data can expose the factors behind that drop-off. Addressing the problems users encounter gives a developer a chance to win back a lost audience and reduce the likelihood of deletion.

Personalisation. Tailoring the experience to the individual user is one of the clearest markers of a successful app, and data-driven design makes it possible.

These advantages are concrete rather than theoretical, which is why so many developers now work actively with data.

Practical guidance for data-driven applications

Collecting data is relatively straightforward. Building a high-quality application on top of it is considerably harder. The following considerations help.

Let the data shape the service

A key factor in building good data-driven applications is understanding what information customers actually need. Early on, it is essential to decide which data will be most valuable. A booking app for flights and trains, for instance, will centre on destinations, departure and arrival times, and the best ticket prices.

The last mile rule

In big data, the “last mile” refers to the tools that turn enormous streams of data into something useful. Developers should concentrate on:

• efficient access when identifying information needs and putting that information to use;
• choosing solutions that make the app simple, social and easy to understand;
• using actionable data so customers can make sound decisions;
• running prompt and effective campaigns.

Plan for scale

Modern data-driven applications need to handle information in a range of formats. Over time a product may expand and require extra resources, the ability to scale, and support for other platforms. The most successful tools work with most formats, social networks, archived documents and cloud storage. Good apps perform just as well in a web browser as on a mobile device.

Listen to the majority

Building a community around an app, whether it is already live or still in development, is a popular and commercially sound move. It supports continuous improvement. Direct feedback from users shows what is missing, which components need work and which ideas should be dropped. Paying attention to the people who actually use the product is a modern development norm, and one that large companies and well-known developers follow.

Gather the right data

Effective applications rely on dependable mechanisms for collecting data, covering:

• interaction tracking;
• preferences;
• user behaviour.

Tools such as Google Analytics and Firebase make that information far easier to collect, process and organise.

Run A/B tests

A/B testing is an important step towards a high-quality product. The idea is to create two or more versions of a feature or design element, test each with different groups of users, then analyse the results to see which performs better or appeals more to the intended audience. The strongest option can then be rolled out, with a marked effect on the final quality of the software.

Monitor continuously

Continuous monitoring is another essential step. Data collection and analysis should not stop once the app launches successfully. Ongoing monitoring tracks current trends, surfaces problems early and highlights the key issues to address, supporting steady improvement. A common mistake is to let monitoring lapse after launch, which tends to have negative consequences, including a marked decline in the product’s popularity.

Keep a data-driven roadmap

The roadmap for future updates and new features should itself be data-driven, built on what user data reveals. Priorities matter at this stage: focus on improving the product and making it more responsive to user needs and preferences. That keeps the app in step with the market, allows timely updates and helps the audience grow.

Work across functions

Encourage collaboration between everyone involved in design. Developers, designers, marketers and data analysts should all take part. This way of working produces more effective cooperation and focuses joint effort on the desired outcome. Cross-functional collaboration ensures data is taken into account at every stage and in every aspect of development and promotion.

Core principles

A handful of considerations underpin successful, efficient and in-demand data-driven applications.

• Simplicity. The product should be simple, both in how it presents itself through its interface and menus, and in how it is operated. The simpler the programme, the wider its appeal.
• A capable tool. Despite that simplicity, the app must be intuitive and useful, able to handle the tasks assigned to it and perform a specific role for the user.
• Responsiveness. The app should be accessible across devices, platforms and operating systems. Concentrating all effort on a single platform is a mistake; developers aim to release even the simplest apps for at least two mobile operating systems. The more accessible the product, the greater the benefit to its creator.
• Sociability. Users matter in developing, improving and maintaining the programme, so the app should remain open to them and to the wider world of data.

Data-driven applications represent the next step in development, so it is worth considering whether such a product suits a given company, organisation or project.

Final thoughts

Building software of this kind can be complex, but plenty of useful tools, solutions and development methods make the problems far easier to solve. What matters is understanding what data-driven applications are, what they are for and what they aim to achieve. By grasping what the end user needs and how to make use of big data, a developer or company can produce an application that is genuinely high quality, practical, engaging and potentially profitable.

Common questions

Where can data for application development be found? Public research is one option, but customer surveys and testing your own programme tend to produce better results.

What are the best methods for app testing? For a data-driven app, try A/B testing. It allows particular ideas and hypotheses to be examined and compared.

How is an app’s success measured? Continuous monitoring is required. Build links between every department, from developers to designers, and compare how different factors influence the final result.