Data continues to fuel the information economy.
Developing breakthrough digital products requires keen insight into your target audience and marketplace. Just gaining the attention of your prospects with an elevated user experience is not enough. User attention is fleeting in the hypercompetitive digital space. To keep your audience engaged over time, you must keep refining, optimizing and innovating user experiences.
Which brings us back to data, the world’s new dominant resource.
All digital solutions produce data that can be leveraged for user insight. However, not all organizations are empowered to make the best use of the data they collect (or could collect). Motivated product teams can realize the benefits of a more data-driven strategy by giving data collection and analysis the same design treatment as critical elements of UX.
We’ve said before that it takes a comprehensive approach to product development in order to create solutions that truly resonate with digital native audiences. Data experience design is an increasingly important part of that holistic process.
Why Pursue A More Purposeful Data Experience Design?
Forward-looking brands are leveraging data to drive better user experiences and more effective decision-making. More impactful insights, achieved faster than ever before, lead to efficiencies and improvements, and bigger leaps in understanding, capability and user adoption. Brands that don’t move in the direction of data are looking to be left behind.
Much of what we touch and work with now automatically generates data. But the real reason why we’re seeing this increase is the growing utility of data analytics and automated responses to analytic decisions.
The past decade’s data explosion created a virtuous circle of data analysis and action, leading to new insights, data creation, and data analysis. We’ve seen companies collect more data than ever before as they’ve raced to transform their businesses and make data-driven decisions.
More data is being generated today than ever. But not all of that data collection is done purposefully or in an organized fashion. In the rush to adopt analytics, many organizations failed to develop a sophisticated understanding of data management methods and practices. In some of these cases, the outcome has been a complicated network of disparate data streams unable to talk to each other or contribute meaningful insights.
This is exactly where data experience design can help.
What Is Data Experience Design?
Data experience design is the process of transforming raw data into a robust and flexible data architecture capable of fully supporting the informational needs of users — external (customers) or internal (your product team). This more purposeful approach to data makes it more accessible, digestible and actionable.
Data experience design occupies the middle ground between data visualization and user experience design. Whereas UX design is all about making things easier for the user, data experience design is all about making it easier to analyze large amounts of data and glean insights.
How is this accomplished?
In short, a data experience designer considers the full data experience — not just the chart or visual used, but the real-world questions the customer needs the data to answer. The designer must consider everything from how the data are collected and structured to the actions a customer needs to take once their questions are answered. From this holistic view, we can start to tackle challenges that plague data-centered products, like siloed data, meaningless dashboards (with charts that often don’t say anything at all), and interfaces that make essential comparisons or actions impossible.
Data experience designers develop custom-built data infrastructures and help product teams create better tools for data analysis and advanced data visualization. Establishing that infrastructure requires you to transform raw data into easily shareable formats.
Data transformation is accomplished by gathering, warehousing, and preparing data for the purposes of insight, tracking and/or measurement. A critical aspect of data transformation is the ETL (Extract, Load, Transform) process, which includes decision-making around a number of key areas, including:
- Data Harnessing
- Data Warehousing
- Data Integration
- Data Management
Data transformation and custom-built infrastructures allow you to develop sophisticated data visualization and analysis tools that help your product team to automate tasks, better track performance, provide custom reporting and share dashboards that support your team’s operational goals. Data experience design also helps to optimize your customers’ user experiences, maximizing the value they receive from your digital solutions.
How to Harness Data to Power Your Digital Solutions
A custom-built data infrastructure gives you the ability to utilize 100% of your data. At Crush & Lovely, we help our clients fully harness their data with a four-step data experience design process. We develop processes to power your applications, utilize analytics to inform smarter products, organize data for maximum efficiency at scale, and visualize data to help product leaders navigate further iterations with speed.
- Develop a purpose-led data strategy.
We begin by helping you identify the important questions, the queries which will be most impactful for your users and beneficial for the success of your digital product and business. From there, we design a tracking plan to effectively capture the data you need in order to answer those questions.
- Create the data infrastructure to capture, store and format your data.
Our seasoned data and development team have considerable experience developing data warehousing strategies to satisfy the most stringent privacy and legal requirements. We review all the ways that data is coming into your organization and help you harness the most important data streams to extract valuable insights. When necessary, we develop additional ways to capture relevant user data to supplement existing data sources.
- Put your data to work immediately.
With the data infrastructure ready, it’s time to deploy that tracking plan and let the data flow. In the next step, a behavioral analytics pipeline and relevancy scoring system help to transform raw data and prepare it for presentation and analysis.
- Drive actionable insights with greater frequency.
Data transformation enables powerful data visualization tools and techniques, driving the ability to personalize customer interactions and glean insights for future growth.
Get the Most Through Data Experience Design
The ability to leverage your data more fully can help you stay better connected with your users and ahead of your competition. To do so, you must take a purposeful approach in designing your data architecture.
For product leaders hoping to engineer a more robust data analysis infrastructure, Crush & Lovely is here to help find the most efficient path to more effective data insights.