Throughout the past year, we've had the privilege of interviewing design leaders from a diverse range of industries, including Cubyts users. The purpose of these interviews was to gain insight into the factors that contribute to their success, as well as the challenges they encounter. Interestingly, we discovered that one of the most important factors for success is also one of the most challenging to obtain: meaningful operational data that offers visibility throughout the entire design process.
Why DesignOps data is important.
Successful design managers recognize the value of real-time data. It not only enables them to make informed decisions but also measures productivity, efficiency, and impact. Design leaders can effectively demonstrate their value to stakeholders by emphasizing the changes that not only align with business objectives but also improve user satisfaction.
Furthermore, improved visibility of the design process can greatly enhance overall design performance. When designers have transparent and clear insights into each stage of the process, they are more equipped to make informed choices and collaborate effectively with other team members. However, getting visibility of design-related activities which includes tasks, people, tools, time, and cost aspects, especially in a product development environment, is notoriously tricky.
Design Teams in Product Development Environment.
Design teams often lack internal processes and they use project, and knowledge management tools defined for engineering. This reliance on project/knowledge tools makes it challenging for design managers to access relevant operational data. Applications like Figma or JIRA do not provide meaningful data on design operations.
Many design managers resort to using tools like Excel, Notion, or CODA to aggregate data manually, but this process can be time-consuming and resource-intensive. Additionally, pre and post-design work like research, UX analysis, and testing often happens outside of product development systems and is not captured in a common platform.
One of the biggest challenges in gaining insights is the fragmentation of data across different tools. Designers prefer to stay within their design environments, such as Figma, where they work on wireframes, receive feedback, and create design documents. Unfortunately, this data is often isolated from project management tools like JIRA, which is further isolated from the measurement data of design impact that resides in product analytics tools.
To tackle this issue, we start by aggregating data from various tools within the product-design environment, such as Figma for design, JIRA/Asana for project management, and Analytics/Hotjar for measurement, into an intelligence system. Let’s call it a Design Intelligence System (DI).
Pic 1: Integrating data from various tools in the product design environment.
The aggregated data can then be categorized into four main categories: Project, Documentation, People, and Product data.
Project health status.
Usage, consistency, and quality of work
Team health, competencies, productivity, and collaboration.
Impact on Business Success
Table 1: Categories for DesignOps Data.
By analyzing data from these categories, the design intelligence system (DI) can provide valuable insights into the design management process so design managers can focus on areas that require improvement.
Pic 2: Categorizing and Analysing Design Operations Data.
Furthermore, you can query DI based on the intent and get the exact answers that you are looking for. Let's look at some examples in the table below:
|Intent / Query||Data||Insights|
|What’s the status of all my projects over the last 6 months?||
Bar Chart Showing Project Statuses
|Status of how many projects are currently delayed, completed or stalled.|
|How much of my team is currently being utilized?||
|Status on current/future planned work and team utilization.|
|Show my team’s competency summary.||
Team Competency is plotted on a spider chart.
|Identifying competency gaps within your team, Planning hiring or skill improvements roadmap.|
Table 2: Example of what queries can be posted to DI and expected answers from the system.
Referring to the table above, imagine having immediate access to all the relevant operational data you need to make informed decisions. DI can fetch data from different tools, run analysis based on your query, and present data in an appropriate format. For example, in the table above, you can see a bar chart that shows the status of all projects in the last 6 months. You can extend this query to know more about which projects are stuck, the project that has the most comments or has received more positive feedback, etc. You can even identify which projects made the most impact on business goals instantly.
But it's not just project data that you can access. Once your data from different apps are integrated, DI can also provide you with your team's health status, including utilization and plot competencies. And what's even better? You can now access all this information via your favorite communication tool like Slack.
By integrating a Slack channel, these design insights can be made readily available to all channel participants, making communication even more effective within teams.
Pic 3: Delivering designops insights to slack channels on-demand.
Thanks to AI, you can have a human-like dialogue with the repository and get exactly what you want. Design data is relevant well beyond the design phase, so DI would also allow you to look into the history of your design process, and assets, and observe trends and actual impact on business over time.
If you are a Design Leader and looking to gain real-time visibility into your design process, contact us for a demo of Cubyts Design Intelligence.