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How critical is data for DesignOps?

DesignOps professionals are recognizing the vital role that data plays in their success. From understanding project progress and team dynamics to optimizing resources and measuring impact, having access to accurate and relevant data is paramount. 

By prioritizing the use of data, businesses can achieve up to 30% higher revenue growth, while non-data-driven companies may experience operational costs that are 10-15% higher due to inefficiencies. It's clear that data is a crucial factor in driving success and growth for businesses.

Types of data in DesignOps

“Data-driven design is not only critical during the user research or user testing phase but also while performing various design activities or tasks in the design projects.”

To gain valuable insights about DesignOps, it's important for a design manager to collect various types of data. This includes information related to operations, deliveries, team skills and health, process maturity, and design impact. Mature DesignOps practices typically collect the data listed in the table below:

Category

Metrics

Example

Project Health

Active Design Sprints
Delayed Design Sprints

Active Projects
Completed/Delayed Projects

Event
Engagement

Metrics that track how many people participated/engaged in an activity or event

  • No. of team members attending team building events

  • % of designers using volunteer time off

Process Adoption

Track how much adoption a process or requirement has achieved in a team or organization

# of products using a design system

People

Metrics that track the way people on a team or in an organization feel about somethings

Designer Happiness, Role Clarity, Communication Quality

  • Overall Health Risk
  • Total planned effort
  • Utilization
  • Top competencies
  • Designer(s) assigned/un-assigned to projects
  • Potential hiring requirement

Time

Track the time that is invested in a particular process or part of the organization.

Deliverable turnaround time, time to market, maker time, time saved

Leadership

Metrics that measure leadership maturity

Designer ← → Developer ratio

Tool Adoption & Diversity

Process adoption, # of projects that have design involvement, Stage at which design gets involved

 

The Challenges in Data Collection

By utilizing data-driven design throughout the project, informed decisions can be made to improve overall design efficiency and effectiveness. However, design leaders often face real challenges when it comes to collecting data, particularly in large or complex team structures. 

  • Data comes from multiple sources and is difficult to collate: Design teams often encounter challenges when it comes to collecting data from various sources. Information is gathered from different project management systems, time-tracking tools, financial reports, customer feedback surveys, and more. These sources have their own unique formats and structures, making it difficult to create a cohesive picture of the data.

  • The cause-and-effect relationship between data is not very clear: The cause-and-effect relationship between data is a complex web that can be challenging to untangle. While DesignOps relies heavily on data, understanding how different factors influence each other is not always clear-cut. For example, an increase in project completion time may be attributed to a lack of resources or inefficient workflows. However, it could also be influenced by external factors like client feedback or scope changes. Drawing definitive conclusions from the data alone becomes difficult without considering these contextual elements.

  • Inferring from short-term vs. long-term trends: Design Managers need to analyze both short-term and long-term trends to make informed decisions. Short-term trends can help identify immediate issues or opportunities that require attention. On the other hand, long-term trends provide a broader perspective on overall performance and areas of improvement.

By analyzing data over time, DesignOps teams can uncover patterns and correlations that may not be immediately apparent. They can identify bottlenecks in the design process or areas where resources are being underutilized. This allows for more efficient resource allocation and better decision-making.

 

“Design leaders need a single unified dashboard/visibility layer to stay on top, draw inferences, and do course corrections as required.”


Design Intelligence (DI) is the future of DesignOps 

Design intelligence is an essential aspect of DesignOps. Data analysis plays a crucial role in shaping decision-making and driving results in this field. Data provides valuable insights that aid in making informed choices at every stage of the design process. By harnessing the wealth of information available through robust data analysis techniques, design teams can elevate their DesignOps game to new heights. 

So if you're serious about achieving success in DesignOps, it's essential to understand the power that data holds and utilize it to its fullest potential.


Design Intelligence (DI) by Cubyts

Design managers can correlate data from multiple sources and design execution platforms to make sense of the efficiency & performance of projects, people, time & tools. Data from multiple applications like Jira, Slack, Figma, CDP Platforms, Drives, and calendars can be collated to infer insights & recommendations around design operations & performance. 

Cubyts is one of the leading platforms that allows Chief Design Officers, Design Managers, and Design Program Managers to use data intelligently to improve the function & its impact.

Book a demo call today to experience Cubyts DI.