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Digital transformation has been measured by the evolution and improvement of technology. This transformation wouldn’t be possible without the humans that use these tools. Humans are the key figures- in this digital-first world- that are not going away.

What is Design Thinking?

Design thinking is a human-centered approach to problem-solving that focuses on understanding the needs of the end-users. It is a methodology that involves a deep understanding of the people who will be using the product or service and using that knowledge to create solutions that meet their needs. The design thinking process typically involves five stages:

The Empathy Stage

The empathy stage is where the design thinking process begins. It involves gaining a deep understanding of the people who will be using the product or service. This is typically done through research, observation, and interviews. The goal is to gain insights into the needs, desires, and pain points of the end-users.

The Define Stage

Once the research has been completed, the next step is to define the problem or challenge. This stage involves synthesizing the information gathered during the empathy stage and identifying the key issues that need to be addressed.

The Ideate Stage

The ideate stage is where brainstorming and ideation take place. This stage involves generating a wide range of ideas and solutions to the problem or challenge identified in the define stage. The Prototype Stage: The prototype stage involves creating low-fidelity versions of the potential solutions generated during the ideate stage. The goal is to create something that can be tested and refined based on feedback.

The Test Stage

The test stage is where the prototypes are tested and evaluated. The feedback gathered during this stage is used to refine the solutions and make them more effective.

What is Data?

Data is a collection of facts and figures that are used to inform decisions. In today’s digital age, data is generated at an unprecedented rate. This data can come from a wide range of sources, including social media, websites, sensors, and more. The challenge for businesses is not only collecting data but also making sense of it. Data can be used to identify trends, understand user behavior, and make predictions. By analyzing data, businesses can make data-driven decisions that can help them achieve their goals.

Design Thinking and Data: The Power Couple of 2022

In our recent Forbes article, we highlight this. Without people in the heart of every digital experience, algorithms wouldn’t succeed in being truly effective. Forrester predicts that 2022 will carry “heightened expectations for digital experiences [and] pivots to human-centered tech transformation.”

This means a shift in thinking- including when it comes to creating effective AI algorithms. We’ve been hearing over and over through the years that data is the “new oil”. We even wrote recently how there never seems to be enough of it. It is true that in order to accomplish digital transformation, you must have lots of data to train an algorithm- you can have vast amounts of data, but we are now realizing that you must know what to do with it and who will benefit from it. 

A new way of thinking is the solution for developing human-centered algorithms. We need to include the human experience- or design experience- into our design processing for quality solutions.

The Hot Duo: Design Thinking and Data

A Human Perspective

2022 is the year where this perspective will be the focal point. Software engineers and data scientists will join forces with design experience professionals to understand the human problem and develop the right solution for it. 

Here are five best practices that focus on the design aspect of data engineering to help you better understand the heart of any issue and create solutions fit for digital transformation.

  1. Don’t develop tech for tech’s sake. Before creating something, developers must ask themselves the “why” behind it. This means an understanding of the individual users, their needs, past experiences, and more; and then using that knowledge to design better solutions. 
  1. Data goes two ways. Good quality data must be a priority. Scientific data helps uncover the patterns of behavior that may need to be addressed, but the human data is essential to identify and develop the right solutions for these patterns. To understand the symbiotic relationship between data and design, consider the challenges faced during the pandemic. Data-driven science led to life-saving vaccines. 
  1. Remove siloes. Software engineers, design, and development must work together to create innovative solutions based on relevant technologies. They need to balance the business and user needs for a possible technology solution that achieves the desired outcome. 
  1. Engage all stakeholders. It is important to engage external and internal stakeholders to prevent biased program algorithms from happening. By having the perspective and data from different departments in the organization and from those out in the world- the people impacted by the technology- we ensure that this does not happen. 
  1. Continually test and improve. Once a solution is created, it is not the last solution to be developed. This solution must be continuously tested to ensure that it is accurately addressing the business problem and effectively meeting human needs. Being human means constant change in our experiences, beliefs, and more. It is important to constantly check that the algorithm works for any new data that is applied to it. 

Humans are the driving force behind technology. This is why we must start with the human experience and work toward a solution for the tools that improve that user experience. This is the line of thinking that will lead to better innovative solutions.

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