Increasingly, the data projects that deliver the most value and sustainability within an organisation are ones where the end users have trust, ownership and pride in the end product.
This requires that there be true empathy and understanding between developers and end users from the outset, as well as continuous enhancement of data projects in order to meet the constantly evolving needs of the business.
Most developers focus purely on the technical elements of the data projects (such as governance, modelling, security and structure), while most end-users are focused on the business benefits that the data can bring. A change in approach is required to converge the thinking of the business users and the developers.
Design thinking is an approach that aims to do exactly that, by looking beyond conventional processes, procedures and practices of problem solving, and toward addressing challenges from the perspective of those who will be ultimately using the product or service. Designers who adopt this approach are more interested in the why, rather than with the how.
Whereas traditional methods stress the importance of data and logic, design thinking requires that designers focus on the desired outcomes and improving the overall experience for end users – all while taking their own biases into account. It encourages designers to scrutinise all the options available, identify potential problems, and choose the best way forward.
Design thinking can be distilled down to five phases: empathising with users in order to understand them and their needs, defining the problem that needs to be solved, ideating multiple ideas to meet the requirements, quickly prototyping in order to learn and refine, to finally testing in order to confirm ideas and plot a course of action. Crucially, these steps are not linear, as prototyping and resulting observation can lead to many more ideas on how to tackle the challenge.
Design thinking in data
Those developers or business consultants who adopt data projects with a design thinking approach can be better aligned to the overall business strategy as they are now focusing on the ‘why’ from a business perspective, rather than the ‘how’ from a technical perspective. On the other hand, developing a data project that focuses purely on technical delivery is like constructing a building without understanding who will be using it.
Rather than building something that is tailored for the users, it will simply check all the boxes from a list of requirements. While there may be an extensive list of boxes to tick, the architect will need to use intuition and experience to guide the occupants on choosing the best possible options, ensuring that the building best meets their requirements. Doing the same with data projects will ensure that developers don’t simply build something that works, but something that works for the right people at the right time.
Don Norman, the father of UI design, states that designers should take the given problem as a suggestion rather than a final statement, and should resist the temptation to immediately jump to a solution to the stated problem and rather look more broadly in order to fully understand the actual problem, which may have only been partially articulated.
A hybrid approach
While this is a departure from traditional project management, there is room in these data projects for a hybrid approach that incorporates the principles of design thinking in each step of the project. This requires that the designers shift away from delivering in a big bang approach and towards keeping users involved in each step of the project.
Traditional approaches to developing data projects have included business analysis, technical analysis, data extraction, data modelling, data integrity testing, data application development, quality assurance, end-user workshops, enhancements, user sign-off and go-live. While nothing changes in this new approach, there is a need to apply the principles of design thinking with each phase. Naturally, some phases, such as business analysis, might have a larger component allocated to the design thinking phases, the challenge for developers is to keep the end-user experience in mind across all steps.
Unlike the conventional approaches, this requires revisiting earlier steps. For example, in the ideation phase of developing a front-end data application, developers might identify a new way of looking at the data in order to bring more value; this in turn might require going back to the data modelling phase to change the model. Fortunately, doing this is made possible by the multitude of business intelligence tools that lend themselves to an incremental, design-thinking approach, allowing organisations to rapidly release prototypes and experiment with various ways of answering crucial business questions.
Ultimately, applying design thinking principles to data projects helps developers and consultants to connect with end-users, empathise and understand their real challenges, and use this context to create solutions that add true value, and foster key partnerships that are long-lasting and symbolic.
