Integrating Design and Advanced Analytics: A Collaboration that Can Bring Greater Business Value

?In our previous article, we discussed, what causes advanced analytics projects to fail? We examined five primary reasons that result in generating such unsuccessful analytics projects. Advanced analytics projects have the potential to tap into greater value, but many times, projects fail to reach up to the potential that the advanced analytics projects are built for. In this present article, we will explore how integrating design and analytics can result in a fruitful collaboration.??

??Often analytics and technical teams fail to foresee the human element, The End-Users, whose adoption of the analytical model entirely affects the project. Failures in advanced analytics occur due to several reasons, misalignment with business proprieties, technical issues, and poor user adoption. However, problems caused by technical feasibility and business viability are no longer common.??

A profound collaboration among designers, data scientists, engineers, and end-users can help mitigate the cause of failure as it provides deep insights into the user desirability and expectations of the project.?Just by keeping in mind the user adoption from the start of the analytics project while developing the model, the chances of needing an expensive change management process also get reduced significantly.??

?On the surface, design and analytics are two separate undertakings; analytics is associated with technical aspects where having expertise in machine learning and artificial intelligence is a must. On the other hand, designers are creative, empathic, and focus on looking at deep patterns to influence people. These two opposing forces can be considered Yin and Yang, whose purpose is to bring balance and solve complex problems. The collaboration of design and advanced analytics provides deep human insights which have the potential to unlock hidden business value.??

?The role of design in advanced analytics is not limited to just user interface and data visualization. Though, considering in such a manner would be a great mistake!??

?Designers add their value in three significant ways, and only through their expertise, businesses can achieve the objectives that analytics projects are meant to deliver:-?

• GRASP OVER THE END-USER'S JOURNEY; Designers provide a very different perspective that data scientists and analytics teams might not be able to see. From the designer's perspective, technical teams?can get a clear idea of how a model will play an essential role in an end user's journey and how it will operate within its structure. Varying perspectives can help see gaps in data or potential bias that could highlight the need to have changes in the model architecture to capture the best business value.??
• PRODUCT DESIGNS THAT MEETS USER'S NEEDS; designers can design products and create model designs that suit the end user's needs. This collaborative approach to model creation brings out the value that the analytics project is meant to provide.??
• PLACING AN ELEMENT OF CHANGE MANAGEMENT; one of the biggest challenges of analytics projects is user adoption; therefore, designers can help in mitigating such risk by informing the analytics team from the user desirability mindset. Having placed a change management process from the start of the project will ensure delivering well-informed model creation and de-risking adoption.??

A balance?between?design?and?advanced analytics?

Projects can achieve tremendous success when each part plays its role well; a sound collaboration among the team members has the potential to nurture greater business value. Advanced analytics is much more concerned with its respective technical and?AI &ML technologies,?which often can become the reason for failing to notice aspects that make the project successful. No matter how well a model gets developed, if the end-users aren't adopting the model and using it in the long term, the project hasn't reached to deliver up to its potential.?

?Designers can help by sharing and informing the data scientists about the end user's journey and how a user sees value in the tool. At every project level, the team should coordinate to develop a model which has greater utility requirements. Of course, the accountability for this collaboration should not be alone on data scientists; instead, designers should take a proactive role in helping the analytics team see the end user's journey.??

?The yielded fruits of this collaboration will add value in three ways:-?

• Objective function; a thorough understanding of the end user's journey??
• Model creation; lowering the risk of user adoption by giving users control over the model configurations??
• Model comprehensibility; designers ensure the end user's understanding of the model and make the project less of a black box.??