| | JULY 20218CIOReviewA PRACTICAL GUIDE TO USING HUMAN CENTERED DESIGN TO DELIVER ADVANCED ANALYTICS PROJECTSBy Neerav Vyas, VP Analytics & Strategy - Head of Analytics, Realogy HoldingsFirms are at risk of wasting billions on failed analytics and data science projects over the next 3 years:Over the past decade the failure rates on analytics projects, particularly advanced analytics projects have been startingly poor. Business adoption of data and analytics continues to be an issue and firms grossly underestimate the failure rate of these projects· The vast majority (77%) of businesses report that adoption of big data and AI initiatives are a challenge often because they're designed in silos, tackle the wrong use cases, and business leaders don't think they'll deliver value· Unsurprisingly, 85% of data projects end up failing and do not move past the preliminary stages let alone transform business processes, functions, or end experiences· Despite high failure rates, firms are looking to increase their investment in data and analytics, with 92% saying they will increase their spend over the next yearThe pace of acceleration might not be alarming, if the level of spend on these initiatives was small and growing rapidly. However, firms spent close to $37.5 billion dollars on AI systems in 2019. IDC estimates that number to reach over $97.9 billion by 2023. If the rate of success on these projects does not improve, that may be as much as $90 billion dollars spent on initiatives that fail.Human Centered Design can help us to better design projects to overcome these barriers:Over 3 years ago we pushed our analytics and data science teams to approach all their projects using design thinking and making human centered design a cornerstone of how we approach all of our projects - end to end. This has resulted in an 90% success rate on projects moving past the POC phase AND having those projects integrated into the day to day of our businesses. We approach all projects in 3 phases:Phase 1- Inspiration and Empathy: Before we start any significant analysis or touch a single piece of data we start with user/ stakeholder research and background research. Our goal is to truly understand the needs and pain points of the space we're looking into. If we were tackling forecasting for our finance team, our goal is to understand what KPIs to model and understand how the team does the process today, what's easy, what's hard, and how they could potentially use our solution in the future. It's all about empathy for understanding the process today and thinking about how we can design solutions to improve experiences for customers and employees in the futureWe want to understand not only what models to use, but also what the solution itself looks like. Will the team need to do simulation modeling or what if analysis, how will they use explanations on from the model, and how do we showcase model results that engenders the most positive experience for our employees or customers?Phase 2 ­ Ideation and Exploration: This phase begins with hypotheses based on the work done in Phase 1. We then proceed IN MY OPINION
< Page 7 | Page 9 >