(US and Canada) Marco Vernocchi, EY Global Chief Data Officer, and Aible CEO Arijit Sengupta speak with Steve Wanamaker, CDO Magazine CEO, in a video interview about the challenges of showing economic value from data initiatives, aligning data leaders with business owners, and developing AI talent.
Data leaders often mention data as a critical challenge in demonstrating value, but that's only sometimes true, according to Vernocchi. Value-first and data-driven are EY's mantras, he says, and he explains them as follows:
Being value-first and shaping the proposition around what the business wants to achieve is the starting point. Failure to do so will lead to problems with value delivery and economic value recognition.
It's critical to recognize that data is a core factor in production, not the end product.
Sengupta stresses the need to laser focus on creating economic value from data projects. He cites Aible as an example. The company decided to follow a 30-day satisfaction guarantee — if customers do not see economic value in 30 days, they do not pay.
He adds that leadership should refrain from assuming they require a new model when the solution might be to stop doing something they are currently doing.
When asked how data leaders can align with business owners, Sengupta suggests they do the following:
Analyze revenue, cost, risk goals, and the business executives’ KPIs.
Recognize that because the world is changing quickly, analyzing data to determine patterns over six months may no longer be helpful.
From Vernocchi’s perspective, the top need is to engage often with the business leaders to gain trust and understand their priorities. This is followed by having clear plans, timeframes, and execution programs. It is also essential to agree upfront on the deliverables or the outcomes. He advises leaders to manage expectations appropriately and ensure they are not overpromising.
When scaling AI in light of a talent shortage, Vernocchi says that starting a high-performance AI practice requires standardizing methodologies and procedures and creating a level of automation that enables scalability.
Next, he suggests finding qualified people interested in different jobs within the organization.
Sengupta adds that because AI lacks curiosity, domain knowledge, and business context, the need is to find people inside the organization who know the business, are curious, and are willing to try something new. Although AI tools are at the point where companies do not need an expert data scientist, he notes, those tools do not understand business.
In conclusion, Vernocchi says that too many data scientists worldwide should spend less time on non-high-value activities like data preparation and cleansing. Automation, he points out, is required to improve efficiency.
CDO Magazine appreciates Marco Vernocchi and Arijit Sengupta for sharing their insights and data success stories with our global community.