Get Started

Top-Down vs. Bottom-Up Data Culture Change

Scaling AI Claire Carroll

Last year when we surveyed over one hundred data professionals, they ranked organizational change as their third biggest data challenge (behind data cleaning and model productionalization). However, this year, organizational change moved up to the second place slot, suggesting that organizations have not yet overcome the culture change obstacles to data integration.

At our EGG NYC panel last week on Magical Experiences And Greater Well-Being: Putting the Human At The Center of AI Personalization, we asked about fostering a company culture that welcomed and incorporated data initiatives. 

woman smiling to a computer

Company Connections

During the panel, Bridgette Rippel Vargas, Director of Customer Engagement Platforms at Disney Parks & Resorts, stated that at companies like Disney, that contain many different business verticals within them, crafting a holistic user experience is critical, and can only happen when business objectives are aligned with technological infrastructure. Vargas stated that tech-native  companies often have a leg up when it comes to the central role of data in business objectives. At Disney, it's a little different; "many technology-enabled companies are well established; we have legacy infrastructure along with a people infrastructure, and it’s hard to put the new into the old. It’s hard and it’s really expensive."


Haile Owusu, SVP of Analytics, Decisions, and Data Science at Turner Broadcasting, confirmed that data and business focus go hand in hand. Since Turner is the parent company for a host of media labels, including CNN, TBS, TNT, and Tru TV, organizational fragmentation is a huge blocker to understanding user interactions. He stated that "these companies have been profitable in their silos, so we have to make the case that artificial intelligence enables new business and new technology. There’s a lot of education involved."

Top-Down vs Bottom-Up

A top-down approach to culture transformation has the benefit of reaching more people faster (and often a bigger budget). Michael Xiao, Divisional VP of Enterprise Data Science at Blue Cross Blue Shield, described a company data culture that is driven by leadership, saying "It's a huge focus and a top priority for our company." Yet if the impetus is only coming from the top, it may not be directly linked to technical and business pain points on the ground, thus impeding grassroots adoption.

However, a bottom-up path to data transformation may be too rooted in pain points and less attuned with overarching business objectives, thus dooming the efforts to be misdirected. Communication (coupled with sincere listening at every level) about the ways data can help improve the user and business experience is critical to encourage adoption. 

Better Together

two woman working together with a computer openThere were several recurring themes at EGG NYC, including how to target data efforts, combatting bias, and how critical the cultural change around data is. Executives from leading corporations and burgeoning startups alike agreed that it was necessary to get buy-in at all levels of the organization to foster data maturity. Often it is just as important for data leaders to drive educational efforts and sync business collaborations as it is that they are up-to-date on the latest technological advancements; business relationships are critical to technical success.

In order for an organization to become truly data-driven, it is critical to prioritize education and support at every level of the organization. Walid Mehanna, Head of Data & Analytics at Mercedes-Benz Cars, stated that you can't depend on the top-down or  the bottom-up approach: "it’s not only important to have the top management, or the grassroots approach, but it’s also important to have middle management." Jon Tudor, the Senior Manager of Self-Service Data and Analytics at GE Aviation, agreed in his talk, saying that, "you can’t just be top-down or bottom-up, you need both."

See Organizational Change in Action

GE Aviation recently drove a targeted data transformation by coordinating the organizational and technological changes they needed to implement. Read the Case Study to learn from their best practices.

Let's Go >

You May Also Like

AI Cultural Change From First Principles

Read More

From the Lab to the Enterprise: Getting Your Work Adopted Across the Organization

Read More

In Search of a New Year’s Resolution? Check Out Proof of Concept!

Read More

Delivering AI Governance for New Roles With Dataiku 10

Read More