(US & Canada) | CDOs Should Leave Organizations That Neglect Data Quality — Starbucks, Director of Global CoE for Advanced Analytics and Data Science

Megan Brown, Director of Global CoE for Advanced Analytics and Data Science at Starbucks, speaks with Diena Lee Mann, Founder and CEO of Spectio, in a video interview about her professional trajectory, the average tenure of a CDO role, getting closer to business, and how companies can better align analytics teams with business functions.

Starbucks Corporation is an American multinational chain of coffeehouses and roastery reserves headquartered in Seattle, Washington. 

Brown’s professional trajectory spans across domains, from academia to data science. She pivoted from academia to see the faster impact of her work and have a sense of team and eventually landed in the data science industry.

When asked about her perspective on the average tenure of a CDO role, Brown states that the function is relatively young. Also, a CDO needs to collaborate with all the organizational sectors, from operations to marketing to HR, to understand their data needs and how the business could use data.

Additionally, Brown notes that all the sectors have the same baseline concern but different pathways to address it. Further, she believes that the greater organization has to learn more about data science and analytics work in general.

Brown shares that while the hype around data science makes it seem like one can get incremental returns with minimal effort, that is rarely the case. Expanding on this, she says that if the organization is new to business, a CDO needs to advocate for data infrastructure.

Now, since these do not deliver immediate returns, it becomes difficult for the CDOs to get people’s attention, because of which they try different strategies, says Brown. Further, the organization’s unwillingness to fund the expensive foundational initiatives adds to the challenge, she says.

Continuing, Brown states that organizations should be working systematically with better definitions, standards, and data quality. She stresses that data quality enables everything, from in-house data science models to GenAI. Therefore, if an organization cannot comprehend the criticality of data and invest in data quality, a CDO should move on.

Moving forward, Brown shares what it means for her to get closer to the business side as a CDO and how it works out. She states that starting the conversation with a beginner’s mindset helps. For instance, she asks all the basic but fundamental questions about marketing because she is not well-acquainted with it. According to her, this reflects interest, and she is able to break her pre-built assumptions about that line of business.

Adding to that, Brown affirms that getting close to business is critical, and Starbucks being a relationship-oriented company, uses immersions. To understand business better, she relies on change management methods because there are numerous useful processes to build, maintain, and communicate across complicated relationships.

Furthermore, Brown feels motivated by business questions, problems, and opportunities where data can play a role. To know these, she gets closer and brings the team closer to business.

When asked how companies can get their data and analytics teams better aligned with the business, Brown highlights that it depends massively on the model. To elaborate on this aspect, she points out the shared service model versus the R&D model.

In shared service, one must be focused on customer service and dedicated to stakeholders. Stakeholders, after getting used to data, get demanding with data. This leads CDOs to work with teams that are aligned with business functions.

On the contrary, the R&D function is siloed, and it follows the vision of the data science and AI analytics leaders. However, their weak relationship with business affects how they launch.

To mitigate that, organizations must have visionaries who can manage the communication and changes that are being inflicted on business from an R&D perspective. She remarks that many leaders take a balanced approach because they know that the future needs AI and GenAI solutions, and that works with product and project management.

In conclusion, Brown states that it is crucial to ensure that R&D is not treated as a side project, nor should shared services overwhelm the R&D function. She adds that while product and project management may seem burdensome to young organizations, there comes a time when they realize the necessity of the functions.

CDO Magazine appreciates Megan Brown for sharing her insights with our global data community.