Headline Partner

Tifani McCann, Vice President, Data and Analytics, Otsuka

Describe your career to date

My academic career began with an early love of puzzles that later evolved into a passion for problem solving and leveraging data to inform solutions. From there, I decided to apply my formal training to life sciences and developing solutions for patients. To date, my professional career has taken a course best described as a career lattice where I have leveraged skills to propel me in various career directions. Each career move provided an opportunity to learn new skills or new domain areas and allowed me to develop beyond a technical expert to an enterprise leader.

In addition, my career path provided me with the honor to lead technical teams of the most talented and committed individuals I could imagine knowing. Often unsung heroes, it has required me to develop my voice to advocate for my team and ensure their talents and contributions are recognized and valued. After earning my PhD in Biostatistics, my career began as a protocol statistician at Bristol-Myers Squibb (BMS). I had amazing sponsors at BMS and was provided various development opportunities over 17 years, including leading: biometrics therapeutic areas; business operations teams; and the enterprise clinical data organization.

I joined Covance in a global leadership role with significant P&L responsibilities and led two transformations. After gaining valuable business and operational learnings, I decided to return to pharma in 2019 as the CDAO at Otsuka Pharmaceuticals for the opportunity to develop and lead an enterprise data strategy.

How are you developing the data literacy of your organization, including the skills of your data teams and of your business stakeholders?

Data literacy is foundational to a data-guided organization. Our data literacy efforts have increased with the maturity of our data strategy and will continue to evolve as the organization’s needs shift.

We began with general enrichment using multiple modalities to share data and analytics concepts and create awareness of actual applications of advanced analytics and artificial intelligence (AI) in our portfolio. Once better established, the Data Office set a corporate goal to further enable a data-guided culture through organizational data literacy and fluency. We then amplified our general data community enrichment, rolled-out role-specific instructor-led training, and this year launched computer-based training as the program’s third component.

What role do you play in building and delivering conventional artificial intelligence solutions, including machine learning models? Are you also involved in your organization’s adoption of generative AI?

Heading the data and analytics organization allows me the privilege to lead a team that is instrumental in determining AI, machine learning, and generative AI (genAI) use cases and delivering against them in partnership with business stakeholders.

The question we are trying to answer or problem we are trying to solve will determine which AI solution we leverage, this includes natural language processing, machine learning, genAI, or other AI approaches as is appropriate.

With the introduction of genAI, it has provided new opportunities to identify enterprise-level use cases whereas the more traditional AI approaches have more often been applied to address more function or domain specific use cases. Given this enterprise-level lens, we have established a company-wide AI Council of which I have a leadership role in driving its mandate which is to accelerate the adoption of responsible AI across the organization.

Have you been able to fix the data foundations of your organization, particularly with regard to data quality?

Impactful progress has been made to address foundational needs to enable trusted data. Two of the key enablers have been:

To establish a balanced cross-functional information governance framework.

To stand-up an enterprise data catalog.

These have both allowed for pain points and priorities to be identified and resourced to address quickly, these have included data quality, findability, access, reuse, classification, and lineage.

Tifani McCann
has been included in:

100 Brands 2023 (USA)
100 Brands 2024 (USA)

Enabling data and AI leaders to drive impact

Enabling the success of Data and AI leaders and their teams.

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