Glenda Crisp: Why Empathy is an Essential Characteristic of a Successful Data Leader

Glenda Crisp, Head of Data and Analytics at Thomson Reuters explains what it takes to become a successful data and analytics leader in the modern era   

This week’s interview with our Global Top 100 Innovators in Data and Analytics features Glenda Crisp, Head of Data and Analytics at Thomson Reuters.

We’re featuring an expanded interview with one of our Global Top 100 Innovators in Data and Analytics every week, so be sure to check back in for more insights from our network of globally recognized data leaders.  

C: In a professional context, what achievements are you most proud of in the past year, and why?

GC: There are quite a few professional achievements that make me proud. One of the main ones is the revamping and streamlining of our data and model governance practices. This includes not just policy development but also a strong focus on data ethics. We established new standards and rolled out comprehensive training on these topics. A significant achievement was our ability to adapt swiftly to the sudden rise of generative AI that we experienced late last year. Thanks to our strong foundation in data governance, we were able to provide immediate guidance and accommodate these new AI advancements within our governance processes.

The second achievement I'm proud of is the significant advancements we've made in our enterprise platforms. This includes our enterprise AI platform, data platform, and BI platforms. We've embedded our data model governance policies and standards within these platforms, essentially automating our governance processes. This reduces the likelihood of deviations and ensures compliance with our standards.

The third accomplishment is related to our goals of delivering high-value data analytics use cases. Last year, we set ourselves a target to deliver data analytics use cases driving significant value, which we exceeded. The use cases we developed delivered substantial value across the organization in terms of increased revenue, expense reduction, risk mitigation, and improvements in both customer and employee satisfaction.

These achievements are a testament to the dedication and talent of our team.

C: What do you think are some of the biggest challenges facing data and analytics leaders today? And how do you think they can be overcome?

GC: One of the greatest challenges for data and analytics leaders today is the balance between exciting new developments like generative AI and the foundational work necessary for their successful implementation, such as data quality and ethics. The appeal of these ‘shiny objects’ can overshadow the importance of foundational data work. Overcoming this challenge is not straightforward, but I believe it hinges on effective communication and education.

Data quality directly impacts the efficacy of AI models. We need to consistently communicate this connection to stakeholders and emphasize the importance of maintaining high data quality. Furthermore, continuous education about data quality and its relevance to AI, conveyed through various channels like newsletters, videos, or training sessions, can ensure that everyone in the organization understands and respects the need for high-quality data.

C: In your experience, what does it take to be a successful leader in the data and analytics space? What characteristics or skills should aspiring data leaders focus on cultivating?

GC: Based on my experience, a successful data leader needs more than technical proficiency; they need a holistic understanding of the entire data process. This encompasses knowing how data is captured, stored, moved, transformed, cleansed, enriched, and eventually used in AI models. Such knowledge allows leaders to guide and challenge the work of software engineers, ensuring the right data quality is obtained from the beginning, eliminating the need for cleanup later.

However, two skills that often get less attention but are equally important are active curiosity and empathy. Being actively curious allows leaders to be forward-thinking, open-minded, and innovative, while empathy is crucial for understanding the concerns and fears of others regarding data and AI, especially in an era where these subjects are often surrounded by hype and fear. Cultivating these characteristics alongside a deep understanding of data is crucial for aspiring data leaders.

C: What are you most passionate about when it comes to data and analytics? What do you think is too often overlooked or misunderstood?

GC: What ignites my passion in the field of data and analytics is the potential it has to drive transformation and add value. I am fascinated by the capabilities of AI and how it can be harnessed for improved decision making and efficiency. I love the process of turning raw data into meaningful insights and seeing the tangible impact on an organization's performance and growth.

However, what is often overlooked or misunderstood is the importance of data quality and the role of ethics in data and AI. People tend to focus on the flashy side of AI and overlook the critical underpinning of high-quality, well-governed data. Poor data quality can lead to flawed AI models, and unethical data practices can result in significant risks.

In terms of ethics, there is a tendency to view it as a side issue, rather than a central concern. The ethical considerations around data collection, storage, and usage, as well as the implications of AI decisions, are incredibly significant and cannot be downplayed or ignored.

So, while I am most passionate about the transformative potential of data and AI, I also firmly believe in the need for a strong foundation of quality data and a robust ethical framework. This, I believe, is key to realizing the full potential of data and analytics in a sustainable and responsible manner.

Download the full Corinium Top 100 Global Innovators in Data and Analytics 2023 report, here