Altavoces

Laura Gent Felker
Director, Data Insights and Scalability at SalesForce
Omar Khawaja
CDAO & Global Head Data & Analytics at Givaudan
Tiffany Perkins-Munn
Managing Director & Head of Data & Analytics at JPMorgan Chase

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Scaling Data ROI: Driving Analytics Adoption Within Your Organization

March 2024

Summary

Data initiatives demand substantial investment in tools and talent, and understanding the return on investment (ROI) is essential for organizations. The discussion accentuated that ROI is not solely about financial gains but also includes department-specific metrics that contribute to overall business goals. Key strategies for enhancing data capabilities include building a strong infrastructure, promoting collaboration between technical and non-technical teams, and nurturing a data-focused culture. Making data insights understandable through storytelling is an important skill for connecting technical insights and business understanding. The vital role of executive support in advocating for data initiatives and the role of change management in overcoming resistance to cultural shifts were also discussed. Lastly, the challenges of expanding analytics capabilities were discussed, concentrating on data quality, infrastructure, skill gaps, and integration.

Key Takeaways:

ROI for data initiatives goes beyond revenue, covering various metrics like operational efficiency and customer satisfaction.
Effective data management requires a blend of the suitable tools, processes, and skilled personnel.
Using storytelling to make data insights relatable and actionable for non-technical stakeholders is vital.
Executive leadership and change management are essential for nurturing a data-focused culture.
Organizations face challenges in expanding analytics, including data quality, infrastructure, and integration.

Deep Dives

Understanding ROI in Data Initiatives

The concept of return on investment in d ...
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ata initiatives is complex, going beyond just financial metrics. As Tiffany Perkins-Munn noted, ROI can be linked to specific business goals, such as customer satisfaction and operational efficiency. "It's really about how do you use data and analytics to achieve a specific business goal," she explained. This approach requires organizations to consider various metrics, including cost reduction, resource allocation, and customer experience. By aligning data initiatives with these metrics, companies can ensure they are delivering tangible value.

Building a Data-Driven Culture

A data-focused culture is essential for using data effectively across an organization. This involves integrating data literacy across all levels, from executives to frontline employees. Omar Khawaja emphasized the need for "targeted data literacy programs" that cater to different personas within a company. Moreover, making complex data insights understandable through storytelling is an important part of this culture. As Tiffany Perkins-Munn articulated, "It's not about simplifying information too much, it's just about learning how to tell the story."

Overcoming Resistance to Change

Change management plays an important role in transitioning to a data-focused organization. Resistance often stems from fear or misunderstanding of data's role in daily operations. Laura Gent-Volker highlighted the importance of involving employees in decision-making to promote a sense of ownership and accountability. Pilot projects can serve as proof of concept, demonstrating the benefits of data initiatives incrementally. Engaging skeptics and understanding their concerns is essential to successfully implementing change.

Scaling Analytics Capabilities

As organizations aim to expand their analytics capabilities, they encounter several challenges. Data quality is vital, as inconsistent or inaccurate data can undermine decision-making. Infrastructure limitations, such as inadequate data storage and processing power, need addressing to accommodate growing data volumes. Skill gaps in data management and analytics also present hurdles. Tiffany Perkins-Munn noted, "The issues are always the same: data quality, infrastructure, skill gaps." Organizations must strategically tackle these issues to enhance their analytics functions effectively.