QUICK SUMMARY
June Dershewitz, a data leader with 25 years of experience, shares three interconnected stories about building trust between data teams and business stakeholders through data quality, strategic alignment, and data literacy. She emphasizes that trust is the foundation of effective data partnerships, requiring proactive approaches to data quality management, early integration of data teams in business initiatives, and targeted data literacy efforts. The session provides practical frameworks and strategies for data professionals and business stakeholders to collaborate more effectively, ultimately enabling data to drive innovation and business value.
KEY QUOTES
- “Trust became the underpinning of my work as a data leader, and I applied it to everything I did in that professional setting.”
- “Data needs to have a seat at the table, even if it’s a cultural shift. Maybe there’s a launch playbook where design is involved, PR is involved, and legal is involved—data should also be involved.”
- “Data literacy is the ability to read, work with, and make decisions based on data. It’s not just about understanding numbers—it’s about knowing how to interpret, question, and act on data in your daily work, no matter where you are in the business.”
FULL SESSION SUMMARY
Introduction and Background
June Dershewitz introduces herself as a data professional with 25 years of experience, currently working as a data leader at a large tech company in Seattle. Beyond her day job, she co-founded an angel investing syndicate called “Invest in Data” and has committed to writing publicly about data topics. She frames her presentation around the concept of trust, which she discovered was a critical need during her onboarding at Twitch nearly a decade ago. Through a listening tour with stakeholders, she identified that people wanted more trust from data and the data team—they were confused about which dashboards were reliable, lacked confidence in self-service tools, and questioned the data team’s priorities.
Data Quality: The Foundation of Trust
Dershewitz begins with data quality, often the scapegoat when trust issues arise. She humorously presents “Data Quality’s a Mess, and It’s All Your Fault,” highlighting how data teams are frequently blamed for quality issues regardless of their cause. To address this challenge, she offers a framework breaking data quality into three categories:
- Prevention: Catching issues early before they reach production, limiting their impact
- Detection: Strategically placing alerts to identify problems as they occur
- Resolution: Maintaining documentation of important data quality issues and standard procedures for handling them
Through audience interaction, it becomes clear that many attendees have faced major data quality issues in the past six months, with some problems reaching executives or customers. Dershewitz acknowledges that while data quality problems with revenue impact are painful, they can help justify investment in prevention and detection systems.
Strategic Alignment: Integrating Data into Business Decisions
The second story addresses the common scenario where data teams are brought in too late to business initiatives. Dershewitz role-plays a situation where a stakeholder asks “How’d we do?” and the analyst responds with “Did we do what?”—illustrating the disconnect that occurs when data needs become an afterthought.
This misalignment happens due to:
- Lack of integration between data teams and broader business strategy
- Insufficient appreciation for analytics’ value
- Time crunches or resource shortages
The ideal state is one where data teams know about major initiatives in advance, business stakeholders can articulate their data needs thoroughly, data gaps are viewed as prioritization decisions rather than quality problems, and analytics is appropriately funded.
To move toward this state, Dershewitz recommends:
- Being proactive in building connections between data and business teams
- Advocating for data to have “a seat at the table” in planning processes
- Educating leaders about analytics’ value through regular demonstrations
- Considering whether the organization truly values data and making career decisions accordingly
Audience participation revealed a split between those whose organizations bring data teams in early versus those who involve them too late, highlighting the varied maturity levels of data integration across companies.
Data Literacy: Bridging the Knowledge Gap
In the final section, Dershewitz shares her journey from skepticism about data literacy to recognizing its importance. She recounts learning from her friend Joe, who led a multi-year data literacy initiative at PBS that improved data skills for over 600 employees. This experience helped her define data literacy as “the ability to read, work with, and make decisions based on data”—not just understanding numbers but knowing how to interpret, question, and act on data in daily work.
Even in data-savvy environments, Dershowitz identifies opportunities for targeted data literacy efforts:
- Pinpointing specific gaps in certain teams or roles
- Ensuring everyone understands available self-service tools
- Addressing recurring questions about definitions and metrics at the executive level
Through audience interaction, she discovers that formal data literacy programs are rare but valuable, and many have witnessed colleagues who claim to be “data-driven” yet struggle with using data effectively.
The Interconnected Nature of Data Partnership
Dershewitz concludes by revealing how these three stories—data quality, strategic alignment, and data literacy—are interconnected. For example, data quality issues often stem from not prioritizing data early enough in initiatives (strategic alignment), while the gap between data teams and business stakeholders can be bridged through education and collaboration (data literacy).
KEY TAKEAWAYS
- Trust is fundamental to effective data partnerships and must be built through quality management, strategic alignment, and literacy efforts.
- Data teams need early involvement in business initiatives to ensure proper measurement, avoid emergencies, and deliver meaningful insights.
- Data literacy should be targeted and practical, addressing specific gaps and needs rather than implementing one-size-fits-all programs, especially in data-savvy organizations.
Delivery on Event Focus: Aligning Innovation with Business Strategy
This session directly addresses the focus of aligning innovation with business strategy by demonstrating how data teams can become strategic partners rather than reactive service providers. Dershowitz shows that when data is integrated early in business initiatives, properly understood by stakeholders, and maintained at high quality, it enables more informed decision-making and innovation. The frameworks she provides help organizations structure their data functions to support strategic objectives rather than operate in isolation.
Delivery on Event Theme:
Harvesting Innovation & Sowing the Seeds of Future Growth
The session supports the theme of “harvesting innovation and sowing seeds of future growth” by emphasizing how proper data partnerships create fertile ground for innovation. Quality data allows organizations to harvest insights from past initiatives, while strategic alignment and literacy enable teams to sow seeds for future data-driven innovation. By building trust in data, companies can more confidently experiment, measure results, and scale successful innovations.
Action Steps for Innovation Experts and Corporate Changemakers
- Assess your organization’s data quality framework using the prevention-detection-resolution model and identify areas for improvement.
- Review your innovation launch playbook to ensure data teams are included early in the planning process alongside design, PR, and legal.
- Identify specific data literacy gaps in your organization and implement targeted training for those areas rather than broad programs.
- Create regular opportunities to demonstrate the value of analytics to leadership through concrete examples of data-informed decisions that led to positive outcomes.
- Establish clear definitions for key business metrics to ensure everyone from executives to frontline employees shares the same understanding.
- Build bridges between data teams and business units through regular touchpoints, shared goals, and collaborative problem-solving sessions.