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Thought leadership
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February 22, 2024

Building Strong Relationships with Business Stakeholders | Webinar (Featuring Nik Acheson)

Join Egor and Nik in this insightful webinar as they delve into the importance of fostering strong relationships with business stakeholders in the realm of data leadership.

Egor Gryaznov
Join Egor and Nik in this insightful webinar as they delve into the importance of fostering strong relationships with business stakeholders in the realm of data leadership.

This engaging discussion highlights key strategies for success, including the use of shared language, understanding business needs, and translating technical capabilities into tangible business outcomes.

Strategies Covered:

- Importance of Shared Language
- Understanding Business Needs
- Translating Tech Capabilities into Business Outcomes
- Delivering Quick Value
- Guiding Data Strategy with Business Needs
- Shared OKRs and Time to Value
- Empathy Towards Business Stakeholders
- Role of Metrics in Business Success

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Throughout the conversation, Egor and Nik draw from their own experiences and share real-world business scenarios to illustrate these concepts. Whether you're a seasoned data leader or just starting in the field, this webinar offers valuable insights to help you navigate the complex landscape of data leadership and build stronger relationships with your business counterparts.

Watch the Recording On YouTube

Connect with Egor on LinkedIn
Connect with Nik on LinkedIn
share this episode
Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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