
Data observability is swiftly emerging as a cornerstone in data governance and quality, making waves for its promise of transforming business insights and reliability. In this episode of Predictable B2B Success, host Vinay Koshy dives deep with Ryan Yackel, a product strategy leader at IBM, to unfold the nuances and strategic imperatives of data observability.
From the groundbreaking strategies that connect data observability to broader governance initiatives to the significant financial impact of poor data quality, Ryan sheds light on how businesses can stay ahead in a data-driven world. He also delves into the art of narrative design, illustrating how compelling storytelling can enhance market engagement and elevate the perceived value of observability solutions.
Whether you’re a data engineer, marketer, or executive, this discussion offers invaluable insights into harnessing data observability for sustainable success.
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About Ryan Yackel
Ryan Yackel has spent the past decade immersed in the dynamic world of high-growth startups. His career trajectory has led him to his current role at IBM Databand, a cutting-edge data observability company. Before joining IBM Databand, Ryan honed his expertise at Symphony and Tricentis in test automation and QA, and then at Key Factor in cybersecurity. He discovered that the data quality and reliability challenges at IBM Databand parallel those he faced in software quality and reliability in his previous roles. This continuity in problem-solving attracted him to the data observability space, where he continues to innovate and drive progress.
The Rise of Purpose-Driven Branding in B2B
Data observability has emerged as a critical component for B2B companies looking to drive revenue growth through improved data quality and reliability. By implementing robust data observability practices, organizations can enhance their decision-making processes, reduce costly data incidents, and ultimately boost their bottom line. This comprehensive guide will explore how data observability can be leveraged to drive revenue growth, backed by expert insights, real-world examples, and actionable strategies.
Understanding Data Observability and Its Impact on Revenue
Data observability refers to an organization’s ability to understand the health and state of data in their systems. It encompasses monitoring, tracking, and troubleshooting data throughout its lifecycle, from ingestion to consumption. For B2B companies, effective data observability can directly impact revenue growth by:
- Improving data quality and reliability
- Reducing data downtime and associated costs
- Enabling more accurate and timely decision-making
- Enhancing customer trust and satisfaction
Ryan Yackel, an IBM product strategy leader, emphasizes the importance of data quality:
“Poor quality data cost companies approximately 12,900,000 US dollars annually, and this financial burden arises from ineffective targeting, lower conversion rates, and increased cost per acquisition due to updated or inaccurate data.”
The Five Pillars of Data Observability
To fully leverage data observability for revenue growth, it’s essential to understand and implement its five key pillars:
- Distribution
- Freshness
- Volume
- Schema
- Lineage
By focusing on these pillars, B2B companies can ensure their data remains accurate, up-to-date, and reliable, leading to better business outcomes and increased revenue.
Implementing Data Observability to Drive Revenue Growth
1. Enhancing Data Quality for Improved Decision-Making
One of the primary ways data observability drives revenue growth is by improving overall data quality. By continuously monitoring and analyzing data, organizations can quickly identify and rectify issues, ensuring decision-makers can access accurate and reliable information.
Ryan Yackel highlights the importance of data quality in B2B contexts:
“Data inaccuracy is also a top challenge for 87% of B2B sales and marketing teams, leading to weakened campaign performance, low engagement rates, and compromised ROI.”
To enhance data quality through observability:
- Implement automated data quality checks throughout your data pipeline
- Set up alerts for data anomalies or inconsistencies
- Regularly audit and clean your data sources
2. Reducing Data Downtime and Associated Costs
Data downtime, or periods when data is missing, inaccurate, or otherwise unusable, can significantly impact a B2B company’s revenue. Data observability helps minimize these costly incidents by providing early warning signs and enabling quick resolution of issues.
Ryan Yackel explains:
“We look in we have calculations that kinda go back and say, hey. How many times did you guys experience late deliveries on your, for your data SLAs? How many times did you not catch a pipeline that should have been executed in earlier this morning and you didn’t find out that it didn’t execute until 3 days, 3 days later?”
To reduce data downtime:
- Implement real-time monitoring of data pipelines and systems
- Develop and maintain a comprehensive data incident response plan
- Invest in tools that provide end-to-end visibility of your data ecosystem
3. Enabling Data-Driven Decision Making
Data observability empowers B2B organizations to make more informed, data-driven decisions by ensuring that the data used for analysis and reporting is reliable and up-to-date. This leads to better strategic choices and, ultimately, increased revenue.
To enable data-driven decision-making:
- Implement dashboards and visualizations that provide real-time insights into data health
- Train teams on how to interpret and act on data observability metrics
- Integrate data observability insights into your decision-making processes
4. Improving Customer Trust and Satisfaction
For B2B companies, maintaining customer trust is crucial for long-term revenue growth. Data observability helps ensure that the data used to serve customers is accurate and reliable, improving customer satisfaction and retention.
To leverage data observability for customer trust:
- Provide transparent data quality metrics to customers
- Quickly address and communicate any data issues that may affect customer operations
- Use data observability insights to improve customer experiences proactively
Tools and Technologies for Effective Data Observability
B2B companies can leverage various tools and technologies to implement robust data observability practices. Ryan Yackel mentions IBM’s Databand as an example:
“Databand basically made their time to detection down to 0. Because we were able to automatically tell them exactly when the defect or a data incident happened, and then tell them what the resolution could be based off the error logs that they had.”
Some popular data observability tools include:
- IBM Databand
- Monte Carlo
- Datadog
- Acceldata
- Bigeye
When selecting a data observability tool, consider factors such as:
- Integration capabilities with your existing data stack
- Scalability to handle your data volume and complexity
- Ease of use and adoption across your organization
- Customization options to meet your specific business needs
Measuring the Impact of Data Observability on Revenue Growth
B2B companies need to track and measure key performance indicators (KPIs) to truly understand how data observability drives revenue growth. Ryan Yackel suggests focusing on metrics such as:
- Mean Time to Detection (MTTD)
- Mean Time to Resolution (MTTR)
- Cost of data incidents
- Data quality scores
- Customer satisfaction ratings
By monitoring these metrics, organizations can quantify the impact of their data observability efforts on revenue growth and continuously improve their practices.
Best Practices for Implementing Data Observability in B2B Organizations
To maximize the revenue-driving potential of data observability, consider the following best practices:
- Develop a data observability strategy aligned with business goals
- Foster a data-driven culture across the organization
- Invest in employee training and skill development
- Continuously evaluate and improve data observability processes
- Collaborate across departments to ensure comprehensive data coverage
Ryan Yackel emphasizes the importance of a holistic approach:
“We can attach it to that higher level top down push, and it gets us more credibility because now we’re speaking in terms of observability for your governance strategy versus observability as this standalone thing.”
Overcoming Challenges in Data Observability Implementation
While the benefits of data observability for revenue growth are clear, B2B companies may face challenges in implementation. Some common obstacles include:
- Resistance to change within the organization
- Complexity of existing data ecosystems
- Budget constraints for new tools and technologies
- Lack of skilled personnel to manage data observability initiatives
To overcome these challenges:
- Start with small, high-impact projects to demonstrate value
- Seek executive sponsorship and support
- Invest in employee training and upskilling programs
- Consider partnering with data observability experts or consultants
The Future of Data Observability in B2B Revenue Growth
As data continues to play an increasingly critical role in B2B success, the importance of data observability will only grow. Ryan Yackel predicts:
“Data observability is very, it’s gonna be a no brainer, for most organizations probably in the next 2 or 3 years, similar to how something like Datadog, New Relic, Instana, Dynatrace, these observability products that are in the cloud and looking at applications.”
Future trends in data observability may include:
- Increased integration of artificial intelligence and machine learning
- Greater focus on real-time observability and incident prevention
- Enhanced collaboration between data and business teams
- More sophisticated predictive analytics for data quality issues
By staying ahead of these trends and continuously improving their data observability practices, B2B companies can ensure sustained revenue growth and competitive advantage in the data-driven business landscape.
Conclusion: Harnessing the Power of Data Observability for B2B Revenue Growth
Data observability has emerged as a critical factor in driving revenue growth for B2B organizations. Effective data observability practices can significantly impact a company’s bottom line by improving data quality, reducing downtime, enabling data-driven decision-making, and enhancing customer trust.
As Ryan Yackel aptly puts it,
“Improving data quality is a top priority for 66% of b to b marketers aiming to enhance their go to market strategies.”
By implementing robust data observability practices, leveraging the right tools and technologies, and fostering a data-driven culture, B2B companies can unlock the full potential of their data assets and drive sustainable revenue growth in an increasingly competitive marketplace.
To harness the power of data observability for your B2B organization, consider conducting a thorough assessment of your current data practices, identifying areas for improvement, and developing a comprehensive data observability strategy aligned with your business goals. With the right approach and commitment, data observability can drive your company’s revenue growth and long-term success.
Some areas we explore in this episode include:
- Community Engagement: Participation in open-source conferences and tech meetups to discuss technical deployments.
- Executive-Level Strategy: Aligning data observability with data governance to enhance prioritization.
- DIY Approach vs. Observability: Comparison between basic alerting/monitoring and comprehensive observability with ML detection.
- Strategic Narrative and Storytelling: The importance of a strong narrative for effective product communication.
- Pilot Testing for Proof of Concept: Using pilots to demonstrate the effectiveness of data observability solutions.
- Data Fabric and Data Mesh: IBM’s hybrid architecture and integrating data observability.
- Data Quality and Observability: The importance of “data quality in motion” and evolving observability tools.
- Data Acquisition Strategy: Combining top-down and bottom-up approaches for integrating Databand.
- IBM Acquisition: The impact of Databand’s acquisition by IBM and cultural integration with AI and quantum computing initiatives.
Listen to the episode.
Related links and resources
- Check out Databand.ai
- Learn from Shanif Dhanani – Customer Success With AI: Simplify Messy Data Challenges Instantly (Without Complex Integrations or Steep Learning Curves.)
- Learn from Guy C. Holmes – Maximizing Scalability in Data Management And How to Cut Costs
- Learn from Cary Sparrow – How to Master Competitive Intelligence With Labor Market Data to Drive Growth
- Learn from Chris Mercer – How to Use Data Insights And Measurement Marketing to Drive Growth
- Learn from Ryan Janssen – How to Use Business Intelligence to Remove Data Complexity And Drive Growth
- Learn from Mark Stouse – Data-Driven Decision-Making: 5 Ways to Use Data Analytics to Drive Business Growth
Connect with Ryan Yackel
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