Thought leadership
December 1, 2023

Building Strong Relationships with Business Stakeholders: A Data Leaders Guide

Data leaders can no longer focus only on the data itself, they must also build strong relationships with business stakeholders.

Kyle Kirwan

With organizations collecting data at an ever-increasing clip and relying on it more than ever to make decisions, data leaders can no longer focus only on the data environment. They must build strong relationships with business stakeholders and truly understand how data can impact the opportunities and challenges they face.

Data leaders play a pivotal role in fostering collaboration and mutual understanding between technical teams and non-technical stakeholders who are focused on real-world business outcomes. 

In this blog post, we delve into the intricacies of this partnership—from identifying key stakeholders and their needs to delivering accurate data, aligning strategies with business goals, and overcoming obstacles. We'll help you navigate the road to successful relationships. 

Understanding Your Stakeholders

Understanding your stakeholders is the foundational step towards building reliable relationships that drive data projects to success. 

Step 1: Identify Key Business Stakeholders

If you are a centralized data team, first identify the lines of business (LOB) owners that most heavily leverage your data. Then, partner with the various business leaders one at a time, taking time to understand where they’re leveraging data (e.g. building a list of their key dashboards or ML models), what types of business-specific checks they need to have monitored, and who should be notified if problems are identified by the observability system. 

We recommend prioritizing the highest impact LOB and solving their needs before moving to the next team, making it easier to get other leaders onboard. This approach may feel repetitive at first, but it allows you to polish your team’s data observability process, and build trust within the organization over time.

Step 2: Open Up the Black Box

Data can seem like an enigmatic "black box" to those outside the technical teams. To the uninitiated, data transformation, algorithms, and analytics can seem like hidden processes happening only behind the scenes. The true stakeholders are often not the data scientists or analysts who have a deep understanding of this black box; they are the individuals looking at dashboards, relying on insights, and making critical business decisions based on the work of those analysts and scientists.

Imagine a scenario where a data leader is constructing a data pipeline. From a technical perspective, it may involve intricate data transformations and complex coding. However, from a business standpoint, the pipeline's purpose is clear and simple—it must provide the data necessary to facilitate a decision that could impact the company's bottom line by millions of dollars. Understanding this business perspective is vital for data leaders.

Work with your stakeholders to help them understand why and how data initiatives will improve their own outcomes, but be sure to do so in a way that's straightforward and easy to understand.

Step 3: Communicate Effectively

Effective communication is the basis of building strong relationships with business stakeholders. 

In the world of data, where complex algorithms and technical jargon often reign supreme, data leaders must become skilled translators. Their task is to bridge the gap between the technical intricacies of data projects and the needs of non-technical stakeholders. The key here is simplification.

Practical Examples:

  • Speak Their Language: Assume that any business stakeholder is non-technical, even if they have some technical knowledge. Use terminology familiar to them. If they refer to a "report" instead of a "dashboard," adopt their terminology to ensure clarity.
  • Results Over Processes: Understand that non-technical stakeholders primarily care about the results, not the technical processes behind them. Avoid delving into the intricacies of data pipelines or modeling techniques unless requested. Instead, focus on delivering actionable insights and tangible outcomes. If a project will require multiple weeks / months to complete, set appropriate expectations.
  • Keep It Simple: Make it simple for stakeholders to understand the information presented. Use straightforward, intuitive visuals and clear explanations to ensure that the message is accessible to all.

Proactive communication is the cornerstone of successful data relationships. To foster alignment and collaboration, data leaders can follow these actionable steps:

  • Periodic Synchronization: Regularly engage with stakeholders through periodic syncs. These sessions provide an opportunity to discuss progress, address concerns, and ensure that everyone is on the same page.
  • Ask About KPIs and Metrics: Understand the KPIs (Key Performance Indicators) and metrics that matter most to stakeholders. Knowing their specific goals and objectives enables data leaders to tailor their efforts towards meeting those needs.
  • Support and Alignment: Initiate conversations by asking stakeholders, "How do I best support you?" This question underscores your commitment to their success. Additionally, share your initiatives and how they align with their goals and initiatives. By showcasing the value of your work in the context of their objectives, you reinforce the collaborative spirit of the partnership.
  • Principle of Least Astonishment: Effective data leadership requires proactive communication with stakeholders to ensure their needs and expectations are met. Data leaders should not wait for stakeholders to discover changes or updates independently; instead, they should be proactive in sharing information. This practice aligns with the principle of "least astonishment," where systems should behave in ways that users expect. (add link) 

For example, consider a scenario where a data leader is working with a department head responsible for inventory management. By periodically syncing, the data leader can inquire about critical inventory-related metrics and learn about the challenges and goals of the department. This information allows the data leader to tailor data initiatives, such as real-time inventory tracking, to directly support the department's success.

Tip: C-suite executives require timely data for crucial budget decisions. By proactively engaging with them, data leaders can bridge the gap between the technical and business realms and ensure that data initiatives are aligned with the organization's overarching goals.

In addition to delivering accurate data, it is equally important to transparently address data limitations. Often, data limitations arise from a lack of clarity in stakeholder requirements. Data leaders should engage in open dialogues with stakeholders to uncover the true intent behind data requests and manage expectations effectively.

The importance of delivering accurate and reliable data cannot be overstated. Trust in data is fundamental for stakeholders to make informed decisions confidently. By communicating clearly and proactively, data leaders can build that trust over time. 

Aligning Data Strategies with Business Goals

Aligning data strategies with business goals is what makes it possible to illustrate the value of data-driven decisions. Data leaders must emphasize the impact of data initiatives on business outcomes, emphasizing the concept of "time to value."

Connecting Data Initiatives to Organizational Objectives: Data leaders should ensure that every data initiative is explicitly connected to organizational objectives. When stakeholders can see a direct link between data efforts and the achievement of broader business goals, their confidence in data-driven decisions grows.

Demonstrating the Impact of Data-Driven Decisions: Time to value is a crucial metric that underscores the speed at which data insights can be transformed into tangible business outcomes. For example, if a data leader informs a stakeholder that a complex data project will take three months to complete, the stakeholder may respond positively by saying, "Great, you help me scope this down, and I’ll figure out how to make it work." This showcases the alignment of data efforts with stakeholder needs and immediate value creation.

Highlighting the Significance: Emphasize that data strategies not only support business goals but also expedite the realization of results. Stakeholders appreciate initiatives that yield actionable insights quickly, particularly when they are aligned with critical decision-making moments.

To illustrate this alignment and time-to-value concept, consider a scenario where a data leader is tasked with providing real-time sales data to inform budget decisions. By clearly connecting the data initiative to the budgeting process and demonstrating how quickly actionable insights can be obtained, the data leader showcases the immediate impact of data-driven decisions on the organization's financial outcomes.

Collaborating on Data Projects

Collaboration is the lifeblood of successful data projects. You can encourage teamwork and collaboration in many ways, but there are three strategies proven to work for any data team.

1. Conducting Collaborative Workshops and Brainstorming Sessions:

Collaborative workshops and brainstorming sessions are invaluable tools in the data leader's arsenal. They bring together diverse perspectives, fostering innovation and shared understanding.

Here's why they matter:

  • Cross-Functional Insights: Collaborative workshops allow technical and non-technical teams to collaborate in an interactive environment. This cross-functional interaction enables a deeper understanding of each other's challenges, perspectives, and goals.
  • Idea Generation: Brainstorming sessions generate creative ideas and solutions. When stakeholders actively participate, they contribute unique insights that can shape data strategies and project directions in unexpected and valuable ways.
  • Alignment: Workshops help align data projects with business objectives. By collectively defining goals and expectations, stakeholders and data teams ensure that projects remain focused on delivering tangible value.

2. Soliciting Feedback and Incorporating It:

The success of data projects hinges on continuous improvement. Data leaders must actively seek feedback from stakeholders and incorporate it into data strategies.

Here's why this approach is crucial:

  • Iterative Improvement: Feedback loops ensure that data projects evolve to meet changing requirements and expectations. Stakeholder input allows data leaders to make course corrections and enhancements throughout the project lifecycle.
  • Stakeholder Buy-In: Involving stakeholders in the feedback process fosters a sense of ownership and engagement. When stakeholders see their feedback being acted upon, they become more invested in the success of the project.
  • Minimizing Misalignment: Soliciting feedback helps prevent misalignment between data teams and stakeholders. It allows data leaders to address concerns and issues early, reducing the risk of misunderstandings and project setbacks.

3. Establishing Common Definitions and Metrics:

Effective communication within data projects requires a shared vocabulary and common metrics.

Establishing these standards is vital to ensure alignment between different teams and departments:

  • Shared Language: Common definitions and terminology eliminate confusion and misunderstandings. When everyone uses the same language, stakeholders can easily grasp the meaning and significance of data insights.
  • Consistency in Measurement: Common metrics and KPIs ensure that everyone is evaluating progress and success based on the same criteria. This alignment prevents discrepancies in evaluating project outcomes.
  • Interdepartmental Collaboration: When different departments or teams within an organization share common definitions and metrics, collaboration becomes smoother. Teams can work together seamlessly, knowing that they are pursuing shared objectives.

Incorporating these collaborative practices not only enhances the effectiveness of data projects but also strengthens the relationships between data leaders and stakeholders. By promoting transparency, shared goals, and open communication, collaborative efforts pave the way for successful outcomes and enduring partnerships.

Overcoming Challenges

Building strong relationships with business stakeholders can come with a unique set of challenges. 

One of the most common obstacles data leaders face is resistance to change.

Stakeholders may be accustomed to traditional decision-making methods and may resist adopting data-driven approaches. 

To address this, data leaders should:

  • Educate and Demonstrate Value: Educate stakeholders about the tangible benefits of data-driven decisions. Use concrete examples and case studies to illustrate how data can lead to better outcomes and increased efficiency.
  • Start Small: Begin with small, manageable data projects that demonstrate quick wins. These early successes can help overcome resistance by showcasing the practical benefits of data.
  • Lack of Understanding of the Value of Data: Some stakeholders may not fully grasp the value that data can bring to their decision-making processes. 

In such cases, data leaders can:

  • Provide Training: Offer training sessions or workshops to help stakeholders and their departments understand the basics of data analytics and its potential impact on their roles and responsibilities.
  • Tell Compelling Stories: Use storytelling to convey the value of data. Share success stories or case studies that highlight how data-driven decisions have positively influenced similar organizations.

Another common challenge is figuring out how to navigate complicated political dynamics within an organization.

To help navigate political and organizational dynamics, data leaders can:

  • Identify Champions: Identify influential individuals within different departments who can champion data initiatives and advocate for their importance.
  • Create Cross-Functional Teams: Form cross-functional teams that include members from various departments. This fosters a sense of ownership and shared responsibility for data projects.
  • Practice Conflict Resolution: Develop conflict resolution strategies that prioritize open and constructive dialogue. Encourage stakeholders to voice their concerns and work towards mutually beneficial solutions.
  • Have Clear Communication: Maintain clear and transparent communication channels to prevent misunderstandings and mitigate potential conflicts before they escalate.

By addressing these common challenges and adopting strategies for collaboration and conflict resolution, data leaders can navigate the intricate landscape of building relationships with stakeholders more effectively. These efforts not only promote the success of data projects but also contribute to a culture of data-driven decision-making within the organization.


The journey to building strong relationships with business stakeholders is not without its challenges. Resistance to change, misunderstandings about data's value, and organizational dynamics can pose roadblocks. However, with proactive strategies, such as education, collaboration, and conflict resolution, these challenges can be overcome.

In the end, the payoff can be huge. Strong relationships between data leaders and business stakeholders pave the way for data-driven decisions that impact the bottom line, drive innovation, and keep organizations ahead of their competition. So, embrace the challenges, cultivate these relationships, and let collaboration become your guiding star.

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Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
Data analyst
Business analyst
Data/product manager
Total cost
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
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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