Bridging the Gap Between Business Stakeholders and Data Modelers

Introduction

Data modelers frequently communicate in terms of entities, constraints, and other technical terms. This does not always translate well for business stakeholders. They are generally more interested in strategy and business outcomes. 

Data modelers need input from the business to understand what data is important and how it should be used. On the other hand, the business relies on data modelers to create strategies and visualize outcomes. Collaboration is critical to creating a data model that allows business stakeholders access to the insights they require. 

In this blog, we will cover the roles and responsibilities of a data modeling project, common challenges in practical collaboration, and strategies to overcome them. 

Roles and Responsibilities 

Successful data modeling initiatives require well-defined roles and responsibilities within the business. This ensures clear communication channels within the project, facilitating collaboration between stakeholders and data modelers.

• Data Modelers: They design and create conceptual, logical, and physical data models that organize and structure data for best performance, scalability, and ease of access. In the 1990s, data modeling was a specialized role. Today, these responsibilities are often integrated into broader roles such as Analytics Engineers, Data Engineers or Data/Business Analysts.
• Business Stakeholders: They define business requirements and validate data models to meet operational needs. They oversee implementation to ensure performance and scalability and may use the generated reports.  
• Data Users: These are analysts and BI developers who use data within the organization. They analyze data, identify trends, and create reports and visualizations to inform strategic business decisions. 
• Data Consumers: Individuals who review and utilize data insights for decision-making, such as executives who regularly check dashboards and reports. Data consumers rely on the analyses and visualizations created by data users to guide their strategic planning and operational oversight.

Delineating roles and responsibilities reduces misunderstandings and overlaps. However, teams often face challenges in cooperation, and strategies are needed to bridge the gap between them. 

Bridging the gap 

Collaboration between data modelers and business stakeholders often encounters challenges such as: 

• Ambiguity over data modeling ownership 
• Business priorities competing for attention 
• Communication gaps due to technical jargon 
• Evolving business needs and differing opinions

These issues can lead to delays and inefficiencies, disrupting workflows and impacting project timelines. To address these challenges effectively, organizations can implement several strategic approaches: 

1. Expectation Management: before starting a data modeling project, it is crucial to manage expectations, especially when working with stakeholders who may not be familiar with their role or what is expected of them. Here is how to approach this scenario: 
   • Roles and Responsibilities: clearly define what the business stakeholder needs to do and what the data modeling team will handle. This includes discussing the entities involved, understanding what data is in scope, and defining deliverables. Stakeholders will also help validate and test the data models and approve the final versions.
   • Commitment: explain how much time and effort they need to invest throughout the project. This includes attending meetings, providing feedback, and reviewing models.
2. Project Meetings: the first step is to align with the business stakeholders if they prefer short stand-ups, regular or workshop-style meetings. It will depend on their availability and conflicting priorities.
   • Short Meetings: conduct 15-minute stand-ups to foster continuous dialogue between business stakeholders and data modelers. This approach ensures meetings stay on track within busy schedules, encouraging active participation without significant time commitments. It also accommodates changing priorities smoothly and guarantees the adoption of an interactive refinement based on continuous feedback.
   • Workshop Scheduling for Faster Deliverables: organize focused workshops to speed up end-to-end deliverables. By dedicating a few intense days, business stakeholders can contribute effectively without stretching commitments over scattered sessions. This approach accommodates different business priorities while ensuring everyone works towards the same goal.
   • Regular Meetings: several regular-length meetings allow for more in-depth discussions and collaboration without overwhelming participants. Regular-length meetings provide a balanced approach, ensuring continuous progress and detailed feedback while respecting stakeholders’ time and priorities. They should be recurring to update on progress and make sure everyone is aligned.
3. Documentation: this is a key step to foster good collaboration between the business and data modelers.
   • Ownership: decide who owns the documentation based on the content type. Business stakeholders should own the documentation for project definitions, ensuring that business requirements and goals are clearly captured. The data modelers should handle the technical documentation, detailing the data structures, models, and technical processes.
   • Review and Update Workflow: assign a workflow for reviewing and updating documentation periodically. This could include setting reminders, tasks on a kanban board or scheduling regular check-ins. One effective strategy is to adopt the approach popularized by dbt, which involves documenting at the same time you are developing. This ensures that documentation is always up-to-date and evolves alongside the project.
   • Centralized Storage: choose a centralized location for storing documentation, such as SharePoint or Confluence. This ensures easy access and visibility for all team members and data users involved in the project.
   • Templates and Guidelines: these can be established to streamline the documentation process and maintain consistency across different team members.
   • Versioning and Approval: implement a system for versioning and approving documentation when changes are made. This ensures that all team members have a unified understanding of the topics and that any changes are tracked and agreed upon.
4. Relevant Use Case Selection: avoid modeling out all your business’ entities simultaneously, as this can lead to complexity and delays in delivering measurable results. Instead, start with a specific use case that aligns closely with strategic objectives and has the potential to highlight the immediate benefits of data-driven insights. By focusing efforts strategically, organizations can efficiently allocate resources and achieve quicker returns on their data modeling investments. 

Conclusion 

Connecting business stakeholders with data modelers is crucial for effectively leveraging data. Clear communication, defined roles, and strategic alignment between data modelers and business stakeholders are essential. This collaborative approach not only boosts operational efficiency but also nurtures a culture of accountability and mutual respect. 

Looking to enhance collaboration in your data projects? Contact Xebia Data today to discover how we can help streamline your processes and maximize the value of your data initiatives.