As a data scientist or marketing analyst, you might evaluate your own work based upon accuracy, completeness, models, or some other criterion. But, if you’re not able to demonstrate the business impact of your analysis, it becomes worthless to stakeholders.
On your team, designate a champion executive who is a data driven leader. Employing logic and reason, this executive should demonstrate how data can be an empowering force in the stakeholder’s organization. On the other side, you might have a business unit leader, their analyst, and a day-to-day point of contact.
When sharing your data analysis with stakeholders, it’s crucial to set expectations and deliver the results so that all parties understand the value. Even if your analysis tells a positive story for the company, if your presentation leaves stakeholders with more questions than insights, your work won’t be recognized. This goes for both external and internal stakeholders.
Unfortunately, stakeholder communication and management is a soft skill not taught in any academic curriculum. You need to have the skills to not only communicate your data science analysis but also to influence business decisions based upon the analysis. Consulting firms like McKinsey, Deloitte, BCG, and Accenture specialize in stakeholder management. Management consultants learn to manage client relationships through a rigorous curriculum during onboarding.
Most data scientists must learn these skills on the fly without a formal curriculum or course. At Retina, we train each of our new team members how to share data with our clients. Over the last three years, we have developed an internal framework to guide new team members on external stakeholder management. (Note: this model will also work well for internal stakeholder communication.)
3 Principles of Stakeholder Management
The three principles of stakeholder management are trust, impact, and communication.
Trust is the most important principle to establish with your stakeholders and maintain on an ongoing basis. You should become a trusted advisor who enables them to make data driven decisions. You can build trust through:
Credibility: Showcase your experience and knowledge of both data science and the stakeholder’s industry.
Reliability: Demonstrate the accuracy of your forecasts and models over time.
Intimacy: Build a personal relationship with the stakeholder through genuine conversation, vulnerability, and shared interests.
On the other hand, self-orientation will adversely impact trust with your stakeholder. When possible, gather baseline information about the company from other sources.
Stakeholders want to see the impact you are making on the organization with data science. You can make a lasting impact with:
Insight: Share actionable insights based upon your data analysis to prepare your stakeholder for strategic decisions and meetings.
Business Case: Provide the next logical step or roadmap for your stakeholder, including cause and effect language for your recommendations.
Accountability: Send deliverables on time, track action items, and focus on project management.
Don’t let fear of being judged or making mistakes reduce your potential for impact.
Communication is one of your most valuable tools for stakeholder engagement. Continue to hone your skills to build trust and make an impact.
To provide useful insights to your stakeholder, figure out their ideal state and the steps they need to take to get there. It’s also important to determine what areas of the business the stakeholder can influence.
When delivering bad news to a stakeholder, be clear, concise, and compelling. Focus on rebuilding trust for the next project, rather than apologizing. It’s important to show the issues, rather than just telling the stakeholder what went wrong, and to follow up to confirm they understood.
To enable stakeholder management, keep communication streamlined by using Excel to share project milestones. Another great option is a project management platform that showcases workflow, such as Trello, Asana, or JIRA. Stakeholder management tools are less impactful.
At the end, if your data analysis doesn’t influence your client’s business strategy or decisions, they likely won’t recognize your value. Remember to share more than the results: tell the stakeholders what the data means and how they can use it to grow their business.