Tackling the Stakeholder Communication Problem

DTP #18

“Whether the implementation is going in the right direction depends on communication with the stakeholder. So I will say number one challenge is–How do you conduct a good and well informed meeting with stakeholders such that the data scientist is doing exactly what is helpful for the company and that drives the business value?”

Are data science initiatives truly driving business value? The answer, as it turns out, hinges on a fundamental yet often underestimated facet of work– communication with stakeholders.

Ensuring that data scientists are aligned with the company's strategic goals and providing solutions that genuinely benefit the organization can be a challenging endeavor.

We spoke to multiple Data Science leaders on how they are tackling this challenge, and expand on actionable ways to implement similar strategies below.

🌐 From the Web:

Improved Communication Needed with Stakeholders on Data Needs: A recent report describing how The National Center for Science and Engineering Statistics (US) ensures its data and analyses meet data quality standards including relevance, and evaluating the extent to which NCSES has processes to identify emerging needs.

How to Create a Stakeholder Strategy: A HBR article emphasizing the importance of adopting stakeholder-based approaches for businesses, as they can lead to greater success and resilience.

Improving Data Analytics in Higher Education Requires Collaboration: Why institutions like Rutgers University must break down data silos, to make data-informed decisions effectively, and include a wide range of stakeholders in decision-making processes.

Fostering Understanding Between Stakeholders and Data Scientists

Stakeholders may need to put in the effort to learn the more technical aspects of their data science teams work:

“So I will say right off the bat, there is an information imbalance before you even start the meeting [and] talk to the stakeholder. Stakeholders don't necessarily know what the code is doing, so we really need to tune things down and then explain in a very understandable language [on] what this code is doing, and specifically how it's helpful.”

The disconnect between stakeholders and data scientists often stems from differences in their educational backgrounds and training. This inherent distinction can lead to a shift in their respective focuses and priorities.

To bridge this gap effectively, stakeholders need to proactively invest effort in gaining a better understanding of the technical aspects of their data science teams' work. This information imbalance, characterized by stakeholders not fully comprehending the intricacies of the code and processes, necessitates a concerted effort to simplify and communicate complex technical concepts in a more accessible and understandable language during meetings and discussions.

This approach can foster better collaboration and alignment between stakeholders and data scientists, ultimately leading to more effective decision-making and data-driven outcomes.

Optimizing Data Science Collaboration Through Requirement Analysis

“The first thing is to set up a call with the data science team or scientist with the business users. End users who are going to use it and try to find out the exact requirements and then bring in the data if it is a new source altogether or if it is adding more field calculations to the existing data. So the requirement analysis is the most important aspect. Once you nail that, you bring the correct data, then it's all smooth.”

Effective collaboration between data science teams and business users hinges on a meticulous and thorough requirement analysis. Set up a process to:

  1. Initiate a call or meeting that brings together the data science team or scientist with the business users.

  2. Engage with end users who will utilize the data insights to extract precise project requirements.

  3. Ensure a clear understanding of user needs, whether it involves incorporating new data sources or adding field calculations to existing data.

  4. Prioritize the requirement analysis phase as the cornerstone of project success.

  5. Once requirements are meticulously defined, proceed to acquire and integrate the relevant data.

  6. Establish alignment with the precise needs and objectives of the business users to streamline subsequent project steps.

The Art of Translating Data to Insights for Data Scientists

The mindset shift when communicating with stakeholders isn’t easy for data scientists:

“Things like that are a very difficult transition to make. But it's incredibly helpful. The [term] we try to remind ourselves [of is] bottom line up front because it's very easy to get lost in the details and then never actually get across your point. It's almost uncomfortable as an analytical person because you feel like you have to build up to that.”

Here are actionable ways to navigate this transition:

  • Bottom Line Up Front (BLUF): Lead with the key conclusions and insights to ensure stakeholders grasp the main points right away.

  • Simplify Complexity: Avoid overwhelming stakeholders with technical details; focus on delivering a clear, understandable message.

  • Highlight Relevance: Emphasize how the data insights align with the business's goals and objectives.

  • Visual Aids: Utilize visual representations like charts or graphs to illustrate key findings, making them more accessible.

  • Engage in Two-Way Communication: Encourage questions and discussions to ensure stakeholders fully comprehend and can apply the insights.

  • Practice Concise Communication: Practice summarizing complex analyses into straightforward, digestible statements.

By adopting these strategies, data scientists can bridge the communication gap with stakeholders effectively, delivering insights that drive informed decision-making.

“Have, like, three options [you can share with stakeholders]. You don't [need to] have a strong opinion. You don't have to really sell anything in particular, but if you provide a bunch of details without providing the three options, you'll have failed to communicate clearly.”

A Middleman Can Facilitate Data Science Communication

“I think having data product managers is fundamental because sometimes you can give projects to data scientists and they can do amazing stuff, but then what? Is this actually helping us achieve this goal or not? Having the product figure in between that understands enough data science but also knows what are the company goals where we're going [is necessary].”

Here's how to leverage data product leaders for better project outcomes:

  1. Appoint Data Product Managers: Assign individuals who possess a strong understanding of data science and are well-versed in your company's strategic objectives to the role of data product managers.

  2. Ensure Strategic Alignment: Data product managers should be well-informed about the company's goals and long-term vision to align data projects with these objectives.

  3. Facilitate Communication: Foster open channels of communication between data scientists and data product managers to ensure that the projects are on track and contribute to the desired outcomes.

  4. Project Oversight: Data product managers play a pivotal role in overseeing data projects from inception to completion, ensuring that they remain focused on delivering value to the organization.

  5. Performance Evaluation: Regularly assess the impact of data projects against predefined company goals, with data product managers taking the lead in this evaluation.

✅ Tools & Resources:

LucidChart: A RACI matrix software, allowing you map out individual tasks, stakeholders, and each stakeholders level of responsibility for that task in one central location

Stakeholder Questionnaires: Use Stakeholder Questionnaires to identify and understand people with influence over your project.

Stakeholder communications plan: A plan will help you figure out how to provide stakeholders with the right information at the right time, and via the right channels.

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