The Process Communication Model for Data Scientists

Amid the current frenzy around generative AI and the enormous technological strides we're seeing, one crucial element is frequently neglected - the art of communication itself. Tailoring your message to land with your audience and their thinking styles is a difficult skill to conquer.

Today I want to talk about a well-loved framework for communication used by presidents and astronauts alike - The Process Communication Model (PCM) and how it applies to data science.

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Grasping the Fundamentals of the Process Communication Model

You've probably heard me bang on about this a thousand times before, but...

To truly thrive in data science, over and above the technical side of the role, professionals must understand the art of effective communication.

Unfortunately, communication is a complex matter that depends as much on how the audience perceives and understands messages as the content of the message itself.

This is where the Process Communication Model (PCM) becomes invaluable.

The brainchild of Dr Taibi Kahler, the Process Communication Model, is a psychological tool that offers a deeper understanding of how individuals communicate and interact. It operates on the premise that each person has a unique personality type and corresponding communication preference.

Understanding the Process Communication Model

The Process Communication Model classifies individuals into six primary personality types:

• Thinkers

• Persisters

• Harmonisers

• Imaginers

• Rebels

• Promoters

By learning about each of these personality types, data scientists can adapt their communication styles to better connect with diverse audiences of individuals more effectively.

The Role of Communication in Data Science

Communication plays a pivotal role in data science for several reasons. Two of the dominant factors are:

1. Data scientists frequently work in cross-functional teams, collaborating with individuals from varied backgrounds and broad expertise. Misunderstandings and misinterpretations can arise without effective communication, leading to project setbacks and less-than-ideal solutions.

2. Data scientists are responsible for explaining complex technical concepts to stakeholders without a technical background. Clear and succinct communication is vital to ensure informed decision-making and action based on data-derived insights.

Delving into the Six Personality Types of the Process Communication Model

Let's delve deeper into each of the six personality types outlined in the Process Communication Model:

Thinker Personality Type

Thinkers are analytical individuals who favour logical and structured communication. They value precision and are often detail-oriented. When communicating with Thinkers, providing data-backed evidence and logical reasoning is crucial.

Persister Personality Type

Persisters are reliable individuals who value stability and consistency. They are methodical in their approach and prefer organised and systematic communication. When interacting with Persisters, it's essential to provide clear plans and objectives.

Harmoniser Personality Type

Harmonisers are empathetic individuals who value interpersonal connections. They prioritise maintaining harmony in relationships and appreciate open and supportive communication. Building trust and fostering a positive environment is essential when engaging with Harmonisers.

Imaginer Personality Type

Imaginers are creative individuals who relish exploring possibilities and generating new ideas. They appreciate communication that is imaginative and inspiring. When communicating with Imaginers, it's crucial to encourage brainstorming and provide opportunities for creative thinking.

Rebel Personality Type

Rebels are spontaneous and energetic individuals who value freedom and individuality. They appreciate communication that is engaging and fun. When interacting with Rebels, providing them with choices and opportunities for self-expression is key.

Promoter Personality Type

Promoters are charismatic individuals who enjoy influencing others. They appreciate communication that is enthusiastic and persuasive. When engaging with Promoters, it's essential to highlight the benefits and engage in lively discussions.

Implementing the Process Communication Model in Data Science

With a better understanding of the different personality types, let's explore how the Process Communication Model can be applied in data science.

Boosting Team Collaboration in Data Science Projects

Data science projects often involve multidisciplinary teams working together towards a common goal. Data scientists can tailor their communication approaches to foster effective collaboration by understanding the different personality types within the team.

For instance, when

Working with a team of Thinkers, providing them with detailed documentation and organising regular structured meetings can help them feel engaged and appreciated. Conversely, when collaborating with a team of Harmonisers, creating a positive and harmonious work environment can enhance productivity and satisfaction.

Enhancing Data Presentation and Reporting

Data scientists present insights derived from complex data sets to non-technical stakeholders. Data scientists can ensure that their presentations are engaging and easily understood by considering the preferred communication styles of different personality types.

For example, when presenting to a group of Imaginers, incorporating visually appealing graphics and storytelling techniques can capture their attention and spark their creativity. When reporting to a group of Rebels, utilising interactive visualisations and incorporating gamification elements can create a more engaging experience.

Promoting Effective Problem-Solving

Data scientists often encounter complex problems that require collaborative problem-solving. By leveraging the Process Communication Model, data scientists can facilitate practical problem-solving sessions that cater to the different communication preferences of team members.

For example, when brainstorming with a group of Persisters, providing them with a structured problem-solving framework can help them feel more comfortable and engaged. Encouraging open and lively discussions can help stimulate their creativity and generate innovative solutions when working with a group of Promoters.

Case Studies: Success Stories of Using Process Communication Model in Data Science

I wanted to share some real-world cases where I’ve used ideas from the Process Communication Model to successfully benefit data science projects.

Data Science Team Slowdown

Working for an online retail platform, I was working with two teams of techies and a mix of business stakeholders. Communication had broken down, and new development took longer because of rework and misunderstanding.

A few short sessions on the Process Communication Model really boosted the output of the tech teams. By understanding the different personality types within the business (and their own teams), they were able to improve collaboration and enhance the effectiveness of their development process.

As a side-effect of this work, the team members felt more engaged and appreciated, creating a more positive and cohesive work environment.

Reporting and Presentation

At an energy trading company we utilised the Process Communication Model to improve the existing data presentation and reporting processes. By considering the preferred communication styles of different stakeholders, they were able to design more impactful and accessible reports that effectively communicated vital insights.

By using visualisations that catered to the preferences of each personality type, the BI team saw an increase in stakeholder engagement and a greater willingness to act on the insights derived from the data. Decision-making processes became more informed, resulting in better engagement and ownership of the data from the non-technical stakeholders.

Final Thoughts

The Process Communication Model provides data scientists with a valuable framework for understanding and adapting their communication approaches to suit different personality types. By leveraging this knowledge, data scientists can enhance team collaboration, improve data presentation and reporting, and facilitate effective problem-solving.

In the ever-evolving field of data science, more than technical skills are needed for success. Effective communication is the key to building strong relationships, gaining stakeholder buy-in, and ultimately deriving meaningful insights from data. By embracing the Process Communication Model, data scientists can excel and make a lasting impact.

All the best,
Adam