What is a high performance data team? You know how popular Google is now. Since Google acquired Android, it has become one of the globally accepted Operating Systems for smartphones. This popularity was not just about individual brilliance but resulted from Google’s high-performing Android team. Google followed certain principles for running the Android team with high-performance standards.

Similarly, high-performing teams are crucial in data-driven decision-making to achieve optimal outcomes for your organization. Achieving high-performance standards with a data team is even more challenging than delivering good results. You must follow certain principles of running a high-performance data team, which involve carefully cultivating critical characteristics, high-performing team models, and best practices. 

In this article, you will understand what a high performance data team is and its characteristics, strategies for building a high-performing team, and principles for running one. Additionally, you will look into one of the best high performing team models that will help you uncover valuable knowledge for your organization’s achievement.

Let’s start exploring the significance of the High-Performance Data Team!

Key Attributes of High-Performance Data Team

Since every team is different, some key attributes of high performing teams separate them from others. Here are a few unique attributes of a high performing data team.

A Small Multi-Disciplinary Team

High performance data team typically have small, cross-functional groups with a maximum size of nine members. This attribute aligns with Amazon’s two-pizza team rule. These smaller teams empower members to execute tasks and adopt agile methodologies. They possess strong technical expertise to handle all data production and management aspects from inception to completion. 

Adopt a Product Mindset

When the data team adopts a product mindset, they can discover, build, and own the data products that drive business transformation. This mindset requires them to focus on understanding business needs and proactively identifying opportunities to utilize data for business transformation. 

Additionally, this will prioritize balancing the cost of developing and maintaining data products with the value they deliver to the organization.

Utilize Engineering Principles of a Modern Data Stack

With engineering principles, a data team can achieve the organization’s common goal, which is rapid and safe delivery of value. Rapid refers to automating processes to remove human involvement by using readily available connectors to increase productivity. Safe refers to eliminating risks and reducing costs.

High performance data team must look to adopt the engineering principles of a modern data stack. Hevo’s integration platform can easily Extract data from 150+ data sources and load it into a desired destination without writing complex code. Optimizing your data stack with such platforms will save your engineering team’s bandwidth and securely integrate your data to the desired destination.

To learn about how modern data teams automate the data integration process with Hevo, refer to the Hevo Pipelines.

Establish Productive Partnerships

The data team must interact with external teams of the organization to adopt the data products. To do this, the data team should establish a productive partnership with them without hindering the flow of value. The external team can be a specialist expertise or a platform team. So, the data team interacts with the users of their products to work on the product based on the user’s feedback. 

With these partnerships, high performance data teams can influence other parts of the organization to achieve their team goals.

Work Practices

Meticulous planning and strategic resource allocation are crucial for establishing and maintaining the effectiveness of a high performance data team. Ineffective work practices make it difficult to organize and evaluate the data, hinder the team’s ability to assess risks, and prevent them from building creative and innovative solutions. 

This impacts innovation and makes it challenging to achieve the team’s goals.

Apart from these unique attributes, there are a few common characteristics of high performing teams. Let’s examine some of them.

  • Shared Vision: High-performing teams share a common vision or goal and collaborate as a motivated team toward this shared vision. Their ultimate vision is to deliver outstanding solutions for the organization’s success.
  • Recognition and Appreciation: High-performing teams should understand that success is not achieved through individual performance but through team effort. Appreciate each individual within the team for their contribution towards success.

Building a High Performance Data Team

Building high performance data team goes beyond gathering skilled data science experts together into a team. You must work hard to create a high-performing team! 

Let’s look into a few tips that help a team leader on how to build a high performing team.

Create a Cohesive Team

Building a high-performing team is like cooking a meal with the right ingredients; if any of the ingredients are too many or too few, the meal will lose its taste. It is crucial to dedicate effort to carefully selecting a cohesive team. Once the team is created, it is essential for a team leader to prioritize maintaining its stability by providing continuous care and support.

By choosing any of the certifications listed here, You can become Certified Data Scientist. Be a part of a high performance data team within your organization!

Build a Vision-Aligned Team Dynamics 

To ensure a highly productive team, build a strong connection and interaction between the team members. This is achieved when they share the team leader’s values and work together towards the shared vision or goal of the team. As team members gain confidence in their skills and knowledge, they get more done. 

Continuous Upskilling

As emerging technologies change daily, the team leader should facilitate documentation tools, technologies, training, and online learning platforms to expand the knowledge and skill sets of the team members

Refer to the article to upskill your knowledge in data modeling tools.

Establish Measurable Goals

Establishing measurable, clear, and purposeful goals is essential for creating a high-performing team. A shared vision or goal motivates, energizes team members, and promotes innovation among team members. 

To learn more about an organization’s measurable goals, refer to the Data Science for Business

Transparent Communication

By encouraging a collaborative environment for open communication, team members can share their own ideas, provide feedback, and engage in constructive criticism. This collaboration will enable the team to address the issues, identify the areas of improvement, and achieve the organization’s goals.

Mutual Respect

Creating a respectful environment is vital for the success of a high-performing team. This will build a strong bond between the team members, which allows for effective knowledge sharing, skill development, and increased productivity.

Leading a High-Performance Data Team

As a team leader, you should have a commitment and investment to drive your team continuously to high-level standards and increase employee productivity. 

Point Data Team to Right Problem

Team leaders must ensure that their data team focuses on the right problem

Let’s say when a data team develops a model to identify target customers for a market campaign. The model identifies the target customers based on those with a high likelihood of making transactions or those likely to transact when targeted by the campaign. 

The team leader will carefully decide on targeted customers to be identified by the model. Additionally, leaders will change the data team’s approach to training data, modeling techniques, and the level of effort, as the individuals in the data science team can be inexperienced.  

Build a Clear Evaluation Metric Up Front

Data teams evaluate different models when solving problems and choose the one with the highest performance. To do so, they use metrics to compare models and select the most effective one

Let’s say the data team is developing a model to assist a sales team in prioritizing customers to connect using historical contact information. Among the various models, the data team chose the best one using the error rate metric. This metric will measure the percentage of data points where the model’s predictions deviate from the actual outcomes. 

Create a Common-Sense Baseline 

To start with the data science projects, the team leader should create a Common-Sense Baseline that helps the data team use without extensive data science knowledge

For example, suppose the data team is developing a recommendation algorithm for an e-commerce site. In that case, a simple Common-Sense Baseline could track visitor browsing history and recommend popular products from those categories. This baseline helps the team to set up the data and evaluation processes, identify data access, quality, or timeline issues, and address any challenges in calculating the evaluation metric.

Develop Research-Oriented Data Science Projects

In most data science projects, the data team should spend a reasonable amount of time researching to build effective models. Initially, the data team can produce a model that is 10% better at predicting business results. However, they can improve their performance by experimenting with other algorithms. This requires extensive research on the business problem and a model developed for the defined problem.

Check for Truths and Consequences

It is crucial to carefully examine results to ensure the benefits are real and prevent unintended adverse effects. Team leaders should check that the calculations are based on the data separate from that used to create the models. If the results appear valid, investigate for any adverse consequences. A model’s improved performance on a particular metric may come at the expense of other metrics. 

For example, an online retailer utilizes a recommendation algorithm to improve the revenue per visitor metric of the model. Revenue per conversion and conversion rate are the key metrics determining revenue per visitor. The algorithm increases the revenue per conversion but decreases the conversion rate, which will negatively impact the organization’s goal of converting visitors into customers.

Log Everything and Retrain Periodically

Despite rigorous testing before the launch of data science models, the risk of unexpected or incorrect model predictions for certain input data cannot be eliminated. Logging all input and output data in detail as much as possible will aid in faster problem-solving

Due to their high reliance on data, data science models require additional work and complexity and must be retrained periodically. Additionally, a lack of understanding of how these models operate can make finding and fixing issues difficult. The data science team should keep all the log activities and retrain the models regularly to overcome the challenges. 

Effective High Performing Team Models

This section will explore one of the best high-performing team models. The following image illustrates Pat MacMillan’s High-Performance Team Model from his book, “Performance Factor.” The model presents a circle where each element is interconnected, and it highlights the equal importance of each component in driving the best business outcomes. 

Principles of Running a High-Performance Data Team: High-Performance Team Model

This model describes six principles for running a high-performance team.

1. Common Purpose

A clear, shared, and compelling purpose is among the most important ingredients to accomplish organizational goals at high-performance levels.

2. Crystal Clear Roles

Team members understand their responsibilities and those of their team members. Roles define how you design, partition, and deploy the team’s work. Implementing roles can be challenging for teams, but when it is successfully implemented, team members realize the significance of enhancing collaboration to achieve outstanding business results.

3. Accepted Leadership

Effective teams require acknowledged leadership that facilitates the team, trains the team, and provides feedback. 

This leadership builds exceptional performance from individuals and the whole team, leading high performance teams.

4. Effective Processes

High-performing teams can identify, design, and map the key team processes. They continuously evaluate the effectiveness of the key processes by conducting weekly meetings. In the meetings, they will find improvements by answering the following set of questions:

  • How are we doing?
  • What are we learning?
  • How can we do it better?

5. Solid Relationships

Strong team performance relies heavily on positive relationships among team members. Building on trust, acceptance, respect, courtesy, and empathy promotes a collaborative work platform where team members feel valued and understood.

6. Excellent Communication 

High performance teams prioritize excellent communication, which should be prompt, unambiguous, and precise to facilitate collective thinking and action.

Five Principles of High-Performance Data Team

This section will overview the five principles of running a high-performance data team.

1. Plan a Strategy 

For maximum effectiveness, data teams thoughtfully plan how to utilize organizational data to create the team’s value or goal. They effortlessly clarify the Key Performance Indicators (KPIs), prioritize projects, and map out road maps with specific models. These terms carefully align their strategy with the overall business strategy, ensuring that priorities and goals reflect the business goals and growth plans. Data teams know well enough to avoid overly detailed plans with their road map.

2. Apply Lean Principles and Design Thinking

Effective data teams apply lean principles to mitigate risk and maximize the team’s values. Initially, they may not require a dedicated data science expert, sophisticated tools, or data repositories. Instead, they focus on understanding KPIs and developing strategies to improve them.

Before implementing any business data analysis, the data team employs design thinking principles to envision the impact of machine learning advancements on business operations. 

3. Establish a Data Governance

The data team prioritizes data governance and quality assurance to set directions, allocate resources, and make strategic decisions. They follow a code of ethics when using data and use the organization’s core values as a framework for making decisions about data handling.

4. Collaborate and Empower Others

Data teams collaborate and empower each other to drive innovation and achieve the best business results. When individuals within the data team feel empowered to share their ideas and provide feedback, it allows an open communication environment. 

5. Utilize Smart Technology to Scale Up/Down

The data team may employ elastic or cloud computing to save storage expenses and optimize operations. These technologies require them to pay only for the computing resources required. Data teams may also utilize technologies such as automated machine learning to streamline processes with limited resources.

Key Takeaways

In today’s data-driven world, highly skilled data scientists are critical to delivering reasonable business values. Achieving high performance data team is not easy; it is a complicated process. The team leader has to highly focus on selecting the right individuals and follow certain principles to lead the high-performance data team.

As this article shows, you can cultivate high performance data team in your organization by following five principles: establishing goals, creating an open-communication work environment, empowering others, forming a steering committee for data governance, and utilizing smart technologies.

Frequently Asked Questions (FAQs)

1. As a team leader, I’ve found Data Governance to be one of the challenging tasks for my high performance data team in developing data science projects. How can I apply the Collaborative Problem Solving approach to data governance?

Apart from the six principles of high performance data team, you can apply a simple Collaborative Solving approach by following the essential steps.

  • Define the problem
  • Collect relevant information for data analysis
  • Choose appropriate stakeholders and assign roles
  • Brainstorm different methods to address the data governance problem
  • Prioritize solutions that align with the data governance goals
  • Plan to execute the best solution
  • Communicate the solution to the stakeholders and get feedback
  • Monitor the progress and performance
  • Investigate potential risks, if any, and adjust the solution accordingly

If you want to learn more about data governance tools, read 15 Best Data Governance Tools for 2024.

2. I am a beginner on my data science team, and I understand the concept of a Common-sense Baseline. What are the best practices for creating a Common-sense Baseline for my data science projects?

You must maintain the following to create a common-sense baseline for your data science project. 

  • Establish a set of guidelines for creating baselines.
  • Apply Naming Conventions for your products to organize them over time.
  • Lock the projects till the baseline is complete.
  • Duplicate the baseline of one product release for the next one.

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Sony Saji
Technical Writer, Hevo Data

Sony is a former Computer Science teacher turned technical content writer, specializing in crafting blogs and articles on topics such as ML, AI, Python Frameworks, and other emerging trends in Data Science and Analytics.

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