In the fast-paced world of data science, a standardized workflow is essential for increased efficiency, collaboration, and long-term success. A structured approach ensures that every stage of the data science process, from problem understanding to model deployment, aligns with business goals and maximizes the data team’s value. By adopting a standard workflow, organizations can streamline project management, enhance reproducibility, and improve the overall quality of your data science initiative.
Not only is a standardized workflow a critical component of your initiative, the absence of a workflow can also be one of your biggest obstacles to success. A 2022 Deloitte Survey of over 250 respondents reported that some of the most difficult barriers to becoming insight-driven are related to processes–or lack thereof. Without a structured approach, organizations often struggle to get consistent value from data science teams. These teams are hampered with inefficiencies, inconsistent results, and communication gaps between business stakeholders and technical workers. Standardizing the data science workflow not only addresses these pain points but also unlocks a wealth of benefits from providing transparency and consistent deliverables to improving collaboration within data science teams and across the broader organization.
There is a transformative effect of workflow standardization on data science teams. Our experience implementing these workflows with a variety of clients and markets has helped us identify these five benefits of standardization that can help organizations maximize the return on their data investments.
CRISP-DM: A Framework to Standardize the Data Science Workflow
One widely recognized framework for standardizing data science workflows is the Cross-Industry Standard Process for Data Mining (CRISP-DM). Developed in the late 1990s, CRISP-DM remains highly applicable due to its adaptable and straightforward six-phase structure. These six phases can be generally defined as:
- Business (Problem) Understanding: Understand the current business processes and determine the business goals for the project.
- Data Understanding: Gather data sources and data definitions, talk to subject matter experts, and conduct exploratory data analysis and data quality checks.
- Data Preparation: Select, clean, format data, and create data features with help from subject matter experts.
- Modeling: Select modeling technique, generate test design, and build model. Assess the model performance.
- Model Evaluation: Evaluate process and model results in the context of the business problem.
- Deployment: Produce deliverables and develop a plan for model monitoring and maintenance.
CRISP-DM provides a roadmap for tackling data science projects systematically, allowing teams to stay focused on solving business problems while maintaining flexibility to iterate and adapt. This methodology ensures that technical progress translates into measurable business outcomes. While other methodologies like SEMMA or Agile Data Science exist, CRISP-DM stands out for its business-centric approach and broad applicability across industries.
Benefit No. 1: Flexibility and Continuous Improvement
Data science projects rarely follow a straight path from start to finish. The unpredictable nature of data exploration and modeling requires teams to adapt as they uncover insights, confront challenges, and respond to changing business priorities. They thrive on flexibility, iteration, and continuous improvement. Standardized workflows like CRISP-DM naturally align with agile methodologies, enabling teams to make steady incremental progress without losing sight of the bigger picture.
By structuring projects into clearly defined phases—such as Data Preparation, Modeling, and Evaluation—CRISP-DM ensures that work can advance in manageable steps. Data teams can deliver tangible value at each stage of the process.
Each phase of CRISP-DM is designed to be revisited as needed, allowing data scientists to refine their work iteratively based on new insights or changing requirements. This keeps teams from getting stuck trying to achieve a perfect solution (i.e. a waterfall approach) before showing incremental progress (i.e. an agile approach).
This standardization and transparency ensures that even those unfamiliar with the specific project can make meaningful contributions quickly without requiring extensive training and ramp-up time.
One of the key benefits of CRISP-DM’s iterative design is its ability to keep data science efforts aligned with business goals. Regularly revisiting the Business Understanding phase keeps the project focused on delivering relevant outcomes, while building trust and confidence with stakeholders. This alignment becomes even more critical when businesses need to reprioritize objectives mid-project.
The framework fosters consistency by standardizing how tasks are approached and completed, ensuring uniformity in both workflow and progress. Combined with the efficiency of delivering incremental value along the way, CRISP-DM empowers organizations to maximize their return on investment in data science projects.
Benefit No. 2: Structured Task Management
Effective task management is critical in data science projects where the complexity of tasks and the diversity of team roles can quickly lead to disorganization. A standardized methodology like CRISP-DM provides a structured approach to technical project organization, ensuring that every team member understands the responsibilities that support a project’s overall success.
CRISP-DM also facilitates timeboxing—a key task management strategy—by providing a framework to define and limit the time allocated to each phase of the project. For example, teams can establish a specific timeline for the Data Understanding phase to prevent overanalysis (analysis paralysis) and keep the project moving forward during the current iteration. This level of organization not only ensures that tasks stay on schedule, but also helps data scientists maintain focus and prevents them from becoming distracted by other interesting, but less critical explorations.
By keeping the team on track and maintaining momentum, CRISP-DM helps organizations achieve their data science goals efficiently and with minimal resource waste. This leads to better quality results and faster progress.
Benefit No. 3: Enhancing Trust and Communication
Effective communication is at the heart of successful data science projects. It is essential for bridging the gap between data science teams and stakeholders, especially when balancing technical complexity with business objectives. CRISP-DM clearly defines phases—such as Data Understanding, Modeling, and Evaluation—that provide a shared vocabulary that fosters data fluency across an organization.
Team members can also communicate easily about what’s been accomplished and what still needs work, ensuring that communication is efficient and focused. This shared vocabulary builds confidence in the process and aligns the entire organization toward consistent communication.
Standardizing your technical project workflows isn’t just about creating a routine—it’s about unlocking your team’s full potential to make a valuable impact on an organization.
The structured approach of CRISP-DM also enhances transparency, a critical factor in building trust with stakeholders. It helps technical workers clearly communicate the status of their work, explain challenges, and outline next steps in a way that resonates with diverse audiences. Stakeholders gain a deeper understanding of the project plan, empowering them to make informed decisions and stay connected to the project’s outcome and value. This consistency and openness foster a culture of collaboration, where everyone—from data scientists to executives—feels invested in the project’s success.
By creating a framework for clear and transparent communication, CRISP-DM ensures that data science efforts not only deliver results but also build trust along the way.
Benefit No. 4: Optimizing Team Management and Development
Managing a data science team effectively requires clear roles and structured processes. The CRISP-DM methodology provides a framework that can be tailored to an industry or organization, giving teams the flexibility to build their own processes around the standard phases.
New team members can quickly familiarize themselves with the processes and project structure, understanding how their responsibilities fit into the bigger picture. Beyond onboarding, CRISP-DM supports ongoing upskilling by offering team members a development path. The modular phases allow individuals to deepen their expertise in specific areas or gain a broader knowledge of the entire workflow. Team leaders can identify specific skills required for each phase of the workflow, then make a plan for filling any skills gaps on the team. This makes it easier to identify opportunities for training and professional growth, ensuring that team members stay engaged and equipped with the skills needed to problem-solve new or evolving challenges.
Benefit No. 5: Fostering Collaboration, Quality, and Efficiency
CRISP-DM facilitates seamless collaboration among data science team members by providing a clear and structured roadmap for the project. New team members can independently assess the current state of the project, understand which tasks need attention, and easily jump in to contribute. This standardization and transparency ensures that even those unfamiliar with the specific project can make meaningful contributions quickly without requiring extensive training and ramp-up time.
Effective communication is at the heart of successful data science projects. It is essential for bridging the gap between data science teams and stakeholders, especially when balancing technical complexity with business objectives.
By aligning all team members around a common methodology, it reduces variability in outputs and provides a reliable basis for quality control, helping teams deliver consistent and impactful results.
Workflows Unlock Greater Impact for Your Organization
Standardizing your technical project workflows isn’t just about creating a routine—it’s about unlocking your team’s full potential to make a valuable impact on an organization. From improving efficiency and maintaining consistent quality to enhancing collaboration, a streamlined process will be a game-changer for your projects.
Building a data science team? Learn more about how you can help them achieve early wins and gain momentum with important stakeholders.
| FEATURED AUTHOR: MAGGIE CONROY, DATA SCIENTIST
-1.png?width=290&name=Blank-Template-Facebook-Post%20(2)-1.png)