Data Science Project Management: From Idea to Implementation
As data science continues to evolve and expand, organizations are increasingly relying on data science teams to make data-driven business decisions. However, managing data science projects and ensuring that they deliver value to the organization can be challenging. According to Gartner analyst Nick Heudecker, 85% of all data analytics projects still fail, which underlines the need for effective business strategy to avoid such pitfalls.
Why is it important?
In order for organizations to maximize the return on their data science project investments and see a real impact on the bottom line, effective data science project management is extremely important. The lack of proper project management can lead to delays in data collection and preparation, hindering the project’s progress. Furthermore, inadequate leadership support, communication breakdowns, siloed data, and a lack of collaboration among team members can all contribute to project failure. By implementing effective project management strategies, organizations can ensure that the insights generated from data science projects are translated into action, providing tangible value to the business.
The Data Science Project Lifecycle plays a critical role in this process. The framework provides a structured method for utilizing data to address key issues, aligning the project with the overall business goals, and ensuring that the results will be beneficial to the organization.
Let’s delve deeper into each stage of the data science project life cycle:
(1) Business understanding
During this stage, business-related research is conducted using domain expertise to specify the needs of the business. The output of this stage is a Business Requirements Document (BRD) that has been approved by all stakeholders.
(2) Data understanding
In this stage, data sources and their owners are researched and documented. An Exploratory Data Analysis (EDA) is used to research the data, record any anomalies and unusual artifacts in the data, and explore ideas for feature engineering. The outputs of this stage include a Data Source Dictionary and an EDA report that outline the method to complete the task and deal with data errors that were discovered.
(3) Data preparation and Optimization
The stage of data preparation and optimization encompasses feature engineering, addressing anomalies found in the stage of data comprehension, and transforming raw data into a usable format for the project’s goal. This stage can take up to 80% of the project’s time, so it is critical to plan ahead, maximize team performance, and utilize open-source tools to speed up the process. To ensure reproducibility and documentation at this point, it is important to focus on data provenance, versioning, reusability, and robust code during code review.
(4) Modelling
The modelling stage is where the collected data is used to train a model using machine learning or deep learning techniques. The goal is to create a model that can make predictions or decisions based on new input data. This stage includes selecting the appropriate algorithm, training the model, evaluating its performance through testing and validation, optimizing the model parameters, and selecting the best model.
(5) Evaluation
This stage is closely related to the modelling stage and is often repeated multiple times to reach the final stage. The evaluation stage involves testing technical and business metrics and checking individual predictions of the model. Two evaluation strategies, online and offline, are used to track metrics and decide which model to deploy.
(6) Deployment
It is the final stage, where the best model is made available to end-users. It involves closely monitoring the model’s performance and meeting security, versioning, and communication requirements. Reusability is important in this stage as the requirements for model deployment are similar for different projects, and using a model-serving framework can save time. The model’s performance is monitored by evaluating online metrics and retraining or triggering alerts if necessary. A/B testing and multi-armed bandit techniques are also used to further evaluate the model’s performance and make decisions in the deployment process.
Conclusion
In conclusion, Data Science Project Management is an essential component for maximizing return on investment for data science projects. The Data Science Project Lifecycle provides a structured approach for managing a data science project from idea to implementation. Each stage of the lifecycle is important and requires careful planning and execution to ensure that the projects are well-designed, well-executed, and provide value for the organization.
