A blueprint for running an agile data science organization
The world of data science is intricate and comes with hidden costs that go beyond budgets. Data scientists are a valuable investment for any organization, but inefficiencies like idle infrastructure can waste significant amounts of data infrastructure investments. Agile methodologies offer a solution by improving workflow and reducing wasted time. By implementing agile practices, the traditional data science process becomes optimized and adaptable, resulting in more cost-effective data science initiatives. This article explores the hidden costs in data science and demonstrates how agile practices can address them.
Data scientists possess intricate knowledge and expertise in handling data, making them a valuable resource. However, when data scientists spend more time on tedious tasks rather than innovation, it becomes an expensive expense without the expected payoff. Additionally, data scientists often work on their own machines to avoid restrictions from central IT, which leads to knowledge discovery becoming burdensome and reinventing the wheel scenarios.
Waste can take various forms. For example, the Boston Consulting Group found that only 44% of models make it to production, and a significant portion of a data scientist’s time can be wasted on IT setup tasks. Infrastructure costs also add up quickly when data scientists are distracted from innovation. Furthermore, the costs of moving data into and out of the cloud can become expensive at large data scales, making it challenging to manage cloud costs across multiple stacks and environments.
Machine learning, particularly Generative AI, requires substantial cloud compute and expensive GPUs. For instance, models like ChatGPT can cost organizations around $700,000 per day in computing costs. The training process for models like ChatGPT can involve thousands of GPUs and months of training. These costs pose challenges for data science leaders who need help scaling their projects effectively.
Agile methodologies are becoming increasingly relevant in data science projects, especially in today’s efficiency-driven and adaptable environment. Agile processes emphasize adaptability, collaboration, and iterative development, which can significantly impact the cost efficiency of data science projects throughout their lifecycle. Data science projects naturally align with key traits of the agile management approach, such as incremental and iterative development, focus on values, empowering the team, and continuous learning.
A typical agile process for a data science project includes defining the problem and potential impact, collecting and analyzing data, developing and testing models, deploying models into production, and continuously monitoring, analyzing, and refining the models. Project management tools like Jira can facilitate the implementation of agile methodologies by organizing units of work and tracking progress.
Infrastructure management is often overlooked in data science, but it plays a pivotal role. Setting up and managing data science environments can result in substantial hidden costs, especially when resources are underutilized. Balancing the need for cutting-edge technology with cost control is crucial in infrastructure planning and investment. Agile strategies can unlock more value from data science investments, turning potential waste into productivity and innovation. They also enable cost monitoring and facilitate ROI calculations for individual data science projects.
Scaling data science projects is a challenging task, but agile practices can help manage rising costs. An agile workflow allows teams to identify storage inefficiencies and minimize redundant data sets through iterative sprints. Version control and feature branching enable efficient data management, reducing the need for additional storage resources. Agile practices also optimize resource allocation, improve transparency, and enable automation, leading to lower costs related to manual labor and error rectification.
However, it’s important to note that agile is not a one-size-fits-all solution. Adaptability and flexibility are essential, as rigid adherence to any one methodology may introduce operational blind spots and unexpected costs. By adopting agile methods and focusing on iterative improvements, transparency, and automation, organizations can scale their data science projects successfully while keeping costs in check.
Efficiency is crucial for data science organizations. Without it, costs spiral out of control, and the time-to-value increases, negating the competitive advantage.
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