For more than a decade, organizations across diverse industries and sectors have made significant investments in data science in the hope of realizing new products and efficiencies that derive value from rich information. One of the most tangible forms of this investment is in the hiring and creation of data teams; however, soon after these teams are formed, many organizations experience a disconnect: how do you align the hypothesis-driven work of data science with the commercial priorities of the enterprise? While this is not a new challenge in the research and development domain, technical advances and well-trained professionals offer tantalizing potential if the disconnect can be repaired -- and the good (and bad) news is that the common inhibitors are almost never purely technical. Instead, these disconnects are rooted in communication and coordination challenges between strategic goals, data teams, and other enterprise-level functions that employ varied organizational strategies. Therefore, to resolve the disconnect, it is essential for data professionals to consider what organizational strategy and data team leadership looks like specifically for data science.
This course is designed to shed light on the patterns that define successful and unsuccessful data science functions. We will explore those patterns as they relate to organizational strategy: the way an organization orchestrates goals, plans, resources, and execution -- and for businesses, the way an en enterprise establishes a competitive advantage. We will also focus on identifying the specific and sometimes idiosyncratic considerations associated with converting data science capabilities into tangible business outcomes. Students taking this course will develop a thorough understanding of the patterns that maximize the impact of the data science function within an organization, and they will also develop awareness of the most common stumbling blocks and pitfalls that organizations face when trying to leverage data science.
This course is part of the Leadership track of the Advanced Data Science Certificate. In particular, it’s part of a three-course leadership sequence that explores different aspects of leadership and management in a data science context: How, focusing on product management and infrastructure; Who, focusing on building effective teams, and Why, What, & Where, focusing on organizational strategy.
Upon successful completion of the course, students will:
Understand the variety of ways in which data teams can add value to organizations, from optimizing user experiences and finding efficiencies in supply chains to developing new business hypotheses and building products outright.
Explore the special case of data science teams within organizations where data is the product, and identify structures that can help leverage data teams’ capabilities in those settings.
Identify patterns and challenges associated with communicating data science capabilities and limitations with stakeholders across different divisions, and explore methods to pre-empt and address those challenges.
Understand how well-managed data teams can rapidly experiment and prototype in effective ways that are sometimes particular to the data science field, and explore how organizations can leverage agile development and rapid prototyping to make better resource allocation and prioritization decisions.
Compare different organizational and reporting structures for data science functions within organizations, and identify the tradeoffs associated with each one.
Enrollment in this course is open to all students and applies credit toward the Advanced Data Science Certificate Leadership tracks.
Applies Towards the Following Certificates
- Certificate in Advanced Data Science : Elective Course