In a World Economic Forum article, Antonio Neri, President and CEO of Hewlett Packard Enterprise, argues that the COVID-19 pandemic was a catalyst for rapid change and the potential for a brighter future. “Today, we are entering the Age of Insight,” Neri writes. “A new era that is defined by insights and discoveries that benefit all and that elevate the greater well-being of every human on this planet.”
The swift advance of data science, machine learning, and AI provides access to enormous troves of information. Harnessing this potential can open vistas of insight and action. Nonetheless, without well-executed digital products and services, those vistas are clouded by the sheer scale of the dataverse.
Helping to guide this emerging evolutionary step in data science, economics, and human understanding is the data science product manager.
Data science product management is the locus of data science and product development teams. It requires a broad-minded leader who speaks many “languages,” including data and computer science, engineering, marketing, and business planning. This comprehensive effort is in service of positive user outcomes from data products.
Let’s briefly explore what a data science product manager does, how the position came to be, and how you can become one.
From Soap to Algorithms: The Evolution of Product Management
To understand the position, we first must review traditional product management. The work is born from the days when packaged consumer goods companies assigned “brand managers” to oversee specific brands.
Brand managers were responsible for a product’s lifecycle and accountable to all stakeholders. With their intimate knowledge of every aspect of a product, particularly its users, brand managers could iterate, track sales, and improve the product.
As the product management field expanded into the digital age, software companies used product managers to guide software development. Today, product managers lead talented multidisciplinary teams creating groundbreaking products many of us use every day.
What Is a Data Product?
An exact definition of a data product is difficult to find, writes Barr Moses on Medium. Nonetheless, Moses identifies essential benefits of data products:
- They increase data accessibility and democratization
- Faster ROI on data
- More precise and customized insights from data
Furthermore, Moses suggests key characteristics found in well-executed data products:
- Reliability and observability: “Data product managers need solutions to identify and solve data product performance issues in near real-time,” says Moses.
- Scalability: A data product must seamlessly scale as customer needs grow.
- Extensibility: Data versatility is crucial. Products must be able to integrate into APIs and be easily digestible for customer needs and preferences.
Usability: Any product, be it a bar of soap or a sophisticated AI-driven data product, must provide an excellent user experience.
Security and compliance: Data leaks and breaches are the bain of our modern existence.
- Release discipline and roadmap: A solid strategy for iteration and improvement is critical for any modern product development project.
Skills and Responsibilities of a Data Science Product Manager
The ability to lead and integrate cross-functional teams is a core skill of any product manager. We’ve often used the analogy of an orchestra conductor to describe how a product manager operates. Product managers are the maestros of diverse teams, shepherding each product element from concept to market.
Other essential skills and responsibilities of a data product manager include:
- Data proficiency: A data PM must be able to leverage programming and data science skills. Most are proficient in SQL and Python and may also have advanced machine learning and AI skills. Data product managers are responsible for overseeing the data product lifecycle and are competent in managing and analyzing large datasets.
- Understand data science industry trends: Staying abreast of leading trends allows data product managers to identify opportunities to solve customer needs with advanced analytics, AI, and machine learning.
- Manage expectations: Data and technology applications vary depending on customer requirements. A data PM manages expectations to guide their customers to the right solution.
- Develop a clear product vision: Data science product managers combine their data proficiency, knowledge of industry trends, and cross-functional leadership skills to create a compelling product vision.
- Set the roadmap: A critical task of any product manager is setting the product roadmap defining launch, user testing, and long-term product strategy
- Lead teams and shepherd processes: A data product comes to fruition through the collective effort of diverse groups across various engineering and data disciplines. A data PM prioritizes the work, bridges communication gaps between teams, and shepherds the project to successful completion.
As of this writing, Glassdoor lists the median annual salary for a data science product manager at $133,003. Commensurate with the growth and multisectoral integration of data science and AI, demand for data product managers will continue to grow in the coming decades.
Learn Data Science Product Management at Merrimack College
Merrimack College allows professionals to specialize in the exciting data science product management field. Two flexible online programs provide fundamental and advanced concepts built around an industry-aligned, experiential learning format.
Students develop foundational data science and analytics skills to support effective product decision-making, leadership, and stakeholder communications.
Master’s in Product Management
The Master’s in Product Management degree program allows professionals to leverage their data skills and experience into the exciting and challenging field of data science product management.
Led by industry experts, the 33-credit hour program covers the broad scope of product management. Courses include:
- New product development and the principles of product realization
- Developing product specifications
- Marketing and business analytics
- Negotiation and conflict resolution
- Organizational leadership and decision-making
- Foundations of data management, statistical analysis, and data visualization
- Data governance, law, and ethics
In addition, students choose between three concentration electives, including:
- Life Sciences
- Complex Technology
Product Management Foundations Graduate Certificates
Data professionals can also parlay their expertise into product management with one of Merrimack’s Product Management Graduate Certificates. These programs provide the relevant PM skills industries seek in a concentrated format focusing on life sciences, software/web/mobile, or complex technologies. Credits are fully transferable to graduate programs.
Student resources include Merrimack’s Career Resources, including the Graduate Cooperative Education/Internship program, Student Immersion program, and Career and Professional Development resources.
We stand at the cusp of a new data revolution–the Age of Insights. Help lead the way into this new era as a data science product manager.