There’s often a lot of confusion between the role of a data scientist and a decision scientist. But the two fields carry out entirely different tasks and work to fulfill different organizational goals. We had both at Instagram and they fulfilled different needs, so I thought I’d explain the main differences I see from my personal experience in the decision science role, working closely with my data science colleagues.
Data Science vs. Decision Science
Data Science: Focused on finding insights and patterns through statistical analysis. This work is often exploratory and data-driven, without being tied to a specific decision.
Decision Science: Seeks insights that directly support business decisions. This process begins with a clear question or problem, like the most optimal way to spend a yearly budget. A decision scientist analysis is tailored to answer that specific question.
What is Data Science?
Data science is centered on using data to improve and develop products through statistical models. Data scientists aim to understand, interpret and analyze data with the goal of building better systems and solutions. Their focus is often on data quality and statistical rigor.
For data scientists, analysis comes first, business challenges are considered afterwards. They think in terms of algorithms, machine learning, data patterns, processing, and experimental statistics. Their work often involves uncovering casual relationships and conducting deep statistical analysis to generate insights related to their product or domain.
Data scientists are deeply interested in data quality because it directly impacts the reliability of their findings. Their north star is to use high-quality data and advanced statistical methods to support product development.
What is Decision Science?
Decision science is the practice of using data to guide and improve business decisions. Decision scientists frame their analysis around a specific question or problem presented by stake holders, and then tailor their methods to support decision-making rather than open-ended exploration.
Unlike data scientists, decision scientists put the business problem first and allow that context to drive the analysis. Individuals working in this field often go by titles like “analyst” or “applied analytics professional,” and focus on making insights usable and actionable.
Decision scientists must take a 360-degree view of the business challenges. This involves understanding various analysis methods, visualization techniques and behavioral insights that will help stakeholders act. They need to integrate multiple data sources, each chosen for their ability to address the specific decision at hand.
Importantly, decision scientists must be comfortable operating in imperfect conditions, as not every business scenario allows for a clean experiment. For example, there is almost no clean way to test for viral or celebrity marketing. They need to balance statistical integrity with practical business judgement and know when to act on correlation and when to demand more testing.
Their ultimate goal is to use data and statistical insight to support decision-making around strategy, budgeting and marketing within the broader business context.
Data Science vs. Decisions Science: Key Differences
Core Objectives
At Instagram, the data scientist’s core objective was to support product development through deep, rigorous analysis. Each data scientist focused on a specific product or features, ensuring that user behavior within that feature was well-understood and accurately measured.
In contrast, the decision scientist supported smart business decisions, particularly in areas like marketing budget, strategy and allocation. My team supported marketing leadership in understanding user behavior across products to inform campaign properties and investments.
Methods
Data scientists at Instagram ensured accurate data logging within their areas. They conducted statistical analyses on usage trends, often relying on complex visualization to communicate nuanced finds. They were responsible for updating logging and measurement protocols whenever a product changes or a new feature is launched.
My work as a decision scientist involved building on the data prepared by my data science colleagues. I used their tables and logs as inputs for broader cross-product analysis. Our team integrated multiple data sources, emphasizing well built data visuals built for executive consumption. The insights gained were rounded in business content and were aimed at answering specific questions or problems.
Results
The data scientist’s results empowered the product teams to build, test and refine features with a high level of precision. Their analysis led to scalable product improvement and helped maintain high data integrity within their area.
The decision scientist’s results informed real-time business decisions, such as adjusting marketing strategies based on user behavior or demographic trends. Our work helped drive campaign efficiency, budgeting effectiveness and strategic clarity for leadership
Ultimately, both roles were essential to business’ success.
Frequently Asked Questions
What is decision science?
Decision is a multidisciplinary field that uses data analysis, statistical modeling and behavioral insights to improve decision-making processes at an organizational level.
Is decision science math?
Yes, there is math involved in decision science. To solve complex business problems, decision science often uses mathematical models to analyze data and understand the decision-making process.
Are decision science and data science the same?
No, decision science and data science are not the same. But they do have some overlap:
- Data science is an exploratory field focused on finding insights and patterns through statistical analysis.
- Decision science seeks to support business decisions by focusing on operational problems and using analysis methods, visualization techniques and behavioral insights to find the most optimal solution.
