Meta (formerly known as Facebook) is a technology company that specializes in social media, online advertising, virtual reality, and more. Data science plays a critical role across Meta’s products and business operations. Here are some of the key ways that Meta leverages data science:
Understanding User Behavior
One of the most important applications of data science at Meta is gaining insights into user behavior on its platforms like Facebook, Instagram, and WhatsApp. By analyzing user data and activity patterns, Meta can understand how people interact with content, connect with each other, and spend time on its apps. This enables Meta to improve user experiences, develop engaging new features, and optimize its advertising efforts.
Some specific ways Meta may analyze user behavior include:
- Studying how users connect and interact with friends, family, groups, and pages on Facebook
- Analyzing usage patterns on Instagram to see how people discover and consume content
- Understanding messaging and calling activity on WhatsApp to improve the communication experience
- Developing machine learning models to predict which content users are most likely to engage with
These insights help Meta create more personalized, relevant experiences for users across its family of apps.
Ad Targeting and Delivery
A core part of Meta’s business is displaying ads to users on its platforms. Data science powers Meta’s abilities to target and deliver effective ads. By analyzing user data including demographics, interests, behaviors, and connections, Meta can understand its audience segments and determine which users are most likely to engage with certain types of ads.
Advanced machine learning algorithms match ads to users based on prediction models. Data science also enables more advanced ad formats like sequential messaging ads optimized across platforms. Meta analyzes vast volumes of user data and ad performance data to continuously refine and improve its ad targeting over time. This enables advertisers to reach relevant audiences and allows Meta to deliver ads users find valuable.
Content Ranking and Recommendations
Meta applies data science to optimize the content it shows users across its apps. This includes ranking and recommendation systems that surface the most relevant and engaging posts, photos, videos, and other content for each user.
By analyzing user interests, interactions, and content preferences, Meta can build machine learning models to predict the content that is most likely to be enjoyed by a given user. These ranking and recommendation systems are customized to each user and are critical to keeping users engaged on Meta’s platforms.
Data science also helps Meta automatically identify objectionable or dangerous content and stop its spread while surfacing more authoritative information. This is crucial to maintaining the integrity of Meta’s platforms.
Fraud and Safety Protection
To protect its users, advertisers, and platforms, Meta utilizes data science capabilities to detect and prevent fraud and safety issues. This includes identifying fake or suspicious accounts, blocking spam and scams, detecting unauthorized data access, stopping abusive or dangerous behavior, and more.
By examining patterns in account creation, user behavior, messaging activity, login attempts, ad clicks, and other signals, Meta can build models to recognize signs of fraud, misuse, or policy violations. These models enable Meta to quickly flag, investigate, and remove any violating or dangerous accounts or activities across its apps.
Protecting user safety and security is a top priority for Meta, and data science delivers essential tools to support this at massive scale across global platforms.
Product Development
Throughout the process of developing new products and features, data science informs key decisions at Meta. By instrumenting new product surfaces for analytics, conducting robust research and testing, and analyzing feedback data, Meta gains insights that shape product design and iteration. This includes new apps and ventures like Meta Quest virtual reality.
Data science also enables “simulations” where Meta can model the potential impact of new products and features prior to launch. This helps accelerate innovation while mitigating risk. Overall, taking a data-driven approach allows Meta to build the most useful and enjoyable products possible for its global community.
Business Operations and Infrastructure
Behind the scenes, data science powers critical aspects of Meta’s business operations and infrastructure. This includes forecasting traffic and platform demand to efficiently plan capacity, allocating computing resources, building reliable infrastructure, optimizing workflows, and more.
Data science also streamlines Meta’s operations in areas like sales, finance, HR, legal, and customer support by identifying insights and automating processes where possible. Across all teams, data science enables more agility, capability, and efficiency as Meta continues scaling globally.
How Data Science is Organized at Meta
To fully leverage data science, Meta has assembled a world-class team of data scientists and engineers:
- Meta has thousands of employees focused on data science research, engineering, and product analytics
- Hundreds of data scientists and engineers make up the Facebook Core Data Science team
- Data scientists are embedded across different product groups (ex: News Feed, Instagram, Messenger, etc)
- Specialized data teams focus on areas like integrity, safety, AI, infrastructure, and more
- Leading academic experts in statistics, machine learning, and computer science work at Meta
This concentrated talent and experience allows Meta to innovate quickly in a fast-moving industry. The various data teams apply scientific rigor to understanding patterns in massive datasets and translating insights into improved products.
Meta prioritizes collaborative, multidisciplinary work between data scientists, engineers, product managers, researchers, and others. This ensures data science directly creates value for users, partners, and all aspects of the business.
Data Science Technology Stack and Tools
To empower its data teams, Meta has built specialized data infrastructure, analytics tools, and ML frameworks tailored to its scale and evolving needs. Key elements of Meta’s data science technology stack include:
- FBLearner Flow – Meta’s proprietary machine learning platform for managing the full lifecycle of models from experimentation to production.
- TensorFlow – An open source library for numerical computation and machine learning.
- PyTorch – An open source ML library focused on deep neural networks.
- Spark – Cluster computing framework for big data workloads.
- Hive – Data warehouse system for querying and analyzing structured data at scale.
- Presto – Distributed SQL query engine designed for big data analytics.
- Atlas – Meta’s observational data storage system.
- DataDog – Service for monitoring, alerting, and troubleshooting Meta’s infrastructure.
On the experimentation side, Meta data teams utilize tools like Notebooks and Sigma to iterate quickly. To share findings and dashboards, data teams use DataHub and Plotly.
This combination of open source technologies and Meta’s own specialized tooling gives data scientists the infrastructure to derive insights from the over a billion people who use Meta’s platforms. The tools are optimized to scale to immense datasets while enabling rapid experimentation and learning.
Data Science Culture and Values
In addition to technology, Meta has developed a unique culture that empowers its data teams to have tremendous impact. Some defining aspects of this culture include:
- Openness and transparency – Data findings are shared widely to align all teams and openly discussed to reach the best outcomes.
- Rapid iteration – Small, agile teams iterate and learn quickly by continually testing ideas.
- Evidence-driven decisions – Data and research inform discussions and decisions at all levels.
- Quick implementation – Effective ideas are rapidly implemented, scaled, and measured.
- Risk-taking – Teams are encouraged to pursue bold, innovative ideas and learn from mistakes.
This experimental, analytical culture allows Meta’s data science teams to consistently drive measurable improvements across its products and business. Even as the company has grown to global scale, it has maintained a culture of embracing ideas backed by data – no matter where they come from.
Impactful Applications of Data Science
Across all of Meta’s apps and operations, data science has enabled breakthrough improvements over the years. Here are some impactful examples:
Ranking Algorithm Improvements
By developing more predictive models for its News Feed ranking algorithm, Meta reduced clickbait articles by 80% and increased exposure for friends and family content by 400%. This dramatically improved the relevance and enjoyment people get from their News Feeds.
Proactive Detection of Fake Accounts
Leveraging machine learning on account patterns, Meta now proactively detects 99.7% of fake accounts before any user reports them. This significantly reduced harmful misinformation and inauthentic behavior.
Suicide and Self-Injury Prevention
Analyzing signals around self-harm content led Meta to improve its violation detection rates from 16% to 97%, allowing it to better connect people with mental health resources.
Election Integrity Efforts
Applying data science to understand platform vulnerabilities enabled Meta to remove over 1 billion fake accounts in the months prior to US elections. This limited spread of misinformation and foreign interference.
Increased Advertising Relevance
By improving its ability to match ads to people’s interests, Meta drove a 40% increase in ad relevance scores, boosting value for advertisers and users.
Reduced Harmful Content Viewing
Algorithmic improvements based on data research led to a 50% drop in time spent by users viewing harmful content on Facebook. This created a safer, more positive experience.
Improved Recommendation Accuracy
Across its apps, enhanced data science recommendations have increased click-through rates by 40%, getting users more of the content they find meaningful and enjoyable.
Data Science at Meta: Looking Ahead
As Meta continues to innovate, evolve, and grow, data science will only become more crucial to its success. Key areas Meta is focusing its data science efforts on moving forward include:
- Developing more natural, intuitive AI interactions powered by transformational advances in self-supervised learning, recommendation systems, and reinforcement learning.
- Building the metaverse by creating immersive, interconnected 3D spaces and photo-realistic avatars enabled by computer vision, simulation, and sensing technologies.
- Unlocking new experiences in augmented and virtual reality through breakthroughs in scene and speech understanding.
- Pushing the limits of privacy-preserving machine learning to unlock insights from encrypted data.
- Leveraging NLP and multimodal learning to tackle harmful content across languages and surfaces.
- Designing sustainable AI systems that minimize data usage, energy consumption, and environmental impact.
At the core of these efforts is Meta’s long-term commitment to applying data science for the benefit of people, partners, and society. As one of the pioneers of big data analytics, Meta will continue exploring new applications of data science, statistics, and AI to create connection and opportunity for the billions who use its platforms worldwide.
Conclusion
Data science powers Meta’s ability to understand its global community, build engaging products, and operate effectively at unprecedented scale. From ranking algorithms to integrity protections to business analytics, data science infuses all aspects of Meta’s apps and operations.
By leveraging a robust infrastructure, specialized tools, massive datasets, leading experts, and an innovative culture, Meta has established itself as a trailblazer in data-driven technology advancement. Even with billions of users, Meta manages to extract meaningful patterns, generate valuable insights, and enable more fulfilling human experiences through the continued evolution and application of data science across its family of products.