Data Science vs Data Analytics in Libraries

Data Science and Data Analytics - these two terms are heard at every library conference these days. But do you know what they really mean in the library context? And which one is more important for you as a librarian?

The Data Revolution in Libraries

Today's libraries are no longer just repositories of books and journals. They have become data hubs:

• Issue/return records - How many books were issued, which are most popular
• E-resource usage - Which e-journals are being read the most
• Website analytics - What users are searching for, what they're clicking on
• Footfall data - When the library is most crowded
• Research data - Theses, datasets, repositories

The question isn't whether data exists. The question is how this data is being used.

Data Analytics in Libraries: "What Happened?"

Data Analytics focuses on: "Understanding what has happened"

Simple definition:
Data Analytics means analyzing existing library data to extract insights so that better decisions can be made.

Real Library Examples:

• How many books were issued this month?
• Which subject books are being read the most?
• What are the library's peak hours?
• Which e-resources are rarely being used?

Tools: Excel, SQL, Power BI, Tableau, Basic Python
Output: Reports, Dashboards, Charts, Evidence-based decisions

Data Analytics is the entry point to data-driven culture for libraries.

Data Science in Libraries: "What Will Happen?"

Data Science goes one step further.
"Predicting what will happen next"

Simple definition:
Data Science uses advanced statistics, machine learning and computational techniques to create predictions, automation and intelligent systems.

Real Library Examples:

• Which books will be in demand next semester?
• Which students are at high risk of late returns?
• Prediction of research trends
• "You may also like" recommendations (for books/articles)

Tools: Python/R, Machine Learning, NLP, Big Data platforms
Output: Predictive models, Recommendation systems, AI-enabled tools

Not every library needs a full-scale data science lab, but in research-intensive libraries (IITs, universities) its scope is growing rapidly.

Data Analytics vs Data Science: Side-by-Side Comparison

Aspect	Data Analytics	Data Science
Focus	Past & Present	Future
Question	What happened? Why did it happen?	What will happen? How to improve?
Complexity	Medium	High
Coding	Limited	Extensive
Output	Reports & Dashboards	Models & Predictions
Library Use	Planning & Evaluation	AI-based Services
Example	Monthly circulation report	Book demand prediction model

Which is More Important for Librarians?

The truth is:
In libraries, Data Science and Data Analytics don't compete - they complement each other.

Reality check:

• Most librarians → Need Data Analytics + Data Literacy
• Special roles → Need Data Science skills

Typical Library Roles:

• Data Librarian → Policy, RDM, metadata, ethics
• Library Data Analyst → Usage analytics, dashboards
• Data Technologist → Text mining, APIs, automation

Data Science in libraries is a "team sport," where every librarian doesn't need to become an ML expert.

Libraries' Unique Strengths

Libraries bring unique values to the data world that aren't found in other organizations:

Privacy & Ethics

Libraries have a long-standing commitment to:

• User confidentiality
• Intellectual freedom
• Responsible data stewardship
• Transparent policies

Organizational Expertise

• Metadata creation and management
• Information organization systems
• Long-term preservation strategies
• Quality control processes

Trusted Community Position

• Neutral, non-commercial space
• Focus on public good over profit
• Established community relationships
• Commitment to equitable access

Future of Libraries: Data-Savvy, Not Data-Obsessed

The important thing is:
Libraries' goal isn't just to handle Big Data, but to make data meaningful, ethical and usable.

Whether it's Data Analytics or Data Science,
The end goal will always be:

• Better services
• Better research support
• Better informed society

Final Thoughts

Data Analytics tells libraries "what is happening,"
and Data Science tells libraries "what could happen."

Only together can they create 21st century smart, inclusive and trusted libraries.

One-Line Takeaway:
In the library world, Data Analytics helps us understand our past, while Data Science helps us shape the future—together transforming libraries into data-empowered knowledge institutions.