Difference Between Informatics VS Data Science

Informatics and data science are distinct fields. Informatics focuses on building systems to collect, store, and manage data. Data science use tools to extract insights through advanced analytics. Informatics provides the infrastructure, data science analyzes data to solve real-world problems.

This guide provides an in-depth comparison of informatics and data science across various aspects including origins, focus, techniques, processes and applications. It also covers how the two domains converge in the world of data-driven decision making.

What is Informatics?

Informatics is about science of processing data for storage, retrieval, and transfer. It focuses on developing systems and infrastructure to manage and analyze information effectively.

Some key aspects of informatics include:

• Designing databases, data models, schemas to structure and organize data
• Developing algorithms, methods and processes for efficient data storage and management
• Building information systems like clinical systems, decision support systems, recommender systems
• Focus on databases, data warehousing, data mining, information architecture
• Applying techniques like data modelling, ontologies, taxonomies to classify and structure data
• Utilizing computational techniques to process, integrate and analyze complex information
• Focus on systems engineering – integrating hardware, software, infra for information flow

Informatics provides the systems foundation to handle data at scale across domains like health, business, education etc.

What is Data Science?

Data science is an interdisciplinary approach to extracting actionable insights from data using scientific methods, processes, algorithms and systems.

Some key aspects of data science include:

• Application of statistics, machine learning, predictive analytics to solve real-world problems
• Leveraging programming languages like Python, R, Scala for data analysis
• Focus on the end-to-end data science lifecycle – data collection, cleaning, modeling, visualization
• Using techniques like classification, clustering, neural networks for predictive modeling
• Applying scientific rigour through reproducible analysis, hypothesis testing, controlled experiments
• Aligning data analysis to practical business contexts and objectives
• Communication of data insights to stakeholders using data visualization and storytelling
• Collaborative discipline spanning across business, technology and analytics domains

Data science provides the comprehensive framework to derive value from data through the scientific method.

Key Differences Between Informatics and Data Science

Informatics builds information systems, data science performs statistical analysis and modeling to create business value. Here is the comparison table of informatics vs data science.

Basis	Informatics	Data Science
Focus	Information systems, data management	Deriving insights and value from data
Scope	Information flows and infrastructure	Statistical analysis and modeling
Techniques	Data warehousing, ontologies, taxonomies	Machine learning, data mining, predictive modeling
Processes	Database design, data modeling, software systems development	CRISP-DM, OSEMN – end-to-end analytics
Programming	Database programming – SQL, PL/SQL	Python, R, Scala – libraries like Pandas, TensorFlow
Infrastructure	Hardware – storage, computing, network	Cloud platforms – AWS, GCP, Azure
Analytics Capability	Descriptive and diagnostic analytics	Predictive and prescriptive analytics
Major Applications	Library information systems, clinical systems, recommender systems	Retail analytics, fraud detection, ad targeting, computer vision
Academic Origins	Information systems, computer science	Statistics, mathematics, business disciplines

Areas of Convergence

Informatics and data science share some common knowledge domains:

Information Systems

Both fields study the flow of information through systems and processes to enable insights.

Databases

Relational databases, data modeling, schema design, and SQL provide shared foundations in working with structured data.

Data Warehousing

Collecting, integrating and structuring organizational data in warehouses, data lakes is a common need.

Data Mining

Techniques like clustering, classification, association rules enable extracting patterns from large data sets.

Statistical Foundations

Basic statistical thinking in terms of distributions, hypothesis testing, error measurement provides the groundwork for both.

Algorithms

Exposure to common algorithms build capacity for working with data systematically.

Data Visualization

Visualizing insights from data analysis aids both domains though at different levels of complexity.

Cloud Platforms

Shared data and analytics services on cloud platforms like AWS, GCP and Azure.

Typical Process Flows

The workflows for informatics and data science projects also highlight their convergence and divergence:

Informatics Process Flow

• Identify business information needs and analytics requirements
• Conceptualize system architecture and design information flows
• Create data models identifying entities, attributes, relationships
• Set up databases, data warehouses, lakes to store data
• Develop schemas, taxonomies, ontologies to structure data
• Build information systems – dashboards, reports, input forms and workflows
• Develop capabilities for searching, querying, updating and maintaining information
• Manage information security, access controls and compliance

Data Science Process Flow

• Frame business problem and identify relevant data sources
• Collect and integrate structured and unstructured data
• Explore, cleanse and preprocess data
• Perform statistical analysis to understand relationships
• Engineer features from data for modeling
• Train machine learning models using algorithms like SVM, neural nets etc.
• Rigorously evaluate models for accuracy and performance
• Interpret results and extract business insights
• Deploy models to applications and business processes
• Monitor and continuously improve model performance

While informatics enables information flows, data science performs advanced analytics.

Career Transitions

The overlaps enable some movement across the domains:

Informatician to Data Scientist

Here, developing statistical and ML modeling skills, coding ability in Python/R and business acumen are key.

Data Scientist to Informatician

Data scientists can become informaticians by gaining systems analysis and design skills along with program management capability.

Hybrid Roles

Crossover roles like clinical data analysts, geospatial data engineers, data architects require blending both skillsets.

Comparing Evolution

Data science has evolved by building upon the foundations provided by informatics:

Information Systems -> Data Science Systems

Informatics provides the groundwork of systems thinking applied to data which data science uses by expanding the types of systems – pipelines, machine learning systems etc.

Data Warehouses -> Big Data

Informatics is focused on warehouses of structured data whereas data science incorporates new unstructured data sources and real-time streams.

Statistical Models -> Machine Learning

The transition from standard statistical models to sophisticated ML algorithms has enabled next-generation analytics capabilities.

DB Programming -> Data Science Toolkits

Informatics relies heavily on SQL and database programming, on the other hand, data science utilizes Python, R and specialized libraries like TensorFlow.

Information Retrieval -> Predictive Modelling

Data science expands capabilities from searching, classifying and visualizing information to predictive and prescriptive modelling.

Domain-focused -> Business Alignment

Informatics domains are academically driven, data science ensures tight alignment with business outcomes and impact.

Future Outlook

As organizations become data-driven, convergence of informatics and data science can be expected across these key dimensions:

• Holistic information architectures incorporating analytical data and systems
• Platform convergence – analytics capabilities assimilating into mainstream business systems
• Democratization of ML through low code and automation
• Expansion of real-time data streaming and event processing
• Cross-skilling and elevation of data-focused roles into leadership
• Shift from domain-specific to integrated enterprise information competencies

Key Takeaways

• Informatics focuses on information systems while data science performs advanced analytics.
• While they share foundations like databases, warehouses and visualization, they diverge in processes, programming, infrastructure and techniques.
• Data science has evolved upon informatics systems by expanding data scope, techniques, tools and business alignment.
• Increasing automation and real-time data will drive convergence, but informatics retains uniqueness at the core.
• Organizations will benefit from bringing the groups together to create integrated data and analytical capabilities.

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