Business Analytics VS Data Science

Business analytics focuses on gathering, analyzing, and reporting business data to drive strategic decisions, whereas data science uses advanced statistical and machine learning techniques to derive deeper data-driven insights and predictions.

This guide compares business analytics and data science in-depth on different parameters – skills, focus, processes, techniques, and applications. It also covers career transitions between the two fields.

Defining Business Analytics

Business analytics relates to the processes, skills, technologies, and practices used by organizations to measure and analyze business performance to make data-driven decisions. It applies statistical and quantitative analysis to derive actionable insights.

The focus of business analytics is on historical data analysis to optimize and improve business processes, performance, and decision making based on facts.

Some attributes of business analytics include:

Use of statistical techniques like regression analysis, forecasting, simulation to drive business decisions
To design and maintain data warehouses, ETL processes and analytics pipelines
To perform quantitative analysis – sales trends, marketing effectiveness, operational KPIs, financial modelling
To build dashboards, visualizations and reports to communicate insights
Align analytics with business goals and strategy
Focus on descriptive and diagnostic analysis – what happened and why it happened
Put to good use of business intelligence and analytics tools like Tableau, Power BI, Looker, Microsoft Azure Analytics

Defining Data Science

Data science is a broad field focused on extracting actionable insights from data through the application of scientific processes, algorithms, and systems.

The main emphasis is on statistical analysis and modelling to understand relationships, make predictions, optimize decisions, and guide actions.

Some key attributes of data science are:

Application of statistics, machine learning, data mining to discover patterns and make predictions
To build machine learning models for tasks like classification, regression, clustering, anomaly detection
To develop algorithms and statistical models to produce data-driven insights and forecasts
To collect, clean, explore and transform structured and unstructured data
Data engineering – build and maintain big data pipelines
Communicate results through techniques like data visualization and storytelling
Focus on predictive modeling and predictive analytics
Imply data science toolkits like Python, R, SQL, Spark, TensorFlow, etc.

Business Analytics VS Data Science

Both business analytics and data science involve working with data to drive better decisions but they differ fundamentally in various manners. Here is a head-to-head comparison:

Basis of Comparison	Business Analytics	Data Science
Goal	Enable data-driven business decisions through analysis and metrics	Discover hidden patterns and extract predictive insights from data
Focus	Analyzing business performance data	Building statistical and ML models for prediction
Techniques	Statistical analysis, SQL, data visualization	Machine learning, statistical modeling, algorithm development
Data Scope	Internal business data in warehouses or databases	Any data – internal, external, big data, real-time streams
Type of Analysis	Descriptive – what happened and why	Predictive – what will happen next
Time Orientation	Historical data	Future predictions
Business Alignment	Tight alignment with business goals and KPIs	Loose alignment, focus on data techniques
Math’s Intensity	Basic math and statistics	Advanced statistical methods and calculus
Programming	Optional, light coding for ETL and dashboards	Heavy coding required – Python, R, Scala
Domain Expertise	Strong business acumen essential	Some domain knowledge required
Tools	BI tools – Tableau, PowerBI, Qlik	Data science platforms – Python, R, Spark, H20
Mindset	Quantitative and business-focused	Scientific and data-focused
Organizational Scope	Single department or function	Cross-functional, company-wide impact

Business Analytics VS Data Science

Areas of Convergence and Divergence

Business analytics and data science converge and diverge across certain dimensions. Some points are mentioned here.

Converging Areas:

Using basic statistics and SQL for structured data analysis
Use of data visualizations to communicate insights effectively
Implementing dashboards for organizational reporting and metrics
Focus on data-driven decision making
Utilizing cloud data platforms and warehouses like Snowflake, BigQuery, Redshift
Working collaboratively with business teams and stakeholders
Applying processes like CRISP-DM to structure analytics or data science lifecycles

Diverging Areas:

Descriptive vs predictive modelling – what happened vs what will happen
Role of machine learning, AI, and algorithm development
Light statistics vs heavy use of statistical methods
Basic dashboards vs interactive visualizations and storytelling
Business KPIs vs data mining for new insights
ETL vs building scalable data pipelines
Stakeholder alignment vs technical autonomy
Quantitative analysis vs computer science foundations
Surface insights vs deep analytical dives
Business acumen vs data intuition

Data is the common thread but the depth of analysis and techniques applied differ. Business analytics focuses on actionable intelligence for business improvement and data science strives for transformative insights through technical rigor.

Typical Workflows Compared

The workflows and activities involved in typical business analytics and data science projects also showcase key differences between them.

Business Analytics Workflow

Frame business problem in terms of metrics to optimize – revenue, conversions, operational efficiency etc.
Identify relevant structured data sources like transaction systems, ERPs, CRMs
Define KPIs, dimensions and data model for analysis
Extract, transform and load data into data warehouses or lakes using ETL tools
Use SQL and BI tools to analyze aggregated metrics and dimensions
Build reports, dashboards and visualizations highlighting insights
Communicate findings to business teams to drive decisions
Monitor metrics and refine analysis to improve business performance

Data Science Workflow

Frame open-ended problems where data can uncover new insights – customer churn, lifetime value, personalized recommendations etc.
Collect relevant structured and unstructured data from diverse sources
Explore, clean, preprocess and transform data programmatically using Python or R
Perform statistical analysis like correlation analysis, ANOVA, distributions to understand data relationships
Engineer features from data and train machine learning models using libraries like SciKit Learn, TensorFlow etc.
Evaluate models using metrics like precision, recall, AUC, error rate etc.
Deploy models to applications for predictions using platforms like SageMaker, Azure ML
Communicate insights through reports, presentations and visualization
Continuously monitor and retrain models on new data

Career Transitions

The intersecting components in areas like statistical modeling, data analysis, and technical capabilities enable individuals to move their careers from business analytics roles into data science positions or vice versa by building on this overlapping foundation of relevant knowledge and experience. The fluidity between the fields helps professionals to expand their skillsets and find new career opportunities.

Business Analyst to Data Scientist

For business analysts moving to data science, learning programming (Python, R), math and statistics, ML techniques, and unstructured data handling are key. Curiosity to discover insights beyond business metrics is essential.

Data Scientist to Business Analyst

Data scientists transitioning to business analytics need to strengthen SQL skills, learn BI tools like Tableau and PowerBI, brush up business acumen, focus on stakeholder alignment, and orient models to optimize business KPIs.

Hybrid Roles

These are the hybrid roles for the two fields.

Business data scientist
Decision scientist
Analytics translator
Data science product manager
Strategic data analyst
Business intelligence engineer

Future Outlook

Democratization of data science through low code tools will make complex modelling accessible to business analysts.
Data science techniques like AI and ML will become integral to standard business analysis and intelligence capabilities.
Tighter alignment will appear between operational analytics and forward-looking data science modelling.
Specialized data roles will consolidate into versatile analytics and insights teams.
Analytics, data, and decision science capabilities will converge into business-focused Centres of Excellence.
Self-service analytics will enable business users to harness data directly without intermediaries.
Data storytelling and visualization will emerge as core skills bridging the two fields.

Key Takeaways

Business analytics and data science both extract value from data but differ fundamentally in focus, techniques, and applications.
Business analytics enables data-driven decisions through statistical analysis on business data using BI tools. Data science builds advanced models using ML for predictive insights.
Areas like statistics, SQL, data visualization, cloud infrastructure converge, they diverge significantly in techniques, workflows, problem approach and business alignment.
Professionals from either field can transition to the other by developing complementary skills like programming, statistical modelling, ML, or domain expertise.
As analytics becomes pervasive, the two domains will gravitate closer through self-service capabilities.
Organizations will benefit from bringing the two groups together – bringing business analytics with forward-looking data science will accelerate data-driven innovation.

More to read

Business Analytics VS Data Science

Defining Business Analytics

Defining Data Science

Business Analytics VS Data Science

Areas of Convergence and Divergence

Typical Workflows Compared