Data Analytics vs Business Intelligence

What is Analytics?

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data which is used to drive decision making.

Goals of Analytics

• Data undestanding: Understanding the structure and characteristics of your data and find patterns, trends and correlations.
• Predictive Modeling: Analytics can be used to identify future trends or outcomes based on historical data.
• Optimization: Analytics can help in optimizing processes and operations by identifying areas for improvement.

What is Business Intelligence (BI)?

Business Intelligence is the use of technology and analytics to collect, analyze, and interpret business data to provide insights into operational performance, customer behavior, market trends, and other key metrics. BI involves using data to support strategic planning, decision-making, and operational improvement efforts.

Goals of Business Intelligence

BI should produce a simple overview of your business, boost efficiency, and automate repetitive tasks across your organization. It should have:

1. Roll-up capability - Visualization over the most important KPIs (aggregations) - like a cockpit in an airplane which gives you the important information at one glance.
2. Drill-down possibilities - from the above high-level overview drill down the very details to figure out why something is not performing as planned. Slice-and-dice or pivot your data from different angles.
3. Single source of truth - instead of multiple spreadsheets or other tools with different numbers, the process is automated and done for all unified. Employees can talk about the business problem instead of the various numbers everyone has. Reporting, budgeting, and forecasting are automatically updated and consistent, accurate, and in timely manner.
4. Empower users: With the so-called self-service BI, every user can analyze their data instead of only BI or IT persons.

Key Differences:

1. Focus:
   • Data Analytics: Focuses on the technical analysis of data to extract insights and knowledge for decision making.
   • Business Intelligence: Focuses on the use of data to inform business decisions and drive strategic initiatives.
2. Scope:
   • Data Analytics: Can involve large-scale datasets and complex algorithms, often requiring specialized skills and expertise.
   • Business Intelligence: Typically involves integration of datasets across multiple systems and requires a broader range of tools and technologies. Multiple stakeholders involved.
3. Technologies:
   • Data Analytics: Uses tools like SQL, R, Python libraries (like Pandas, NumPy), advanced spreadsheets (Excel), or dedicated analytics platforms (e.g., Tableau, Power BI).
   • Business Intellignce: Uses BI tools like Microsoft Power BI, Looker, Tableau, or commercial BI platforms to design dashboards, reports, and interactive analytics solutions.
4. Process:
   • Data Analytics: Often involves a more analytical approach with a focus on data cleaning, modeling, and interpretation of results.
   • Business Intellignce: Involves collecting data from various sources within the business (internal systems, external databases, web logs) and presenting it in formats suitable for decision-makers. It may also involve creating reports and metrics to guide business decisions.
5. Output:
   • Data Analytics: Outputs primarily in the form of statistical analyses, models, or reports that are meant to be shared with stakeholders.
   • Business Intelligence: Outputs typically include interactive dashboards, reports, and visualizations designed for users who can explore data at will, without requiring technical expertise.