Less than half (49%) of business leaders say they can reliably generate timely insights.
Build a self-service analytics culture
Empower teams to explore reliable data quickly, safely and independently for faster insights and informed decisions.
Improve reporting efficiency and business operations
Streamline and automate data management processes to support scalable, timely and trusted enterprise reporting.
Accelerate AI & ML with analytics-ready data
Leverage AI-ready, high-quality and well-governed data for training models to generate deep, actionable insights.
Transform raw data into actionable insights with AI-powered data management
About Analytics & Business Intelligence
Business intelligence (BI) refers to the technologies, strategies and practices used to collect, analyze and present business data. It allows organizations to make informed decisions by transforming raw data into actionable insights, improving efficiency and driving growth.
Key characteristics of BI:
- Data integration from multiple sources
- Real-time and historical data analysis
- Interactive dashboards and visualizations
- Automated reporting and alerts
- User-friendly tools for non-technical users
Common use cases of BI:
- Sales and marketing performance tracking
- Customer behavior analysis
- Financial reporting and forecasting
- Supply chain and inventory management
- Identifying market trends and opportunities
Data analytics is the process of examining raw data to uncover patterns, trends and insights. It involves cleaning, transforming and modeling data to support decision-making, improve business outcomes and predict future scenarios through statistical and computational techniques.
Types of data analytics:
- Descriptive analytics: Analyzes past data to understand what happened (e.g., sales reports).
- Predictive analytics: Uses models to forecast future outcomes (e.g., customer churn prediction).
- Prescriptive analytics: Suggests actions based on data-driven recommendations (e.g., optimizing supply chain decisions).
How data analytics differs from business intelligence (BI):
- Data analytics focuses on exploring data to explain why things happen and predict future events.
- Business Intelligence primarily summarizes historical data to provide reports and dashboards for monitoring performance.
- Analytics often involves advanced statistical methods and machine learning, while BI emphasizes data visualization and user-friendly reporting.
- BI is more about descriptive insights; analytics includes predictive and prescriptive capabilities.
Business intelligence (BI) organizes and visualizes historical data for descriptive insights, while data analytics uses statistical models to explain causes and predict trends. Together, analytics and BI tools form enterprise analytics solutions that help drive data-driven decisions and competitive advantage.
Modern trends
AI, real-time processing and cloud-native platforms are transforming analytics and BI tools by automating insights, enabling instant responses, and offering scalable, flexible solutions to meet emerging use cases.
Why data integration matters
Analytics and BI require unified and trusted data to enable accurate insights and faster decision-making. Data integration helps reduce data silos and improve data and AI readiness, boosting operational efficiency for better business outcomes.
SUCCESS STORIES
Why analytics leaders choose Informatica
90% reduction
in data wrangling time
Paycor partners with Informatica and Snowflake to generate reliable data-driven insights, reducing data wrangling times by 90%.
$4M
in retained revenue
La Trobe, alongside Informatica and Microsoft, democratizes analytics and BI to identify at-risk students and achieve over $4M in retained revenue.
Democratized
real-time data access
Yamaha unifies its data and analytics into one comprehensive, AI-powered platform, streamlining operations and improving customer experiences.
-
90% reduction
in data wrangling time
Paycor partners with Informatica and Snowflake to generate reliable data-driven insights, reducing data wrangling times by 90%. -
$4M
in retained revenue
La Trobe, alongside Informatica and Microsoft, democratizes analytics and BI to identify at-risk students and achieve over $4M in retained revenue. -
Democratized
real-time data access
Yamaha unifies its data and analytics into one comprehensive, AI-powered platform, streamlining operations and improving customer experiences.
Solve your analytics and BI challenges
Enterprises have access to vast amounts of data from diverse and siloed sources, making reliable data management the key to business insights.
Help ensure data accuracy, completeness and consistency
Reduce the complexity of using different data formats and latencies
Accommodate increasing volumes, velocities and varieties of data
Monitor, control and optimize cost
Improve data accessibility while maintaining privacy and security
How IDMC can help
The Intelligent Data Management Cloud™ supports advanced analytics with an easy to use, efficient and cost-effective data management solution.
Comprehensive data integration
Intelligent, unified data cataloging & lineage
Enterprise-scale data quality and profiling
AI-powered multidomain master data management
Reliable data privacy and protection controls
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FAQs for Analytics and Business Intelligence
Analytics focuses on exploring data to uncover patterns, predict outcomes and provide deeper insights using statistical and advanced techniques. Business intelligence primarily summarizes historical data through reports and dashboards to monitor performance. While BI answers "what happened," analytics explains "why" and "what’s next," making both essential for comprehensive data-driven decision-making.
Ensuring data accuracy for analytics and BI involves validating data sources, cleaning and transforming data to remove errors and maintaining consistent data standards. Regular audits, automated data quality checks and using reliable data integration tools help prevent inaccuracies. Accurate data helps ensure trustworthy insights, enabling better decision-making and effective business strategies.
You can integrate data from multiple sources for analytics by collecting data from databases, APIs and cloud services. Use ETL/ELT tools to clean, transform and unify data into a central repository like a data warehouse or data lake. Help ensure consistent data mapping and apply data governance for accuracy and security. This process enables comprehensive, reliable analytics across the organization.
To scale analytics for growing data volumes, leverage cloud-based platforms that offer flexible storage and computing power. Implement data partitioning, indexing and caching to optimize performance. Automate data pipelines and monitor system resources to ensure scalability and maintain fast, reliable analytics as data grows.
Maintain data privacy and security in analytics by implementing strong access controls, data encryption and anonymization techniques. Regularly update security protocols and conduct audits to detect vulnerabilities. Ensure compliance with regulations like GDPR and HIPAA. Use secure data storage and limit data access to authorized users only, protecting sensitive information throughout the analytics process.
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