The Rise of Commercial Artificial Intelligence in BI
Even as global businesses continue to embrace big data and data analytics, the challenge many faces is: how to derive the most value from the big data. The latter refers to very large sets of data that cannot be handled with traditional methods.
With artificial intelligence (AI) & its subset machine learning (ML) becoming mainstream, i.e. moving from the laboratory to the commercial market, one option companies today have to handle their voluminous data is machine learning.
Analytics has moved on from traditional methods to automated solutions for better business intelligence. In fact, it's gone beyond the scope of a standalone human analyst.
ML-based algorithms thrive on big data – both structured & unstructured – so using ML for analytics helps companies maximize their big data potential.
Companies can now use ML to ingest and identify data across marketing & sales to:
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Identify trends
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Get insights into vast reams of data
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Make faster business decisions
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Leveraging Big Data and ML for Business Insights
But how do you leverage the combination of big data and ML to glean business insights?
Machine learning-based big data analytics can decipher the data by unveiling patterns and forecasting trends. ML-based algorithms can classify the data, recognize patterns & interpret the data into business insights. That's how they are of value in business intelligence.
How is ML-Based Business Intelligence (BI) Implemented
First & foremost, a business cannot just add ML to the top layer of its IT stack or BI operations & think the job is done.
ML-based algorithms, useful for gathering, analyzing, and integrating data, must be executed across all aspects of the business operation. Here let's not forget the way ML works.
Data scientists train the "machines" by providing them with continuous datasets to observe & analyze to help them improve over time. They rely on data models that include disciplines like data labeling, segmentation & analytics.
Benefits of Machine Learning in Big Data Analytics
Big data analytics can lead to major cost reduction, saving of time, and a decrease in risk in business decision-making.
Looking for Patterns
The most important benefit of ML in data analytics is the ability to recognize patterns within the data. ML can be very efficient in decoding patterns within large volume data sets, something that's impossible for humans.
In retail, for example, this can be a big help as it helps in understanding consumer patterns. ML-based analytics can help better service customers by throwing up offers in advance, based on their previous buying patterns.
Predicting the Future
At the same time, machine learning in big data helps forecast future trends in the market. For example, using predictive analytics, if a smartphone manufacturer can scientifically analyze the likely demand of a dual-screen phone in the next year, it can then predict future sales, based on which, it can manufacture x amounts of handsets.
Predictive Analytics
ML in data analytics helps companies understand almost to a degree of accuracy, what their customers want. It allows the calculation of the probabilities of various outcomes. This can be useful for cross-selling and upselling products or calculating the chances of the efficacy of a certain treatment for a patient.
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Key Applications of AI in Business Intelligence
Customer Analytics
AI-powered BI solutions excel in customer analytics by:
- Customer Segmentation: Advanced algorithms can segment customers based on behavior, demographics, and value
- Predictive Customer Behavior: ML models can predict customer churn, lifetime value, and purchase patterns
- Personalization: AI enables hyper-personalized marketing and product recommendations
Operational Intelligence
AI enhances operational efficiency through:
- Process Optimization: ML algorithms identify bottlenecks and optimization opportunities
- Resource Allocation: Predictive models help optimize resource distribution
- Quality Control: AI systems can detect anomalies and quality issues in real-time
Financial Analytics
AI transforms financial decision-making with:
- Risk Assessment: ML models provide sophisticated risk analysis and mitigation strategies
- Revenue Optimization: Predictive analytics help optimize pricing and revenue strategies
- Fraud Detection: AI systems can identify fraudulent activities and patterns
Implementation Challenges and Solutions
Data Quality and Integration
One of the primary challenges in implementing AI-based BI is ensuring data quality and seamless integration across systems. Companies must:
- Establish robust data governance policies
- Implement data quality monitoring
- Create unified data platforms
- Ensure real-time data processing capabilities
Change Management
Successfully implementing AI in BI requires significant organizational change:
- Leadership commitment and sponsorship
- Employee training and upskilling
- Clear communication of benefits and expectations
- Gradual rollout with pilot programs
Technology Infrastructure
Building the right technology foundation is crucial:
- Scalable cloud infrastructure
- Advanced analytics platforms
- Secure data storage and processing
- Integration with existing systems
Future Trends in Commercial AI for BI
Advanced Analytics Capabilities
The future of AI in BI includes:
- Natural Language Processing: Conversational analytics interfaces
- Computer Vision: Image and video analytics for business insights
- Edge Computing: Real-time analytics at the data source
- Augmented Analytics: AI-assisted insights and recommendations
Industry-Specific Solutions
AI-powered BI is evolving to address industry-specific needs:
- Healthcare: Patient outcome prediction and treatment optimization
- Retail: Inventory optimization and customer experience enhancement
- Manufacturing: Predictive maintenance and quality control
- Financial Services: Risk management and portfolio optimization
Getting Started with AI-Powered BI
Assessment Phase
- Evaluate Current Capabilities: Assess existing BI infrastructure and data maturity
- Identify Opportunities: Find high-impact use cases for AI implementation
- Resource Planning: Determine required skills, technology, and budget
- Risk Assessment: Identify potential challenges and mitigation strategies
Implementation Strategy
- Start Small: Begin with pilot programs and proof-of-concepts
- Focus on Value: Prioritize initiatives with clear ROI and business impact
- Build Incrementally: Scale successful programs while learning from challenges
- Measure Success: Establish clear KPIs and success metrics
Conclusion
The rise of commercial artificial intelligence in business intelligence represents a fundamental shift in how organizations approach data analysis and decision-making. As AI and ML technologies mature and become more accessible, companies that successfully integrate these capabilities into their BI strategies will gain significant competitive advantages.
The key to success lies in a systematic approach that combines the right technology, processes, and organizational change management. By starting with clear business objectives and gradually building AI capabilities, organizations can unlock the full potential of their data and drive sustainable growth in an increasingly competitive marketplace.
The future belongs to organizations that can effectively leverage AI-powered business intelligence to transform their operations, enhance customer experiences, and achieve their strategic objectives.
