Challenges:
1. Data Variety and Unstructured Nature: Farm Bureau encountered data from various sources, often unstructured, making it challenging to extract meaningful insights.
2. Multi-Source Decision-Making: Businesses typically relied on an average of five data sources to make informed decisions, highlighting the intricacies of their data landscape.
3. Unstructured Data Overload According to IDC, 80% of data is unstructured, posing a significant challenge for traditional data warehouses.
4. AI and ML Technology Adoption: With Gartner predicting a 75% adoption rate of AI and machine learning technologies by 2024, Farm Bureau felt the urgency to modernize its data capabilities.
5. Exponential Data Growth: Analysts forecasted a 5x increase in data volume by 2025, with an estimated 6 billion consumers interacting with data daily, necessitating advanced data processing solutions.
6. Demand for Faster Data Processing: Businesses demanded faster access, processing, and data analysis for innovation, competitive advantage, efficiency, and compliance.
Solutions:
Enkaytech proposed a transformative solution for Farm Bureau by leveraging a cloud data lake house. This innovative approach combined the strengths of a data lake and a data warehouse, providing a comprehensive platform for processing diverse enterprise and streaming data for business analysis and machine learning.
Key Features of the Data Lake House Architecture:
1. Operational Efficiency: Seamlessly use data without replication across the data lake and warehouse.
2. Resource Optimization: Decouple storage and compute resources, consuming only what is needed at any given time.
3. Diverse Data Support: Enhanced multi-model and polyglot architecture supporting various data types.
4. Versatile Use Cases: Support for streaming analytics, business analytics, data science, and machine learning.
Implementation Road Map:
Enkaytech initiated the transformation of Farm Bureau’s Azure Data Lake infrastructure into a platform capable of leveraging AI and analytics. The roadmap included integrating various Azure services to enhance data processing capabilities:
– Azure Event Hubs
– Azure Synapse Analytics
– Azure Data Lake Gen2
– Azure Cognitive Services
– Azure IoT Hub
– SQL Data Warehouse
– Azure Purview
– Azure Stream Analytics
– Azure Machine Learning
– Azure Data Factory
– Microsoft Power BI
– Azure DevOps
– Azure Active Directory
– GitHub
Business Use Cases/Benefits:
- AI Model Reuse: Reusing AI models built on the data lake for multiple applications, enhances operational efficiency.
- Text Extraction from Images: Converting images of forms or driver’s licenses to text for easy accessibility via APIs, benefiting various internal applications.
- Fraud Detection: Utilizing advanced analytics for effective fraud detection within the enterprise.
- Personalized Price Quotes: Analyzing customer characteristics to generate personalized price quotes, enhancing customer engagement.
- Sentiment Analysis for Customer Care: Predicting customer churn/attrition through sentiment analysis of customer care responses.
- APIs for Data Lake: Building APIs to the data lake and replacing Operational Data Stores (ODS) for streamlined data access.
Enkaytech’s cloud data lake house solution implementation empowered Farm Bureau to overcome its data challenges, providing a foundation for enhanced business intelligence, innovation, and competitive advantage. The collaborative effort showcased the potential of advanced data processing technologies in addressing complex business requirements.
