Data analytics strategy
We explore your current infrastructure and challenges to determine which Azure Analytics tools we can use to solve them. The choice of tools varies depending on the project’s task, whether it’s data engineering, migration, custom integrations, or another. With all the information collected, we devise your unique data analytics implementation strategy.
Data engineering
Building a data warehouse can help you overcome the usual complexities of handling unstructured information, such as health records, emails, or log files. By pushing the limits of Azure Analytics, we provide a structured environment that allows you to aggregate data into a centralized location.
Data discovery and orchestration
Leverage dedicated Azure Analytics discovery tools for scanning, profiling, and classifying data distributed across your organization to make it easy for users to understand and navigate. You can also enhance your data orchestration capabilities by automating data coordination. The automation will ensure seamless data workflows from processing to analytics.
Data migration and integration
With LeverX's dedicated migration and integration services, you can make a crucial move towards a more agile and secure platform. We ensure a seamless transition to Azure Analytics and bring you numerous strategic advantages with this cloud solution, such as better data and service security, optimized deployment costs, improved information availability, and reduced management complexities.
Data visualization
Harness the benefits of Azure Power BI to improve your business intelligence with intuitive and interactive data visualizations and customization features. Within this system, users can create aggregated dashboards, reports, graphs, and charts that are easy to understand and use. Additionally, they can update visuals of these items in real-time.
Analytics, insights, and intelligence
Broaden your analytics capabilities with Microsoft Azure Synapse Analytics services that combine data warehousing, data integration, and big data to provide more insights and predictions. Also, automated machine learning tools allow you to build data models quickly and accurately, ML-based DevOps tools improve model lifecycle management processes, and the real-time analytics service handles information from multiple resources, detecting anomalies in data streams without your manual supervision.
Data lakes and data lake analytics
Set up a scalable data lake that allows you to store data of any size, type, and processing speed. With Azure Data Lake Storage, you can:
- Handle large data volumes with high throughput.
- Safeguard your information with encryption algorithms and access controls.
- Leverage a distributed service to collect and analyze data from the lake whenever required.
- Assign and track related jobs.
- Scale your processing power up and down to accommodate changing data requirements.
GenAI integration
Tap into AI's unlimited potential to streamline content generation through faster information retrieval, semantic searches (i.e. searches that extract data from images, videos, and documents), content personalization, and even automated programming code generation.
Manufacturing
Optimize supply chain operations for manufacturers to reduce their operational costs and ensure on-time delivery:
- A unified data lake to efficiently store and manage large data volumes.
- Real-time analytics of supply chains to define bottlenecks and trends in demand and find areas for improvement.
- Dedicated dashboards to monitor supply chain performance, make informed decisions, forecast demand, and optimize inventory levels accordingly.
Transportation
Ensure more efficient fleet management for transportation companies to increase service quality and cut operational costs:
- Real-time monitoring of vehicle performance and location.
- Analytics of vehicle data streams, including their usage, mileage, technical conditions, and other info.
- Intelligent forecasting models that predict maintenance needs.
- Interactive dashboards that allow managing fleet operations in real-time.
Finance
Detect and prevent transaction fraud to secure customer data, reduce their outflow, and prevent financial losses:
- Centralized storage for distributed transaction data.
- Predictive machine learning tools spot unusual patterns that may indicate fraud.
- Real-time transaction monitoring to enable prompt detection of suspicious activities.
- Intuitive visualization of fraud metrics and patterns.
Retail
Provide unique shopping experiences to ensure continuous customer satisfaction and increase their loyalty:
- Quick aggregation and analytics of data from various sources.
- ML-based customer behavior predictions determined from historical info about their previous purchases.
- Interactive dashboards that visualize customer segments and their purchasing patterns.
- Personalized content based on customer analytics, including purchase recommendations, promotions, and inventory management alerts.
Healthcare
Improve preventive medical equipment maintenance procedures to reduce unplanned breakdowns, downtime, and expenses for healthcare facilities:
- IoT-enabled collection and monitoring of real-time data from connected devices.
- Automated processing of data streams to promptly detect possible failures.
- Predictive analytics models that forecast equipment maintenance needs.
- A single storage for historical and real-time information.
Energy
Improve energy distribution management to prevent resource overuse, cut expenses, and meet energy demands:
- IoT-powered data collection from smart meters, sensors, and other devices in the grid.
- Real-time dashboards enable the continuous monitoring of grid performance.
- ML-based models that predict energy demand and help to optimize distribution.
- Universal storage for real-time and historical data.
- Visualization of major performance metrics and usage patterns.
Jumpstart and scale on the go
Benefit from backup and restore functionality to seamlessly transfer the existing data models to the Azure Analytics cloud platform. With it, you will gain the flexibility to scale your data architecture and pause the service whenever required, only paying for what you use.
Ensure quick time to market
If you plan to launch a business intelligence platform for the market, Azure Analytics can help. Its unique lifecycle management tools allow you to quickly deploy and test your BI solution, resulting in a predictable delivery schedule.
Unify dispersed information into a single source of truth
Collect information from multiple sources into a single data storage with Azure Analytics’ semantic model, which presents distributed datasets in aggregated views that are easy to understand and process.
Fuel your business intelligence
Get faster response times when working with large datasets to drive prompt business analytics, make far-reaching decisions, and take proactive actions accordingly to gain a competitive edge.
Enhance data security
Provide role-based access to your data, so only authorized users can see it. That is, users can only access the information they need, according to their job responsibilities.
Develop fast and easy
When building data models on Azure Analytics, solve business problems instead of dealing with tech issues. The platform enables you to work with Visual Studio, a familiar development environment that allows you to quickly deploy data models.