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Gather data from multiple, diverse sources with different attributes and create a single trusted, unified view. 

Example industry-specific use cases: 

• Customer 360 data hub: Unify and enrich customer data from diverse data sources and enable governance to power customer churn reduction and revenue generation solutions. 

• IT service intelligence data product: Unify, correlate and enrich service, customer, and service incidence/IoT events data from multiple data sources to power proactive customer service management. 

• Omni-channel sales data product: Unify, transform and enrich sales data from multiple online and offline channels to power channel-agnostic KYC insights and automation to boost customer loyalty and sales. 

• Stakeholder engagement data product: Unify, transform and enrich stakeholder profiles and engagement data from multiple engagement channels to power positive project outcomes through intelligent stakeholder management. 

• Crop health data product: Unify, transform and enrich data from IoT sensors and satellite imagery to power crop health monitoring and actionable insights. 

• Farm management data hub: Unify, transform and enrich IoT, weather, satellite, and pricing data to power predictive agricultural operations. 

The process of making an AI solution ready for production is notoriously difficult.  Pilot projects showcase potential, but scaling the required data and environments to fully realize the benefits often leads enterprises to abandon these initiatives.  Platforms with self-service provisioning, deployment orchestration and data fabric capabilities enable enterprises to overcome these challenges. 

Example industry-specific use cases: 

• AI chatbot with an opensource LLM: Implement, deploy and scale an AI powered chatbot connected to an opensource LLM. 

• AI chatbot with a custom LLM: Implement, deploy and scale an AI powered chatbot connected to a custom LLM. 

• Fine-tune and extend opensource LLM: Fine-tune, extend, deploy and scale an opensource LLM to power custom business use cases. 

• Optimized food-truck placement recommendations: Implement, deploy and scale food truck placement recommendations based on location and weather data. 

• Food-truck menu recommendations: Implement, deploy and scale food-truck menu recommendations based on location and historical sales data. 

• Farm crop health issue prediction and recommendations: Implement, deploy and scale farm crop health issue prediction (e.g. disease and pest) and recommend corrective actions based on IoT sensors and satellite imagery data. 

• Optimized farm crop plant schedule recommendations: Implement, deploy and scale farm crop schedule recommendation to optimize storage space utilization and boost profits through spoilage reduction. 

• Farm produce price recommendations: Implement, deploy and scale farm produce price recommendations based on market data. 

• Farm produce yield prediction: Implement, deploy and scale farm product yield forecasts based on historical data, weather patterns, and current crop health to mitigate risks of under production and spoilage. 

Enable non-developers to create data products with low/no-code tools, freeing expert developers to focus on strategic projects. This empowers employees to independently meet their reporting and visualization needs.   

Example industry-specific use cases: 

• Employee expense data analysis: Create simple data pipelines to load, transform and clean employee expense data from multiple spreadsheets and expense tracking database sources. Create a simple unified database of all employee expenses to enable visualization and insight creation. 

• Project progress/status analysis: Create simple data pipelines to load, transform and clean project related data from multiple spreadsheets and create a simple unified database of projects to enable visualization and insight creation. 

• Office visitor analysis: Create simple data pipelines to load, transform and clean office visitor data from multiple spreadsheets to create a simple unified database of office visits to enable visualization and insight creation. 

• Purchase order analysis: Create simple data pipelines to load, transform and clean purchase order data from multiple spreadsheets and purchasing databases to create a simple unified database of purchase orders to enable visualization and insight creation. 

• Office equipment/asset lifecycle analysis: Create simple data pipelines to load, transform and clean office equipment and asset data from multiple spreadsheets and asset management databases to create a simple unified database of office equipment and assets to enable visualization and insight creation. 

Eliminate environment complexities, reduce developer cognitive load and wait times, automate tasks, and enable self-service during development. Turn your ideas into usable, useful applications more effectively.  

Example DevEx use cases: 

• Developer self-service environment provisioning: Pre-configure access to tech stacks, development machine sizes. and cloud infrastructure access and enable development teams to self-provision their environments to eliminate developer wait times and dependency on infrastructure and operations teams. 

• CI/CD tooling and automation: Pre-configure tech stack template specific CI/CD tooling and automation to simplify DevOps activities, automate repeatable tasks and boost developer productivity. 

• Deployment orchestration and promotions: Facilitate developer self-service software and data product deployment and promotion to different environments (e.g. dev to test, test to staging, staging to production) through orchestration to target containers (e.g. Docker, Kubernetes) and other workloads to minimize dependency on infrastructure and operations teams. 

As businesses grow, friction, distrust, and inefficiencies between IT and business teams often intensify. IT teams become overstretched by growing demands to fulfill infrastructure and application development demands whilst also being burdened by security and operational risks. 

Enabling business teams to be self-sufficient in their IT and application development needs, while providing enforceable guardrails, can significantly improve collaboration and outcomes across all teams. 

Example use cases: 

• Product release orchestration across multiple teams: Gain centralized access to product release status and resource utilization across multiple teams and projects through release trains and release management. 

• Self-service technology enablement and orchestration across the entire software development lifecycle: Enable product teams to leverage pre-integrated tech stacks like Jira and Confluence, and more to define, design, develop and deploy their products and enhance collaboration across product teams. 

• Self-service, single-pane-of-glass access to complete up-to-date overview of digital portfolios, products and Agile and DevOps metrics: Gain centralized access to digital portfolios and product insights as well as Agile and DevOps metrics for governance and performance measurement and improvement purposes. 

Starting a cloud journey can seem overwhelming. Sorting through numerous cloud technologies and vendor options, then setting up teams to manage them, can take a lot of time. 

Adopting a procurement approach focused on choosing a single platform vendor that integrates with the best technologies and is fully managed offers great advantages. This approach simplifies the process and accelerates your use case development. 

No use cases required for this.  Take a look at our integrations and our offering. 

The growing number and diversity of technology stacks in the cloud ecosystem can be overwhelming to enterprise IT teams. 

IT leaders face the burden of ensuring that empowering application development teams across the enterprise is paired with implementing pragmatic and enforceable guardrails to mitigate risks through standardization and governance. 

Example use cases: 

• Provide flexible tech stack selection guardrails: Pre-configure a list of tech stack options across multiple functional areas (e.g. cloud platforms, database, data warehouse, web app, API, etc.) that may be selected and used by project teams based on their preferences. 

• Set and enforce team specific template-driven development policies: Setup policy templates with tech stack specific and other development policies that needs to be enforced across the entire software development life cycle to ensure technology governance and adherence to architecture standards by product teams throughout the enterprise.