What Challenges Have You Faced With Cloud Integration?
To provide insights on overcoming challenges in cloud integration, we asked technology leaders to share their experiences. From developing innovative APIs and connectors to enforcing enterprise-wide data consistency rules, here are four key strategies they used to tackle these challenges.
- Develop Innovative APIs and Connectors
- Leverage Machine Learning for Data Integration
- Optimize Data Intake for AI Platforms
- Enforce Enterprise-Wide Data Consistency Rules
Develop Innovative APIs and Connectors
When Riveraxe LLC started offering cloud services, integrating client systems was our biggest challenge. Many healthcare organizations rely on outdated technology and proprietary software, so finding ways to connect these disparate systems to the cloud was complex.
To overcome this, my team developed innovative APIs and connectors that could link nearly any healthcare IT system to cloud platforms. We worked closely with clients to map their data and processes, then built customized integrations to suit their needs. For example, one client used a legacy EHR system from the 1990s that we were able to connect to a modern telehealth solution, allowing them to launch virtual care services.
Constant communication and a willingness to learn from each other were key. My engineers had to understand the nuances of various healthcare IT systems, while clients needed education on the benefits of the cloud. By facilitating knowledge-sharing in both directions, we built solutions that addressed specific challenges and enabled new capabilities for organizations looking to transform care delivery through technology.
The innovations we’ve created provide a roadmap for others in overcoming the obstacles of integrating healthcare and the cloud. With the right expertise and partnership, nearly any system can be connected, paving the way for improved data access, improved workflows, and better patient outcomes.
Leverage Machine Learning for Data Integration
In my role managing a cloud-based digital marketing platform, I faced a significant challenge integrating historical data from disparate sources. After acquiring three competitors, we had customer data in various stages of completeness spread across five systems.
I started by identifying the minimum viable dataset needed to provide value to customers. We extracted this critical data from each system and combined it into a single customer profile in our platform. Though incomplete, this allowed us to keep serving customers without disruption.
We then evaluated advanced data-mapping and automation tools to fill in the gaps without putting extra work on staff. After testing, we implemented software leveraging machine learning to connect data points and populate missing fields. Within six months, 75% of profiles were 90% complete.
While daunting, staying focused on customers’ needs, starting small, and leveraging technology enabled us to overcome data-integration challenges at scale. The single customer view allows us to provide more personalized service and help our customers grow their businesses.
Optimize Data Intake for AI Platforms
As an experienced CPA and software engineer, I've tackled many cloud-integration challenges over the years. One that stands out involved integrating an AI platform for financial forecasting with a client's accounting software and CRM.
The AI model required accessing data from multiple sources, so we built an integration to automatically sync data in real-time. However, the huge volume of data overloaded the AI platform, causing system crashes. We resolved this by optimizing data intake and caching frequently used data.
A second hurdle was handling seasonal fluctuations in data. The AI had been trained on historical data but struggled when new seasonal patterns emerged. We retrained the model on current data, then implemented a mechanism to automatically retrain it on the fly using the latest data.
Constant monitoring and improvement have been key. We analyze performance metrics regularly and make tweaks to improve scalability, data integrity, and AI model accuracy. Integrating cutting-edge tech like AI with cloud infrastructure is an iterative process, but the benefits to clients make the challenges worthwhile. Experience, persistence, and a thirst for continual learning help overcome them.
Enforce Enterprise-Wide Data Consistency Rules
Managing data consistency across multiple cloud platforms had been an ongoing challenge for our integration teams. While this issue can be addressed individually within each platform, doing this at scale is not realistic, and it became even more difficult with multiple cloud integrations in use.
To address this challenge, we defined data consistency rules for enterprise data sources and performed data cleansing on our existing data stores when possible. Going forward, we are adhering to a more structured data governance framework that enforces enterprise-wide data consistency rules. Although we still have work to do, as a result of these changes, we have seen far fewer issues with data consistency across our multiple cloud platforms.
