At this year’s Informatica World, a few notable themes stood out: Cloud continues to drive tremendous change in business processes and operations, complexity reduction is a critical goal for enterprise executives, and the intelligent, AI-driven enterprise is becoming reality. Data is the core component that these themes have in common. However, the truth is that enterprises are still struggling to properly control and harness the enormous amount of data assets they possess. This has given birth to the myriad of iPaaS (integration platform as a service) tools in the market today as integration becomes an increasingly strategic capability. But are these tools evolving to keep up with the geometric pace at which data is growing? (Learn more about iPaaS.)
Cloud is forcing businesses to reconsider how they consume and deliver technology. Many enterprises are considering re-hosting, re-architecting, or re-platforming their application stack as they take one of the multiple routes to the cloud.
As they start consuming more cloud applications, business process fragmentation grows, and manually moving data from system to system becomes impractical and costly. Companies are pushing for ways to streamline and automate the integration of data across these varied and diverse cloud apps, as vendor point-to-point solutions and customized scripting are not practical or scalable solutions anymore. Attempting cloud data integration and cloud application integration without an iPaaS solution just isn’t feasible at scale.
Not all iPaaS tools are at the same maturity level. Some excel at cloud data integration, others at cloud application integration, and yet others at a combination of both. The best-of-breed tools tend to have all these capabilities as table stakes. However, you need more than just base integration capabilities to keep pace with today’s data challenges, among them:
A next-gen iPaaS goes a step further to incorporate patterns and capabilities such as data quality, master data management, data governance, data security, data cataloging, B2B partner integration, support for streaming data and data lakes, and so on. As you consider an integration strategy and evaluate solutions, avoid the added complexity of having to piece together capabilities from various data management tools and look for one end-to-end solution.
There is a rush happening to capitalize on the treasure trove of enterprise data being created on a daily basis. Organizations need AI algorithms to process and learn from this big data, as the volume of data goes far beyond what humans and traditional systems are able to process. Ironically, the same AI models need big data to continually improve over time. (Read more here: AI needs data and data needs AI.)
Businesses are rapidly moving to adopt AI in the enterprise. To ensure the best outcomes, it’s critical to develop the right frameworks and adopt the right technologies.
With regard to an iPaaS, determine whether your choice of solution clearly helps accelerate your journey toward becoming an intelligent enterprise. Ask yourself whether the solutions you’re considering have real built-in AI capabilities that will help optimize the entire data management process.
Your choice of iPaaS can have a profound impact on eventual business outcomes. With that in mind, I recommend you start at a high level by considering your organizational mindset around data and AI (learn more about Informatica’s AI engine).
Finally, for an expert overview of Informatica’s iPaaS, watch our upcoming webinar: Meet the Experts: How to Get the Most Out of Your AI-driven iPaaS.