The state of iPaaS in 2022: Powering SaaS, Data Intelligence, and AI

Olga Annenko integration best practices

Data intelligence powered by iPaaS

The iPaaS market is clearly growing now at a faster pace than ever anticipated. This is not only due to the pandemic forcing companies to accelerate the digital transformation, but also in general due to the rise of SaaS. The replacement of older, server-bound software solutions with more modular, user-friendly and flexible SaaS solutions means customers need to connect these disparate cloud systems somehow if they want their business to become truly data driven. 

The increased awareness of iPaaS has driven the emergence of new usage scenarios such as creation of native integrations for SaaS products, or new experiments with combining services, technologies and practices such as AI and RPA. It is also slowly becoming the “secret sauce” of the data intelligence and automation industry, where not just the quantity but also the quality of data play a crucial role in providing accurate analytics and insights. 

So, let’s review the state of integration platform as a service in 2022 by diving deeper into the three emerging trends in the integration segment. 

Trend #1: An Integration Layer for SaaS

More and more SaaS vendors have come to realize that nowadays, it’s not enough to build a great new product and provide superb services along with it. While it’s flattering to think that your product is THE solution to a client’s problem, in reality it is only truly effective when it can exchange data with related business software applications. 

Just ask yourself this question: How often did you think while using a new software product “Hmm, how can I get this data into my customer relationships / resources planning / marketing /… system”? I know I did; for example, to sync customer appointments from our booking system with our cloud CRM. Or if you’re a SaaS vendor, ask yourself how often your customers and prospects asked your support team for an integration with a system XY to get all the information in one place. 

The problem is – integrations are a costly affair that diverts the dev team’s focus from the core product. To counteract this state of affairs, SaaS vendors have been increasingly looking for white-label iPaaS offerings and embeddable cloud integration platforms to decrease the total cost of integrations and help their dev teams build them considerably faster. It’s largely about standardization, efficient user management, and providing developers with SDKs for custom connectors creation and a number of tech connectors such as for SOAP, Webhook or REST to free them to create integrations with almost any modern application or API in a fraction of time.

The ability of a SaaS solution to fit seamlessly into the IT landscape of potential clients is the real game changer that allows for a proper data exchange and data synchronization to be enabled right from the start. When this happens the value of the SaaS offering increases dramatically and SaaS vendors who embed into client ecosystems experience much lower churn rates. 

Trend #2: AI powering iPaaS… or better vice versa? 

The AI and machine learning are still quite hyped terms, it is therefore no wonder that they didn’t “bypass” the field of application and data integration either. There have been several product announcements in the past years involving machine learning capabilities in the context of Enterprise Integration Platform (EiPaaS). As fancy as it sounds, at the moment, these capabilities seem to be largely limited to automatic recommendations of suitable integration patterns and flows to help iPaaS vendors drive the self-service agenda. 

The shift of focus in the AI and ML segments from the sheer volume of data to the variety and dynamism of datasets used drives, however, also the reverse relationship. Most big data initiatives nowadays are not just about collecting as much data as possible in one pool – that is largely a thing of the past. Now it’s more about the quality of information; about combining these data with valuable contextual information so that real insights and value can be mined and then applied to multiple use cases and industries. Just think about customer experience management, business analytics (BA), or business intelligence.

So how do companies looking for a competitive edge access enough data from various sources so they can derive real value? Ultimately, integration of both enterprise and external systems is the answer, and making integrations as easy, yet powerful, as possible. Nothing slows AI & ML better than poor data quality, so you need to be sure you can groom and transform data ‘on the fly’ as it flows. And there is hardly a better way to do so than working with an integration platform with strong ETL and data transformation capabilities.  

Considering this, it is to expect that integration will soon be regarded as key to enabling AI and ML approaches. And that brings us also to the third trend.

Trend #3: Driving the Data Intelligence and Automation industry

Organizations have been longing for data driven intelligence for a very long time. Now, with the unprecedented volumes of data being collected and thanks to the latest advancements in the areas of intelligent automation, AI and machine learning, being able to make data driven and AI-based decisions – for example with regard to customer experience optimization – finally became a reality. 

Business process optimization is another area where the need for intelligent automation and quality data increased threefold during the pandemic. In this respect, Data Intelligence and Automation industry is definitely on the rise. 

The trends that are particularly worth highlighting are RPA (robotic process automation) and IPA (intelligent process automation) as well as cognitive APIs1 that provide a set of AI features such as NLP (natural language processing) and semantic technology to add a certain level of intelligent automation to routine tasks and processes. Interestingly, RPA in particular used to be pegged as immature, faulty and therefore largely useless, but 2020 fostered considerable advancements in this field and proved RPA to be very useful for business process optimization after all. 

This being said, any data intelligence and automation initiatives are tightly married with data integration technology because it is imperative to collect and bring together all necessary data for accurate business analytics. So, in the future, we can expect to see even more collaboration between data integration solutions providers and data intelligence / automation solutions providers, as well as more tools, services, and platforms that aggregate these disciplines and support the initiatives.

Conclusion

In today’s fast-paced and highly competitive business climate, organizations need a discipline to optimize their value, manage risks, and minimize costs now that they have the ability to acquire huge amounts of diverse internal and external data. In this light, data governance, proper data management, as well as tools and services that support data intelligence have become a must. 

While iPaaS originally was thought of as a tool to get data from one cloud application to another, it has evolved into a more comprehensive integration platform that supports data synchronization between on-premises systems and cloud applications, as well as between clouds. Not only that; It has been breaking down barriers by embracing new trends such as microservices-based design, API integration, and cloud computing to connect not just applications but also services, people, and devices with utmost flexibility and efficiency. It is exciting to continue to watch this space because one thing is for sure – with the current pace of iPaaS adoption, we will see many new exciting implementation scenarios and further merging of various services and technologies.

1. For more details on what a Cognitive API is, click here. The article is somewhat outdated but still by far the best at explaining this concept.


About the Author

Olga Annenko

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Olga Annenko is a tech enthusiast and marketing professional. She loves to write about data and application integration, API economy, cloud technology, and how all that can be combined to drive companies' digital transformation.


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