Artificial intelligence (AI) requires data integration for collecting data from applications, IoT devices, and other sources, and for delivering results to end users. In this article, Boomi Chief Product Officer Steve Wood describes the five key requirements for any data integration platform supporting AI.
Data analytics – ranging from traditional business intelligence (BI) to advanced artificial intelligence (AI) techniques such as Machine Learning – is proving increasingly valuable to enterprises across industries.
AI in particular is now considered table stakes for digital transformation. If any organization is going to apply digital technology to radically transform its business, having access to split-second, highly accurate and predictive AI-powered analytics will likely be part of that transformation. No wonder, then, that enterprise use of AI has grown 270%Opens a new window over the past four years.
But investing in data analytics means more than standing up platforms for BI, AI, big data, deep learning, and other cutting-edge technologies. It also means delivering data to these analytics platforms in the first place.
BI and AI platforms can’t analyze data they don’t have. Crucial to supporting any kind of analytics platform is having an integration platform that can deliver the data needed for analysis—regardless of where that data comes from and what sort of transformation the data might require in transit.
And once that data is analyzed, it might need to be delivered back to other applications, such as BI dashboards, salesforce automation applications, ERP systems, and other business-critical services. Data integration prevents a data analytics platform from becoming just another data silo.
Data Analytics Depends on Data Integration
What’s the lesson here? It’s this: Data analytics turns out to be only as effective as an organization’s data integration capabilities.
Have great financial data that’s locked away in a dozen data silos scattered across business units? Chances are, your analytics platform won’t be able to analyze it.
Is customer data only available to BI tools through batch processes? Then your sales and support teams will lack up-to-the-minute data about prospects and key accounts, even if something urgent is happening in the field.
Have an integration platform that can connect data silos, but requires extensive hand-coding and expensive on-premise middleware? Then your BI and AI platforms will get their data only after your IT organization has invested in months of work and expensive server hardware.
In contrast, a low-code, cloud-based data integration platform that supports rapid development and connects easily to different data sources will bring speed and efficiency not only to your integration projects, but to your data analytics initiatives as well. Suddenly, your AI, BI, and big data investments will be able to get the data they need, even if those data sources are new or constantly evolving.
5 Requirements for Modern Data Integration Platform
If you’re looking for an integration platform that will enable your organization to make the most of its data analytics investments, including BI and AI, make sure the platform meets these five requirements.
Low-code development
With ready-to-use connectors for most popular business applications and a graphical, low-code development interface, a modern integration platform makes it easy to build integrations for all kinds of business applications and data services five or six times faster than previously possible. Development times shrink from months and weeks to days and hours. The result? Analytics platforms can more easily get the data they need.
Integration with trading partner networks, including EDI networks
If a data integration platform also supports EDI networks, then enterprises can more easily deliver trading partner network to BI and AI platforms for analysis. Integrating with trading partner networks is useful for analyzing trends involving commerce, logistics, and shipping.
Data quality and data governance
If enterprises are analyzing data from two business applications, they’ll want to ensure that those applications use the same data types and data definitions. After all, you don’t want to create business intelligence metrics based on conflicting definitions of a closed sale. An integration platform should support data quality and data governance features, so that enterprises can identify “golden records†for various data types and ensure that data is used consistently both in operations and analytics.
Support for API lifecycle management
Enterprises are increasingly relying on Application Programming Interfaces (APIs) to power data services, such as the microservices making up an application or data feeds that can deliver business-critical data upon request. If an integration platform offers API lifecycle management features, then IT organizations don’t have to invest in, provision, and configure a completely separate platform for managing APIs. They can manage APIs along with more traditional point-to-point connections in a single platform, reducing complexity.
Workflow automation
If a low-code integration platform supports workflow automation, then data in workflows can be fed easily into analytics platforms, and data coming out of those platforms can be easily applied to workflows. Want to automatically leverage AI insights for process automation? Choosing an integration platform that provides workflow automation as well makes this possible.
AI, big data, and other analytics technologies can transform a business. They can support real-time pricing systems, aid in the speed and accuracy of medical diagnoses, and help executive teams with decision-making.
All these use cases require the efficient collection of accurate data for analysis. Data integration and data governance make fast, accurate data collection possible.
As you develop your organization’s AI strategies, remember to include data integration and data governance in your plans. They’ll literally put the data in your data analytics.