Today marks a new chapter for StreamSets. StreamSets’ parent company, Software AG, just announced a new category of integration for large enterprises — the Super iPaaS — and the StreamSets data integration platform plays a critical role.
For the first time, enterprises will be able to integrate anything, anywhere, any way they want.
This means you can integrate your data and your applications from one unified platform, connecting from on-prem to the cloud, developed anywhere, deployed anywhere with central control and distributed execution.
I’m genuinely thrilled that StreamSets is part of this new game-changing category.
In the past decade, technology innovation, adoption, and evolution have moved faster than at any point in history. The long-awaited convergence of various forms of integration–data, application, B2B, API-driven and event-driven is finally upon us, from the perspectives of market and technology maturity.
With the imminent mass adoption of AI, that pace is about to accelerate to one we can hardly imagine. And while the possibilities for good are stunning, so too is the potential for calamity. AI models rely on a constant influx of high-quality data for training and inference. Yet data management is still a huge challenge for enterprises. 72% of technology executives surveyed in a recent MIT study say that should their companies fail to achieve their AI goals, data issues are more likely than not to be the reason.
As 78% of enterprise technology leaders put scaling AI and machine learning use cases to create business value as the top priority of their enterprise data strategy, it’s time for enterprises to address their data management challenges once and for all.
Since my area of expertise is data integration, I’m going to focus on that in this blog.
How Modern Data Integration Removes AI Scaling Obstacles
A recent PWC survey found that the top tech-related challenge for AI is identifying, collecting, or aggregating data from across the company, ensuring its completeness and accuracy in preparation for use in AI.
As you upgrade your technology and architecture, they suggest focusing on two imperatives: integration and data. “With technology tools that help you overcome your data challenges, you can achieve much faster (and much more cost-effective) operationalizing of AI.”
Using a modern data integration platform like StreamSets helps organizations overcome AI scaling challenges like:
HOW MODERN DATA INTEGRATION HELPS
Pre-built connectors gather data from various data stores and infrastructure, including legacy systems like mainframes. You can then transform disparate data formats into a consistent, analysis-ready data set using various data integration patterns–ETL, ELT and Reverse ETL.
Poor data quality
Automate data cleaning tasks like handling nulls, deduplication, normalization, and validation. Cleaning the data used for AI training and decision-making reduces the risk of biased or inaccurate models.
Lack of observability, monitoring, and explainability
Data integration tools can ensure that input data used for AI models is reliable, accurate, and representative of real-world scenarios. These tools also help explainability by providing complete visibility into where AI model data came from and what changes happened before entering the model.
Get the Data Integration Advantage for Scalable AI
I wrote this blog using excerpts from a white paper I wrote with Arvind Prabhakar, co-founder and CPO of StreamSets. Get the whole paper: The Data Integration Advantage: Building a Foundation for Scalable AI.
And learn more about Super iPaaS:
What it is
What led to this new category in this blog by CEO Sanjay Brahmawar
What’s available today in this blog from Integration General Manager Suraj Kumar
The complete vision and roadmap in this blog from CPO Stefen Sigg