Enterprises need a solid data foundation as they prepare to scale AI. Getting there requires modern data integration. Adding more integrations to the same platform? Even better, writes Girish Pancha, CEO at StreamSets.
This year 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, events, B2B, and APIs – from one unified platform connecting from on-prem to the cloud.
I’m genuinely thrilled that StreamSets is part of this new game-changing category. You can read more about Super iPaaS in this message from our CEO, on our website, or in this white paper.
Why now?
In the past decade, technological innovation, adoption and evolution have moved faster than at any point in history. 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. According to a recent MIT study, 72 percent of technology executives surveyed say that should their companies fail to achieve their AI goals, data issues are more likely than not to be the reason.
As 78 percent 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.
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,” the survey stated.
Using a modern data integration platform like StreamSets helps organisations overcome AI scaling challenges like:
- Data silos – Prebuilt connectors gather from various data and infrastructure, including legacy systems like mainframes. You can then transform disparate data formats into a consistent, analysis-ready format.
- Poor data quality – Automate data cleaning data like handling nulls, deduplication, normalisation, 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 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 a white paper with Arvind Prabhakar, co-founder and CPO of StreamSets on this too. Get the whole paper: The Data Integration Advantage: Building a Foundation for Scalable AI to learn more.