If good data is the fuel for AI/ML and advanced analytics, smart data engineering is foundational for setting these systems up for success. Without proper data engineering practices in place, even the most advanced models and applications will fail to deliver value.
Jerod Johnson, senior technology evangelist, CData Software, and Phillip Miller, senior product marketing manager, AI, Progress, joined DBTA’s webinar, Powering AI/ML & Analytics with Smarter Data Engineering, for a roundtable discussion about how smarter data engineering can best accelerate AI/ML performance, streamline analytics, and enable real-time decision making.
Creating a modern data foundation for the enterprise is both more urgent and complicated than ever, according to Johnson, who noted that 62% of IT leaders say their organizations aren’t equipped to harmonize data systems to fully leverage AI. This is due to the:
- Exponential growth of data sources and destinations
- Growing diversity of users who need to access data
- Increasing demand for diverse integration types
However, the CData platform has enabled advancements in this space, with Johnson detailing a few customer success stories.
The CData platform is a comprehensive connectivity data solution that provides real-time data access across enterprise apps and infrastructure. With it, Innomotics, a globally leading provider of electric motors and large drive systems and Siemens business, enabled real-time operational intelligence at global scale.
Challenged by a diverse range of data sources—including SAP ERP, Dynamics 365, CSV files—Snowlake warehousing in multiple regions, and a need for real-time manufacturing insights, Innomotics chose the CData platform to cultivate a unified framework for all its data. It also helped power high-performance extraction and automated data movement to Snowflake, which led to:
- Real-time operational intelligence
- Quality optimization
- Potential for predictive maintenance
- Standardized data engineering across global ops
According to a manufacturing director at Innomotics, “CData's platform allows us to extract and analyze manufacturing data from our global operations in real time, giving us the foundation we need for predictive maintenance and quality optimization initiatives."
Miller explained that the key to enabling successful AI/ML and advanced analytics is transforming isolated, independent data into semantically associated data through knowledge graphs.
Creating this “contextualized, connected data ecosystem [is] what’s improving the performance for our customers,” said Miller.
Data engineering is what enables a highly contextual, connected data estate. It transforms raw data into “data connected and contextualized into the domain that business operates in, the language that’s used to express the data, the regulations, the security, the governance that goes around the data, the business rules—all of that goes into making AI successful in any organization, but especially when you’re scaling AI into the enterprise,” Miller explained.
The Progress Data Platform “is a way of simplifying the architecture needed to allow you to have the agility… for these new AI applications, systems, products that are coming out every week, every day,” said Miller. Ingesting data regardless of source, the platform curates the data, governs the data, applies metadata, semantically enriches it, enforces business rules, and feeds downstream systems.
Progress’ agile, secure enterprise AI Data Platform enables organizations to build trustworthy and accurate AI-powered apps, applying human intelligence to data at machine scale, moving businesses from research to production faster, and rapidly delivering business value from AI projects.
This is only a snippet of the full Powering AI/ML & Analytics with Smarter Data Engineering webinar. For the full webinar, featuring more detailed explanations, a Q&A, and more, you can view an archived version of the webinar here.