Operational Intelligence

A core focus of DCP Midstream is our commitment to operational excellence. Leveraging the NetSpring Operational Intelligence platform to analyze real-time data gives our team members key information to prioritize critical work, support quick response, and more effectively serve our customers.

In an increasingly digitized world, it is critical for enterprises to get a deeper analytical understanding of product and customer experiences. But enterprises struggle to do this well due to a gap between current first-generation product analytics tools, AI/ML tools, and business intelligence tools. The next generation of behavioral analytics systems need to bridge this gap and advance the sophistication of analytics, but with a simple business user friendly self-service interface.

The history of analytics is rooted in relational data models and batch SQL querying on data at rest. As the need for temporal, streaming, and event-oriented analytics increased in recent years, we saw specialized systems emerge focussed on one primary aspect of time-series, product analytics, real-time monitoring, alerting etc. In the process, these specialized systems have lost the analytical power of relational systems. My bet is that the next revolution in analytics will come from platforms that deliver the best of both worlds.

Every business process can be thought of as a time-ordered stream of events. Deep analytical insights into business processes require systems that not only treat events and time as first-class citizens, but can also model and analyze complex events that cut across multiple data streams and sources. With the maturity of streaming and data integration infrastructure in the Cloud, there is an opportunity for the next generation of AI-driven complex event processing systems that can bring about significant business process optimizations at enterprises.

Data analytics is core to operations-intensive segments like ridesharing, food delivery and others. Successful companies in those spaces build complex infrastructure to support tasks like vehicle routing, dynamic pricing, fraud detection, personalized promotions, and continuous customer engagement. Data analytics vendors who provide scalable platforms can help such companies focus on core, day-to-day customer needs rather than generic data infrastructure

AI/ML-driven detection of anomalies and other patterns of interest in data has the potential to greatly improve the depth of analytics in enterprises. However this potential is still vastly unrealized. The main reason is the inability to effectively operationalize ML models at scale. Operationalizing ML models includes sourcing and combining (often in real-time) context data from multiple sources, and deploying trained models for use in business rules, alerts and operational applications. Platforms that can solve this problem well can greatly further the ability of enterprises to do advanced analytics.

In an increasingly fast-paced business environment, real-time data analytics is no longer a nice-to-have but a must-have for companies, to stay relevant and competitive. Data engineering teams today struggle with the infrastructural complexities associated with providing business teams a scalable, self-service platform for real-time analytics integrated with their operational business processes. There is a huge opportunity for a next-generation, low-TCO, Lakehouse-style analytics platform that can seamlessly layer on top of event buses such as Apache Kafka and moderm cloud data lakes with stores such as AWS S3.