Real-Time, Data-Driven Decision-Making with Databricks
Data has become a crucial strategic asset for organizations in today’s rapidly changing business landscape. However, simply having data is not enough to drive successful decision-making and increase profitability. This blog will explore the importance of leveraging data to maximize value and drive strategic insights. By accelerating the time to value your data, organizations can stay ahead of the competition and achieve their goals. Whether you’re in finance, healthcare, retail, or any industry, the principles discussed in this blog will provide valuable insights for organizations looking to get the most out of their data.
Over the past decade, several trends have disrupted the Data & AI landscape, requiring traditional data platforms needing to evolve to support changing needs and opportunities, including:
- Capitalizing on data in all its variety, sizes, and forms and applying it to analytic use cases (data science, artificial intelligence, and machine learning)
- Combining traditional deductive and reactive (past-looking) analytics with forward-looking predictive and prescriptive analytics
- Performing reverse ETL (delivering data to consumers in their chosen consumption pattern (e.g., API, BI Tool, queue, specialized Analytic Engine)
- Factoring the above into real-time decision-making
Companies are at varying levels of maturity concerning the above requirements. Many have moved away from legacy data warehouse platforms that are no longer suited to these emerging needs onto cloud-based modern architectures and platforms. Many have incorporated a shift from high latency, nightly batch processing to near real-time, low latency streaming processing. Some have gainfully deployed artificial intelligence into production workflows. Nearly all are still figuring out how to institutionalize, perfect and harden these capabilities.
Manufacturing and Operations
An overarching goal for manufacturing companies is to accelerate essential data ingestion and turn it into insights supporting inventory, logistics, ERP, operations, and their products. Real-time data ingestion needs exist for ERP, SCM, IoT, social, and other sources to realize predictive AI & ML insights. Many companies use this data to create digital twins (a replica software model of a physical entity) to facilitate design, perform integration testing, and run simulations on those physical components.
Most manufacturing companies create massive sensor infrastructure for their operations or products, which generates enormous volumes of IoT data in the form of telemetry logs, images, etc. This data must be ingested in near real-time to monitor the health of equipment, infrastructure, and other assets.
Additionally, manufacturing companies are often required to perform the following:
- real-time ingestion of transactions
- operational data
- market and competitive data
This activity addresses analytic use cases around their markets, competitors, and customers. A modern manufacturing data pipeline collects this data in near real-time and often couples it with continuous analytics, AI & ML to perform predictive maintenance, anomaly detection, real-time workflow optimization, supply chain management, equipment effectiveness, and health & safety risk assessment.
Figure 3. Databricks Lakehouse for Manufacturing
Consumers expect banking and financial applications to accurately and immediately reflect the up-to-the-minute status of their accounts. The best time to combat fraudulent activity is while those transactions are happening.
Streaming data pipelines collect
- transactional data
- market data
- merchant products and services
- clickstream data
- customer service data
This data can be combined with AI to flag and combat fraud, make targeted recommendations and advertisements, predict customer churn, and aggregate risk.
Figure 4. Databricks Lakehouse for Finance
Care providers must be able to monitor conditions continuously so that patients receive the timely interventions and care that they need.
Two significant objectives of healthcare companies are
- improve patient outcomes
- optimize the cost of care
The key to both is collecting and real-time monitoring of critical patient information (a 360-degree patient profile), including:
- vital electronic health records
- insurance claims
- medical monitors
- lab reports
- genomic data
Intelligent data pipelines coupled with AI/ML can:
- shortened inpatient hospital stays
- aide in the pre-detection of life-threatening conditions
- build intelligent clinical alarm systems
- predict demand for hospital beds, supplies, and medications
Streaming facilitates CMS interoperability and data exchange with internal and external systems via FHIR.
Figure 5. Databricks Lakehouse for Healthcare
In this article, we have shown how the requirements for modern data pipelines are both increasingly time sensitive and complex. They need to support data in its immediacy (velocity), format, and size requirement and augment it with analytics, data science, artificial intelligence, and machine learning to support business use cases. These pipelines need to operate at the speed of the business for there to be actionable data-driven decisions.