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Oracle Strategy for Artificial Intelligence Becomes Clearer
Early October’s Oracle OpenWorld provided some helpful granularity relative to the company’s overall message about cloud-delivered business. The cloud message was still a dominating theme, building upon last year’s OpenWorld theme that was all-in on the cloud all the time. However, the company also delivered some specific insight into its industrial IoT solution and, overall, its approach for artificial intelligence and machine learning.
2017 OpenWorld cloud speakers and marketing continued to tout the company’s strong growth, the speed of migration in its core markets, and highlighted progress provided by last year’s NetSuite acquisition. NetSuite was acquired to give Oracle SaaS inroads into smaller and middle markets. A simple message was delivered to more traditionally cautious market players that face volatility. It touted the value of cloud as evident when you don’t know what your future might be in terms of scale and complexity of need. Oracle believes on-premises approaches lack the necessary agility and flexibility for those situations.
In addition to some of the cloud messages, the company demonstrated more definition for its strategy for new technologies. A good deal of time and content was spent on artificial intelligence, blockchain, and cyber security.
To Oracle, artificial intelligence (AI), including machine learning (ML), is best when it is pervasive but goes unnoticed. The company aims to position these capabilities as feature-centric, perhaps to remove the perception, true or not, that new resources and high-cost competencies must be acquired by users to consume AI and ML.
It makes sense from a system-oriented viewpoint, and it should hold true down to control layers. Consider:
- Adaptive machine learning that proactively recognizes and improves performance
- Voice control of interfaces and functions
- Suggestions, recommendations, and automation based on dynamic interactions with systems
- Auto-population to improve accuracy and efficiency
An example of this was the company’s announcement for what it called the world’s first autonomous database cloud. Using machine learning-driven automation, the database tunes, scales, and repairs without human interaction. It also works in conjunction with the newly announced cloud-based cyber security solution suite. Touted as cyber defense, the suite uses machine learning and high levels of automation to deal with the complexity, vulnerability, and speed issues associated with scaled data, such as patching and downtime.
Oracle Industrial IoT Solution Takes Shape
I was fortunate to spend some time with Oracle’s IoT Cloud Services group. We reviewed the company’s solution for industrial use. Built upon Oracle’s enterprise IoT cloud platform, the solution consists of seven core capabilities and five applications. Oracle indicates the capabilities are:
- Device connectivity
- Digital twin
- Business KPIs and rules
- Machine learning and anomaly detection
- Predictions and recommendations
- Digital thread
- Integration and extensibility
The company also communicated that AI and machine learning are built in to the solution, rather than standalone. As such, they underlie single-pane dashboards for business rules and predictions/recommendations. In turn, these are integrated into supply chain, customer experience, human capital, and enterprise resource planning business apps.
These seven capabilities support the five applications, which include:
- Asset monitoring – monitor assets, their health, utilization, and availability
- Production monitoring – manufacturing equipment and production line monitoring and prognostics
- Fleet monitoring – of fleet vehicles, driver behavior, and costs
- Connected worker – safety monitoring of people and environments
- Service monitoring for connected assets – automation of assets and customer service
By extending the digital twin, Oracle’s industrial-use digital thread is designed to leverage the completeness of the company’s supply chain and customer experience capabilities. Oracle indicates the solution can leverage IoT data to automate workflows across the company’s capabilities in engagement, maintenance, manufacturing, product lifecycle management, transportation, and project management.
As evidence, Oracle presented a use case for manufacturing that blended IoT and ERP data. When predictive and correlative analysis is applied, the benefit is improved yield and quality.
Oracle Integrates into Industrial Operations Via IIoT Data
As it builds out additional applications, it is clear Oracle is taking more direct aim at converging IT applications, IoT data, and operational processes. To that end, the company plans to roll out an IoT-based predictive maintenance application in 2018.
The company stated that the new application will stay true to Oracle’s vision. AI and machine learning will be features. For example, time-series algorithms will analyze data that then is pushed into visualization. The visual will make failure patterns easier to recognize for non-data scientists. For health predictions, the analytics will automatically invoke best-fit machine learning algorithms based on the sensor data and KPIs.