Oracle SaaS applications provide organisations with a powerful foundation for managing their financial, operational, human resources, supply chain and other business processes. However, the value of enterprise data does not have to stop at recording transactions and producing traditional reports.
By combining Oracle SaaS data with Oracle Database, Oracle Machine Learning and Oracle Analytics Cloud, organisations can take their business data further by using machine learning to identify patterns, generate predictions and provide deeper insight into what may happen next.
This creates an opportunity to move from simply understanding what has happened to making more informed decisions about what could happen in the future.
From Business Data to Predictions
Every day, Oracle SaaS applications can generate large amounts of valuable business data.
Financial transactions, invoices, purchasing activity, customer behaviour, employee information and operational events can all contribute to a significant historical dataset.
Traditional reporting can provide answers to questions such as:
What happened?
How much revenue was generated? Which invoices were paid late? Which products were purchased? How much did the organisation spend?
Machine learning introduces another dimension:
What is likely to happen next?
For example, historical business data could be used to develop a machine learning model that predicts the likelihood of delayed customer payments.
The model can learn from relevant historical information and then be used to score new or existing records.
Instead of simply showing which customers have previously paid late, the organisation can identify customers or transactions that may have a higher likelihood of delayed payment in the future.
That can give the business an opportunity to act earlier.
Scoring Data Inside Oracle Database
One of the important capabilities within this architecture is the ability to perform machine learning scoring directly in the Oracle Database.
The data and machine learning model can reside within the database environment, allowing the model to be applied to large datasets without requiring the data to be moved to a completely separate processing environment.
Once the data has been scored, the resulting dataset can also be stored within the database.
This provides a practical data flow in which the organisation can:
- Collect and prepare its business data.
- Develop or use an appropriate machine learning model.
- Apply the model to relevant data.
- Generate predicted results or scores.
- Store those results in the database.
- Make the results available for analytics and reporting.
For organisations working with substantial volumes of enterprise data, keeping the processing close to the data can provide an efficient foundation for machine learning.
Bringing the Results into Oracle Analytics Cloud
Machine learning predictions become significantly more useful when business users can easily understand and analyse them.
This is where Oracle Analytics Cloud can play an important role.
The scored data generated through Oracle Machine Learning can be presented through interactive dashboards, reports, charts and other visualisations.
For example, a finance dashboard could display customers according to their predicted payment risk.
Business users could then analyse the information by customer, region, business unit, transaction type or other relevant dimensions.
Instead of asking finance teams to work directly with a machine learning model, the predictions can become part of the familiar analytics experience.
This helps connect advanced analytics with everyday business decision making.
A Real Business Use Case
Consider an organisation that uses Oracle Fusion Cloud Applications for its financial operations.
The organisation has several years of historical accounts receivable data. This information contains payment history, invoice information, customer details and other factors that may be relevant to payment behaviour.
The organisation could use this historical data to develop a machine learning model designed to identify patterns associated with late payments.
Once the model has been trained, it could be applied to relevant invoice or customer data.
The result might be a score representing the likelihood of delayed payment.
That score could then be stored in Oracle Database and exposed through Oracle Analytics Cloud.
A finance executive could see a dashboard showing:
- Customers with a higher predicted payment risk
- The value of invoices associated with those customers
- Historical payment behaviour
- Predicted risk across different regions or business units
- Changes in predicted risk over time
The organisation could then use this information to prioritise its resources.
Instead of waiting for invoices to become overdue, the finance team could identify potential risks earlier and determine where further attention may be appropriate.
Beyond Financial Management
The same principle can be applied to many other areas of an organisation.
For example, machine learning could be used to support:
Demand forecasting
Historical purchasing and operational data could help identify potential future demand and support planning decisions.
Customer analysis
Customer behaviour and historical activity could be analysed to identify patterns and potential changes in customer behaviour.
Fraud and anomaly detection
Machine learning can help identify transactions or activities that appear unusual compared with historical patterns.
Supply chain planning
Historical supply and demand information can be used to help identify potential supply chain risks and changing demand patterns.
Workforce planning
Historical workforce information can provide insights that support workforce planning and resource allocation.
The specific application will depend on the organisation’s data, objectives and business requirements.
From Reporting to Predictive Analytics
Traditional business intelligence remains extremely important.
Organisations need to understand their financial performance, operational activity and historical results.
However, machine learning can extend that capability.
Instead of relying exclusively on historical reporting, organisations can begin using their data to generate predictions and identify potential risks or opportunities.
The progression can be viewed simply as:
What happened?
Traditional reporting answers this question.
Why did it happen?
Analytics can help identify the underlying factors and relationships.
What is likely to happen?
Machine learning can provide predictive insight.
What should we do?
Business leaders can then use those insights alongside their own knowledge, experience and business processes to determine the appropriate action.
This creates a much more valuable role for enterprise data.
Building on the Oracle Technology Foundation
One of the advantages of this approach is that organisations can build on technologies that are already part of the broader Oracle ecosystem.
Oracle SaaS applications provide the operational foundation.
Oracle Database provides a platform for storing and processing enterprise data.
Oracle Machine Learning provides machine learning capabilities that can be applied to that data.
Oracle Analytics Cloud provides the dashboards, reports and visualisations that allow business users to explore the results.
Together, these technologies can create a connected pathway from operational business data to predictive insight.
The goal is not just about incorporating machine learning into the current technology infrastructure.
The goal is about making the existing enterprise data more useful.
Turning Data into Business Value
As far as business managers are concerned, what really matters is not whether the company will be able to adopt a machine learning model.
What really matters is what the company can achieve by applying machine learning to its business data.
A successful implementation should connect the technology to a genuine business requirement.
It could help a finance team identify payment risks earlier, help a supply chain team anticipate demand, help management identify unusual activity or help business leaders understand potential future outcomes.
When machine learning predictions are combined with Oracle Analytics Cloud, these insights can become accessible to the people who need them.
This creates an opportunity to move beyond simply reporting on the past and start using enterprise data to support more informed decisions about the future.
At CushySky, we believe extending Oracle SaaS with AI, machine learning and analytics can help organisations unlock more value from the data they already have.
The opportunity is not just to produce more data, but to transform enterprise data into insight, insight into better decisions, and ultimately, better decisions into tangible business value.
