Bring AI to Data , A Smarter Way with Oracle!

 “Bring AI to Data” is a term I recently heard during the Oracle AI World in Singapore last week and it really caught my attention. It sounded simple but the idea behind it is quite powerful. So I thought it’s worth exploring a bit more!

Normally, working with data and AI meant one thing, which is, moving data around. We would extract data from databases, send it to external tools or platforms, build machine learning models and then push the results back into the database. But this approach adds complexity, increases costs and introduces security risks.

But now, Oracle is changing that model by bringing AI to where the data already is!

The concept of “Bring AI to Data” is straightforward but powerful. Instead of moving large volumes of data across systems, Oracle allows you to run AI and machine learning directly inside the database. This means that data do not have to leave its secure environment. This results faster processing, reduced data duplication, improved security and a much simpler architecture. For organizations dealing with sensitive data, such as banks, healthcare organizations and government institutions, this approach is especially valuable.

Lets consider a simple banking scenario where we want to predict customer churn. Traditionally, we would extract customer data, move it to a machine learning platform, train a model and then integrate the predictions back into our system. With Oracle we can create and run the model directly using SQL.

For example, we can build a churn prediction model using a built in package as below.

BEGIN
  DBMS_DATA_MINING.CREATE_MODEL(
    model_name => 'CHURN_MODEL',
    mining_function     => DBMS_DATA_MINING.CLASSIFICATION,
    data_table_name     => 'CUSTOMER’,
    case_id_column_name => 'CUSTOMER_ID',
    target_column_name  => 'CHURN_FLAG'
  );
END;
/

Once the model is created, generating predictions is just another SQL query.

SELECT
  CUSTOMER_ID,
  PREDICTION(CHURN_MODEL USING *) AS CHURN_PREDICTION,
  PREDICTION_PROBABILITY(CHURN_MODEL USING *) AS PROBABILITY
FROM CUSTOMER;

As per above example, in just a few lines, we can implement AI without exporting a single row of data. This simplicity is what makes Oracle’s approach much practical for real world systems.

Another useful example is building recommendation systems. Whether it’s suggesting products in an ecommerce platform or recommending financial services to customers, Oracle allows us to generate recommendations directly within the database. Using built in models we can produce recommendation sets with simple queries, without the need for external processing engines.

Oracle also supports integrating Python with the database, thus giving us the flexibility to use popular machine learning libraries while still keeping data nearer. For eg, we can load data into a Python environment directly from Oracle, train a model using libraries and store the model back in the database. This approach allows us to combine the familiarity of Python with the performance and security of Oracle.

One of the biggest advantages of this approach is security. When data stays within the database, there are fewer risks associated with data movement. There are no intermediate files, no unnecessary copies and fewer integration points that could be exposed. This makes compliance with data protection regulations much easier as well.

Performance is another key benefit. Since AI models run where the data is, there is no network latency or overhead from data transfer. Oracle databases are already optimized for high performance processing and by executing AI workloads within the databases, organizations can take full advantage of parallel processing and optimized query execution.

In real world, this approach can be applied to fraud detection, customer segmentation, predictive maintenance etc. Financial institutions can run fraud detection models in real time on live transactions. Retail companies can segment customers and personalize marketing campaigns. IoT systems can analyze sensor data directly within the database to predict equipment failures before they happen.

What makes Oracle’s “Bring AI to Data” approach stand out? It is its simplicity!

Instead of building complex data pipelines and managing multiple tools, everything happens in one place. This reduces operational overhead and allows us to focus more on insights rather than infra.

So, “Bring AI to Data” is a smarter way of thinking about data and analytics. By keeping data in the same place and bringing AI to it, organizations can build faster, safer and more efficient solutions.