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Phase 6 - Operationalise

Phase 6: Operationalize

Overview: Operationalize is the final phase where the project transitions from a testing environment (sandbox) to a live production environment. Data is monitored to ensure that the model performs as expected, and adjustments are made if necessary. The team also communicates the project's benefits widely and sets up pilot projects for controlled deployment.

Key Processes:

  1. Moving to Live Environment:

    • Data is moved from the sandbox to a live environment for real-world application.
    • The model's performance is closely monitored to ensure it produces the expected results.

  2. Communication and Pilot Deployment:

    • Benefits of the project are communicated to a broader audience, and pilot projects are set up to deploy the work in a controlled manner.
    • Pilot projects help manage risks effectively by testing the model on a small scale before full deployment.

  3. Efficient Execution and Monitoring:

    • Algorithms may need to be executed more efficiently in the database rather than using in-memory tools like R, especially with large datasets.
    • The model is tested in a live setting, such as a production environment, for a specific set of products or business lines. Model accuracy is monitored, and the model is retrained if necessary.

Key Outputs: Successful analytics projects yield several key outputs:

  • Business users determine business benefits and implications.
  • Project sponsors assess business impact, risks, and return on investment (ROI).
  • Project managers evaluate if the project was completed on time, within budget, and if goals were met.
  • Business intelligence analysts determine if reports and dashboards need to be updated.
  • Data engineers and database administrators share code and documentation.
  • Data scientists share code and explain the model to peers, managers, and stakeholders.

In Simple Terms:

Imagine you've built a robot and now it's time to unleash it into the real world.

First, you move the robot out of the workshop and into a live environment where it can perform its tasks. You keep a close eye on it to make sure it behaves as expected.

Next, you tell everyone about your amazing robot and set up some small-scale trials to see how it performs in different situations. This helps you identify any potential problems before rolling it out everywhere.

You also make sure the robot runs efficiently and smoothly, especially when dealing with lots of data. And if it doesn't work perfectly at first, you tweak it until it does.

Finally, you share your robot's success with everyone involved, highlighting its benefits and ensuring that everyone understands how it works. It's a team effort to make sure the robot – or in this case, the analytics project – is a success.