Operationalizing analytics models is critical if you want these models to remain relevant and useful. However, successfully operationalizing the advanced analytics application lifecycle is often easier said than done. Below, we’ve outlined some essential considerations to incorporate as part of a framework for operationalizing advanced analytics:

  1. Business Engagement and Strategic Planning 

It’s important that analytics models are aligned with the company’s strategic plan, meet business needs, and are accepted and agreed upon by all relevant stakeholders. As part of this step, many organizations establish a center of excellence and governance framework to define how to manage and monitor analytics models in production. This approach also ensures that standards and policies are implemented to verify that the data feeding the models is accurate and complete, even as the business and data sources change. 

  1. Building Data Pipelines 

Prior to building data pipelines, data scientists first need to assess the raw data to understand where the value lies for the business. This step doesn’t require continuous data feeds with a fully operational model; a snapshot of the data is sufficient to develop and test the analytic models. Once the data science team has determined which data is valuable, they should share their profiling, quality, and transformation code with the data engineering team. This enables the latter to evolve it into production-quality code and create data pipelines. 

  1. Model Development, Improvement, and Measurement 

Another important consideration is developing and refining models and measurements, including the iterative development, refinement, and improvement of the models to reflect business changes. As part of this, organizations need reliable, automated feedback mechanisms to measure performance, tooling, and processes for business-as-usual model management. 

  1. Insights to Action 

Successfully operationalizing processes entails rapid prototyping of models with engaged business partners, integrating data into operational systems, and taking prescriptive actions with little to no human involvement. 

  1. Adoption and Measurement 

The final step is to create metrics that measure analytics’ business value. These should track to financial results, KPIs, and other outcomes used to measure the company’s success. 

Armed with the capabilities outlined above, companies can operationalize advanced analytics applications supporting corporate objectives. For more on why this is so critical, take a look at this recent APEX of Innovation post examining why some analytics projects are destined for failure.