From a logistical perspective, implementing data science and AI operationalization in enterprises can be notably more challenging than self-service analytics. This increased complexity stems from the necessity for coordination, collaboration, and substantial changes not only at the organizational level but often extending to system architecture and deployment/IT domains. 

Nevertheless, data science and AI operationalization represent the ultimate and pivotal phase of any data project. Without proper operationalization, these projects remain incomplete, unable to deliver tangible, revenue-generating impacts for the business. So, what steps should you take to ensure a seamless execution? We recently outlined four primary reasons operationalization efforts may stumble, enabling you to proactively avoid these pitfalls within your organization. Now, we’re presenting a compilation of tips to facilitate smooth execution.

1. Early Business Alignment

Start by collaborating with business teams from the project’s outset. This ensures your operationalized solution aligns with business goals. Often overlooked, this step prevents data teams from investing time and effort in solutions that don’t deliver actual value. While data teams excel in their work, they may lack the business context to optimize solutions. 

For instance, if a data science team working on a loan website’s fraud detection model doesn’t consult with the fraud operations team, they may create a technically sound but impractical model. Close collaboration provides crucial insights for refining the model to meet real-world operational challenges. 

2. Streamlined Packaging and Deployment

From a methodological perspective, operationalization demands a consistent and efficient workflow encompassing integration, testing, deployment, performance measurement, ongoing monitoring, and iterative enhancements. Discrepancies in packaging and deployment can subtly erode a model’s performance during the transition from development to production. 

Traditionally, the responsibility for adapting the data product to meet IT ecosystem requirements, including performance and security standards, falls upon the data engineering or IT team. However, this transition between data and IT or data engineering teams becomes considerably smoother when both teams operate with shared tools and align their objectives. Thus, effective communication, even among technical teams, becomes paramount in ensuring a seamless process. For more insights on how IT can contribute to preventing AI project failures, refer to this blog. 

3. Streamlined Model Retraining

After deployment, the efficient retraining and updating of models is paramount. Adopting a retrain-in-production approach is a pivotal aspect of operationalization success. Without it, model retraining becomes a full-fledged deploy-to-production process, demanding substantial resources and impeding agility.

4. Effective Monitoring and Performance Communication

A successful operationalization strategy also entails functional monitoring, which serves as a means to convey the model’s performance to business sponsors, owners, or stakeholders. This presents an opportunity to showcase the model’s real-world results for evaluation. The nature of the functional information shared can vary, depending largely on the industry and use case. Examples of such data include case contact counts, system flow interruptions, and performance drift measurements. Functional monitoring is closely tied to having a viable rollback strategy in case of issues. 

This step is crucial because ongoing knowledge transparency must be consistently disseminated and championed throughout the organization at every available opportunity. Any lapse in communication can jeopardize the significance and value of employing ML technology within the organization. 

Bringing It All Together 

It should now be evident that operationalization is not a one-time project, but an ongoing investment that demands a significant allocation of resources, both technical and personnel. Operationalization is inherently rooted in the notion that data science requirements are intricately linked with business needs, emerging from various lines of business or departments. In essence, data initiatives cannot thrive within an isolated, detached central team devoid of a profound understanding of the business or connections to those possessing such insights. To illustrate, successfully executing a customer churn prediction and prevention project necessitates input from teams like marketing and sales. 

Instead, operationalization thrives in organizations that establish a central data team as a center of excellence—a kind of internal consultant that can be mobilized to drive data efforts across the entire company, incorporating both self-service analytics and operationalization. In practice, this organizational model entails: 

A Comprehensive Data Platform

This platform provides access to data, facilitates discovery and exploration, supports visualization and dashboarding, and enables machine learning modeling and production deployment. It forms the foundation for both a thriving self-service analytics and operationalization environment. It ensures that everyone across the organization, regardless of their technical skill set, can work with trusted data and achieve desired outcomes, whether in the form of dashboards or predictive models. 

A Centralized, Unifying Data Team

This team, far from operating in silos, manages the platform, ensuring that all data is accessible, accurate, and usable within a self-service context. Additionally, they take responsibility for broader deployment and operationalization efforts stemming from self-service projects or other data initiatives in collaboration with business units. 

Seamless Collaboration and Communication

Establishing efficient channels for collaboration and communication between business units and data teams is essential. This facilitates addressing questions arising from data projects within their context, particularly concerning data origin, interpretation, and accurate utilization in projects. This collaboration also ensures that any significant data project developed through self-service analytics undergoes validation by the data team. 

Feedback Loops for Continuous Alignment

Implementing feedback loops is crucial to align operationalized data projects with business objectives and ensure their continued alignment. These loops enable easy adjustments if necessary, ensuring that data-driven initiatives consistently meet their objectives and adapt to evolving needs.