Step 4: Execute Minimum Viable Product (MVP) Implementation and Iterate

With the MVP defined in Step 3, the focus now shifts to implementing and refining the MVP to deliver immediate value. This step involves assembling the right team, selecting appropriate tools, adopting agile methodologies, and proactively addressing potential blockers. The objective is to develop a functional solution swiftly, validate its effectiveness, and establish a foundation for scaling and continuous improvement. This foundation will also serve as a launchpad for more advanced capabilities, such as deeper analytics and, when the time is right, AI-driven insights.

Building a capable and collaborative team is crucial for the successful implementation of the MVP. The team should encompass a diverse range of skills and expertise pertinent to the project.

Key Roles:

Selecting appropriate tools and technologies accelerates development and ensures scalability and flexibility of the solution.

Implementing agile practices facilitates flexibility, rapid response to changes, and incremental value delivery throughout the project. Agile practices have four key pillars:

Planning: Break down the project into actionable tasks. Choose between the Sprint method, which focuses on delivery within a set timeframe (usually two weeks), or the Kanban method, which emphasizes continuous task delivery. Each approach has its merits depending on team dynamics and discipline. We tend to favor Kanban for its flexibility and flow.

Frequent Stand-Ups: Establish a cadence for stand-ups and project catch-ups that aligns with team needs and dynamics. For newer teams or those needing closer coordination, daily meetings may be beneficial. For others, once or twice a week might be sufficient to plan and review the week’s progress. The frequency can also depend on team discipline in keeping task lists updated.

Continuous Integration and Testing: Regularly integrate new code and conduct testing to catch issues early. Version control tools like Git offer built-in CI/CD pipelines, enabling engineers to test their code with each pull request. This ensures quality and stability with every iteration and streamlines code review processes.

Retrospectives: After each sprint, evaluate what went well and what could be improved

Embracing an iterative approach that allows for rapid testing and learning can mitigate risks associated with unforeseen challenges.

Test and prototype early. Develop small-scale prototypes to validate concepts and identify issues promptly. Use these prototypes to gather immediate feedback and refine approaches.

Communicate often and transparently. Maintain open lines of communication with all stakeholders about risks and uncertainties. Address challenges openly to build trust and collaborative problem-solving.

Respond with flexibility. Be prepared to pivot strategies based on new insights or changing circumstances. Encourage a culture where learning from failures is valued and leveraged for improvement.

Proceed to build the MVP as per the defined scope, focusing on delivering core functionalities that address the primary business problem.

After deployment, collect feedback to refine the MVP and enhance its value. Focus on performance monitoring (tracking key metrics), user engagement (feedback from end-users through surveys or interviews) and continuous improvement (prioritize and plan improvements based on feedback and impact).

As you implement and refine your MVP, planning for its maintenance is vital to ensure its longevity and adaptability. Maintenance protocols address potential issues before they escalate, keep the solution aligned with business changes, and uphold data integrity and performance standards.

Develop a Maintenance Plan by defining maintenance activities and assigning dedicated roles (regular data quality checks, scheduled updates, backup and recovery procedures, and compliance).

Implement Monitoring and Alert Systems by setting up monitoring tools (tracking system performance, data flows, user engagement) and alert mechanisms (real-time alerts for errors, downtimes and performance drops).

Establish Support Channels, most important user support (documentation, FAQs and a support ticketing system) and feedback loops (regularly gathering positive and negative user feedack).

Plan for Continuous Improvement by setting up regular reviews (scheduled MVP evaluations) and documentation (configurations, processes and version history).

Identifying and mitigating potential blockers early ensures the smooth progress of the MVP implementation. These challenges typically fall under stakeholder, technical, or organizational barriers.

When facing stakeholder challenges, such as resistance or lack of engagement, involve key decision-makers from the start, provide regular briefings, and share progress openly to build trust. Demonstrating early wins shows tangible benefits and aligns the initiative with stakeholder goals. If there is cultural resistance to change, encourage cross-functional teamwork, recognize and reward openness to new approaches, offer training sessions to address concerns, and empower change champions who can advocate within their teams.

Technical challenges often arise from legacy systems and technical debt that hinder integration and scalability. Consider middleware solutions to connect old and new systems, choose compatible tools, and plan incremental upgrades to avoid disruptions. Data quality and availability issues should be addressed by conducting thorough data audits, implementing data governance practices, and improving data collection and storage infrastructure.

Organizational challenges, such as bureaucratic delays or resource constraints, may slow the project’s momentum. Work with leadership to simplify approval chains, obtain executive sponsorship to remove obstacles, and show early value to justify the need for streamlined processes. If skilled personnel or budget is limited, optimize resource allocation to focus on critical tasks, consider external support, and adjust the MVP scope to fit available resources without undermining core objectives.

Keep stakeholders in the loop and maintain motivation by communicating progress and milestones achieved.

As soon as your MVP has proven value by achieving a measurable improvement to one or more key metrics, move on to Step 5: Transition to Organization-wide Adoption.

If you’d rather avoid execution delays, schedule a free 30-min consultation with our Head of Data. We’ll show you how to efficiently implement, test, and refine your MVP without costly setbacks.

Here’s how this looked for a mid-sized consumer packaged foods company:

Assemble a Cross-Functional Implementation Team:

After identifying OEE (Overall Equipment Effectiveness) as the most impactful KPI to improve, the company brought together a lean, cross-functional team.

This included a data engineer familiar with pulling operational data from the packaging lines, an analytics engineer with experience in transforming raw data into actionable insights, and a production floor supervisor who understood process bottlenecks and seasonal throughput variations. By working closely from the start, the team ensured technical solutions aligned with practical manufacturing realities.

Choose the Right Tools and Technologies:

Initially, data was tracked on clipboards and whiteboards—a manual process that couldn’t scale with the plant’s output. The team extracted data from an on-premise SQL Server that logged packaging line events and moved it into Snowflake to handle the growing volume and complexity of data. Azure Data Factory streamlined ingestion, enabling quick, reliable updates.

This modernized setup not only supported the MVP’s immediate needs but also positioned the company to integrate additional data sources, such as raw ingredient quality checks and finished goods inventory levels, in the near future.

Adopt Agile Development Methodologies:

Rather than launching a months-long waterfall project, the team worked in iterative sprints. Early prototypes revealed that combining event data to create hourly OEE snapshots was computationally intensive. By reviewing performance daily and holding brief, focused stand-ups, the team rapidly tested different data modeling approaches. This enabled them to quickly move from a proof-of-concept dashboard to one that updated reliably every 30 minutes—fast enough to catch production slowdowns before they impacted daily output.

Adopt a "Fail Fast, Recover Quickly" Mindset:

The first approach to aggregating line events was inefficient, running two-hour refresh cycles and missing the target update interval. Instead of treating it as a failure, the team used it as a learning opportunity.

By leveraging Snowflake’s Python integration and libraries to efficiently sequence downtime events, they reached the 30-minute refresh target. This quick course correction turned a stumbling block into a stepping stone, sharpening the team’s understanding of both the data and the tools at hand.

Develop and Deploy the MVP:

Once the dashboard stabilized, shift managers and plant floor leads accessed a near real-time view of uptime, speed, and yield percentages. Instead of relying on end-of-shift reports, supervisors could immediately spot where packaging lines were underperforming and adjust staffing or maintenance schedules.

The MVP’s deployment introduced a new level of responsiveness, ensuring that issues like jammed fill nozzles or misaligned packaging sleeves were addressed before they affected entire batches.

Iterate Based on Feedback:

As operators began using the OEE dashboard, their suggestions guided incremental improvements. They requested clearer trend lines to highlight persistent slowdowns, automated alerts for unexpected stoppages, and the addition of upstream ingredient quality metrics. Each iteration made the solution more intuitive and more attuned to real-world needs, driving greater adoption and trust on the production floor.

Establish Maintenance Protocols:

To safeguard long-term reliability, the team implemented regular data quality checks, monitored processing times for anomalies, and documented protocols for handling system updates. Maintenance tasks, scheduled during non-peak production hours, ensured the dashboard’s performance wouldn’t degrade over time. This proactive approach kept the OEE insights accurate and actionable, sustaining the momentum gained from the initial MVP release.

Proactively Address Potential Blockers:

While some IT staff were initially skeptical about moving beyond their Microsoft-centric environment, demonstrating tangible value from Snowflake’s scaling capabilities allayed those concerns. As the dashboard delivered meaningful, timely data and improved production outcomes, skepticism gave way to enthusiasm.

Ultimately, this success story eased future tool adoption, paving the way for similar analytics solutions in areas like quality assurance, supply chain, and demand forecasting.

Want to ensure you get to ROI as quickly as possible? Book a free 30-minute consult to learn how our years of experience can help you implement efficiently and effectively.