Maximising ROI in Machine Learning: Best Practices for Success

Many companies stumble in their machine learning journey, often dazzled by the technical complexities and missing a crucial step—clearly linking their efforts to tangible business outcomes. Additionally, they often significantly underestimate the effort required to have high quality ML use case in production.

To bring these challenges into sharper focus, consider a real-world scenario where poor planning and operational roadblocks derail a machine learning initiative aimed at slashing customer churn.

Case Study: Predicting Cable TV Customer Churn

Picture this: A customer experience team at a cable TV provider is tasked with using machine learning to curb customer churn and offer timely discounts. Yet, the business overlooks a crucial first step—assessing the financial upside. If they had done the math, they’d have realised that cutting churn from 20% to 10% could unlock a $1 million boost in annual revenue, underscoring just how vital this project should be. However, missing this, they loose the opportunity to gather right investment and sponsorship for the effort.

With no proper planning in place, the data scientist is left scrambling to build the model, hitting roadblocks right out of the gate. Access to critical customer support data drags on for an entire month, and when the first version of model is finally built in development, it limps in with a disappointing 67% F1 score.

In an effort to boost accuracy, the data scientist digs deeper, adding purchase and consumption history into the mix. But the effort is plagued by slow data discovery and access and poor data quality and standards, turning what should have been a quick fix into a drawn-out process. Even with these struggles, F1 score inches up to 75%. Furthermore the entire project grinds to a halt when the team realises that there is a need for software integration with operational system which was not planned.

Worse yet, with predictions only updated monthly, many customers are already long gone by the time insights hit the table. Fast forward six months, and the operations team is raising red flags as the model’s accuracy nosedives, leaving them to question if the entire initiative was worth the effort.

Frustrated by endless setbacks and minimal progress, the data scientist finally throws in the towel, seeking out a company that’s serious about data-driven transformation. They find a new home where the right tools, streamlined processes, and leadership commitment make a real difference—delivering the kind of results that their previous organisation could only dream of.

Challenges

This case study highlights a critical truth: building machine learning models is about much more than just crunching algorithms. The real battle lies in overcoming hurdles such as data access, quality, and scalability—any of which can sink even the most promising initiative if left unchecked.

These challenges are far from unique. Whether it's a small company just dipping its toes into machine learning or a large enterprise grappling with ROI, these roadblocks pop up time and again, derailing efforts across the board.

Business Aspects

1. Improper understanding of business outcome
2. Improper understanding of investment needed
3. Improper understanding of success KPIs

Capability Aspects

1. Delay in discovering and getting access to trustworthy data
2. Slow refresh of data
3. Data quality issues
4. Data harmonisation problems
5. Deployment challenges
6. Underestimating integration effort with operational systems
7. Too much manual work and hence slow speed of experiment iteration
8. Lack of automation and standardisation in move to production
9. Lack of model monitoring in production
10. Difficulty running A/B tests at scale

Tackling these roadblocks doesn’t just boost the performance of a single machine learning project—it sets the stage for success across dozens, if not hundreds, of use cases. These same solutions can supercharge analytics and business dashboards, creating a ripple effect of efficiency and value across the organisation

Want to explore how well-executed data and AI platforms can give your organisation a winning edge? Check out 'Well-Implemented Data and AI: A Top Competitive Advantage for Large Companies'. To learn more about prioritising use cases, read second section in customer support automation article.

Solution

Solution for this challenge lies in five steps

1. Clear Business Outcomes - Having clear problem definition of use case along with clear business value ($1 million/100k/10k additional revenue) and success metric direction (reduce churn) before building any model
2. Set Expectations - Setting right effort expectations for ML adoption is crucial. Success is much more than running algorithms
3. Set KPIs for Success (business)
4. Invest in Data Platform
5. Invest in Data Science and MLOps Platform

Business Outcome

For any machine learning initiative, understanding the business outcome is not just important—it’s critical. It’s not about checking a box that says you’ve used an ML algorithm or implemented MLOps; those are just technical milestones. What truly matters is the business impact. Take a cable TV provider as an example: cutting churn from 20% to 10% could unlock an additional $1 million in revenue. That’s the kind of measurable success companies should be aiming for.

Or alternatively, at a bank it could mean reducing time to offer loan from six weeks to two weeks could increase offer acceptance by 20% giving bank additional 100 million in loan offered.

Unless such outcomes are clear, it would be tough to invest into the foundational capabilities for AI and data transformation. Without clear business outcomes, it would also be tough to gain the buy-in from the sponsor or stakeholders.

Setting Expectations

Setting the right expectations is key to success in ML adoption. All too often, organizations underestimate the complexity of building production-grade models, leading to POCs that barely got off the ground due to half-hearted support. The result? Missed deadlines, unmet quality standards, and a project that fizzles out before it even begins to show its value.

That’s why it’s critical for Data and AI leaders to set clear, realistic expectations upfront. Building strong foundations—like data quality, governance, user-friendly tools, and streamlined automation—ensures faster iterations with ML use cases and prevents costly surprises down the road. Success in ML depends on getting these basics right from day one.

Too often, teams fall into the trap of treating these efforts as a mere 'checkbox exercise.' Without well-defined business KPIs—like faster time to market, quicker insights, or higher customer satisfaction—these initiatives risk becoming little more than empty technical milestones, offering no real business value.

Business KPIs for Success

Implementing a strong data and AI platform is just the beginning—it’s a means to an end, not the end itself. The real question is: have you measured its impact? How much faster can your AI use cases move from inception to production now compared to before? Without tracking these metrics, you’re missing the chance to prove data and AI platform's true value to your business.

Additionally, when such KPIs are defined early in the project, they end up influencing "how" the data and platform is implemented, focusing clearly on tradeoffs that help deliver the business outcomes such as as time to market, time to insight, improved customer satisfaction or business growth etc.

Data Platform Investment

To tackle persistent data challenges, companies need to invest in a comprehensive data platform that goes beyond the basics. A strong platform ensures data governance, automates quality checks, streamlines access, and makes data easy to use across the organization. These features are the backbone of any successful ML initiative.

1. Business Data Catalog
2. Data Access Workflow
3. Data Lineage and Profiling
4. Automated Data Classification
5. Data Quality Automation
6. Data Harmonisation
7. Data Ops Automation
8. Ease of Use , Standardisation and Simplification

A. Data Governance

1. Business Data Catalog - A well-organized business data catalog is crucial for speeding up use case requests. Instead of wasting time tracking down data sources or asking multiple teams for information, data scientists can quickly locate trustworthy data in minutes, unlocking faster insights and results.
2. Data Access Workflow Automation - Fast access to data is a game-changer for accelerating time to market. Once trustworthy data is identified, approval and access should happen within hours, not days. This can be done with approval and permission system with zero admin in loop. With data science projects requiring constant iteration, delays in access can bring progress to a halt, putting both timelines and project feasibility at serious risk.
3. Data Lineage & Profiling - Data lineage and profiling are key to identifying trustworthy data. If a dataset is widely used across multiple downstream systems and regularly refreshed, it's a strong indicator of reliability. For example, a customer table that’s updated daily and feeds several systems is far more dependable than one updated sporadically with fewer downstreams. This visibility into data usage builds confidence in its accuracy.
4. Automated Data Classification Workflow - Classifying the right access level of datasets and fields within them should be semi automated to scale the effort and enable faster access to data fields.

B. Data Quality

1. Data Quality Workflow Automation - In machine learning, the adage 'garbage in, garbage out' couldn’t be more true. To ensure high-quality results, organidations need to automate the detection of data issues at scale. By setting up workflows that instantly flag and assign problems for resolution, you can prevent low-quality data from derailing your entire ML project. This proactive approach keeps data quality under control, ensuring your models perform as expected. Systems such as ServiceNow or Jira could be integrated into automated data quality checks.
2. Data Harmonisation - When dealing with data from multiple systems, managing master data is essential for successful analysis and machine learning. By harmonizing this data across your cloud warehouse or data lake, you ensure consistency and accuracy. This not only improves the quality of your analysis but also allows your machine learning models to draw insights from a unified source of truth..

C. Data Automation

1. Automating DataOps processes—such as version control, continuous integration, and deployment—transforms how quickly your team can iterate and deliver results. By ensuring that every code change triggers automatic testing and deployment, you free up valuable time for your team to focus on innovation rather than manual tasks. This streamlining boosts productivity and accelerates your time to market.
2. Automation of technical complexities such as security, networking, autoscaling, cost and performance optimisation can help teams focus on productive work. Even compliance checks to regulations such as PCI DSS, HIPPA could be automated to 80-90% using infrastructure as code tools such as CDK or CDK-TF.

D. Ease of Use

1. Ease of Use & Simplification - Making data easy to use is essential for empowering your team. With a standardized, SQL-based interface, 80% of your end users can access and automate their workflows without needing to understand technical details such as file formats or complex processing frameworks. This not only reduces dependency on data engineers but also speeds up decision-making, allowing your organisation to act on insights faster.
2. Standardisation - Standardization is the key to scalability. By creating easy-to-use platforms with clear documentation and strong support, you empower every business unit to adopt and benefit from data-driven initiatives quickly. This consistency not only accelerates adoption but also ensures that every team works with the same tools and processes, driving efficiency and collaboration across the organization.

Better Data Science and MLOps Platform

Ease of use, fast iterations, automation etc

1. Ease of Use - AutoML
2. Ease of Use - Notebooks
3. Automation
4. MLOps Platform
5. Model Governance and Explainability

1. Ease of Use with AutoML User Interface - User-friendly AutoML tools, like SageMaker Canvas and Dataiku, are game-changers for speeding up machine learning experiments. These platforms empower both citizen data scientists and experienced professionals to quickly test ideas and build models without getting bogged down by repetitive tasks like fine-tuning parameters. By simplifying experimentation, AutoML tools help your team deliver results faster, boosting innovation and cutting time to market.
2. Ease of Use - Notebooks - User Friendly Notebook. Making machine learning tools user-friendly is essential for broad adoption and productivity. Platforms with intuitive notebooks, like the latest version of JupyterLab, simplify the experience, allowing data scientists to focus on solving problems, not wrestling with software. Additionally, providing easy access to data and streamlining feature engineering pipelines removes bottlenecks, enabling teams to move from data to insights faster.
3. Automation - Data scientists shouldn’t have to worry about managing infrastructure, security, or cloud optimization—that's not their area of expertise. By automating these complex tasks, your team can focus on what they do best: building models and driving insights. Automating infrastructure, compliance, and performance management not only reduces risk but also boosts efficiency, allowing your data science efforts to scale seamlessly
4. Fully Integrated MLOps - MLOps is a powerful tool for accelerating the deployment of machine learning models, allowing your organization to move quickly and scale efficiently. By leveraging platforms with built-in MLOps support, you can streamline workflows across teams and reduce the time from experimentation to production. However, it's important to remember that MLOps alone won't guarantee success—it enhances speed and scale, but the quality of your use case identification and best practices will determine the true value you gain from it.
5. Model Governance and Explainability - Model governance and explainability are crucial for building trust in machine learning predictions. Businesses need clear, transparent insights into the reasons behind predictions to confidently move models into production. Furthermore, ensuring fairness in these models is often essential, both for maintaining regulatory compliance and meeting ethical business standards. A robust governance framework not only strengthens trust but also safeguards your organization against potential legal and reputational risks.

Fully Integrated MLOps

For a deeper understanding of building successful MLOps platforms, I highly recommend this 9-minute video on Uber’s Michelangelo platform, which highlights the key decisions behind its success. You can also explore the accompanying blog for insights into its current implementation. For those interested in more in-depth learnings, a comprehensive one-hour video provides valuable lessons from Uber’s experience in scaling MLOps effectively.

MLOps is designed to enhance the speed and scalability of your machine learning deployments. Services such as Amazon SageMaker offer built-in MLOps support to streamline workflows across teams. However, it’s important to note that while MLOps accelerates processes, it isn’t a magical solution. Without strong use case identification and best practices in place, faster deployment may simply lead to faster failure. Success depends on combining MLOps with sound strategic planning.

When implementing MLOps, start with the basics—like version control and CI/CD—and gradually scale up as your needs grow. There's no need for a full-scale launch on day one. By automating processes and offering easy-to-use interfaces, you simplify adoption, ensuring that teams can onboard quickly and efficiently. This step-by-step approach helps you avoid disruption while setting a strong foundation for long-term success.

MLOps standardises and enables following

1. Version control
2. CI/CD
3. How do you store training data ?
4. How do you track experiments ?
5. Model Training
6. Model serving
7. Feature store - Where are reusable features stored for online prediction ?
8. Models registry - for easy deployment for api/batch predictions of different versions of models
9. How do you automate container based deployments
10. How do you do canary model deployment
11. Model monitoring

Conclusion

To unlock the full potential of machine learning, businesses must first understand the problem, define measurable outcomes, and determine the investment required to secure the right sponsorship. It’s not just about algorithms—success hinges on setting the right expectations, driving business outcomes with clear KPIs, and investing in robust data analytics, data science, and MLOps platforms. These investments should go beyond checkbox exercises to truly accelerate time to market, speed up insights, and improve customer satisfaction, ultimately delivering meaningful, lasting impact.