Many companies in a rush to adopt machine learning (ML) may be missing some key elements necessary for capturing ROI. Successfully using ML to improve business decision-making takes more than technology. A well-planned, strategic approach and the right data science expertise are equally critical.
For organizations that are new to AI/ML, partnering with outside specialists can be the fast-track to better results. In our data science collaborations with companies across many industries, we’ve seen certain factors have added significant value for our clients. Here are three of the most important to consider:
1 – Increase impact by aligning ML efforts with business strategy
Using machine learning for predictions doesn’t start with data. To get actionable insights, your hypotheses and datasets need to align with real business practices and decisions. That’s why we focus first on understanding how the business operates, including interviewing the client’s internal business experts and stakeholders about problems they need to solve, and what they think is causing them.
These brainstorm sessions often generate dozens of ideas. We also layer in perspectives based on our decades of work in customer experience and advanced analytics. Our data scientists then translate ideas into nuanced hypotheses the ML models and data need to answer.
After initial testing of predictive models, we re-engage the business experts so they can assess if the predictions are sensible and actionable. It’s important that models be explainable in how they derive certain predictions. That helps build trust in the models, the datasets, and how the data was prepared.
Beyond the Arc’s CEO Steven Ramirez adds, “If the predictions can’t be used for taking action, machine learning is just a thought exercise.”
2 – Capture better decisioning insights using the latest ML techniques
Many organizations rely on analytics using traditional regression models, but the latest advances in machine learning can achieve significantly better results. An outside partner like Beyond the Arc can help you increase predictive power using sophisticated ML techniques like neural networks, decision trees, and support vector machines (SVMs).
Another advantage of collaborating with outside experts is how you benefit from their broader experience. For example, based on working with an extensive range of modeling scenarios, they have a solid understanding of which algorithms and data types may work best to solve specific problems.
It’s also highly valuable to have a data science partner that really understands the key issues and complexities of your business. That industry expertise equips them to recommend which data sources and variables may help models generate better insights.
3 – Amplify the quality of insights with feature engineering
80% of data science work centers on
One of the most difficult challenges companies face is how to prepare the right data to ensure the chosen algorithm can deliver optimal results. That’s where feature engineering comes in. It’s one of the most crucial tasks of a data science project, and the most time-consuming. Having the right expertise is essential.
Feature engineering brings order to the chaos of raw data. Within databases, data often has many inconsistencies, idiosyncrasies, and duplications based on how it was entered. That’s not what you want to feed into your ML models.
Instead, it’s critical to first organize and engineer data features into new variables that align best with a chosen algorithm and the hypothesis you want to test. It often entails filling in missing values, splitting or grouping values, recoding categorized data into numeric inputs, removing dupes, and more.
The key is to identify the right variables and values that target the specific nuances you need. An expert in feature engineering can remove the noise in the data, and identify variables that make actionable patterns easier to spot.
Domain expertise also makes a substantial difference in data preparation. Data scientists that have built numerous ML models for specific industries (e.g., lending) can more quickly identify and engineer the most useful variables from the raw data.