Maybe you’ve been in a meeting like this: Weeks ago, your executives posed a question to your team that had critical business implications. It might have been, “Which customers are likely to bring us repeat business?” or “How can we get ahead of potential consumer fraud issues in our credit card business ?” Whatever the question, it was your team’s job to leverage data to reach an answer.
Led by human intuition on which data sets would best predict the answers the business needs, your team applied machine learning and came back to the table. Now, your business leaders are questioning the accuracy and of your results.
This common scenario illustrates a couple of the most frustrating machine learning challenges for data scientists, software engineers, developers and business leaders:
- It’s time-consuming to manually define prediction problems, identify relevant features, collect disparate data and feed it into machine learning models.
- Taking the time to do so doesn’t guarantee accurate outcomes.
Data science starts with a human process – the business question and the ideas about which data can help answer that question. But businesses need more automation at the front end to incorporate valuable data quickly and efficiently, and extract patterns executives can confidently incorporate into business initiatives.
Without this automation boost, organizations struggle to make their peace with a slow process that yields questionable results.
If your business leaders are weighing the investments they’ve made to collect data versus what they’re getting from it, and the scales aren’t balancing as expected, focus on the feature engineering work upon which machine learning depends.
Here’s what happens when you take that complex, multi-step, iterative process and simplify it with automated feature engineering:
- You get better results from the human expertise on your team.
- You improve the accuracy of your results.
- You save weeks of effort, enabling your executives to pose more business questions.
Depending on what those questions are, you can measure results in improved customer experience, increased revenue, streamlined operations and more.
Is your company making the most of the data it's collected? The answer is likely “no,” especially if your machine learning services have stalled out and stakeholders doubt their veracity. Making machine learning easier to use can solve both challenges.
Contact Feature Labs to succeed with data science and predictive modeling.