frustrated data scientists
frustrated data scientists
frustrated data scientists
ML Fundamentals

Data prep for Machine Learning doesn’t need to be so hard

Building powerful machine learning models capable of accurately predicting customer behavior takes a lot of data. Unfortunately, as a data scientist, there’s a good chance that you don’t have enough internal data to build the best models and deliver the insights your management team is looking for. As a result, you’re probably relying on second- and third-party data (or even data from other parts of the business) to augment what you’ve got. And while that may be standard procedure for many, the reality is that it comes with some pretty significant drawbacks. Afterall, working with different data sets is really hard.

In this post, we’ll look at some of the pain points you’re likely experiencing every time you try to prepare second- and third-party data for your machine learning models. We’ll then explain why using Signals can help you avoid those challenges while also increasing your models’ predictive power.

The data prep dilemma

Whenever you work with second- or third-party data, there are some important considerations that you have to keep in mind while you’re prepping it that can hinder your success. This includes issues like:

  • Low match rates. Any consumer data you acquire will only help boost the effectiveness of your machine learning models if it specifically pertains to your customers and the data that you already have about them in the dataset you use to train your model. If there’s little crossover between the dataset you’ve used to train your model and a dataset you’ve acquired, your model won’t improve in any meaningful way. Unfortunately, this isn’t something that you can ascertain until you’ve actually got the external data in hand.
  • Too much data aggregation. To help preserve consumer privacy, data is often aggregated at some macro level, such as by postal code. The problem with this is that it creates average values that reduce the variability within the data, thus rendering the data less useful overall.
  • Too many dimensions. Consumer datasets often include vast numbers of features about each customer, most of which aren’t particularly relevant when it comes to improving your model’s predictive power. That means you’ve got to take the time to reduce the number of dimensions in any data you acquire, cutting out any irrelevant information before building the data into your model.
  • Joining datasets is time consuming. Unless all of the data you’re working with is at the exact same level of granularity, joining disparate data sets can be tedious and time-consuming. Often it can take days, if not weeks, of painstaking work to join everything together.

Ultimately, because of challenges like these, you inevitably wind up spending far more time prepping your data than actually building the best machine learning models possible.

That’s problematic for a number of reasons. For example, it means that despite having spent a lot of money on costly datasets, it’s going to take a long time for your models to improve and start delivering value. Not only that, having less time to optimize your model is another blow to model performance. And then there’s the fact that you’re stuck doing a lot of grunt data prep work that’s tedious and isn’t exactly going to keep you and your team excited and engaged in your job.

Signals offer a solution

The good news is that there’s a much better alternative to using second- and third-party data in the way that you’re used to. Here at we’re creating what we call Signals, which are much easier to work with. Fit for purpose and machine learning-ready, Signals are rich, machine-learning derived insights about consumers’ needs that are generated from real consumer behavior.

Not only can Signals be appended as additional features to your existing training and test sets with a simple join, when you use them, you only receive Signals that are relevant to the model you’re working on, which helps boost model performance. As a result, you’ll see a much faster increase in model performance (often in days, instead of weeks). Not only that, you and your colleagues will be a lot happier since you’ll have more time to do the work you enjoy doing like algorithm selection and model tuning, rather than just manipulating data. Best of all, when you’re able to quickly improve model performance and increase predictive power, it leads to better business outcomes faster, which in turn leads to happy stakeholders across the organization.


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