With so much data in your own stores, it’s tempting to think you have all you need to start producing great predictive insights. This might be true initially, but you’ll quickly run into one (or more) problems. The reason? Your internal data is only looking at your past performance, and not accounting for the broader landscape. Moreover, the rapidly changing landscape means that your historic data quickly becomes obsolete.
When this is the case, machine learning (ML) can’t give you accurate predictions because the underlying conditions have changed drastically. To fully tap into the potential of data science and ML, you need to look outside your organization. Even so, finding the right external data is a complicated and resource-intensive process, often with less than optimal results. But it doesn’t have to be.
Augmented data discovery is the process of automating your search for external data using a platform like Explorium, which connects you to thousands of pre-vetted sources. More importantly, it gives you more than just extra data — it makes your datasets more robust by adding the most relevant data for your specific predictive question.
The alternative is to manually handle your data discovery, a process that’s not just slow, but also highly unscalable. It starts when you have to scour a nearly infinite number of potential sources to find one relevant source. It continues through the legal, validation, regulatory, and integration processes, which can take anywhere from a few weeks to several months — again, for a single source.
But what if instead, you could do all of this automatically, and for thousands of datasets at once? Augmented data discovery lets you scan thousands of potentially relevant data sources, and automatically connects your data to the right ones. More than simply scanning for relevant data, however, augmented data discovery handles this process in minutes, not days or weeks.
When it comes to getting better data, you need more than just access to a massive catalog that will add new sources to your dataset. Augmented data discovery is about making your data better. Better machine learning models don’t come from having the most data, but from having the best data for your predictive questions. Explorium’s platform looks to comprehend your dataset to make sure that what you’re getting is data enrichment, not simply enlargement.
Explorium’s augmented data discovery has two main functions — to quickly connect you to as much external data as possible and to give you the most relevant data for your predictive questions.
The first part is simple. Augmented data discovery tools have access to catalogs of data sources that have already been vetted, cleaned, harmonized, and are ready for use. You simply need to connect to the right platform, and you’ll have access. The second step is more complex, and it’s where tools like Explorium stand out from the pack.
Let’s see just how Explorium augments your data discovery with an example. Imagine you’re an online lender looking to better understand the risk that a customer will default on their loans. You’re using the standard FICO score and a few other basic internal metrics to determine borrowers’ default risk, but you’re missing the mark a little too often.
One of the big problems with FICO scores is that they measure creditworthiness and risk in a very narrow way, one that doesn’t account for the myriad factors that could impact a borrower’s ability to repay. You need to gain a better understanding of your customers, and to do so, you need to look beyond simply credit scores and a few financials.
It’s time to add a new perspective. Here’s how the platform enriches your data:
First, you connect your data to Explorium and our thousands of external sources. From here, our AI engine spins up and scans your database based on your predictive question and goal. It’s trying to understand which sources are relevant to your needs, and which will give you the most significant uplift and best insights. The Explorium Enrichment Catalog includes thousands of public, premium, partner, and proprietary data and signals that are pre-vetted and ready to use.
In our example, your goal is to better predict default risk, so you’ll be connected to sources on a few different axes. The first is financial and income data, including census information, regional tax and income averages, home prices, and demographic data. Next comes person-level information, which can include social media, search engine queries, and even things such as employment and income data. Now, instead of one data source, you have potentially hundreds or thousands.
Next, Explorium automatically identifies and ranks the most relevant sources during the enrichment process. Because better data science doesn’t come from just having more data, but from having better data, this step is all about finding those sources that will give you the biggest uplift.
At this point, you’ve gone from, let’s say, 500 possible sources, to perhaps the 50 to 100 that are the most relevant, all ranked and scored based on their impact on your models. From here, we start the extraction and enrichment process.
Now, it’s time for the magic, as Explorium’s feature engineering and generation tools take over. Instead of just giving you the data and calling it a day, leaving you to break your head thinking of the right features, Explorium takes it to the next level by engineering features based on this enriched data.
Once your dataset is enriched, the platform will automatically scan your new, unified data and find the most relevant features that are likely to lead to the predictive outcomes you’re looking for.
You might still be relying on old standbys like a FICO score threshold of 600, or a history of loans in the past 12 months. These aren’t necessarily wrong, but they’re also limited in the type of answers they’ll give you. Explorium’s AI-driven feature generation tools scour through your dataset to find even the most obscure connections that could give you a better prediction.
This could be features such as whether borrowers are highly active on social media, or whether they have a long online purchase history that doesn’t track with their income. It could even be that something as simple as where they live compared to their income might be a better predictor.
The best part? Instead of days, weeks, or months, the process takes minutes. This way, you can focus on the real task at hand — finding the insights that will give your organization the most significant ROI — instead of wasting time digging for needles in the haystack of data available online.
Traditional data discovery means that your team must spend hours, days, and even weeks combing through an ocean of possibly irrelevant data to find the one set that might give your models an uplift. Augmented data discovery means focusing on your models, and letting Explorium handle the heavy lifting of getting you the data you need to keep your data science team running at peak efficiency and giving you the best outcomes for every predictive question you have.