As organizations move from data analytics to data science, their data needs evolve, too. Without data, there is no machine learning (ML). But we’re not talking about any old data; in fact, the kind of data you may have relied on for more straightforward analytics or BI just won’t cut it for machine learning, either.
When you’re building complex, predictive models, your organization’s internal, historical data — such as past sales figures or customer information — can only tell you so much. To be as accurate as possible, models need to combine a lot of nuanced detail on one side and a lot of high-level context on the other. That means you need to look to external, alternative sources of data to fill in the gaps.
Alternative data encompasses everything from information on sector-level growth and performance, economic trends, location data, customer footfall, public domain data like business addresses and registration details, mortgage/ beneficial ownership information and criminal records, politically exposed person lists, and even text scraped from social media and blog content.
Exactly what kind of alternative data you prioritize will depend on the kind of industry you’re in, what your company does, and the questions you’re trying to answer. The point, really, is to identify gaps in your knowledge (and datasets) that stop you from making business-critical decisions with confidence. Once you’re clear on what you’re missing, you can ask yourself who does have that information — and how you can acquire the data and feed it into your models.
Increased demand for alternative data has created some really complex headaches for organizations, though.
Yes, it’s great that all you need to do is track down the right datasets. That this will give you the final piece of the puzzle for predictive models that elevate business performance, making your operations more efficient and profitable. But how do you navigate the thousands of available data sources? How do you sift through the irrelevant stuff to find the important details?
And how do you handle the fact that these data sources are so disparate, with different owners? That they’re provided in different formats that may not be compatible with one another?
How do you avoid spiraling costs if a promising dataset turns out to be of lower quality than expected, or contains just a slither of relevant data points? Or that the information you need is scattered across multiple sources, all of which have to be purchased separately?
Overcoming these hurdles isn’t only expensive, it’s also time-consuming. Which is frustrating, as any delay to your model development can cause you to miss out on lucrative opportunities.
Fortunately, there is a straightforward technical fix for this: a platform designed to deliver vastly improved external data discovery.
External data platforms work by automating your connections to thousands of pre-vetted data sources. Not only have these been curated for quality and reliability, they form a single, collective catalog, so you don’t have to pay for access to each one separately. In fact, these datasets are all inter-compatible, so you can essentially treat them as a single resource, lifting out just the details you need to enhance and augment your existing datasets, or combining them into brand new datasets for your data science projects.
Even better, a top external data platform is far more sophisticated than just a data catalog. These don’t just connect you to thousands of data sources; they will also help you find your way around them, suggesting just the most relevant data points and providing ways to automatically integrate them into your pipelines.
An external data platform can help your organization to centralize the entire alternative data acquisition process. It may even help you to automate most of this process, including cleaning up and harmonizing datasets so that they are ready to use in your machine learning projects. However, there can be significant variation in quality from platform to platform, so before you choose one for your business, it’s important to pay close attention to the details.
The key is to ensure that you are opting for a data science platform, not only a data catalog platform. In other words, one that’s specifically set up with machine learning in mind. Can you use it to enhance your datasets? Will it suggest useful, relevant details you may not have thought of? What about feature engineering?
Ultimately, your external data platform should remove as many of the roadblocks associated with finding and acquiring data sources as possible. It should also make it easier to streamline your data pipelines and get your model development off to a great start. That’s where the true value lies.
Want to learn more about the challenges and opportunities in getting the right alternative data for your risk models? Download your free copy of the research report 2021 State of External Data Acquisition