Pinpointing the most useful machine learning data and figuring out how to combine sources to create accurate, meaningful models is one of the most business-critical areas of data science.
Increasingly, data scientists are realizing that the data they have in-house might not be extensive or complete enough to do that on their own. That solely using in-house data sources is limiting their visibility, and that they need to look beyond their own silos, to external data, to enrich their models and get the insights they need.
Sources of external data include other companies, data marketplaces, government websites that host publicly available statistics, figures and demographic information, as well as nontraditional or hard to access “alternative” data sources such as IoT sensors, PoS transactions, satellites, blogs, and social media networks.
Once you’ve acquired this data, you can then apply machine learning or deep learning algorithms to help you draw out insights from the data and make predictions about patterns, performance, and behaviors.
Until recently, the options for data scientists fell into three categories:
This is when data brokers collect up data from a number of sources and prepare it for delivery to clients, as an additional input for their decision-making process.
The next step up from this is when data is enhanced using calculations and analytical rules. Typically, this applies to data services provided by organizations like credit rating agencies and marketing data providers, with results provided in the form of scores or tagging.
This is when customers hand over their own data for providers to combine with data they own, or from other sources, in order to deal with specific analytical requests.
Today, though, there has been a rapid evolution of sophisticated platforms that go that step further to meet the demands of today’s rapidly expanding data economy.
The best of these automate the processes of connecting to and harmonizing multiple external data sources, mining for insights, improving efficiency, effectiveness, and data analysis automation.
Key functions of data connection platforms designed to improve the way you interface with and utilize external data include:
This involves merging third party data with your own sources, enhancing, and improving the performance of your models in the process.
Incorporating alternative data to add depth and nuance to your prediction models is fast becoming the gold standard across many different industries.
In late 2019, the Federal Reserve Board issued a statement with advice on improving consumer protections while using alternative data, demonstrating how commonplace using alternative data in the finance and banking sector is today. Use cases include fraud detection, credit writing, account management, and banking operations for online lenders, banks, and fintech companies.
Built-in tools are used to interpret the underlying meaning of datasets so that you can identify and connect to the most relevant and beneficial external sources.
This is vital if you intend to scale up and expand your field of insights fast. You should also be able to connect to the data silos you have in-house through the same platform so that you can treat all the data in your possession as one holistic resource.
As we’ll discuss in a moment, cleaning and harmonizing data from disparate sources is an arduous task. It’s essential that you use a platform that streamlines and automates as much of this process as possible. This means you’ll find it much more efficient and realistic to connect to external data sources and incorporate this into your data discovery processes and prediction models.
The more data sources you use, the more approaches you’ll encounter to organizing and presenting data — and the more quirks, idiosyncrasies, and potential inconsistencies you’ll find along the way. Here are a few things that can make things tricky for you:
Data quality and accuracy
When you connect to an external source, you have to trust that the data being provided to you is correct, up-to-date, and reliable. Quality data is, of course, foundational for meaningful insights and accurate prediction models.
There’s not much you can do to retrospectively improve the quality and data of someone else’s dataset, but you can make better choices about who to work with by going through trusted data marketplaces or going through a platform that has pre-vetted these connections for you.
Standardization and data preparation
There are many, many ways an organization could choose to collect, organize, and store data, especially when you combine structured and unstructured sources. If you’re combining data sources that haven’t been standardized, the process of harmonizing them can be very time and resource-intensive. Again, the more you can automate the process by going through a system or platform that handles this for you, the better.
A misaligned data provider market
At present, the process of buying and selling data isn’t always frictionless. One-to-one agreements mean lengthy negotiations over pricing, licensing, and liability. However, as data marketplaces proliferate, making the purchase of data an increasingly self-service endeavor, many of these problems will iron out. What’s more, going through a purpose-built platform removes the vast majority of these problems for you, so that you can focus on connecting to the most valuable data.
There’s simply no need to limit yourself to your proprietary data any longer. The world is your data oyster — and top data scientists know that their jobs are all about exactly that: data.
At the same time, connecting to swathes of complex, sometimes incoherent pools of big data means you need the right automated data science solutions in place to support you. Make sure you get the right technology in place first, or you’ll make your job harder, not easier.