10 Powerful, Free Big Data and AI Data Sources

AI relies on powerful data sources to jump start the learning process. These free resources will help grow your analytics database.

Data fuels innovation. Without huge chunks of data, artificial intelligence and BI platforms would be useless. But finding data can be tricky. There are three major concerns you need to factor for when analyzing a new source of data for your project:

  1. Is the data you’re sourcing unbiased and objective? A lot of surprising things can skew data in a way that makes it less useful for the purposes of broad-based AI projects.
  2. How much does the data cost? Even if there’s no upfront monetary cost, how much time will it cost you and your team to harvest, organize and insert the data into your platform?
  3. How current is the information? AI is a fast-paced environment. Outside of teaching your platform about historical trends, old data (information that is more than 2 years old) is past its expiration date.

If you’re looking for reliable AI data sources, keep reading. We’ll cover ten of the best tools to supplement your analytical database.

1. US National Center for Education Statistics

The United States has one of the largest public-school systems in the world. With more than $115,000 being spent by taxpayers on each individual student, the US education system represents a massive investment of manpower, funds and natural resources.

All of the activity surrounding education in the United States is statistically compiled in the US National Center for Education Statistics. For analytical platforms leveraging AI to help provide humans with information, education data can be useful. Everything that is placed into the platform is first collected and then fact-checked by the US Department of Education.

2. Financial Times Market Data

For fintech startups, the data that is provided by the Financial Times could be very useful for building an analytical database centered on the investment markets. The data available encompasses market information from around the globe, and includes a well-stocked treasure-trove of historical data.

3. Social Media APIs

Most social media sites, including Instagram and Twitter, offer developers API access to pull raw data from their platforms. Data scientists looking to source public data for their analytics platform can use these tools to train their AI systems with real-world population sets. It’s important to note that due to recent developments, many social media sites are changing the level of access they give companies to their user’s data. This is a very fluid resource that could change dramatically in the coming months.

4. Machine Learning Data Set Repository

Did you know that there’s a free and open exchange of ideas taking place on a big data scale? The Machine Learning Data Set Repository is a collaborative space where data scientists and data nerds can share the information they’ve compiled. It’s an excellent resource for anyone creating an analytics database with diverse sets of information. Although, it’s a good idea to treat this resource kind of like academics treat Wikipedia – it’s best to verify any information you receive for free.

5. MS MARCO – Microsoft Machine Reading Comprehension

Microsoft is rolling out a ton of new software every day. Their army of coders are working hard to deliver solutions and tools to both businesses and consumers at the speed of light. In their quest to deliver these services to the world, Microsoft collects an unfathomable amount of information. Microsoft makes some of this data available to data scientist via their Microsoft Machine Reading Comprehension platform – MS MARCO. This specific resource is designed to help analytics database managers provider their AI projects with contextual answers to help train systems using anonymized real-world question and answer sets.

6. Reddit Comments Data Set

If you’re trying to teach your AI platform to understand the citizens of the internet, the Reddit comment data set could be incredibly useful. It is a few years outdated, but a comprehensive collection of more than 14 months of Reddit conversations is quite a treasure trove for almost any analytical database.

7. US Government Open Data Initiative

The US Government has set a policy that public data should be freely available to the public – as long as privacy and safety concerns are negated. More than 160,000 individual data sets are available on data.gov. And more is being added daily as new information meets their release requirements.

8. European Union Open Data Portal

Not to be outdone by the yanks, the European Union has also stepped up to the plate to create public data sets. You can peruse through more than 12,000 data sets at your leisure by visiting the EU Open Data Portal.

9. Google Public Data

Google is the world’s leading search engine provider, among many other things. They’ve leveraged this technology to help researchers discover new sources of data. Google’s Public Data Search platform compiles data from a variety of hand-picked sources.

10. Open Data on AWS

Amazon Web Services powers a large percentage of the world’s online traffic and stores a massive amount of information in their cloud platform. There are several public data sets that are maintained within their infrastructure. Amazon catalogues these resources and has presented them to the public for data mining purposes. You can access them for your analytics database at the Registry of Open Data on AWS.

Use the Above Resources to Jumpstart Your Analytics Database

An analytics database can help jumpstart almost any tech project. An unbiased, current database of information relevant to your industry will improve your decision making, and power a more compelling AI experience for your end-users. Even if AI isn’t part of your platform, BI needs to be. Business Intelligence is the difference between shooting in the dark and scoring a major goal.

You can toss out the phrase: “You get what you pay for.”  When it comes to data, it isn’t about how much you pay for it. It’s about how relevant it is to your industry, and how much of an advantage it provides your tech.