As we continue to define the API landscape as part of our work here at Streamdata.io, we are continuing to identify different patterns in how APIs can be put to use. There are some APIs that have direct opportunities to subscribe to relevant data, and other APIs that have more secondary opportunities to subscribe to complimentary data. An example of these primary and secondary API opportunities can be found within the market data sector, when it comes to stock quote data. You can directly subscribe to stock data from one of Xignite’s APIs, getting all the recent stock prices for a particular company. Secondarily, you can also subscribe to information on the same company via social media APIs like Twitter and Facebook–providing two separate, but complimentary ways of subscribing to valuable market data.
The bigger opportunities exist within understanding where to find the secondary sources of data.
A stock market quote API will give me real time quotes using a ticker symbol, allowing me to get real time updates of the price of that company’s stock. Secondarily, social media APIs provide me with a potential firehose of data about any topic I want, but if I query it intelligently, I can also get relevant data about a particular company, including it’s stock. The viability of primary sources of market is pretty clear, and companies like StockTwits represent the opportunity around secondary sources of market data. The question is, where are there other untapped streams of data we can put to use, that will give us similar benefits, helping us understanding how well, or how poorly a company is doing, providing us with analyst or customer sentiment, and other valuable information.
Market data is just one example of primary and secondary sources of data available via APIs. Opening up bigger questions around what the opportunities are there available in healthcare, education, or the environment. As with market data, the opportunity doesn’t just lie in providing real time streams of primary and secondary data to humans, allowing them to make decisions. There is also a growing opportunity around using these data streams to train machine learning models, so that can be used to make decisions as part of a larger artificial intelligence system. Expanding the playing field for the value of primary sources of data, as well as the more interesting ways in which we can augment, enrich, and expand on these sources with other secondary types of data.
This is where APIs really can become interesting. There are only so many web or mobile applications you can build using APIs, but when it comes to building dashboards for humans to make decisions, and develop machine learning models to help computers make better decisions, the possibilities are endless. The most obvious, and lowest hanging fruit can be found with subscribing to the primary sources of data, but the harder work, and bigger opportunities exist within understanding where to find the secondary sources of data, subscribing to them, and understanding how to take things to the next level. We’ll keep exploring this concept beyond the area of financial data, and see what other examples we can come up with to help light your imagination about what is possible with Streamdata.io.
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