Artificial intelligence seems to be everywhere at the moment. ChatGPT features in headlines every day, and Microsoft and Google have both announced that they will be integrating AI in their search engines. Closely related to artificial intelligence is machine learning. Following on from his previous post looking at how APIs can yield new insights into data, Kasra Aghajani takes a look at how machine learning can work with APIs to improve data analyses.
APIs are useful for combining datasets from different sources to generate new insights, and for gathering data from external sources to build new applications. When coupled with machine learning (ML), APIs can unlock even more potential within the data.
Machine learning is a type of artificial intelligence concerned with developing algorithms that enable computers to learn from data and improve their performance without being explicitly programmed. It is not about creating a computer that thinks like a human or acts independently of humans. Instead, machine learning involves algorithms that can be trained to look for specific patterns in data and make predictions or decisions based on that data.
So, when we talk about machine learning we’re not talking about OpenAI’s ChatGPT system, something which has a mind of its own (or at least “appears” to have a mind of its own). While machine learning can be a powerful tool to assist data analysts by automating certain tasks or providing insights they may have missed, it is not designed to replace human analysts. Skilled operators are still required to develop and fine-tune algorithms, and creative insights are necessary to ensure that the data is being analysed correctly.
In my last post, I looked at how Get the Data had created an application for the Georgia Primary Care Association that allowed users to see the healthcare needs of a specific geographic area by clicking on a map. But what if we wanted to analyse trends in those healthcare needs over time? Machine learning allows us to quickly analyse historical data and extrapolate them to future trends. And as well as picking up on trends that might not be immediately apparent via traditional methods of analysis it can also be configured to continuously pull in new data and analyse it in real-time.
In the example given above we could combine public health data and geographic data with other data sources such as socioeconomic data, data about educational attainment, or rates of public transport usage, to find trends that might not be apparent using traditional methods of epidemiological analysis.
All of this means that machine learning opens up possibilities for analysing data that may not have previously been available to smaller organisations. It can provide organisations with a cost-effective way of generating new insights from their data. Machine learning can help you to find new ways of improving outcomes while targeting scarce resources to the places they are most needed.
Get the Data’s evaluation packages – Measure, Learn and Prove – use a wide variety of cutting-edge analysis methods, including machine learning approaches. If you would like to discover how these packages want to discover how these packages can unlock your data’s potential to meet your goals and improve your outcomes, can unlock your data’s potential to meet your goals and improve your outcomes, then contact Alan or Jack for further information.