In 2019, artificial intelligence and big analytics were two of the most important and fastest-growing technologies. However, this upward trajectory wasn’t isolated to 2019 and has been the trend for half a decade. This advancement and the resulting proliferation also means that AI, ML, and big analytics have found their way to consumer applications in every industry. But how do these technologies fit into your company?
Despite being closely associated with each other (and often used together), It is important to establish that these three topics are not synonyms. They serve different purposes and can be used independently of each other. In this article, we’ll clearly differentiate between AI, ML, and big analytics and also take a brief look at how each of these three technologies fit into the existing workflows of a non-tech business.
What is artificial intelligence?
Contrary to countless Hollywood portrayals, artificial intelligence is not a sentient robot (not yet anyway). For now, artificial intelligence is a way to remove the human element from a wide range of business processes and services. Now because the technology has been in the market for a while now, it has had time to develop and become accessible to more industries. As a result, some implementation of AI can be found nearly everywhere.
For instance, AI is being used with disease mapping, manufacturing robots, conversational bots (we see this one a lot), and social media monitoring tools that help alert us to inappropriate content. In fact, Most services that we benefit from nowadays are powered by AI. A simple Google search is powered by AI on the backend. Did you use your credit card at the grocery store this past week? Then there was some AI powering the validation of your card, as well as monitoring your account for fraud. Today we see AI as a way for us to leverage computer intelligence as a substitute for human intelligence.
How is machine learning different from AI?
Machine learning is a subset, an extension, of artificial intelligence. In a nutshell, machine learning is the process of programming a computer to analyze new datasets and modify its behaviour based on its analysis – without any human interference.
Here is an example to help you understand: typically, a programmer will write code that tells a computer what to do, usually it is a code that evaluates, at the lowest level, to true or false, a 1 or a 0. When you develop an ML model, you are writing code that will change dynamically in response to the data it is being fed. Through the training of the model and the testing of it for accuracy, you can come up with some pretty fascinating applications! Just remember – the quality of the insights you receive from your model is only as good as the training that you provide the model.
The beauty of machine learning is that it is able to use many different data types, as well as structured and unstructured data. By using Google Cloud’s AI platform, anyone can get started creating a new ML model. Google already has some built-in algorithms that you can use to test against your input data. Google AI platform helps you create your model, deploy it, and get predictions based on your model.
Machine learning is also growing fast! 95 percent of North America has already adopted uses for machine learning (and $28 billion dollars already invested worldwide). So investing in machine learning will likely lead to a powerful and long-lasting competitive advantage.
Sources: Google AI platform Guide, Enterprisers Project
How Big Data Analytics fits into the bigger picture?
Oftentimes, AI and ML are used in tandem, especially in the realm of Big Data Analytics. These two technologies are changing the way we analyze data. AI helps us answer the questions that may be asked after a large amount of data has been parsed and analyzed. It can help us see critical data points that help companies make their business decisions, ultimately driving processes to a more streamlined workflow. In fact, more than 60% of businesses that plan to adopt machine learning in their analytics process have noted that it has helped with their decision making.
By using tools such as Google’s BigQuery, which has a machine learning component, you are able to leverage computation at a massive scale to handle enormous inputs of data (and with Google Cloud’s recent announcement of BigQuery Omni, this tool is now multi-cloud friendly). Tools like BigQuery help data analysts to derive insights from data. By developing machine learning models that program themselves dynamically in response to the data that is being fed to them, the process of data analytics has become less cumbersome and more robust.
Sources: Finances Online, Google BigQuery
Wrapping up…
Artificial intelligence, machine learning, and big analytics are key components of a modern data-driven company and while you might feel that these technologies aren’t an ideal for a non-tech organization, it’s important to remember that these technologies also represent the near future. And that when things like automation, AI customer service, big analytics powered marketing become the norm, the early adopters will be at the forefront of a new digital era.
If you’re interested in learning more about your organization can harness these emerging technologies, reach out below to set up a free strategic consultation with our certified cloud experts.
