Artificial intelligence (AI) has been in the news a lot recently. ChatGPT, the chatbot, has become part of many conversations.
And, it might seem like a gimmick—AI that has been “fed” data to see how it responds. However, real-time AI has many more uses than just answering questions.
One of the uses that you might have heard about is self-driving cars. The AI is programmed to monitor the road and propel the vehicle safely. To do so, it takes in information from sensors that monitor the road, speed limit, location, and more.
It, then, uses this information to make split-second decisions to navigate the car through the traffic.
Similarly, AI can be used to improve safety and operational efficiency in airports, detect fraud in online banking transactions, provide patients with better care in hospitals and much more.
According to this article about Unlocking the Power of AI With a Real-Time Data Strategy on CIO.com, AI can do all that and more.
However, to do so, ”data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making.”
The Importance of Real-Time Data
In order for AI to make real-time decisions, it needs real-time data. Using the example of AI in self-driving vehicles again, the computer has to constantly get a flow of information for it to make decisions.
A person needs to have all the evidence before they can make an informed decision. AI is no different.
Like a person, AI needs some input to determine the output.
Unlike a person, however, AI can handle vast quantities of input in a second and still be able to process it quickly. That, in turn, means that AI can be used to detect fraudulent transactions, make product recommendations, and optimise just-in-time business processes in real-time.
Automation Is the Driving Force
Businesses are looking to leverage AI and enable automation. We are living in a world where data capturing is the easy part. Our phones are gathering information about us and our lives. We can have medical information tracked through wearables. Every online interaction we have is providing someone with some information about us.
Data gathering is not the problem here.
The problem is ensuring data quality so that AI can make better real-time decisions—because high-quality data can help Machine Learning (ML) models deliver better outcomes.
So, if we wanted to accelerate automation, we’d need to look at data management and strategy differently.
In order to be more data-driven, you would need to develop a holistic vision for your business. Information cannot be siloed within various ecosystems. It needs to be integrated so it can be analysed. Only then will it be able to help you make a change.
Instead of trying to fit new methodologies into your legacy software applications, you need to invest in “data and model governance, discovery, observability, and profiling.”
Powering Functional ML Models With Data
So, what’s a machine learning model?
An ML model is a software program within which the AI goes through relevant datasets to identify patterns. It will then use this information to train itself to make decisions.
Once the model is trained, it can be deployed to determine the best course of action based on data input.
As you can see, since the model uses pre-existing data to teach itself, it’s important that the ML processes and data are integrated to get the most out of them.
And, ML models use data for everything—from building to training to deploying. You could say that data is the fuel required to power ML models. The two need to be aligned for you to get the maximum benefit from either of them.
Managing Data Strategy and Storage For AI Consumption
For AI to make real-time decisions, it needs the parallel support of ML process flow and data flow. Real-time AI needs the following from its data ecosystem:
- A real-time data ingestion platform
- A real-time operational data store
- The ingestion platform and data store working together to reduce the data complexity
- Change Data Capture (CDC) that returns high-velocity events back into the data stream or analytics platforms
- An enterprise data ecosystem that optimises data flow in both directions
In order for AI to make real-time decisions quickly and accurately, you need ML models that exchange data at high speeds. It’s difficult to build such an ecosystem within your organisation, so a cloud-native approach might be best for you.
Cloud-native is great for scaling your operations, is reliable, and helps portability across deployments.
Of course, you would need a data strategy that would help you optimise your data and how you use it. Fortunately, experts like Agile Solutions should be able to help you prioritise and plan a data strategy that meets your business’s objectives.
A strong strategy would be able to help you automate your processes with the help of AI.