Integration’s Role in AI and Machine Learning

Integration is all about the connecting and moving of data, so that it can be safely stored and used to help you run your business and make decisions. Good integration distributes good data through your systems: if you think of data as cars, integration is like the road network: if it’s done wrong, there are traffic jams, wrong turns and lots of frustration.

You might wonder how this is related to artificial intelligence (AI) and machine learning (ML).

Again, it’s all down to data. AI and ML might sound futuristic and sci-fi but the hardest part of getting value from these technologies comes from supplying them with good training and testing data.

And seeing as good integration means a good flow of data, businesses with good integration systems are well placed to enhance their data processing via AI and ML.

The power of AI and ML

You’re bound to have come across old-school data warehousing, where data is gathered and stored before being sent for after-the-fact analytics, perhaps through a nightly job.

Lots of businesses have had enough of that. They want better and faster data analysis so that they can make better decisions and get a competitive advantage.

Businesses that are leaders in making data work for them have abandoned nightly data extracts and instead pipe the data direct into an analytics platform to perform streaming analytics in real time. Throwing AI and ML into the mix allows for near-instant smart analysis of data, transforming the way that businesses adapt products and services for their market. Sounds good, right?

Tools such as Apache Spark and Databricks are making the leap from integration to AI easier and cheaper than ever. It’s no surprise that people are excited.

AI and ML don’t just stop at fancy streaming analytics. Here are some more examples from a range of industries to get you thinking about what they can do:

manufacturing: decide at speed whether products are fit for sale, via photo or video analysis.
customer service: transcribe callcentre calls with speech to text and perform sentiment analysis.
finance: monitor transactions at scale to quickly identify unusual or fraudulent behaviour.
logistics: optimise processes by studying vehicle routes and patterns of delivery.

The examples show what’s possible: either doing things better or faster (or both), which means less manual faffing and lower costs. So, while AI might sound like something from The Terminator, in practice it’s what clued-up businesses use to improve performance and save cash.

The good news is that you don’t need to understand the rocket science behind any of this to use it. We love digging in and doing the under-the-hood techie work to make it all happen. So, perhaps we can help you get one up on your competitors (who might not even be aware that AI/ML could help them – oh well).

I have a particular interest in all of this because my area of study was aeronautical engineering and aerodynamics. Modern aerodynamics uses complex computer calculations to model airflow, and this is founded on the same numerical calculus that makes machine learning work. The parallels in the maths of aeronautics and the maths of AI means we’ve got the grounding needed to help businesses that want to use AI and ML in their workflows.

We think machine learning or artificial intelligence could help your business. Let’s talk and see what might be possible. Drop us a line ¬– we’d love to geek out about it with you.