Is there a need to overhaul the plethora of tools available out there to address the latest and greatest integration problems businesses are facing? Firstly, we may need to go back and see how systems are integrated today. And consequently, find the shortcomings in the existing methods of enterprise solutions?
The Background
I have been in the Integration space for a while now. I have seen technology shifts where new integration tools come and go. While few of them failed in their inception phase itself, a few have walked the mile and stood tall, catering to the customer’s needs and providing a viable solution to run their businesses. With that said, would you agree if I ask you – “Are we there yet?”.
Well, the answer is “No”. And why would I say that? Probably you need to scroll down to get into more details.
The Problem
For the starters – It’s the Data. But before we dive deep into analyzing why Data is a reason, let’s go back a few steps and see what the current integration landscape is, and how the solutions are designed to address the enterprise’s needs.
The traditional integration technologies work by pulling and pumping data across disparate systems, while applying the necessary business logic on its way in/out.
Data processing has been the core for generations. And with the amount of data going to increase manifold in the future, the traditional enterprise integration solutions will have a tough time addressing the core problem of data distribution. Gone are the old ways of data warehousing where enterprises follow the gather-store-analyze methodology. Yes, this has worked wonders, but with the emerging business needs and the pace at which decisions are to be made based on data analysis in order to adapt to the emerging market trends, the traditional enterprise technologies would not be a go-to solution.
The Solution – AI/ML
Businesses need to adapt to a faster way of data analysis which would help them make decisions on-the-go. A real-time data feed piped through a robust data analytics platform would definitely help. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), businesses now have a solution. Above all, no more running of your overnight jobs on the data stored in your warehouses to perform ex-post-facto analysis. The results of which then would be used to take business decisions. Well, that sounds a bit lethargic in cases where you need to run ahead of the growing market trends, ain’t it?
Inject AI/ML into your solution and the results would be quite impressive. Certainly, this allows for smart analysis of your data and help in taking instant decisions, making your business adapt to the market.
But wait, is that all? All the hunky-dory stuff about AI/ML ends there?
No, not at all. For instance, here are a few use cases to give you an idea about the capabilities of AI/ML,
Use Cases
Targeted Advertisements
Ever seen how those annoying ads pop up while you watch your favorite shows on any of your online streaming services? And surprisingly those ads appear to be connecting you to your most recent browsing history or even that fancy gadget you have just wanted to purchase online. Again – AI made that happen.
Food Delivery Apps
A while ago, no one ever thought of purchasing their daily breakfast/lunch/dinner or even that yummy dessert at the click of a button. This has taken the whole food industry by storm, allowing you to open up a plethora of opportunities while expanding your business manifold. Targeted offers and suggestions about available products can be made using AI/ML.
Healthcare
With machine learning algorithms, it will be a lot easier to detect and analyze multiple patterns across many health conditions. In other words, this could help identify many chronic diseases that may go undetected, if not for the introduction of AI.
Finance/Trading
Predictive analysis is one of the keys to trading. On the other hand, we have multiple applications which take care of analyzing the possibilities of stock market fluctuations, which in turn helps investors take the right decisions in real-time.
Transportation
Heard about Auto-Pilot? Tesla? The transportation industry has come a long way since the days of its first motor vehicle. Machine Learning helps in analyzing various feeds of live traffic data, and sensory information of your automobile with surroundings perception. Combined with AI, it makes you relax in your seat while having a cup of coffee or even attending those early office calls on your way to work. Yeah, the machines are taking care of your commute to your destination. The additional perks – They can even park themselves!
AI is the Future
Certainly, the future is AI and it’s already here. Heard that a zillion times? If needed, I suggest you revisit your integration solutions. In short, bring some AI/ML techniques into the mix.
I would like to close it out here before I lay out a few of the AI/ML tools which may get you started, which include (but are not limited to),
- Amazon Machine Learning
- Apache Spark/Databricks
- BigQuery ML
- Google Cloud ML Engine
- TensorFlow
