How Artificial Intelligence/Machine Learning can make Integration easy

Every modern application from desktop to a mobile platform uses database. Whether they are flat files or in memory or NoSQL, every application needs databases. And today, apart from database, most applications today are seeing Artificial Intelligence as a critical component.

But despite the era of AI, many businesses are yet to adopt it. Perhaps the fear of AI taking over human jobs is one of the reasons. However, history has been kind to those who have moved in early and in the process acquire an undeniable competitive advantage.

Most of the organization doesn’t succeed because they don’t innovate enough. They don’t succeed because they do more of the same things. In short, it’s the repetitive kind of work which eats up enormous amount of time and therefore productivity of employees. And the place which is often overlooked when it comes to getting ideas for innovation is the vast amount of data that gets collected during running a business. AI can help exactly in these places. Automating the routine task so that business can focus on bringing innovative solutions to life will be the way forward.  And that’s where AI will revolutionize the Integration Industry. Just like how GPS did to automobiles, AI will do the same to Integration systems. And in the process free up time for humans to do higher value work.

When routine tasks which are of low intelligence level and rooted in operations can be ‘outsourced’ to AI, it will result not only in transformational initiatives for the business but also get integration-platform-as-a-service (iPaaS) into focus.

As the data volume keeps increasing, the need for deeper dive into making sense of it all will continue to compound. And that’s where AI will come into force for Integration. Data Integration systems are increasingly using AI for exploring the tons of data. A lot of what is called Dark Data (data points which are available in large numbers but not put to use) which use to be a bane for businesses, will now be the play area for AI.  Metadata is being inferred now with AI. Inference of schema and data distribution is now being done with machine learning algorithms.

For example, sensitive data can be detected and protected automatically using classification algorithms. This is just one of the examples of how data integration systems are working along with AI to improve data usability.

Two key points that will determine the success of blending AI in Data Integration would be on cloud and having comprehensive metadata architecture.

The future will see humans and machines co-existing harmoniously on the automated integration platform. Be it manufacturing, retail, finance or insurance, businesses (and ultimately consumers) will see tangible benefits of AI blending with Integration systems.

One of the easy steps to take is to start with AI driven APIs. For example, converting call records into text and analysing them for a pre-defined rating score. The advantages are tremendous as you can get ‘live’ feedback from customers in volumes and analyse the same to take real time decisions. Most of customer engaging apps will increasingly use AI help to handle tasks better.

Going forward, a significant portion of data management and analytics will eliminate the human intervention on a day to day basis. It will open new revenue generating streams and change the way work will be done. In future the learning curve and new innovative product/service launch cycles will be dramatically reduced with AI driven data integrations.

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