Applying AI to Data Integration: How Important is it for Your Business?
The world of data integration is rapidly changing. Currently, businesses are contingent heavily on data analysis as well as information in real-time to make decisions, raising the bar for data integration. The proliferation of data (with the introduction of non-traditional data sources such as social media post, machine log, streaming data, etc.) along with the traditional ones all challenge old models of data integration.
In this new reality, relying on processes such as ETL to handle the data deluge is not enough. For those of us in the data management business, it should not be a surprise that in a TDWI survey, nearly 37 percent of respondents experienced difficulty in accessing and integrating all relevant data. Organizations need to adopt transformative technologies such as AI to capitalize on data and enable faster decision-making.
So, What Are the Challenges?
With intricacies in big data growing each day, data integration is becoming increasingly complex. Data no longer lives in an organization – it lives in the cloud and across myriad cloud platforms. Further, the dawn of new data types and compute tiers are adding to the diverse data fabric many organisations have in place today.
Majority of data integration tools today are mostly pigeon-holed into the functions of moving data from one place to another. According to their perception, that’s the difficult part so to speak. However, in reality, integrating data is the difficult part. What they expect is that their tools will magically integrate data and their expectations will be met without a hitch.
What Are the Main Hurdles to Cross for Successful Data Integration?
- First, unlike before, data now resides across various segments and departments of an organization. Not only it exists in the cloud and across various cloud platforms but also in different schemas with different data dependencies.
- Next, the business landscape has become more disruptive than ever. The data flows in myriad places and is copied as well as duplicated. With each system being handled by a different owner, data is created and managed differently. While the data is flowing, it is accessed by users, all making changes to fulfil their specific needs.
Business leaders need to consider data as a corporate asset to capitalize on it properly. Or else, data will always be viewed and used as a by-product of the business, ultimately keeping data integration as a sizable hurdle for organisations. In order to change this mindset, organizations need to adopt modern technologies such as AI.
How Can AI Simplify Data Integration?
The emergence of artificial intelligence and machine learning has improved both the processes and outcomes of data integration. According to the Harvard Business Review, AI will add $13 trillion to the global economy over the next decade.
Data integration technologies are embracing AI capabilities in its existing framework to drive the business forward. The AI functionalities allow users handle large volumes of data and transform the way they do business with their trading partner network:
AI-powered data mapping: Using this feature, business users can map data faster for faster data transformation and decision-making. Users can leverage machine learning algorithms to make data mapping predictions from the existing library of tested and validated data maps. This reduces time required to create data mappings, accelerating data transformation. AI-powered data mapping enables business users with less technical knowledge to simply kickstart the data mapping process through a simple drag and drop feature.
Improved big data processing: Machine learning can help business users process big data with ease and speed. Traditional solutions lack precision and speed while handling big data. ML, on the other hand, can easily parse through the big data structure of all data formats to form data models with less human coding intervention.
Great computational speed: Artificial intelligence and machine learning-based technologies help business users decipher business insights from the enterprise dataset in a faster and more efficient manner than traditional business intelligence (BI) techniques.
Intelligence through autonomous learning ability: Since AI automates data transformation, business users can learn the hidden patterns and trends from the curated large datasets and implement statistical modelling on it to generate precise inference of business insights.
AI and ML are radically changing the data integration landscape. The elimination of manual efforts has transitioned data integration from being a one-way process into one that is multi-lateral. And the future of these technologies is so bright that eventually, data will be able to integrate itself based on what it has learned and share its learnings with machines and man.
