The equations around data integration are constantly changing. Presently, businesses are heavily dependent on data analysis as well as real-time information in order to make decisions, raising the bar for data integration. What remains a challenge for data integration is the data deluge that transpires with the introduction of different kinds of data sources.
In the current business scenario, processes such as ETL are not equipped to handle such data evolution. Even surveys such that in a TDWI survey echoes the same fact. As per their survey, nearly 37 % of respondents experiences difficulty in accessing and integrating data. In order to deal with this complexity, organizations must adopt transformative technologies such as artificial intelligence to leverage data for better and faster decision-making.
Challenges Faced by Organizations
As the intricacies in big data grow each day, data integration is becoming increasingly complex. Not only data lives in organizations but also in the cloud and across different cloud platforms. With the dawn of new data types that are combining with the diverse data fabric, the complexity has risen further.
A plethora of data integration tools is mostly pigeon-holed into the functions of data movement from one place to another. And according to their perception, that’s the difficult part so to speak. However, in reality, integrating data is a difficult part. What they expect is that their solutions will magically integrate data and their expectations will be met without a hitch.
First, data resides in myriad segments and departments of an organization. Not only has it existed in the cloud and across cloud platforms but also in different schemas with different data dependencies.
Next, the business landscape has turned out to be disruptive than ever. The data flows in myriad places and is copied and duplicated. With every system being handled by a different owner, data is created and managed differently. During the course of data movement, all users can access and make alternations in data.
To utilize data, organizations need to consider data as a corporate asset. Otherwise, data will always be viewed and used as a by-product of the business. For this mindset to change, companies need to incorporate transformative technologies such as artificial intelligence.
AI to Simplify Data Integration
The emergence of artificial intelligence as well as machine learning has improved processes and outcomes of data integration. Even the Harvard review states that AI will add $ 13 trillion to the global economy over the next decade.
Data integration has incorporated AI capabilities to drive businesses forward. With AI-powered functionalities, business users become capable to handle voluminous information with ease and precision. Here are some functionalities:
AI-Powered Data Mapping: Artificial intelligence mapping allows users map data faster, thus accelerating the data transformations and decision-making. The machine learning algorithms can help users make accurate data mapping predictions from the existing library of tested and validated data maps.
This cuts down the need to create data mappings, and users can kick start the data transformation process with ease. AI, ML-powered data mappings enable users (with little or no technical expertise) to map data and then integrate it into a database.
Improved Speed: Artificial intelligence and machine learning-based technologies empower business users extract insights from the enterprise dataset in a faster way.
Better Big Data Processing: Artificial intelligence-powered technologies act as an aid for business users and enable them process big data without difficulty. Conventional solutions, on the other hand, lack the speed and precision to handle big data. AI solutions can easily parse through the big data structure to form data models without a lot of human intervention.
Enhanced Business Intelligence through Autonomous Learning Ability: It’s a known fact that AI speeds up data transformation, business users can get a grasp of the hidden patterns embedded in data and leverage statistical modeling on it to produce insightful information.
In the current digital transformative era, AI is helping organizations adapt to the changes especially in the manner data is analyzed. As the manual effort gets reduced to a minimum, it has transformed data integration from being a one-way process into one that is multilateral. The future of these technologies is, in fact, so bright that data will be able to integrate itself on the basis of what is learned and share its learnings with machines as well as man.