The article discusses:

• Challenges with data integration
• The need for faster decision-making
• The role of AI in enabling efficient data integration and business decisions

Fast decision-making is a key ask for the business to stay competitive in market. It is crucial that the business gains insights from its enterprise data set and takes necessary and timely action. However, the challenge the business faces in enabling this is that data is growing at a fast rate due to incorporation of non-traditional data sources ( Machine log, social media post, streaming data etc.) along with the traditional ones (CRM, ERP, RDBMS, file system data etc.) in the data governance ecosystem. Hence, data integration and summarization of the data deluge into useful information for developing insights is becoming a big necessity.

The prime ask for organizations is how to spend more time on data analysis rather than data curation. Most business users currently spend more effort on data preparation than analysis. Apposite strategies on data integration plays a vital role here to assist business in reversing this trend. Data Integration (DI) with Artificial Intelligence (AI) capability is the perfect-fit strategy to accomplish automation of data preparation task while additionally bringing in the agile and efficient method analysis of big data into its core competence. In the DI with AI framework, human intervention is an option, which ought to be applied only when necessary.

State of automation in data integration

The current data integration frameworks experience three levels of context-setting information:

1. Complete knowledge–The schema structure for the incoming data content is pre-known 
2. No knowledge –The schema of the incoming data content is not pre-known and AI is used to decipher the schema by parsing the content
3. Partial knowledge–A combination of the above two approaches, where partial schema structure is pre-known and the dynamic part is deciphered using the AI

The degree of cohesion of the enterprise data with the defined schema model lays down the level of AI infusion in data integration and the proportionality of human assistance in the entire data flow. As the current DI tools have vast experience of handling business data, it can infer the metadata of the enterprise dataset and document the same in a catalogue format for reuse.
An exhaustive and efficient information catalogue assists in standardizing DI, governance, and subsequent data discovery framework by defining common and infrequently referred data names, meanings, and usage for the enterprise. Though business is the custodian of this information and can be consulted for creation of such catalogue requiring human intervention, the DI tools, with its nearly five-decade involvement in cataloguing and modelling business data across all industries, is in ideal shape now to imbibe AI into its framework to automate the creation of business-specific information catalogues.

AI capabilities to simplify integration

Current DI technologies are imbibing growing AI capabilities in its framework to cater to the enterprise demand. These AI capabilities in the DI platform help change the way businesses make decisions:

• Prebuilt mapping and metadata catalogue - Using prebuilt DI template and system metadata catalogue, AI can automate the data transformation mapping creation. This will enabling business users with less technical knowledge to use the DI tool through simple drag and drop feature and clock more time on data analysis and trend identification using their domain knowledge.
• Fast computational speed - Apposite utilization of machine learning (ML) with adequate input parameters leads to the deciphering of business insights from the enterprise dataset in a faster and more efficient manner than the traditional business intelligence (BI) techniques. ML invokes fast computation power and less coding, which assists in achieving the speed objective.
• Big data processing- Utilization of ML in DI is best endorsed for its capability to efficiently and quickly process big data. Traditional DI tools lack processing speed for handling large volumes of data(in Zeta byte range or more)as well as handling unstructured/semi-structured data format to derive its hidden business insight. ML can parse through the big data structure of all data formats to generate accurate data models and data pipelines with less human coding intervention.
• Intelligence through the ability to learn autonomously – As AI automates the data transformation mapping creation in the ETL process, business users involve more in learning the patterns and hidden trends from the curated large datasets and applying statistical modelling on it so that the accurate inference of business insights from those data set sare derived.

The case for embedded recommendation engine

One of the other notable improvisations in the DI space leveraging AI/ML is embedding Recommendation Engines in the integration platforms, which can automate data integration process utilizing the metadata sharing and analysis information obtained through deciphering large corporate data set. It advises the best-fit data pipeline by performing graph and cluster analysis based on the way data is accessed in different enterprise-wide applications. The inline technology of recommendation engines probes data-access frequency, commonly used data component in various queries/data mining methods and user roles in the data analytics. The advent of embedded engine sets the ground for maximum business user involvement in the data integration process through the best possible automation of the data pipeline-creation process.

Advantage AI with Ml

Artificial Intelligence with ML techniques solves complex data integration problems. For instance, conventional methods cannot be handling huge volumes of data gathered from different sources like streaming and IoT. In such scenarios, AI/ML techniques not only solve data processing but also improve integration flow.

Optimization of the data integration platform by imbibing AI into it improve execution performance by simplifying the development lifecycle, reducing the learning time for the technology, and lowering the dependency on high skill requirement for ETL workflow creation. Another notable advantage is ML can train the data set to make it apt for configuration of statistical modelling on it without any manual intervention, hence alleviating the human imposed issues. Advantages of AI with ML also include:

• Reduction in the integration total cost of ownership and timeline due to a decrease in the usage complexity and empowerment of business users to perform the DI with less or no assistance from technical experts
• Access to a variety of pre-packaged and configurable data integration templates imbibed into AI for optimized alignment along with intuitive and self-guiding steps for easy deployment of data integration and application tasks
• Conversational user experiences that enhance efficiency through the creation of assisted integration procedure and querying the platform for its operational state. This assists the business leaders of different departments in an organization to connect to the system and create their own data structures and application independently for any of their specific data curation and analysis needs.

AI-enabled decision-making

Data integration infused with AI is gradually automating organization-wide application flow and creation of data pipeline. With the advent of big data storage(HDFS/ Hive/ Cloud storage), data integration tools are accessing large volume of diverse data, enabling its embedded recommendation engine to infer the data structure components intuitively out of this and utilize the same for automating the repetitive and redundant data integration tasks. The AI engine is gradually evolving its inferred and tagging analysis logic, metadata discovery framework and acquired knowledge base to cater to the growing demand of DI pipelines.

Thanks to AI that manages most of the data preparation task, business users are leveraging their domain knowledge armed with ML and statistical concepts on the enterprise dataset for extracting business insights that drive the organization towards success.