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We live in a disruptive era, with data and analytics solutions transforming industries. AI and machine learning help technology organizations achieve their goals, make critical decisions, and develop goods and services.
Emerging technologies are frequently accompanied by intense levels of hype–
artificial intelligence (AI) is a prime example. The ongoing debate over its applications and potential impact on society has spread beyond the tech industry and the public sphere. Indeed, nine of the thirty emerging technologies featured in Gartner’s latest hype cycle are related to artificial intelligence. Given the breadth of potential applications for AI, the fanfare does make sense.
Through tailored data cycles that meet the individual needs of end clients, AI can assist enterprises in making better data-driven decisions for long-term operations that increase revenue.
ML-driven business sales can also use hyper-personalization, a significant step forward in fine-tuning client business cycles.
If we can figure out how to combine all disparate data sources, we can answer many more complicated and interesting questions than simply focusing on use cases. One way we can do this is by allowing easy integration and collaboration with other systems using an open API. An API-first architecture helps customers integrate monitoring, provisioning, configuration management solutions, and other tasks.
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Artificial Intelligence and Digital Transformation
Ultimately, digital transformation is all about extracting more value from technological innovations. Incorporating Artificial Intelligence (AI) and Machine Learning (ML) into various business strategies is perhaps the most significant way these concepts have been linked to digital transformation.
Artificial intelligence solutions are already being used in businesses across various industries. The reason it is widely accepted is that it is capable of boosting productivity and streamlining key processes. By implementing a solid AI-inclusive digital transformation strategy and data, technology stakeholders can unlock ultimate efficiency within their organizations and move closer to becoming an ideal data-driven company.
The Challenges of Data Integration for Businesses
Data no longer resides within an enterprise but in the cloud and across multiple systems. The emergence of new data types and formats adds to the diverse data fabric that organizations have in place. As the complexities of big data grow, data integration will become correspondingly more difficult.
Several data integration tools are tasked with transporting data from one location to another. That, according to most businesses, is the tricky part. However, the reality is quite different. It is challenging to integrate large, complex data streams. Using legacy solutions will take a lot of effort and time; information technology teams will be charged with EDI mapping and complex coding. Tasks like data mapping, data onboarding, and data integration will take months to complete.
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What Does Data Integration with AI/ML Mean?
Integration involves connecting and moving data to be safely stored and used so you can run your business and make decisions. When you think of data as a car, integration becomes like the road/highway network– if it’s done wrong, there are wrong turns, traffic jams, and frustrations to contend with.
You may wonder what this has to do with artificial intelligence (AI) and machine learning (ML). It all comes down to the data. AI and ML technologies may sound futuristic and sci-fi, but the most difficult part of extracting value from these technologies is to provide them with good data training and testing capabilities. Since good integration implies exemplary data flow, businesses with sound integration systems are well-positioned to improve their data processing through AI and ML.
Obstacles to Successful Data Integration
As businesses continue to count on data, integrating data from streaming data services, relational databases, and other real-time sources has become more complex. Well-architecture data integration processes ensure that data is managed, governed, and trusted, allowing for the extraction of hidden business intelligence. Attempts at integration can be hampered by
- The challenges of handling data from multiple sources.
- The difficulties of managing various data integration tools
- A business landscape that enables numerous data handlers to access, edit, copy, and duplicate data
- Slow data movement from the cloud and data lakes
How Can AI/ML Simplify the Data Integration Process?
Data Mapping: With machine learning algorithms, ML-powered data mapping can automate data transformation by providing advanced features and aiding in agile data mapping predictions. AI also allows users with less technical knowledge to begin the data mapping process with simple drag-and-drop features, reducing the time required to create data mappings.
Data Processing: Traditional data integration tools require a more extended setup and processing time to handle volumes of unstructured data or semi-structured formats, whereas ML-powered data integration tools can parse data and generate precise data models that require less human intervention.
Wind-up: AI-infused data integrations solve complex data processing problems and improve integration flow, propelling the business forward and providing a competitive advantage across the enterprise. These cutting-edge integration tools assist enterprises in gaining insights from big data, allowing for better and more timely decision-making, and ensuring business competitiveness.
