Data management is key to all modern business initiatives, including the success of AI initiatives, which are a priority in all boardrooms today. However, the complexity and diversity of data being ingested and stored today poses a huge challenge for the typical organization. Data teams increasingly find traditional data management infrastructure stretched to its limits, and traditional approaches riddled with inefficiencies.
These challenges cannot be solved by adding more engineers or doubling down on custom hand coding or ad-hoc additions to an already complex stack of point solutions. What is needed is a way for data engineers to build agile, intelligent data pipelines that can be operationalized at an enterprise scale without compromising fidelity, performance or security.
The Role of AI in Data Integration
AI projects are a priority for any data-led organization today; however, they cannot succeed without good data. Moreover, good data cannot effectively be created and managed at scale without AI. It is no surprise that AI is proving to be a game-changer in data integration and the end-to-end data management process. Adding an AI copilot or generative AI solution can significantly impact how enterprises manage data, providing advanced capabilities that streamline processes, improve decision-making and unlock the full potential of their data assets.
AI can automate and simplify tasks related to data management — whether you need to discover and understand, access and integrate, connect and automate, cleanse and trust, master and relate, govern and protect, and share and democratize. GenAI can learn and take over mundane, repetitive data management tasks, freeing developers and users to work on high-value strategic work, like building business logic. Intelligent data pipelines provide faster access to accurate and prepared datasets, which are critical for enterprise analytics and AI initiatives. AI is the perfect partner for developers, analysts, stewards and less technical data users. It helps to increase productivity by automating data integration tasks and augmenting execution with proactive recommendations, natural language processing experience and next-best actions.
AI for Data Integration: Three Key Impact Areas
AI is the only way forward to help data teams keep pace with the volume of inbound data and the resulting complexity of data integration. AI automates repetitive and labor-intensive processes, such as data mapping and schema management. It also enhances effort-intensive tasks in data quality and governance and opens up access to data-led analytics for more business users across the organization.
#1 Operational Efficiency and Productivity Enhancements
AI accelerates data pipeline development, automates repetitive tasks and allows a broader range of users to connect and integrate data quickly with automated data mapping and reusable no-code pipelines.
Automated data mapping, which includes predictions for subsequent transformation and expressions, can reduce manual effort and errors while enhancing the productivity of data engineers.
AI-powered systems can quickly build mappings to extract, transform and deliver data by learning from existing mappings and metadata catalogs. They understand the business content and context of databases and file systems and suggest appropriate transformations for standardizing and cleansing data before delivering to target systems and data consumers. Data engineers also save time and effort by leveraging pre-built components and reusable no-code pipelines powered by an intelligent co-pilot.
At the user level, AI can enable far wider access to data by allowing even less technical users to query data warehouses in commonly spoken natural languages rather than requiring knowledge of any coding languages. It executes SQL code behind the scenes to generate an answer. This further democratizes access to data and drives the adoption of analytics models by making it easier for users to get the answers they need in a timely fashion.
#2 Data Quality Enhancements
AI is essential for automating data quality and governance tasks, helping to mitigate human error in these crucial areas.
Anomaly detection
Machine learning (ML) algorithms can learn from historical data patterns and monitor thousands of simultaneously ongoing data integration processes to identify and predict potential data quality issues impacting the models. This automated data error and anomaly detection, along with intelligent data enrichment and virtualization, enables data teams to proactively cleanse and enhance data quality before integration.
Data cataloging
To govern data effectively, data cataloging is required to document business artifacts, definitions, stakeholders, processes, policies and more. To enable a truly aligned view, users must be able to associate definitions and business views with the underlying technical implementations in their data estate.
This process of associating business terms with physical data elements to establish the context and relevance is a tedious and time-consuming step in data governance. In many cases, AI can automatically link business terms to physical data using natural language processing (NLP) techniques and business-type identification. This can dramatically reduce the drudgery of this error-prone task.
Data matching and entity resolution
When data comes from disparate sources, ML algorithms can leverage probabilistic matching to automatically identify similar or matching records across multiple datasets. This minimizes duplication and improves data consolidation outcomes, even when the information is inconsistent or incomplete.
#3 Performance Optimization at Scale
Data workloads are not constant. AI can help automate the process of adjusting to workload patterns, recommending the right mix of resources and pre-setting parameters to increase system efficiency in a highly dynamic business environment.
Auto-tuning of pipelines and auto-scaling of workloads
Each time data moves in and out of the cloud — for various transformations, pulled out for analytics, or transfers to another cloud region or service — your company incurs egregious costs. With thousands of data integration operations running simultaneously across the organization, these costs can quickly spiral if workloads are managed manually and optimization is reactive instead of proactive.
However, because the runtime environment can be unpredictable and compute workloads often fluctuate, optimizing data integration tasks must be continuous and ongoing. AI and ML can help automate and optimize at scale with auto-tuning and auto-scaling capabilities. Autonomous learning from historical data and usage patterns allows the models to predict when usage may spike or dip. This proactively helps developers build self-tuning tasks that boost productivity and optimize performance for data integration tasks.
Real-time streaming data processing for agility at scale
Data sources today span IoT devices, sensors and multiple SaaS systems streaming real-time data. AI can help process vast data streams in real time, without missing anomalies or changes in data patterns which humans could easily overlook. An intelligent AI copilot can also trigger appropriate corrective actions based on event-driven architectures so that issues can be fixed in real time or necessary adaptations can be made in response to the changing situation.
Choosing the Right AI-Powered Data Integration Solution
From data mapping and pipelines to data quality, AI significantly impacts every aspect of the data integration process.
With the Informatica CLAIRE AI copilot capabilities, for instance, you can automate a wide range of data management tasks while reducing complexity, enabling scale and speeding up data delivery to data teams. Data scientists can cut data classification time by up to 50%, data engineers can enhance data discovery up to 100x faster and get productivity improvements of up to 20% or more; while data analysts can uncover key data insights in far less time.
By leveraging CLAIRE's capabilities, data integration outcomes are improved through automation, intelligent recommendations and enhanced data quality, resulting in more efficient and accurate data integration processes.
To learn more about how the machine learning-based innovations in CLAIRE are driving new advances in enterprise data management, visit www.informatica.com/CLAIRE.