AI is a vast field focused on developing computer systems capable of mimicking human intelligence, especially our decision-making processes. The applications for AI-powered business systems and solutions are endless. As a result, AI is a high priority for most boardrooms today. A recent survey of over 1500 AI decision-makers worldwide revealed that 69% of respondents have at least one AI project in production.1 These AI projects vary widely, including discriminative AI models that recognize and classify data and generative AI models that create new information based on their current learning. Machine Learning (ML) models can improve their performance without explicit programming for each new task.
How Quality Training Data Drives AI Success
All of these advancements are undoubtedly exciting. However, this rapid expansion of AI and ML projects is putting unprecedented pressure on the data infrastructure., This impacts both data management and computational power. When it comes to data, legal and regulatory questions of data governance, security and privacy also enter the fray. As a result, data complexity is one of the key factors blocking the speedy and smooth development, deployment and scale of strategic AI initiatives. Gartner® estimates that: “Through 2025, at least 30% of GenAI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs or unclear business value.”2
ML models can deliver reliable results in an unpredictable business environment only if they are trained and retrained with a continuous stream of high-quality real-time data. These models learn iteratively, with each new data set introduced strengthening the algorithm and adding more context., This allows them to handle increasingly complex tasks intelligently in a dynamic environment.
If the AI development cycle begins with data to train the models, the data cycle begins with data integration. The process of identifying, collecting, preprocessing, ingesting, and transforming raw data into a unified format tailored to a company’s requirements is the first step to training the models that power AI.
With the help of DataOps and the right data management solution, seamless and robust data integration can lead to a continuous stream of high-quality data to help the algorithm learn, recognize patterns, and make predictions.
Navigating the Business Risks of Poor Data Quality in AI
Typically, a business collects diverse types of data, which are stored in many disconnected systems, on-premises and in the cloud. These can include:
- Structured, semi-structured and unstructured data
- Quantitative and qualitative data from different business systems, platforms, and devices
- Internal (company and domain-specific) data and external (general/ industry-specific) data
- Historical and real-time data
Data engineers work behind the scenes to bring all this multi-format, multi-source data into a unified repository of business-ready data. However, the data integration process is severely hindered by several practical realities, including:
- Data collection and storage issues that lead to silos, fragmentation and format inconsistencies.
- Data quality issues such as inaccurate and incomplete data or unknown data lineage.
- A lack of data and AI governance frameworks that could expose the data to misuse and the business to liabilities.
- Resource constraints such as understaffed and overworked IT departments as well as budgetary limits.
Without a standardized process to overcome these challenges, data ingestion and integration will continue to remain bottlenecks in the AI development process. Worse, gaps and errors in training data can lead to serious consequences for the business. The main purpose of AI models is to improve decision making. However, poor or inadequate input data leads to incorrect predictions, unintended biases, and even hallucinations which can misinform and lead decision-makers astray. Increased variance or limited, non--representative responses, especially when applied at scale, present another serious challenge to the credibility of AI models.
Aside from the losses caused by poorly trained AI models, rectifying such sampling biases and data quality errors poses another challenge. Once such data is vectorized, it's almost impossible for the model to ‘unlearn’ what it has assimilated without being retrained from scratch. This is because such models keep reinforcing their understanding of a topic based on what they have learned in the past. Such ‘unlearning’ and ‘retraining’ of AI models, if at all possible, can be prohibitively expensive, and set projects back by years.3 If AI is on your radar, it is important to first invest in an unshakable data management foundation, starting with data integration.
How to Get the Data Your AI Projects Need
The best ML models are trained on accurate and representative internal and external datasets, depending on the applications for which they are built. The more diverse the data to which they are exposed, the more scenarios it can address. Data should be correctly classified and labeled. The challenge, as we have seen, lies in making diverse data formats housed in disparate systems all speak the same language. A robust data integration foundation is the only way to ensure a single source of truth to continuously feed the LLMs with high-quality, high-integrity data.
For the volume, speed, accuracy and scale of data that AI projects demand, data integration can no longer remain a manual process. You need high operational efficiency, automation-led accuracy and intelligent process optimization. That’s where the right data integration solution comes into the picture.
Finding the right data integration solution to handle enterprise AI projects can quickly become complicated, given the significant number of tools in the market. However, custom hand coding, or a large stack of point solutions, will not be able to cope and evolve with your AI ambitions. Such approaches may seem more cost-effective in the short term, but they will only add to the complexity, cost and technical debt over time and with scale. This puts your AI initiatives at risk.
Data integration as a process occurs in a hugely dynamic space. For instance, data storage has evolved rapidly from Excel sheets to data warehouses, data lakes and lake houses for on-premises, cloud and hybrid systems. Likewise, data usage has evolved from a small set of data scientists to a large group of business users with democratized access to data and analytics products. Additionally, data formats have evolved from structured to highly unstructured formats across a plethora of source systems.
Your data integration foundation needs to remain strong, stable and scalable in the face of these rapid changes in the data management landscape. In other words, if you want to lead the AI-powered future, it is imperative to invest in a future-proof data foundation that can scale and grow with you and the industry.
It is important to bear in mind that data integration itself is not an isolated process. It is deeply enmeshed with data quality and governance, master data management (MDM), and security. Data governance and security must be baked into each stage of the data integration process, along with clear observability of data lineage.
Key Elements of a Successful Data Integration Strategy for AI
A better approach, from both the technical and strategic perspective, is to start right and invest in a data integration solution that can go the distance. A holistic approach to data integration that encompasses the necessary capabilities will look for solutions that deliver:
Efficiency: Data integration is an ongoing and growing process, and if costs grow with scale, you will soon find yourself over budget. Look for a data integration system that proactively optimizes the use of budgets and engineering resources with no code, reusable, intelligent pipelines for data ingestion and transformation.
Effectiveness: The sources and formats of inbound data are only going to get more complex. Check if your data integration solution can perform with data in any format and can connect any source to any target, along with pushdown optimization for your ETL and ELT workflows.
Governance: Data governance should begin the moment data enters your systems. The best data integration solutions automate and build observability and data quality governance frameworks into the standard workflows. Look for solutions that offer transparency in data lineage from start to finish.
Scale: Your AI initiatives will grow and scale. Multiple point solutions for different data management processes, as well as excessive custom hand-coding, will lead to operating inefficiencies and technical debt. Ask your vendor about their product roadmap and how the solution can expand and scale with your ambitions, without impacting data quality, security, or escalating costs.
Future-proof peace of mind: Data storage and management innovations will continue to disrupt business as usual. Prioritize ecosystem-agnostic data integration solutions that can operate natively to most major data storage ecosystems.
Ease of use: Data management or training the model is not your endgame. The real outcome of AI and ML initiatives is to help business users make smarter, faster decisions. For this, an easy-to-use, plug-and-play, GUI-driven intelligent data integration platform facilitates data democratization and self-serve capabilities.
Seamless integration: Data integration doesn’t exist in a vacuum. Ensure your solution seamlessly plugs into other key upstream and downstream components of the data management ecosystem, such as master data management, data security, access and distribution.
Ensuring Sustainable AI Through a Strong Data Integration Foundation
No business can escape an AI-powered future. While that future depends on data, no AI model, however advanced, can correct the challenges posed by poor-quality data or deliver results with an unstable data foundation. A strong data integration foundation powered by an intelligent, AI-powered solution will deliver better ROI. This is achieved by extending the operational life of your AI models, driving higher adoption of more trusted models, and ensuring immutable compliance with legal and regulatory requirements.
Next Steps
To learn more about how to bring AI to life with next-level data integration, understand key use-cases and explore real-world examples, get your copy of “Bridge the Gap to Real-World AI with the Help of Data Integration.”
2 Gartner Article, Highlights from Gartner and Data Analytics Summit, Alexis Wierenga, March 13, 2024, https://www.gartner.com/en/articles/highlights-from-gartner-data-analytics-summit-2024. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
