The power of next-generation data integration

Poor data quality can derail even the most ambitious artificial intelligence (AI) initiatives, leading to financial losses and strategic setbacks. Modern data integration solutions, such as IBM® DataStage®, addresses these challenges by empowering developers, engineers and enterprises with technology designed to enhance:

• Productivity: A machine learning-assisted, no- or low-code interface to quickly connect and integrate data from hundreds of data sources, targets and formats.
• Performance: An industry-leading parallel processing engine complimented by proactive data pipeline observability and monitoring.
• Flexibility: Process data on your terms across any cloud, virtual private cloud (VPC), geography or on-premises with remote engine architecture and use various reusable integration patterns tailored to use case needs.

By adopting a robust data integration framework, businesses can help ensure that their data is accurate, timely and valuable, unlocking the true potential of their AI investments and driving informed decision-making across the organization.

Business leaders are under intense pressure to implement generative AI (gen AI) due to its significant potential to impact the bottom line: gen AI is expected to raise global GDP by 7% within the next 10 years. With Gartner estimating 80% of enterprises will have deployed or plan to deploy foundation models and adopt gen AI by 2026, the imperative to support AI initiatives is higher than ever.

However, businesses scaling AI face significant barriers to entry, primarily data-related issues. Organizations require reliable data to build robust AI models and gain accurate insights, yet today’s technology landscape presents unparalleled data challenges that hinder AI initiatives. According to Gartner, at least 30% of gen AI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality.

Clean, consistent and reliable data is essential for maximizing AI return on investment, especially considering the explosion of data in different formats and locations. AI-ready data can be accelerated by an enterprise approach that uses a data fabric architecture, which democratizes data across the organization, helping to ensure timely and trusted, business-ready data. A key pillar of a successful data fabric is data integration.

Data integration is a crucial element of the data fabric and one of the key components for improving data usability across AI, business intelligence (BI) and analytics use cases. It is now essential for companies to thrive; by merging data from various sources, businesses can gain valuable insights, make better decisions, discover new revenue opportunities and streamline operations. However, traditional data integration practices and technologies often face several hurdles:

1. Data silos and complexity: Data is rapidly propagating on-premises and across clouds, applications and locations in various formats and structures, creating inconsistencies that hinder analysis. These isolated data pockets prevent a holistic view, slowing the discovery of valuable insights. As a result, data teams often face lengthy cycles to manually standardize data, a complex and time-consuming process.
2. Code silos: Code-driven data integration, while powerful, can be cumbersome and costly. Complex logic is needed to handle diverse data, and hand-written structured query language queries are error-prone and require constant maintenance. This approach to data integration pipelines creates a significant development and upkeep burden. Data engineers need to focus on building transformation logic in a repeatable, maintainable way, with the DataOps tools to reduce time and risk to deliver to production.
3. Scalability and performance: Traditional data integration approaches, even when using mature tools, struggle with the growing volume and real-time processing needs of modern data, especially across on-premises and cloud workloads. These methods often fail to scale to meet the high-performance requirements of today’s organizations.
4. Skills barrier: Seasoned data teams face increasing pressure to respond to growing data requests from downstream consumers, which is compounded by the push for higher data literacy and a shortage of experienced data engineers. A strategy that empowers less technical users while accelerating time to value for specialized data teams is critical.

Modern data integration solutions address these challenges by offering:

• Power to the developer: A no/low code, intuitive user interface that empowers developers to rapidly build reusable, repeatable data pipelines with minimal coding, while offering flexibility for extensibility. Its open ecosystem of prebuilt connectors for diverse data sources and formats simplifies integration, making the process faster and more efficient.
• Power to the engineer: Industry-leading data processing performance helps ensure timely data delivery, while proactive pipeline monitoring identifies and resolves issues before they impact downstream workflows.
• Power to the enterprise: Deployment flexibility—the ability to design jobs once and run them in any geography or VPC—provides scalability for evolving business needs. Also, runtime flexibility, which allows for toggling between extract, transform, load (ETL) or extract, load, transform (ELT) processing patterns without manual recoding, enables organizations to optimize their integration style to match use case needs, enhancing cost management and performance.

IBM has remained a trusted vendor in the data integration space, offering industry-leading tools for nearly two decades. To meet enterprises’ needs in today’s hybrid-cloud and AI landscape, IBM has introduced the next-generation DataStage. This is a modern data integration solution that helps design, develop and run jobs to move and transform data with industry-leading performance and flexibility, enabling enterprises to unlock the true potential of their data.

Read the technical blog to learn how next-generation IBM DataStage empowers developers, engineers and enterprises

Book a live demo to discover the benefits IBM DataStage can bring to your organization