Your enterprise, no matter the industry or its scale, is working to leverage data to achieve its strategic objectives. As an IT leader, you and your team need to ensure the business will not be hamstrung, or even worse, tripped up, by limited data access capabilities.
To be able to leverage data, it must be efficiently accessed, combined, governed, and managed. New sources of data, or new data in current sources, must also be found, understood, integrated, and managed.
While the pain caused by a lack of mature data integration may be related to people or process, you must also ask if your enterprise has adopted the right product set or if it is stuck in outdated tools. The technical debt that has accumulated from years of workarounds and gap-fixing existing processes may seem too expensive to simply rip and replace with more capable modern tools. However, the demand for mature, modern data integration is becoming too strong to ignore. Getting by with an outdated platform, or not using a modern platform to its fullest, is no longer a sustainable choice.
We’ve seen the evolution to truly data-driven organizations through digital transformation. Now, we see the latest evolution where mature enterprises are leveraging artificial intelligence (AI) and machine learning, powering data integration to automate tasks and guide the user experience. You want this.
Although the latest evolutionary stage -- and the new high watermark -- of data integration is AI-powered automation and enablement, there are more requirements such as cloud-native deployments and enterprise scale and trust.
You need to be able to orchestrate the ebb and flow of data among multiple nodes, either as multiple sources, multiple targets, or multiple intermediate aggregation points.
The data integration platform must also be cloud native today. This means the integration capabilities are built on a platform stack that is designed and optimized for cloud deployments and implementation. This is crucial for scale and agility -- a clear advantage the cloud gives over on-premises deployments.
Additionally, data management centers around trust. Trust is created through transparency and understanding, and modern data integration platforms give organizations holistic views of their enterprise data and deep, thorough lineage paths to show how critical data traces back to a trusted, primary source.
Finally, we see modern data analytic platforms in the cloud able to dynamically, and even automatically, scale to meet the increasing complexity and concurrency demands of the query executions involved in data integration. The new generation of some data integration platforms also work at any scale, executing massive numbers of data pipelines that feed and govern the insatiable appetite for data in the analytic platforms.
The “Egregious Toil and Labor” of conventional ETL, where development and change takes months, must become an approach of the past. Intelligently driven automation suggests and generates new data pipelines between source and target without manually mapping or design, saving and optimizing steps. Here are the critical capability categories which define the capabilities for data integration competitive advantage:
- Comprehensive Native Connectivity
- Multi-Latency Data Ingestion
- Data Integration (in all of: ETL, ELT, Streaming)
- Data Quality and Data Governance
- Data Cataloging and Metadata Management
- Enterprise Trust at Scale
- Artificial Intelligence and Automation
- Ecosystem and Multi-Cloud
Data integration has always been the most important component in leveraging data to achieve enterprise strategic objectives. It is evolving with artificial intelligence and other critical capabilities. The opportunity exists to truly make a difference in not just the data architecture, but also the enterprise, by leveraging these capabilities.