BrainBlog for Next Pathway by Jason Bloomberg
Enterprises have been moving their data around for decades as technologies age and platforms become legacy. Today is no different: moving enterprise data to the cloud has become a necessity for any organization still running on legacy data warehouses.
However, there is more to this story, as the rise of AI has redoubled the importance of well-organized, well-governed, and cloud-based enterprise data. As a result, legacy data migration initiatives have taken on new urgency.
There is now a two-pronged business motivation for such efforts: supporting business users with data as well as feeding data into AI applications to extract additional value from data assets.
What is a data product?
Once IT organizations have migrated data to the cloud, business owners take ownership of it and make it reusable across many different business-centric use cases. In other words, the business leverages newly-migrated corporate data as data products.
Data products have a number of characteristics that differentiate them from the vast sea of corporate data washing on the shores of every organization:
- Data products are trusted – organizations must ensure that the data delivered as data products are accurate, consistent, timely, and traceable. To guarantee such trustworthiness, enterprises must implement appropriate governance tools and practices.
- Data products are reusable – by establishing standard data assets, every department can leverage data products for a single source of truth. Whether it be customer profiles, sales metrics, or inventory numbers, everybody across the organization sees the same data.
- Data products are business ready – business users must be able to understand and leverage data products without additional number crunching, filtering, or data enrichment. In other words, data products are ready to use out of the box.
Understanding the dual role of data products
When data products follow these guidelines, they are ready to power analytics and reporting, as well as a range of AI-based applications.
In fact, the rise of AI generally and AI-based automation in particular has transformed the role data products play – from supporting analytics and reporting tools to becoming foundational assets for the AI-driven enterprise.
AI requires high-quality, accurate data. Poor or incomplete data sources lead to hallucinations and other undesirable results. AI systems require well-governed, consistent, and context-rich data, even more so than humans do.
Furthermore, AI may consume data products on a real-time, 24 x 7 basis, representing a different consumption model from humans using such products.
As a result, today’s data products must support both human and machine consumption patterns. In addition to being trustworthy, reusable, and business ready, such products must also offer real-time access, deliver rich metadata context, and support continuous validation.
Building AI-ready data products with legacy data
As AI drives the expansion of business requirements for data products, the burden for delivering such products falls upon legacy data migration initiatives.
Next Pathway, for example, helps enterprises deploy reusable, governed, and scalable foundations for data products, both for human and AI consumption, by ensuring data remains trusted, reusable, and business ready.
Given the complexity of legacy data environments, building data products is a difficult challenge. To address those requirements, Next Pathway offers CRAWLER360, a tool that analysts use to decompose legacy data estates into business-centric capabilities to meet the needs of data products for both human and AI consumption.
CRAWLER360 automatically scans and analyzes a variety of complex legacy data platforms and environments, including Hadoop and Teradata, as well as legacy ETL workflows, SQL code, and business intelligence reports from various legacy reporting tools like Crystal Reports.
From this analysis, CRAWLER360 creates a detailed inventory and dependency map for the organization’s data assets, uncovering data lineage, system interdependencies, and other migration complexities including buried business logic.
Next Pathway is then able to develop new data models that preserve legacy business logic while maintaining the data quality and consistency all data products require, especially as they feed AI applications.
Next Pathway also supports the governance so essential for AI applications via metadata management, data lineage tracking, and standardized business definitions that enable organizations to establish a trusted ground truth across legacy data sources, supporting both analytics and AI applications.
Once organizations have leveraged Next Pathway to migrate their data to cloud platforms like the Snowflake AI Data Cloud, they are able to leverage this modern platform to deliver data products that support AI applications (including autonomous AI agents) as well as the real-time analytics essential for running the modern business.
The Intellyx take
As Jensen Huang, CEO of NVIDIA points out, migrating data to the cloud is the prerequisite for participating in the AI economy.
For Huang, the ‘AI economy’ depends upon ‘AI factories’ that leverage vast data centers built with NVIDIA GPUs, as well as the company’s strategy to expand beyond GPUs to the full breadth of AI infrastructure.
This vision for Huang’s AI economy would not be possible, however, without data – including the data locked away in enterprise systems.
Modernizing data infrastructure and migrating data to modern data platforms, therefore, is essential for competing in the increasingly AI-driven economy.
Huang is not the only tech leader to realize that legacy data modernization can be the single most limiting bottleneck to achieving this long-term strategic goal for AI.
It’s no wonder, therefore, that he singles out Next Pathway as a critical enabler – not just of legacy data migration, but the AI economy at large.
Copyright © Intellyx BV. Next Pathway is an Intellyx customer. None of the other companies mentioned in this article is an Intellyx customer. Intellyx retains final editorial control of this article. A human wrote every word of this article. Image credit: Craiyon.
