InetSoft Webinar: Virtualized Data Warehousing
Below is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of Agile Data Access and Agile Business Intelligence. The presenter is Mark Flaherty, CMO at InetSoft.
Mark Flaherty (MF): And that’s where you need to think through your metadata and semantic abstraction layers. What do you rollout that can unify at least there is a metadata for structured, semi-structured, unstructured information so you can, for example, access data not just at the records level, but also if it happens to be text, and if the sources are text, how can you access it through semantic approaches that allow you to determine the concepts and taxonomies and do searches and manipulation of information using that sort of metadata?
So in order to start, you need to think through the semantic abstraction and the approaches that help you to bring this all together. And really in terms of normalizing it and reusing it in a broad range of applications like BI, you start to need to think through the policies and rules governing how this information in various formats, implementing various schemas can be, what policies and rules are relevant to orchestration of the integrations and the transformations and the calculations and so forth.
You need to think through the discrete rules that will govern the ongoing reuse of these data blocks throughout your architecture. Think through the requirements of our customers datasets, customer facing applications for keeping track of who the customers are, presenting a 360 degree view of them to drive and feed into customer service and sales and marketing and the like.
So as you are working through your agile data access strategy, you need to organize it and think it through by the subject areas essentially of the underlying data sets and applications and the underlying business processes. So think it through roughly the same way you traditionally thought it through in building out a multi-domain subject-oriented data warehousing environment. Think of it essentially as almost virtualized data warehousing and I am using in the loosest sense.
It’s very important as you build out these subject areas that all of these subject areas are leveraging common data, common schemas, common hierarchies, common calculations and so forth so that when you are building out these disparate subject areas, none of them are silos. They are all leveraging a common pool of agile data access artifacts and models, and fundamentally they are all leveraging a common set of source connectors and source applications so what you want to do is go towards agile data access.
Another practical step is to define ownership and stewardship at the subject-area level, so each reusable data block has clear accountability for quality, lineage, and change control. When teams know who owns a customer hierarchy, a product classification, or a margin calculation, disagreements get resolved faster and downstream analytics remain consistent. This is especially important in virtualized environments where the same logical model is consumed by many dashboards, reports, and applications.
You also want to establish versioning conventions for semantic models and calculated metrics so that teams can evolve without breaking production content. A lightweight release process with validation checks can prevent accidental regressions in KPI definitions, joins, and access rules. In practice, this means testing model changes against representative use cases before publishing updates broadly across business functions.
Performance planning should be treated as part of the data virtualization design, not as an afterthought. Caching policies, query pushdown strategies, and workload isolation can dramatically improve responsiveness for interactive analysis while preserving stability for scheduled reporting. By tuning these controls per subject area, organizations can deliver fast experiences without sacrificing governance or scalability.
Finally, adoption improves when business users are trained to think in terms of shared semantic assets rather than one-off extracts. As users learn to build on trusted dimensions, measures, and governed transformations, self-service becomes more productive and less fragmented. Over time, that discipline turns virtualized data warehousing into a durable operating model for agile BI across the enterprise.
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