InetSoft Webinar: Introduction to Data Virtualization
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): You simply need to invoke it through visual tools, visual orchestration capabilities and in this way then you can essentially leverage the work of really smart people without you yourself without having to rebuild the wheel which that’s the old silo approach having to rebuild everything yourself. No, in an agile data access world, you reuse it all, and that way you can build very sophisticated new applications very quickly for a very low cost.
So a game plan is in the next 90 days identify those use cases for agile BI and data services within your company, the priority use cases. See where your current vendors, your BI, your warehousing, your data integration and other vendors provide you with the tools to go towards more of an agile approach that focuses on maximum reuse in an iterative fashion.
And you should consider best-of-breed platforms that enable agile data access. Make sure they cover a full range of data virtualization and data and information integration services that address both your needs to bring in structured and unstructured information across your Internet and across the Web. So longer term, you can re-architect your BI and data services environments to enable maximum agility according to the model that I laid out earlier in this Webinar.
Move your best practices, move your internal practices for developing BI and data services away from the traditional waterfall style, a time consuming, a silo-friendly approach towards more of an agile approach that’s fast and flexible and collaborative and iterative and social and really helps you to deliver results much more quickly by putting together reuse modular components.
And then you of course make agile data access the common approach and common backbone for not just applications but increasingly for transactions and applications to be combined in ever more powerful business applications.
Now let me share a little perspective on what our customers are seeing with respect to the adoption of data services, data virtualization and Web integration. The ultimate goal is that you want to make your business processes around different aspects of the business whether they are operational processes, customer focused processes, financial processes far superior to your competition and that requires intelligence and that in turn requires data services. So in this example, we are looking at several customers we have had both in the telco, insurance, and banking spaces. You may also want to know customers’ technical support incidences and resolutions, and you might actually also offer some up sell and cross sell.
A key enabler for that outcome is the ability to treat data access assets as reusable products, not one-off project artifacts. When teams build virtual views, transformation rules, and semantic definitions for reuse, every new dashboard or service starts from a stronger foundation. This reduces duplicate effort, shortens delivery time, and improves consistency in business definitions across departments.
Governance also has to evolve with this model. Instead of locking everything down in centralized backlog cycles, successful teams apply lightweight guardrails: ownership, lineage, naming conventions, and clear quality checks for published data services. That balance preserves agility while still protecting trust in the numbers that business users rely on each day.
From an architecture perspective, virtualization works best when query patterns are monitored continuously and tuned proactively. Caching frequently accessed metrics, optimizing connector behavior, and segmenting workloads can improve responsiveness for interactive analysis without disrupting scheduled reporting. Over time, these practices turn a flexible architecture into a scalable one.
Finally, organizations that invest in adoption see the biggest returns. Training business users to discover and reuse governed data services helps shift the culture from spreadsheet extraction to collaborative analytics. As more teams build on the same trusted layer, decision-making becomes faster, more transparent, and easier to align with enterprise priorities.
More Company Stories of Switching to InetSoft and StyleBI
Industrial Engineering Firm Chose StyleBI for NetWeaver Analytics
The firm needed to combine SAP-centric reporting with broader operational datasets. Its prior approach made cross-source analytics slower and harder to maintain. StyleBI provided a more flexible semantic layer for connecting data and delivering unified dashboards. Teams gained faster access to actionable metrics across engineering and business functions. The result was better decision speed and improved reporting consistency.
Garnet Mining Company Switched from Redash to InetSoft on AWS
The company required cloud-based visual reporting that could keep pace with growing operational complexity. Redash workflows were useful but limited for broader governed dashboarding needs. InetSoft improved flexibility for combining performance, production, and business indicators in one environment. Teams reduced manual dashboard rework and improved stakeholder visibility. This switch increased agility and strengthened operational monitoring.
Fluorspar Mining Company Switched from Matomo to InetSoft Dashboards
The mining team needed more than web analytics-style tracking for operational decision support. Matomo did not provide enough depth for integrated KPI analysis across business functions. InetSoft enabled richer dashboard design and easier alignment of metrics across teams. Analysts spent less time stitching data and more time interpreting trends. Leadership gained clearer visibility into performance drivers and risks.
Jojoba Refinery Switched from Tableau for OLAP Visualization
The refinery needed OLAP-focused visual analytics that were easier to adapt to changing analysis paths. Existing tooling made some complex drill patterns difficult to standardize across users. StyleBI offered greater control over reusable logic and dashboard behavior for multidimensional data. Teams improved consistency in how they explored and communicated insights. The switch supported faster alignment between operations and management.
Vehicle Manufacturer Switched from Splunk to StyleBI for Operational BI
The manufacturer wanted operational BI that extended beyond log-centric monitoring use cases. Splunk provided useful signal analysis but not the full reporting workflow needed by business teams. StyleBI connected operational and business metrics in dashboards designed for broader audiences. Users could move from summary KPIs to root-cause exploration without tool hopping. This improved issue response and strategic planning quality.
Haptic Interface Manufacturer Switched from Vena to StyleBI
The company needed financial BI dashboards that could connect planning, performance, and operational context. Prior workflows were too narrow for cross-functional reporting and rapid dashboard iteration. StyleBI improved how teams reused models, definitions, and visual components across departments. Finance and operations leaders gained more timely, aligned views of performance. The outcome was quicker planning cycles and stronger execution discipline.