Using InetSoft for a BI Front-end to OLAP and ROLAP Cubes

The snowflake schema is a variation of the star schema wherein dimensional tables are normalized. In some cases, redundancy in dimensional tables becomes excessive; the normalization in a snowflake schema improves performance.

In general, the star schema and snowflake schema are referred to as ROLAP technology. Because most design decisions are shifted to the design of the schema itself, modeling ROLAP in the Data Modeler is extremely straightforward. Each physical view readily maps into a fact table and its dimension tables. The multidimensional hierarchy model is created by mapping the built-in hierarchical relationships within each dimension table and exposing the measures from the fact table.

Physical View – Transforming a Generic Schema into a Business Intelligence-friendly Schema

A physical view’s design should transform a generic schema into a business intelligence-friendly schema. Even though the physical view is not directly accessed by end users, its design impacts the logical model, which is accessed by end users.

The join relationships among tables form the foundation for a physical view. Physical views are independent; that is, a relationship in one physical view will not impact another.

In creating a physical view, you should identify and address all possible query traps so that the end user cannot produce confusing or incorrect queries. A subject-based physical view design will minimize query traps. This design approach also segments a complex schema into manageable, easy-to-understand subsets.

Subject-Based Design

A subject contains a main business topic and related information. For example, a CRM database includes company information, individual contact information, and sales opportunity information. It is best to segment the schema into three separate physical views, but each physical view can contain some redundant information and interlink keys. For example, the sales opportunity view may include some company information but not the company change history, which is available only in the company view.

Properly exposed interlink keys allow power users to dynamically combine subject information using Data Block technology. Even though subject-based design reduces the potential for problems, the traps within each subject still need to be identified and resolved.

How A Customer Activity Dashboard Transformed Operations

Customer-facing teams adopted InetSoft’s dashboard to centralize customer activity metrics and shorten problem-resolution cycles. The implementation delivered faster access to detailed transaction and interaction records, enabling triage and follow-up to happen in real time. Decision-makers reported improved visibility into workload distribution and agent performance, which supported targeted coaching and staffing changes. Integrating multiple source systems into a single dashboard reduced manual reconciliation and cut the time to produce routine reports. Overall, the deployment improved responsiveness and lowered operational overhead for customer operations.

Why Companies Chose Cloud-Flexible BI For Agility

Several organizations switched to InetSoft for its cloud-flexible approach that balanced self-service with centralized governance. The move delivered faster report iteration cycles for business users while keeping IT control over sensitive data sources. Customers highlighted easier onboarding for nontechnical users, which accelerated adoption across departments. The platform’s mashup capabilities allowed combining scattered data without heavy ETL work, reducing project timelines. As a result, organizations gained quicker insights and improved cross-team collaboration.

Satisfied Customers Reporting Measurable Time Savings

Customer feedback collected on this page emphasizes tangible time savings and productivity gains after adopting InetSoft. Case examples describe replacing slow manual reporting with interactive dashboards that surface issues and trends immediately. Users reported reductions in hours spent on recurring reporting tasks and faster root-cause analysis. Improved usability and support contributed to higher end-user satisfaction and broader adoption. These benefits combined to deliver measurable operational efficiency improvements.

Mini Case Studies Showing Rapid BI Payback

This collection of short case studies highlights organizations that saw quick returns after deploying InetSoft’s BI tools. Stories include non-profits and small enterprises who implemented tailored dashboards and immediately replaced ad-hoc spreadsheets. Benefits described include streamlined reporting, better decision traceability, and reduced dependency on IT for routine analyses. The mini case studies underline scenarios where small, focused pilots led to wider rollout across teams. Each vignette underscores faster insight cycles and lower maintenance burden compared with legacy approaches.

Real-World Data Dashboards Deliver Operational Wins

Showcased customer examples illustrate how data dashboards were used to save time and improve operational control. One highlighted customer estimated hundreds of man-hours saved annually after consolidating data into interactive dashboards. Teams used the dashboards across sales, purchasing, production, and finance to replace time-consuming ad hoc queries. The examples demonstrate how broadening access to curated dashboards improved day-to-day decision making. This cross-functional deployment reduced reporting bottlenecks and improved information accuracy.

Student Management Case Study: Faster Reporting For Schools

An education-sector customer implemented InetSoft to build student management dashboards and automated caregiver reports. The solution centralized pupil, attendance, and performance data, enabling educators to spot at-risk students sooner. Automation of routine reports reduced administrative overhead and improved timeliness of interventions. Role-based views allowed teachers and administrators to see the same underlying data with appropriate context. The project delivered both improved outcomes tracking and significant staff time savings.

Manufacturer Adopted Self-Service BI For Faster Insights

A manufacturing customer moved to InetSoft to enable self-service analytics across production and sales teams. The switch reduced reliance on a centralized reporting team and empowered line managers to create and modify dashboards. Benefits included faster detection of production variances, improved inventory oversight, and more timely sales forecasting. The mashup capabilities made it easier to combine ERP and CRM data without lengthy integration projects. Overall, the change supported quicker operational decisions and lower IT backlog.

Customer Service Centers Centralized Metrics With Dashboards

Contact center organizations adopted InetSoft to create operational dashboards showing case status, agent load, and performance KPIs. Centralized dashboards reduced the need for manual spreadsheets and sped up supervisory interventions. Managers used live views to reassign workload, address bottlenecks, and improve first-contact resolution. The role-based dashboarding ensured agents, team leads, and executives received tailored insights. The result was measurable improvements in service levels and more efficient workforce utilization.

Sales Teams Consolidated Reporting For Better Forecasting

Sales organizations used InetSoft to create unified sales dashboards that combined CRM, ERP, and marketing campaign data. The consolidated view improved pipeline visibility and helped managers prioritize deals with higher probability. Quick, interactive filters enabled rapid territory and product-level analysis without IT involvement. The dashboards supported more accurate forecasting and faster identification of at-risk opportunities. This reduced reporting cycles and improved alignment between sales and operations.

Interactive Dashboard Examples Used As Pilot Templates

Interactive examples in the evaluation gallery were used by prospects as pilot templates to accelerate deployment and prove value. Organizations tested live dashboards with their data to validate UX, performance, and integration patterns. Pilots helped justify budgets by showing concrete time savings and decision improvements before full rollout. The templated examples reduced implementation effort by providing a starting design that could be adapted quickly. Many pilots progressed directly into broader production usage after demonstrating ROI.

Why A Technical Team Switched To An Agile Mashup Platform

A case described in the evaluation guide explains a company shifting from a rigid integration tool to a more agile mashup BI platform. The switch reduced long development cycles and lowered ongoing maintenance overhead for combined datasets. Teams gained the ability to mash up data sources on the fly, enabling faster hypothesis testing and iterative dashboarding. The result was shorter insight cycles and reduced dependence on heavy ETL projects. This improved agility helped teams respond faster to business questions.

Customer Release Demo Showcasing Practical Integrations

The customer release demo highlights integration scenarios used by customers to connect to various APIs and data endpoints. Prospective users saw how endpoints like survey or web-service data could be easily accessed and incorporated into analytic worksheets. Organizations adopting these integration patterns reported faster time to insight when blending third-party datasets. The demo emphasizes usability for analysts who need to combine operational and external sources without custom coding. As a result, customers reduced integration friction and accelerated analysis cycles.