Data Analytics & BI Excellence — Turning Data into Outcomes

Moving from Cognos, SSRS or BusinessObjects to Power BI, Qlik or Tableau typically consolidates licensing, retires on-premise report servers and removes the specialist dependency that slows every change. Reporting teams spend their capacity on new analysis instead of maintaining legacy infrastructure.

Users gain interactive, in-memory analytics in place of static, parameter-driven reports. Associative exploration in Qlik, visual discovery in Tableau and self-service semantic models in Power BI let business teams answer follow-up questions themselves rather than raising another report request.

Because every migrated report is reconciled against its legacy original before cut-over, finance, regulatory and operational users keep the numbers they already trust — while the rationalization step removes unused and duplicate reports that never needed to move.

Every migration starts with a tool-based inventory of the legacy estate: Cognos packages, Framework Manager models, Report Studio reports and Transformer cubes; SSRS report definitions and subscriptions; BusinessObjects universes and Web Intelligence documents. Usage analysis identifies which reports are active, which are duplicates and which can be retired.

We then reverse-engineer report specifications and map each legacy construct to its modern equivalent — Framework Manager query subjects to Power BI semantic models or Qlik associative data models, pixel-perfect Report Studio and SSRS layouts to Power BI Paginated Reports, and cross-tab calculations, conditional formatting and bursting logic to native expressions and subscriptions.

Rebuilt reports run in parallel with the legacy platform while automated reconciliation compares figures line by line. Cut-over happens only when numbers match, followed by hands-on user training and post-launch support so adoption does not stall after go-live.

With certified, reusable datasets in place, new dashboards are assembled from trusted building blocks instead of re-engineered from raw tables. Delivery accelerates and every report inherits the same definitions, security rules and refresh schedules.

Leadership gains confidence in the numbers. When revenue, margin or headcount is defined once in the semantic layer and endorsed by the CoE, meetings move from debating data to acting on it.

Platform costs and risk come under control. Workspace strategy, capacity planning and access reviews run as defined CoE processes, and a trained community of business champions reduces the volume of basic requests reaching central teams.

We start by choosing the right CoE model for your organization — centralized, federated or hub-and-spoke — based on how business units operate, where data skills sit and how much autonomy each domain needs. Roles are defined explicitly: CoE lead, platform administrators, data stewards, BI developers and business champions.

Next we establish the semantic layer: shared dimensional models, certified and endorsed datasets, a KPI catalog with owners, and a promotion path from development to test to production workspaces. Row-level and object-level security are designed once and applied consistently.

Finally we codify BI standards — report templates, themes, naming conventions, documentation and review checklists — and launch an adoption program with role-based training, office hours, playbooks and usage scorecards that show where the CoE is working and where it needs attention.

Well-modeled data makes dashboards fast and consistent. Measures are defined once, filters behave predictably and new pages can be added without rewriting logic — which keeps delivery costs down as the reporting estate grows.

Storytelling layouts put the most important signal first. Executive scorecards highlight performance against target, operational dashboards surface exceptions that need action and analyst views provide drill-through detail, so each audience gets what it needs without hunting.

Embedded analytics extend insight beyond the BI portal. Dashboards delivered inside customer portals, SaaS products and line-of-business applications put data where decisions are made.

Our BI specialists design dimensional models, semantic layers and star or snowflake schemas before a single visual is built. Transformation logic lives in Power Query, dataflows, Tableau Prep, Qlik load scripts, Domo Magic ETL or LookML — wherever it is most maintainable for your platform.

We prototype dashboards early to secure quick wins and validate the design with real users, then harden the model with DAX and MDX optimization, query tuning and incremental refresh before scaling to the full audience.

Every deliverable follows consistent UX standards for navigation, themes and layout, and ships with documentation so your team can maintain and extend it confidently.

Row-level and object-level security let one dataset serve every region, business unit and role, each seeing only what it should. That removes the need to maintain separate copies of the same report for different audiences.

Consistent workspace strategy, naming standards, versioning and documentation make the BI estate auditable. Compliance and internal audit teams can see who has access to what, and platform owners can retire unused content with confidence.

Performance tuning protects adoption. Dashboards that load in seconds get used; dashboards that time out get abandoned. Optimized datasets and queries also defer the need for larger capacity or server upgrades.

We audit the existing environment first: workspaces, permissions, dataset lineage, refresh schedules, gateway configuration and usage patterns. Findings are prioritized by risk and by the number of users affected.

Governance is then implemented as a repeatable setup — workspace strategy, RLS/OLS, metadata management, naming conventions, versioning and documentation templates — across Power BI, Tableau Server and Cloud, Qlik and Domo.

For performance, we check query folding, optimize DAX and data models, tune extracts and incremental refresh, and right-size server or capacity configuration, measuring load times before and after each change.

Refresh failures and performance issues are detected and resolved before business users open their morning dashboards, so leadership meetings start with current data rather than apologies.

Governance stays current. Access reviews, workspace audits and naming standards are maintained as a routine service instead of an annual clean-up project.

Monthly usage analytics and adoption scorecards show which dashboards drive decisions and which can be retired, giving sponsors a clear view of the return on their analytics investment.

We begin with a structured transition: documenting data sources, refresh dependencies, security rules and known issues, then agreeing service levels, escalation paths and a prioritized enhancement backlog.

A dedicated onsite–offshore team provides continuous coverage for monitoring and incident resolution, while a named lead works with your business owners on enhancements and roadmap priorities.

Every change is version-controlled and released through test and production workspaces, and each month you receive a service report covering incidents, releases, performance trends and adoption.

Automated insights surface anomalies and trends across thousands of data points, so analysts spend their time investigating what matters instead of scanning every chart.

Predictive models embedded in dashboards put forecasts beside actuals — demand, revenue, churn or risk — giving managers a forward view in the same place they already track performance.

Generative report summaries and natural-language Q&A widen access to analytics. Executives receive a written narrative of what changed, and business users can explore data without learning a BI tool.

We identify the decisions where earlier warning or a forward-looking view would change outcomes, and define how success will be measured before any model is built.

AI features are grounded in the same certified datasets and security rules as your dashboards, so a natural-language answer or generated summary never shows data a user is not entitled to see and always agrees with the governed KPI.

Models and alerts are embedded directly in the reports and workflows people already use, then monitored for accuracy and usage so they continue to earn trust after launch.