Build trusted data foundations for reporting, analytics and AI-ready decision support.

Para helps organizations connect business questions, source systems, data models, KPIs and reporting into a reusable analytics foundation.

The focus is not simply on producing more dashboards. It is on creating a clearer relationship between business decisions, data and measurable performance.

Para's published Business Intelligence capability emphasizes data collection, storage and analysis, while its project portfolio demonstrates experience building data models, enterprise data warehouses, tabular models and Power BI reporting environments.

Perspective

Reporting problems often become visible at the dashboard but begin much earlier.

A KPI may be calculated differently by different departments. A report may depend on manual spreadsheet preparation. A source-system change may break downstream reporting. Two teams may use the same business term while working from different definitions.

When that happens, adding another dashboard rarely solves the underlying problem.

Para starts with the decision and works backward through the data.

We identify the questions the organization needs to answer, the measures that matter, the systems that contain the underlying information and the structures required to make reporting consistent and reusable.

This creates an analytics foundation that can support today's reporting requirements while providing a stronger starting point for future analytical initiatives.

Para's published BI material specifically highlights data accuracy and reliability as important to building confidence in reporting, while its banking work demonstrates the use of information architecture, data models and data hubs as foundations for analytics.

Decision-led

Start with the decisions, business questions and performance measures the organization needs to manage.

Define what leaders, managers and operational teams actually need to know before deciding what data or dashboard should be built.

Shared definitions

Create consistent measures, dimensions and calculation logic.

A revenue number, utilization rate, customer measure or operational KPI should have an understood definition and source rather than being recreated independently in every report.

Build once, reuse

Create models and data structures that can support multiple reports and analytical use cases.

A well-designed foundation reduces the need to repeatedly rebuild data preparation and business logic for every new dashboard.

Ready to extend

Prepare data, metadata, access and governance practices that can support more advanced analytics when the organization is ready.

AI should not be treated as a substitute for sound data foundations. Better data structures, clearer definitions and stronger governance create a more credible starting point for future analytical capabilities.

What We Help Solve

Para helps organizations address the data and reporting problems that make decision-making slower, less consistent or harder to trust.

Common situations include:

• Business units calculate the same KPI differently.
• Management reporting depends on repeated spreadsheet preparation and reconciliation.
• Teams manually combine information from multiple operational systems.
• Dashboards are built directly against source systems with limited reuse.
• Reporting logic is duplicated across reports, teams or departments.
• Source-system changes frequently affect reports and require manual fixes.
• Data ownership and quality responsibilities are unclear.
• Financial, operational, customer and workforce information is difficult to combine.
• Management spends time validating numbers before discussing what the numbers mean.
• Reporting teams repeatedly rebuild similar datasets for different audiences.
• Existing dashboards provide visibility but do not provide a consistent underlying measurement model.
• Data quality issues are discovered only after reports have been produced.
• Organizations have data available for potential AI or advanced analytics use cases but lack the definitions, quality or governance required to use it confidently.
• Business leaders need a more consistent view of performance across functions, products, branches or operating units.

What the Solution Contains

Para's Data & Analytics capability can combine data foundations, integration, modeling, visualization and governance according to the organization's reporting and decision requirements.

Data Foundation

Identify the priority data domains, source systems, ownership, dependencies and structures required to support trusted analytics.

This can include understanding where information originates, how it is currently transformed and where manual preparation is creating unnecessary effort or inconsistency.

Para's published banking work includes information architecture, data models and data hubs designed to gather information for further analytics, data science and AI.

Data Integration & Pipelines

Bring information from relevant enterprise applications and source systems into the analytical environment through controlled data movement and transformation.

Integration reduces reliance on manually combining spreadsheets and creates a more repeatable path from operational data to reporting.

Para's published hospitality example demonstrates this approach by extracting information from an OPERA hotel reservation system and an iScala back-office system and aggregating financial and reservation data into an enterprise data warehouse.

KPI & Semantic Models

Define the measures, dimensions, relationships and calculation logic required for consistent reporting.

The model should reflect the business language used by decision-makers so that reports can reuse the same definitions rather than independently recreating business logic.

Power BI & Dashboard Delivery

Create executive, management and operational reporting experiences using agreed data and KPI models.

Para's published portfolio identifies Power BI as a core BI capability and includes Power BI dashboards for banking and hospitality environments.

Dashboards can be designed around different audiences while maintaining consistency in the underlying measures.

Data Governance & Quality

Establish practical ownership and quality practices around the data included in the engagement.

This can include data ownership, stewardship, metadata, validation, quality checks, issue management and agreed definitions.

The goal is not governance for its own sake. It is to create enough accountability and control for people to understand where reported information comes from and how it should be used.

Analytics & AI Readiness

Assess whether priority data is sufficiently structured, accessible, understood and governed to support more advanced analytical use cases.

Para's published banking experience describes data models and hubs intended to support further data science and AI work.

The emphasis should remain on readiness rather than promises: establish the data foundation first, then determine which advanced analytics or AI applications are appropriate.

How It Works

Analytics delivery should connect business priorities with the data and technology required to support them.

1. Define the decisions

Start with the questions the organization needs to answer.

Clarify reporting audiences, management objectives, operational decisions, KPIs, current pain points and the information that people currently use to make decisions.

Identify which measures are genuinely important before expanding the reporting estate.

2. Assess the data

Map the relevant source systems, data owners, existing reports, calculations, manual preparation and known quality issues.

Understand where the data originates and where transformation or reconciliation currently takes place.

This provides the basis for deciding what should be integrated, modeled, governed or retired.

3. Build the shared model

Create the structures that connect business definitions to source data.

Define KPI logic, dimensions, relationships, source-to-report mappings and reusable analytical models.

Where appropriate, this can include data hubs, enterprise data warehouse structures or tabular models. Para's published work includes all three concepts in its analytics portfolio.

4. Deliver priority analytics

Implement the highest-value reporting experiences first.

Build dashboards, analytical models and integrations in reusable layers rather than creating isolated reports for every requirement.

Para's published banking example included high-performance tabular models and Power BI dashboards designed around individual and company performance.

5. Govern and improve

Put ownership around the analytical environment and use experience from the first delivery to improve the next.

Review data quality, definitions, report usage and changing business requirements. Retire unnecessary reports, improve existing models and identify the next opportunities for analytics or advanced use cases.

Business Outcomes

A strong analytics foundation should make information easier to understand, easier to reuse and more useful in decision-making.

Organizations can use Data & Analytics capabilities to achieve:

• More consistent management reporting through shared KPI definitions and calculation logic.
• Less manual data preparation by reducing repeated spreadsheet consolidation and reconciliation.
• Greater confidence in reported numbers through clearer sources, definitions, ownership and quality practices.
• Reusable analytical models that support multiple dashboards and reporting audiences.
• Improved visibility into business performance across financial and operational measures.
• Faster development of new reporting once common data and KPI structures are established.
• Better connection between operational and financial information where data previously existed in separate systems.
• Improved management of reporting assets through clearer ownership and governance.
• A stronger foundation for advanced analytics when the organization is ready to move beyond descriptive reporting.
• A more scalable analytics environment that can evolve as data sources, business questions and reporting requirements change.

The aim is not to maximize the number of dashboards, it is to create a reporting and analytics environment where the right people can access the right measures with greater confidence and less repeated effort.

Typical Use Cases

Para's published analytics experience includes banking, hospitality and enterprise reporting environments, providing a practical basis for broader Data & Analytics engagements.

Banking analytics

Bring together retail, corporate, financial and portfolio information into structured reporting models.

Support performance reporting with reusable measures, analytical models and dashboards rather than disconnected reporting outputs.

Para's published banking example involved data models, data hubs, tabular models and Power BI dashboards covering individual and company performance.

Executive performance reporting

Create a consistent management view across strategic, financial and operational KPIs.

Bring key measures together so leadership can spend less time reconciling different versions of performance and more time understanding what the measures indicate..

Operational dashboards

Monitor service, workflow, workforce, production or field performance.

Operational dashboards can combine information from the systems where work occurs with shared definitions that allow managers to monitor trends and identify areas requiring attention.

Financial visibility

Connect financial and operational information to provide a clearer view of revenue, cost, billing, performance and related measures.

This can reduce dependence on manually assembled management packs and help connect financial outcomes with the operational activity driving them.

Hospitality & Service Analytics

Bring together reservation, financial and operational information to provide a more complete view of performance.

Para's published hospitality engagement combined OPERA reservation data and iScala back-office information in an enterprise data warehouse and exposed KPIs covering occupancy, financials, operations and food and beverage performance.

GRC & Management reporting

Provide structured visibility into risks, controls, policies, findings, actions and related management information.

Use shared reporting models to make governance information easier to consolidate, monitor and communicate..

AI-Ready Data Foundation

Assess and improve the data structures, definitions, quality and access patterns required before introducing advanced analytics or AI use cases.

The focus is on establishing the conditions that allow future analytical initiatives to build on governed data rather than starting again with every new use case.

Connected Para Capabilities

Data & Analytics can connect directly with Para's broader enterprise capabilities.

Para's public portfolio combines Business Intelligence and Power BI with SharePoint, business process automation, custom applications, portals and Microsoft technologies.

Enterprise Architecture & Data Strategy

Connect data domains, analytical assets and reporting capabilities to the wider enterprise technology environment.

Consider source systems, ownership, integration, security and future requirements when defining the analytics architecture.

Power BI & Business Intelligence

Use Power BI and Microsoft BI capabilities to transform governed data models into reporting and visualization experiences.

Para's published portfolio identifies Power BI and the Microsoft BI stack as core specialties and includes both banking and hospitality reporting examples.

Saas & Enterprise Integration

Connect information from SaaS and enterprise applications into the analytical environment.

Integration can reduce manual data preparation while providing a more consistent route from operational systems to reporting.

Custom Applications & Portals

Embed analytics into the applications and portals where users perform their work.

Rather than requiring users to leave the operational environment to find insight, reporting can become part of a broader business application or enterprise portal experience.

Para's portfolio includes custom applications, portals and Power BI capabilities, creating an opportunity to connect operational experiences with analytics..

Automation

Use reporting and operational signals to identify processes that can be improved or automated.

Analytics can help identify repeated work, bottlenecks, exceptions and performance gaps, while automation can then address selected process opportunities.

This creates a feedback loop between measurement, process improvement and automation.

Business Process & Workflow

Connect analytics to the processes that generate the underlying data.

Where reporting identifies an operational issue, workflow and automation capabilities can help create a structured response rather than leaving the insight disconnected from action.

Para's public positioning combines business intelligence with business process modeling and automation as part of its enterprise Microsoft capabilities.

Need trusted reporting before adding more dashboards or AI use cases?

More dashboards do not necessarily create better decisions.

Para helps organizations establish the data, definitions, models, integrations and reporting structures required to make analytics more consistent and reusable.

Talk to Para about a Data & Analytics engagement that connects business questions, KPI definition, data foundations, Power BI, integration and practical governance into one analytics approach.

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