Turn scattered data into views you can trust and better decisions.

A dashboard is often the most visible part of a data problem. It is rarely the root cause.

Teams may disagree on what a KPI means. Nobody may own the data. People may reconcile figures by hand across spreadsheets. Reports may rely on calculations nobody wrote down. In each case, a better-looking dashboard does not fix the real problem.

Trusted analytics starts before the dashboard.

Para treats Data & BI first as a business and governance question. We choose technology only after we understand the results you need.

We start with these questions:

• What decisions need to be made?
• What performance needs to be understood?
• Which measures matter?
• What does each measure really mean?
• Which source can we trust?
• Who owns the information?
• How should data move through the organization?
• How does insight reach the people who must act on it?
• How will reporting stay trusted after launch?

Decision before dashboard

Start with the business decision you need to make. Then design the report that supports it.

The goal is not more dashboards. The goal is better decisions made with more confidence.

One governed meaning

Agree on what each measure means and who owns it. Teams then share one view of performance.

A KPI should mean the same thing wherever people use it. Everyone should know how to calculate it and where it comes from.

Design for ongoing use

Analytics needs an operating model, not just a build.

Agree on who owns each report and how often it refreshes. Reports then stay useful and trusted after the first release.

Technology follows the model

Platforms should support the business model. They should not dictate it.

Para helps you set the architecture first. We then choose platforms to match it, usually Power BI and the wider Microsoft cloud.

What we help solve

When organizations have data everywhere but clarity nowhere

Data & BI Advisory adds value when reports and data have piled up over time, yet you still lack one consistent view of performance. The signs show up across the organization:

• Inconsistent performance measures Departments calculate the same KPI in different ways. Result: Leaders spend time arguing about the number instead of acting on it.
• Manual management reporting Key reports rely on spreadsheets that teams rebuild by hand every month. Result: Reporting is slow and open to human error.
• Dashboard proliferation Teams build a new dashboard for every need, with no shared model underneath. Result: More dashboards do not always mean more visibility. They can create competing versions of the truth.
• Unclear data ownership Nobody clearly owns the quality of key data. Result: Data issues become everyone's problem and no one's job.
• Broken reporting dependencies A change in a source system can quietly break the reports that depend on it. People then doubt which numbers they can still rely on. Result: Reporting becomes reactive instead of dependable.
• Disconnected operational and analytical data Teams can see single transactions or tasks. They struggle to link them to wider performance. Result: Teams see what happened but not why, or what should change.
• Quality and reconciliation challenges Finance reports and management dashboards show different numbers. Teams must reconcile them again and again. Result: Trust in reporting drops.
• Too many requests and no clear roadmap Business teams have a long list of requests and no clear way to rank them. Result: Analytics teams stay busy but may not deliver the most valuable work first.
• Technology-led analytics programs Organizations start with a new platform before they define the results they need from it. Result: The technology goes live, but nobody can say what value it brings.

What the work looks like

Build the foundation behind trusted insight.

Para links data strategy to the way you plan and deliver reporting. Your analytics then rest on a clear model the business agrees on.

Data Foundation & Landscape

Understand where you are today before you define the future.

Para assesses:

• Key data domains
• Source systems and data stores
• Data flows and dependencies
• Existing integrations
• Current reports and dashboards
• Data ownership
• Data quality concerns
• Existing analytical datasets
• Manual reporting processes
• Critical dependencies and limits

Outcome: A clear view of where key information comes from and how it moves. You also see where the major gaps and risks sit.

KPI & Performance Model

Define what performance means before you decide how to show it.

Para helps set up:

• Business questions
• Key KPIs
• KPI definitions
• Calculation rules
• Dimensions and hierarchies
• Targets and thresholds
• Reporting frequency
• KPI ownership
• The management questions each measure should answer

Outcome: A shared performance language that teams and reports can use the same way.

Data Governance & Ownership

Build accountability into the data model.

Para helps define:

• Data ownership
• Data stewardship
• Quality duties
• Governance roles
• Metadata requirements
• Access rules
• Data quality controls
• Definition management
• Issue escalation and resolution
• Governance routines

Outcome: Clearer accountability for the information the organization depends on.

Semantic & Reporting Architecture

Create a reusable layer between raw data and business decisions.

Para defines:

• Business definitions
• Semantic structures
• Data models
• Source-to-report mappings
• Reporting layers
• Reusable measures
• Reporting dependencies
• Executive and operational report patterns
• Architecture principles

Outcome: Reporting that scales beyond single dashboards and one-off calculations.

Analytics & Dashboard Roadmap

Turn demand into a ranked delivery plan.

Para helps you rank:

• Executive reporting
• Management reporting
• Operational dashboards
• Functional analytics
• Self-service analytics
• Data foundation work
• Integration needs
• Data quality fixes
• Governance tasks

We rank work by:

Business value + decision importance + readiness + dependencies + effort + risk

Outcome: A realistic roadmap instead of a loose backlog of dashboard requests.

Modernization & Integration Direction

Define how your current setup should evolve.

This can include:

• Legacy reporting tools
• Existing BI platforms
• Power BI
• Data platforms
• Cloud services
• APIs
• Integration patterns
• Custom applications
• Chances to automate
• Existing Microsoft tools
• Data modernization priorities

Outcome: A practical path from where you are today to a more connected and lasting data and analytics model.

People, Process, Data, and Technology

Analytics works when the operating model works.

Para's approach rests on the same value chain behind all our advisory and transformation work:

People + Process + Data + Technology

You cannot separate Data and BI from the people who make decisions. Nor can you separate it from the processes that move information. The data those processes create and the technology that links them matter just as much.

People

The people who define and use performance information.

This can include:

• Business owners
• Decision makers
• Data owners
• Data stewards
• Analysts
• BI teams
• Report owners
• Enterprise architects
• Transformation teams
• Technology teams

Question: Who needs the information, and who answers for it?

Process

The tasks that make reporting reliable and repeatable.

This can include:

• Data preparation
• Reconciliation
• KPI approval
• Report production
• Data quality management
• Issue resolution
• Governance routines
• Change management
• Release processes
• Continuous improvement

Question: How does information become a trusted management output?

Data

The information and definitions that form the analytics foundation.

This includes:

• Data domains
• Source systems
• Measures
• Dimensions
• Metadata
• Business definitions
• Data quality rules
• Historical information
• Analytical datasets
• Semantic models

Question: What information do we need, and what does it mean?

Technology

The platforms and architecture that support the model.

Depending on the target setup, this may include:

• Power BI
• Microsoft 365
• Azure
• Cloud platforms
• Data platforms
• Integration services
• APIs
• Custom applications
• Automation platforms
• Reporting and analytics infrastructure

Question: What technology can reliably support the business and information model?

How Para works

From decision needs to a change path you can act on.

Para's advisory approach goes beyond assessment. It produces a practical path to implementation.

• Clarify Decisions & KPIs Start with the business. Identify:
  • Priority decisions
  • Business questions
  • Performance objectives
  • Current reporting audiences
  • Existing reports and dashboards
  • Priority KPIs
  • Pain points and information gaps
  We want to understand what the organization needs to know and why. Only then do we decide what to build.
• Assess Sources & Reporting Understand where you are today. Review:
  • Data sources
  • Source system dependencies
  • Existing reporting
  • Data flows
  • KPI calculations
  • Manual processes
  • Data quality issues
  • Ownership
  • Governance
  • Integration dependencies
  This gives you a fact-based view of where you stand.
• Define the Target Data & BI Model Turn business requirements into a target model. Set up:
  • Priority data domains
  • KPI definitions
  • Governance roles
  • Data ownership
  • Semantic structures
  • Reporting architecture
  • Target integration patterns
  • Technology direction
  • Operating requirements
  You get a shared view of what the future setup needs to look like.
• Prioritize Analytics Releases You do not need to do everything at once. Para helps order the work based on:
  • Business value
  • How critical each decision is
  • Readiness
  • Dependencies
  • Data maturity
  • Complexity
  • Risk
  • Delivery effort
  This can produce a phased roadmap that starts with quick wins and builds toward longer-term modernization.
• Mobilize Delivery & Governance Move from advice into action. You can turn the approved roadmap into:
  • Power BI implementation
  • Semantic models
  • Data integration
  • Data quality fixes
  • Reporting modernization
  • Automation
  • Custom applications and APIs
  • Cloud and data platform work
  • Governance practices
  • Managed reporting and support
  What we advise is what you prioritize, and what you prioritize is what we deliver.

A KPI should mean the same thing wherever people use it. Everyone should know how to calculate it and where it comes from.

What you get

Practical outputs that help you decide and deliver.

Depending on the engagement, Para may deliver:

• A review of your data and reporting landscape
• An assessment of current reporting and data
• A catalog of key business questions
• A KPI catalog
• KPI definitions and calculation rules
• A KPI ownership model
• A source-to-report map
• A map of data flows and dependencies
• A data domain assessment
• A data governance model
• A data ownership and stewardship model
• Data quality requirements
• Semantic model principles
• A target data and reporting architecture
• Concepts for the report and dashboard experience
• A Power BI and analytics roadmap
• A framework for ranking analytics work
• A data modernization roadmap
• Direction for integration and APIs
• A phased delivery roadmap
• Advice on governance and the operating model
• Delivery requirements and an implementation backlog
• Handover into delivery and managed services

Where it fits

Advice should not end with a document

Advisory proves its value when you can carry the agreed direction into implementation.

Para links advisory work to the skills needed to turn a data roadmap into working reports.

Enterprise Services

• Data, BI & Analytics Build reporting around the business model you approved.
• Power BI Create governed dashboards and set up self-service analytics.
• Power Platform & Automation Automate the manual steps in your reporting.
• Custom Apps & APIs Build custom solutions when standard reports are not enough.
• Cloud & Integration Connect your data sources through modern cloud architecture.

Solutions

Depending on the business context, Para can apply its skills to areas such as:

• Financial and management reporting
• Banking analytics
• Operational performance
• Customer analytics
• Employee and workforce analytics
• Service performance
• GRC reporting
• Business performance management
• Executive visibility
• Operational intelligence

Products & Platforms

Where relevant, Para can use reusable assets that cut delivery effort.

Examples may include:

• Banking Analytics Accelerator
• Para Billing & BI
• Reusable reporting and analytics components

Managed Services

Data and reporting setups need care after go-live.

Para's managed services can support:

• Application and reporting support
• Service management
• Report and dashboard operations
• Release management
• Issue resolution
• Operational analytics
• Performance monitoring
• Continuous improvement

This links the first advisory engagement to the full life of your analytics setup.

See how Para works for where this sits in an engagement.