Turn Business Data into Trusted Decisions
Organizations generate data from ERP systems, CRM platforms, finance applications, operational tools, and countless other sources. Yet without the right analytics framework, much of that data remains fragmented, inconsistent, and underutilized.
SourceMash helps businesses transform raw data into trusted insights with Power BI. From data integration and transformation to semantic modeling, DAX development, dashboard engineering, and Microsoft Fabric implementation, we create analytics environments that support faster, smarter, and more confident decision-making.
We build scalable, enterprise-ready analytics solutions that establish a single source of truth, improve reporting accuracy, and empower business users with self-service analytics while maintaining governance and security across the organization.
Data Modeling & Business Metrics
Design scalable semantic models and accurate DAX measures that ensure every KPI, revenue figure, and performance metric is trusted across the business.
Power Query & Data Integration
Connect, cleanse, and transform data from multiple systems to create a reliable analytics foundation for reporting and decision-making.
Microsoft Fabric & Enterprise Analytics
Leverage Fabric, OneLake, Warehouses, Dataflows, and advanced analytics capabilities to support enterprise-scale reporting and performance.
Governance, Security & Self-Service BI
Enable business users to explore data independently while maintaining consistent definitions, role-based access controls, and governance standards across all reports.
Solution 01
BI Strategy, Architecture & Analytics Roadmap
Most Power BI initiatives fail commercially not because Power BI is the wrong platform, but because the implementation lacked a well-defined analytics strategy. Without a clear understanding of the business questions that need answering, the data sources required to answer them, the intended audiences for analytics, and the governance model needed to maintain trust, organisations often end up with disconnected reports and conflicting metrics.
In many organisations, analytics environments evolve organically rather than strategically. Different departments create their own reports, KPI definitions vary across teams, and executives lose confidence in dashboard outputs when figures cannot be reconciled. As adoption declines, reporting assets become underutilised despite significant investment in Power BI and related technologies.
SourceMash helps organisations build a solid foundation for Power BI through a structured strategy and architecture engagement. The outcome is a clear blueprint covering data architecture, semantic modelling, governance standards, security controls, and a prioritised implementation roadmap aligned to business objectives.
Whether the organisation is deploying Azure SQL Database, SQL Server, Azure Synapse Analytics, Microsoft Fabric Lakehouse, or a hybrid ecosystem, our approach ensures analytics initiatives deliver trusted insights, measurable business value, and long-term scalability.
BI Strategy Deliverables
Strategic planning and architecture components that establish the foundation for a successful Power BI programme.
Assessment of existing reports, dashboards, KPIs, data sources, and reporting processes across the organisation. The review identifies duplicate reports, conflicting metric definitions, reporting gaps, adoption challenges, and data quality concerns that may impact decision-making. The findings establish a baseline for future BI transformation initiatives.
Design of the target data architecture that defines how data moves from source systems into Power BI. Depending on organisational requirements, architectures may include Microsoft Fabric Lakehouse, Azure Synapse Analytics, Azure SQL Database, SQL Server Data Warehouse, or Medallion-based data platforms. The blueprint ensures scalability, security, performance, and long-term maintainability.
Definition of the semantic layer that will power reporting and analytics across the organisation. This includes evaluation of enterprise-wide certified datasets, domain-specific semantic models, shared KPI definitions, reusable dimensions, and governance controls that promote consistency and reduce duplicated development effort.
Design of governance standards that ensure Power BI content remains trusted, secure, and maintainable. The framework defines workspace ownership, report publishing permissions, certification policies, endorsement processes, access controls, row-level security standards, data lineage requirements, and change management procedures.
Creation of a prioritised 12-month implementation roadmap that sequences initiatives according to business value, technical dependencies, and data readiness. The roadmap provides clear development phases, analytics milestones, resource planning, and expected business outcomes to maximise return on investment.
A focused strategy engagement typically requires 3-6 weeks depending on organisational complexity, number of data sources, stakeholder availability, and architecture requirements. The engagement includes discovery workshops, data assessments, architecture design sessions, governance planning, and roadmap development.
From Business Questions To Enterprise Analytics Success - Four Stages
Structured methodology used to design and govern modern Power BI environments.
Business Questions Inventory
Conduct stakeholder interviews across Finance, Sales, Operations, HR, Marketing, and leadership teams to document the key analytical questions each department needs answered. Define reporting frequency, decision-making impact, and KPI requirements to ensure analytics development remains aligned with business objectives.
Data Source & Quality Assessment
Assess ERP, CRM, databases, cloud platforms, spreadsheets, and operational systems that hold the information required to answer priority business questions. Evaluate completeness, consistency, accuracy, and timeliness to identify data quality risks before report development begins.
Architecture Design
Design the optimal data and analytics architecture using Microsoft Fabric, Azure Synapse Analytics, Azure SQL Database, SQL Server, or Medallion Lakehouse architectures. Define semantic model strategy, shared datasets, domain models, and reporting architecture to maximise scalability and reuse.
Roadmap & Governance Design
Develop a prioritised 12-month implementation roadmap based on business value, technical dependencies, and data readiness. Establish governance policies covering workspace structure, report publishing controls, certification standards, security, row-level access, data lineage, and change management.
Solution 02
Semantic Data Modelling & Power Query Engineering
The Power BI semantic model is the foundation of every successful analytics environment. Report performance, DAX calculation accuracy, scalability, and self-service reporting capabilities are all determined by the quality of the underlying data model. While dashboard design is important, the semantic model ultimately decides whether analytics remain fast, reliable, and easy to maintain as the organisation grows.
Many Power BI environments become difficult to manage because datasets evolve without a structured modelling approach. Flat tables, excessive many-to-many relationships, inconsistent naming conventions, and poorly designed measures can lead to slow reports, incorrect calculations, and reduced user confidence in reporting outputs.
SourceMash helps organisations build robust semantic models and data transformation frameworks that support enterprise-scale analytics. By combining dimensional modelling best practices, Power Query optimisation, storage mode strategy, and performance engineering, we create solutions that deliver trusted insights while remaining maintainable and scalable.
Whether your data resides in SQL Server, Azure SQL Database, Microsoft Fabric, Azure Synapse Analytics, SharePoint, Salesforce, SAP, or custom business applications, our approach ensures the data foundation behind Power BI is optimised for performance, governance, and long-term success.
Semantic Modelling & Engineering Deliverables
Technical architecture and data engineering components that enable scalable, high-performance Power BI solutions.
Comprehensive review of existing Power BI datasets, measures, relationships, storage modes, and report performance. The assessment identifies modelling issues, DAX inefficiencies, refresh bottlenecks, relationship challenges, and governance gaps that may impact analytical accuracy and scalability.
Design and implementation of enterprise-grade star schema architectures that improve performance, simplify reporting, and support long-term scalability. This includes fact table design, dimension modelling, relationship structures, hierarchy creation, and dimensional modelling best practices.
Development of enterprise date dimensions that support standard and custom business calendars. The framework enables advanced time-based analysis including Year-to-Date (YTD), Quarter-to-Date (QTD), Month-to-Date (MTD), Prior Year (PY), Year-over-Year (YoY), rolling periods, and fiscal calendar reporting.
Design and optimisation of Power Query transformations using the M language. Services include data cleansing, standardisation, table reshaping, parameterisation, query folding optimisation, reusable transformation logic, and ETL design that improves refresh performance and maintainability.
Evaluation and implementation of Import, DirectQuery, Composite Models, and aggregation strategies based on reporting requirements and source system capabilities. The objective is to balance performance, scalability, data freshness, and resource utilisation.
Design of relationship architectures that ensure accurate filter propagation and reliable DAX calculations. This includes active and inactive relationships, role-playing dimensions, bridge tables, many-to-many resolution patterns, and filter context optimisation techniques.
Configuration of secure and scalable connectivity across enterprise data sources including SQL Server, Azure SQL Database, Synapse Analytics, Microsoft Fabric, SharePoint, Salesforce, Dynamics 365, SAP, REST APIs, OData services, and Dataflows Gen2. This includes gateway configuration and refresh architecture design.
A semantic modelling and engineering engagement typically requires 2-8 weeks depending on source system complexity, data volume, model size, performance requirements, and governance objectives. The engagement includes discovery workshops, model design, transformation development, performance testing, and deployment support.
From Raw Data To Enterprise-Ready Semantic Models - Four Stages
Structured methodology used to design, optimise, and govern high-performance Power BI semantic models.
Model Assessment & Discovery
Review existing datasets, reports, business requirements, and source systems to identify modelling weaknesses, performance challenges, relationship issues, and opportunities for optimisation.
Schema & Transformation Design
Design star schema structures, date dimensions, hierarchies, Power Query transformations, naming standards, and reusable modelling components that support business reporting requirements.
Performance Engineering & Optimisation
Implement storage mode strategies, aggregation tables, query folding improvements, relationship tuning, and DAX optimisation techniques to maximise report performance and scalability.
Governance & Deployment
Deploy certified semantic models, establish documentation standards, implement governance controls, configure refresh processes, and define maintenance practices that ensure long-term consistency and trust.
Solution 03
DAX Development, KPIs & Advanced Analytics
DAX (Data Analysis Expressions) is the analytical engine behind Power BI. Every KPI, business metric, time-based comparison, and interactive calculation within a report is powered by DAX. While data modelling provides the foundation, DAX delivers the intelligence that transforms raw data into meaningful business insights.
Many organisations invest heavily in dashboards yet struggle with inconsistent metrics, inaccurate calculations, and conflicting figures across reports. These issues are often caused by poorly designed DAX measures, misunderstood filter context behaviour, or a lack of standardised KPI definitions. As reporting requirements become more sophisticated, these challenges can significantly reduce confidence in analytics outputs.
SourceMash helps organisations develop robust, scalable DAX frameworks that ensure business metrics remain accurate, reusable, and aligned with strategic objectives. Our approach combines KPI standardisation, advanced measure development, time intelligence, scenario modelling, and analytical best practices to enable confident decision-making across the organisation.
Whether the requirement is executive reporting, financial performance analysis, operational monitoring, customer analytics, or advanced business modelling, we create DAX solutions that deliver trusted insights and long-term analytical consistency.
DAX Development & Analytics Deliverables
Business-focused calculation frameworks that enable accurate reporting, advanced analytics, and enterprise-wide KPI consistency.
Design and implementation of standardised business KPIs across Finance, Sales, Operations, Customer Success, Marketing, and Human Resources. Each KPI is documented with clear business definitions, calculation methodologies, validation rules, and reporting standards to ensure organisational consistency.
Development of enterprise-ready time intelligence measures that support Year-to-Date (YTD), Month-to-Date (MTD), Quarter-to-Date (QTD), Prior Year (PY), Year-over-Year (YoY), rolling periods, fiscal calendar reporting, and custom business-specific reporting periods.
Creation of complex DAX measures that leverage filter context manipulation, CALCULATE(), variables, virtual tables, iterator functions, and advanced analytical techniques. Measures are optimised for accuracy, maintainability, and long-term scalability.
Implementation of ranking and Top-N analytical capabilities that allow users to dynamically explore products, customers, regions, or business units. Solutions may include Pareto analysis, contribution analysis, cumulative metrics, and interactive ranking frameworks.
Development of interactive scenario planning solutions that allow users to model potential business outcomes through adjustable parameters. Common use cases include revenue forecasting, pricing strategies, margin analysis, discount modelling, capacity planning, and financial sensitivity analysis.
Design of advanced analytical measures that support trend analysis, moving averages, customer segmentation, percentile analysis, cohort reporting, contribution analysis, inventory classification, and predictive business indicators.
Creation of a governed DAX measure library with consistent naming conventions, business definitions, calculation logic, testing procedures, and documentation standards. This ensures future report development remains efficient and aligned to organisational reporting requirements.
A DAX development and analytics engagement typically requires 2-6 weeks depending on reporting complexity, number of KPIs, business rules, model architecture, and stakeholder requirements. The engagement includes KPI discovery, measure development, validation testing, optimisation, and deployment support.
From Business Metrics To Advanced Analytics - Four Stages
Structured methodology used to design, validate, and optimise enterprise Power BI DAX frameworks.
KPI Discovery & Requirements Analysis
Conduct workshops with business stakeholders to identify reporting objectives, KPI definitions, calculation requirements, performance targets, and reporting expectations. Establish a standardised framework for metric consistency across the organisation.
Measure Design & Development
Develop core KPIs, time intelligence calculations, business performance measures, and advanced analytical logic using Power BI DAX best practices. Design calculation frameworks that remain scalable and reusable across multiple reports.
Validation & Optimisation
Test measures against source systems and business expectations to ensure calculation accuracy. Optimise DAX performance, simplify complex logic, improve query efficiency, and validate behaviour across different filter contexts and reporting scenarios.
Documentation & Analytics Enablement
Publish a governed measure library, document KPI definitions, establish calculation standards, and provide knowledge transfer to reporting teams. Ensure analytics assets remain trusted, maintainable, and aligned with future reporting initiatives.
Solution 04
Reports, Dashboards & Executive Analytics
The report and dashboard layer is where business users experience the value of their analytics investment. Well-designed Power BI reports transform complex data into intuitive insights, helping decision-makers quickly understand performance, identify issues, and take action. Effective reporting environments guide users from high-level summaries to detailed analysis without overwhelming them with unnecessary information.
Many organisations struggle with dashboard adoption because reports focus on displaying data rather than supporting decisions. Excessive visualisations, inconsistent layouts, poor navigation, and unclear KPI presentation often result in dashboards that are rarely used despite significant development effort.
SourceMash designs Power BI reports and dashboards using proven information design principles, focusing on clarity, usability, performance, and business outcomes. Every visual, interaction, and navigation element is intentionally designed to help users move from understanding what happened to discovering why it happened.
Whether supporting executives, operational teams, sales managers, finance leaders, or frontline personnel, our reporting solutions deliver actionable insights through intuitive user experiences and enterprise-grade design standards.
Reports & Dashboard Development Deliverables
User-focused reporting, dashboard design, and analytics experiences that turn data into actionable business intelligence.
Design and development of executive-level dashboards that provide a consolidated view of organisational performance. Dashboards highlight critical KPIs, business trends, variances, targets, and exceptions, enabling leadership teams to monitor performance and make informed decisions quickly.
Creation of interactive reporting experiences that allow users to explore data across multiple levels of detail. This includes drill-through pages, drill-down hierarchies, cross-filtering interactions, dynamic navigation, bookmarks, and guided analytical workflows that support deeper investigation.
Development of Power BI themes that align reports with organisational branding standards. Services include colour palette implementation, typography standards, visual styling, accessibility design, and theme governance to create consistency across the reporting environment.
Implementation of advanced visualisation techniques and custom visuals that enhance analytical storytelling. Solutions may include financial reporting layouts, waterfall analysis, Sankey diagrams, advanced KPI cards, geographic mapping, and specialised visual components tailored to business requirements.
Assessment and optimisation of report performance to improve responsiveness and user experience. Services include visual performance analysis, page optimisation, DAX tuning, visual reduction strategies, query optimisation, and report architecture improvements.
Design of responsive mobile reporting experiences and pixel-perfect paginated reports for operational, financial, and regulatory requirements. Solutions are optimised for desktop, tablet, mobile, print, and export scenarios.
Implementation of accessibility and usability best practices to ensure reports remain intuitive, inclusive, and compliant with organisational requirements. This includes accessible colour selections, screen-reader support, navigation standards, and responsive layouts.
A reporting and dashboard development engagement typically requires 2-10 weeks depending on the number of dashboards, complexity of analytics requirements, data readiness, visualisation needs, and stakeholder involvement. The engagement includes discovery workshops, UX design, report development, testing, optimisation, and deployment.
From Business Requirements To Executive Insights - Four Stages
Structured methodology used to design, develop, and optimise enterprise Power BI reporting solutions.
Analytics Discovery & User Requirements
Conduct stakeholder workshops to understand business objectives, key performance indicators, reporting requirements, decision-making processes, and user expectations. Define the dashboards, reports, and analytical experiences required to support business outcomes.
Information Design & Dashboard Architecture
Design reporting wireframes, KPI scorecards, navigation flows, visual layouts, and interaction patterns. Establish dashboard structures that prioritise clarity, usability, and effective communication of insights.
Development & User Experience Optimisation
Develop interactive reports, dashboards, drill-through experiences, custom themes, and advanced visualisations. Optimise report performance, navigation, and responsiveness to ensure a seamless user experience across devices.
Deployment, Adoption & Governance
Deploy reporting solutions, establish governance standards, validate business requirements, deliver user training, and implement reporting best practices that drive long-term adoption and analytical maturity.
Solution 05
Microsoft Fabric - Unified Data Analytics at Enterprise Scale
Microsoft Fabric brings together data engineering, data warehousing, real-time analytics, data science, and business intelligence into a single integrated platform. By unifying these capabilities under a shared storage layer and governance framework, organisations can eliminate data silos, reduce complexity, and accelerate the delivery of analytics across the business.
Many organisations operate fragmented analytics environments where data pipelines, warehouses, reporting platforms, and governance processes are managed separately. This often leads to duplicated data, inconsistent metrics, higher operational costs, and challenges in maintaining security and compliance across platforms.
SourceMash helps organisations design, implement, and optimise Microsoft Fabric environments that support modern analytics requirements. By combining OneLake, Lakehouse architecture, Fabric Warehouses, Data Engineering, Real-Time Analytics, and Power BI integration, we create scalable platforms that support trusted insights across the enterprise.
Whether you are modernising Azure Synapse Analytics, Azure Data Factory, SQL Server, Azure Data Lake, or Power BI environments, our approach ensures a smooth transition to Microsoft Fabric with long-term scalability, governance, and business value.
Microsoft Fabric Implementation Deliverables
Integrated data platform capabilities that enable scalable, unified analytics across the organisation.
Assessment of existing data platforms, analytics environments, reporting processes, and business requirements. The engagement identifies migration opportunities, architecture improvements, governance considerations, performance requirements, and implementation priorities for Microsoft Fabric adoption.
Design and implementation of Microsoft Fabric Lakehouse environments based on Medallion Architecture principles. The solution establishes Bronze, Silver, and Gold data layers that support data quality, scalability, governance, and efficient analytics consumption.
Implementation of Direct Lake connectivity that enables Power BI to access OneLake data with minimal latency and without traditional dataset refresh dependencies. This approach improves report responsiveness while ensuring users have access to current data.
Development of Fabric Pipelines and orchestration frameworks that automate data ingestion, transformation, validation, and delivery processes. Solutions include incremental loading strategies, workflow automation, monitoring, scheduling, and secure credential management.
Design and deployment of Fabric Warehouses that support enterprise-scale analytical workloads using familiar T-SQL development practices. Solutions provide an optimised environment for structured reporting, analytics, and business intelligence initiatives.
Migration planning and execution services for organisations transitioning from Azure Synapse Analytics, Azure Data Factory, SQL Server Data Warehouses, or other legacy analytics platforms. The objective is to modernise analytics infrastructure while reducing complexity and operational overhead.
Implementation of governance standards, workspace structures, access controls, data lineage processes, security policies, and compliance frameworks that ensure analytics environments remain trusted, secure, and manageable.
A Microsoft Fabric implementation engagement typically requires 4-12 weeks depending on organisational complexity, number of source systems, migration requirements, data volumes, and governance objectives. The engagement includes discovery, architecture design, implementation, optimisation, and knowledge transfer.
From Modern Data Platforms To Unified Analytics - Four Stages
Structured methodology used to design, implement, and govern Microsoft Fabric environments.
Discovery & Platform Assessment
Review existing analytics architecture, data sources, reporting platforms, governance processes, and business objectives. Identify opportunities for consolidation, optimisation, and Fabric adoption.
Architecture & Lakehouse Design
Design Microsoft Fabric architecture including OneLake strategy, Medallion data layers, Warehouse structures, Direct Lake integration, security controls, and governance standards aligned with business requirements.
Implementation & Integration
Deploy Fabric workspaces, Lakehouses, Warehouses, Pipelines, Dataflows, and Power BI integrations. Configure ingestion processes, transformation frameworks, automation workflows, and monitoring capabilities.
Optimisation & Governance
Establish governance processes, security controls, operational standards, monitoring frameworks, and adoption strategies. Ensure the platform remains scalable, secure, and aligned with future business growth.
Solution 06
Self-Service Analytics & Business User Enablement
The true value of a Power BI investment is realised when business users can answer their own questions confidently without relying on IT teams or analytics specialists for every report request. Effective self-service analytics empowers employees to explore data, uncover insights, and make informed decisions independently while maintaining consistency, governance, and trust across the organisation.
Many organisations struggle to balance user empowerment with analytical control. Without a governed framework, self-service reporting can lead to inconsistent KPI definitions, duplicated reports, conflicting metrics, and reduced trust in analytics outputs. The challenge is enabling flexibility while ensuring everyone works from the same trusted source of truth.
SourceMash helps organisations establish scalable self-service analytics programmes that combine governance, training, user adoption, and shared semantic models. Our approach enables users to create their own reports and analyses while maintaining data quality, security, and consistency across the enterprise.
Whether the goal is increasing Power BI adoption, reducing dependency on analytics teams, developing internal analytics capabilities, or creating an Analytics Centre of Excellence, our enablement programmes help organisations build a sustainable data-driven culture.
Self-Service Analytics Deliverables
Training, governance, and enablement frameworks that empower business users while maintaining analytical consistency and control.
Design and implementation of governed, certified semantic models that serve as the foundation for self-service analytics. The semantic layer provides standardised KPI definitions, business-friendly terminology, reusable measures, and controlled access to trusted data assets.
Structured training programmes tailored to different user groups including report consumers, analysts, and power users. Training focuses on practical use cases, hands-on learning, and real organisational data to accelerate adoption and improve analytical capabilities across the business.
Configuration and optimisation of Power BI Q&A and Copilot capabilities to improve user accessibility and productivity. Services include natural language query setup, business terminology mapping, featured questions, AI-assisted report creation, and Copilot-based analytical workflows.
Design and establishment of an Analytics Centre of Excellence that provides governance, ownership, support, and strategic direction for analytics initiatives. The framework defines roles, responsibilities, operating models, and processes that ensure long-term analytics success.
Implementation of automated report distribution, personalised subscriptions, KPI alerts, and data-driven notifications that keep stakeholders informed without requiring constant access to Power BI Service.
Assessment, migration, and consolidation of existing reporting environments. Services support transitions from legacy BI platforms and help organisations eliminate duplicate reports, standardise KPI definitions, and create a streamlined reporting portfolio.
Development of user adoption strategies that encourage engagement with analytics tools and reporting platforms. This includes communication plans, champion networks, support models, and best practices that drive long-term usage and business value.
A self-service analytics and enablement engagement typically requires 3-8 weeks depending on organisational size, user maturity, training requirements, governance objectives, and the complexity of existing reporting environments. The engagement includes assessment, framework design, training delivery, governance planning, and adoption support.
From Analytics Dependency To Data-Driven Decision Making - Four Stages
Structured methodology used to establish sustainable self-service analytics capabilities across the organisation.
Analytics Maturity Assessment
Assess current reporting processes, user capabilities, governance structures, reporting assets, and organisational readiness for self-service analytics. Identify barriers to adoption and opportunities for improvement.
Governance & Semantic Model Design
Create the foundation for self-service analytics through certified semantic models, KPI governance, reporting standards, security controls, and ownership frameworks that ensure consistency and trust.
User Training & Enablement
Deliver role-based training programmes covering report consumption, report development, advanced analytics, Power BI best practices, Q&A functionality, and Copilot capabilities to build analytical confidence across the organisation.
Adoption & Continuous Improvement
Establish support frameworks, Centres of Excellence, analytics champions, measurement processes, and governance practices that drive ongoing adoption and continuous improvement of analytics capabilities.
Solution 07
Power BI Embedded - Analytics Application Integration
Modern organisations increasingly require analytics to be delivered directly within customer portals, business applications, SaaS platforms, and operational systems. Power BI Embedded enables organisations to seamlessly integrate interactive dashboards, reports, and analytical experiences into existing applications while maintaining security, scalability, and a consistent user experience.
Many software providers and enterprise organisations struggle to deliver analytics to external users without introducing licensing complexity, fragmented user experiences, or security concerns. Traditional reporting approaches often require users to leave their primary application, resulting in lower engagement and reduced business value.
SourceMash helps organisations design and implement Power BI Embedded solutions that deliver secure, scalable, and fully integrated analytics experiences. Our approach combines embedded architecture, multi-tenant security, custom application integration, and capacity optimisation to ensure analytics become a natural extension of the applications users already rely on.
Whether you are building a SaaS platform, customer portal, partner ecosystem, ERP solution, or internal business application, we help you deliver rich analytical capabilities directly within the user experience.
Power BI Embedded Deliverables
Secure and scalable embedded analytics solutions that bring Power BI directly into business applications and customer-facing platforms.
Design and implementation of Power BI Embedded architectures that allow reports and dashboards to be securely integrated into applications, portals, and software platforms. Solutions are designed to support scalability, governance, and seamless user experiences.
Deployment of App-Owns-Data embedding models that allow organisations to provide analytics to external users without requiring individual Power BI licences. Solutions include service principal configuration, secure token generation, authentication architecture, and user access management.
Implementation of dynamic Row-Level Security (RLS) frameworks that ensure every customer, tenant, department, or user only accesses authorised information. Security models are designed to support multi-tenant environments while maintaining performance and governance standards.
Integration of Power BI reports into existing application experiences using APIs, SDKs, and embedded analytics frameworks. Services include custom filtering, navigation controls, event handling, dashboard personalisation, and UI integration that aligns analytics with the application's design language.
Configuration of embedded analytics environments that align with corporate branding and application design standards. This includes custom themes, visual styling, navigation controls, and embedded user experiences that feel native to the host platform.
Assessment and optimisation of embedded analytics infrastructure to balance performance, scalability, and cost efficiency. Services include capacity sizing, workload planning, usage analysis, licensing evaluation, and operational cost management.
Implementation of governance frameworks covering security controls, tenant isolation, usage monitoring, performance management, access auditing, and operational support processes.
A Power BI Embedded implementation typically requires 3-10 weeks depending on application complexity, security requirements, user volume, integration architecture, and customisation needs. The engagement includes discovery, architecture planning, development, testing, deployment, and operational handover.
From Application Data To Embedded Analytics Experiences - Four Stages
Structured methodology used to design, deploy, and scale Power BI Embedded solutions.
Discovery & Solution Architecture
Assess application requirements, user access models, security needs, reporting objectives, and scalability expectations. Define the optimal embedding architecture aligned with business and technical goals.
Security & Integration Design
Design authentication frameworks, service principal configurations, embed token generation processes, Row-Level Security models, and integration standards that ensure secure analytics delivery.
Development & User Experience Integration
Embed reports and dashboards within applications, configure custom interactions, implement branding standards, and create seamless analytical experiences that integrate naturally into user workflows.
Deployment & Optimisation
Deploy the embedded analytics solution, validate security controls, optimise performance, implement monitoring capabilities, and establish governance processes that support long-term growth and scalability.
Solution 08
Power BI Governance, Security & Centre of Excellence
Successful Power BI programmes depend on more than technology. As analytics adoption grows, organisations require governance, security, and operational standards that ensure reports remain trusted, data remains protected, and users work from consistent business definitions. Effective governance transforms Power BI from a collection of reports into a managed enterprise analytics platform.
Many organisations experience challenges as their Power BI estate expands. Reports become duplicated, KPI definitions vary between teams, access permissions become difficult to manage, and users struggle to identify which reports can be trusted. Without a structured governance model, analytical confidence declines and reporting investments deliver less value than expected.
SourceMash helps organisations establish comprehensive Power BI governance frameworks that combine workspace management, security controls, certification processes, monitoring, and operating models. Our approach ensures analytics assets remain secure, compliant, maintainable, and aligned with organisational objectives.
Whether the organisation is beginning its Power BI journey or managing hundreds of workspaces and reports across multiple business units, our governance programmes create a scalable foundation for sustainable analytics succes
Power BI Governance & Security Deliverables
Governance, security, and operational frameworks that establish trust, consistency, and control across enterprise Power BI environments.
Design and implementation of structured Power BI workspace architectures that support development, testing, and production environments. Establish deployment standards, access models, content ownership, and lifecycle management practices that promote scalability and governance.
Development of content endorsement and certification programmes that help users identify trusted analytical assets. Establish clear standards for report quality, KPI validation, stakeholder approval, documentation, and ongoing maintenance.
Implementation of security models that ensure users access only the data relevant to their responsibilities. Services include dynamic and static Row-Level Security (RLS), Object-Level Security (OLS), access mapping frameworks, testing procedures, and governance controls for sensitive information.
Configuration and optimisation of Power BI tenant settings to align with organisational security and governance standards. This includes workspace creation policies, sharing controls, export restrictions, external access management, data connection settings, and compliance requirements.
Implementation of reporting and monitoring frameworks that provide visibility into workspace usage, report adoption, refresh performance, content ownership, and platform activity. Analytics help organisations identify optimisation opportunities and improve governance effectiveness.
Creation of end-to-end lineage frameworks that provide transparency into how data moves from source systems through transformations, semantic models, and reports. This enables effective impact analysis, change management, and governance oversight.
Establishment of Analytics and Power BI Centres of Excellence that define ownership structures, governance responsibilities, operating models, support processes, and long-term analytics strategy. The CoE provides leadership and oversight for enterprise analytics initiatives.
A Power BI governance and security engagement typically requires 4-10 weeks depending on organisational size, number of workspaces, governance maturity, compliance requirements, and stakeholder involvement. The engagement includes assessment, framework design, security planning, implementation guidance, and governance enablement.
From Uncontrolled Reporting To Enterprise Analytics Governance - Four Stages
Structured methodology used to establish secure, scalable, and trusted Power BI environments.
Governance Assessment & Discovery
Review existing Power BI environments, workspace structures, security controls, reporting assets, user permissions, governance policies, and operational challenges. Identify risks, gaps, and improvement opportunities.
Governance & Security Framework Design
Design governance standards covering workspace architecture, certification processes, security controls, deployment pipelines, access management, compliance requirements, and operational policies.
Implementation & Operational Enablement
Implement governance controls, security models, monitoring processes, tenant configurations, certification workflows, and administrative controls that establish a trusted reporting environment.
Continuous Monitoring & Optimisation
Establish ongoing governance processes, usage monitoring, compliance reviews, impact analysis procedures, and Centre of Excellence operations to ensure long-term analytics maturity and continuous improvement.
Power BI Ecosystem & Analytics Technology Stack
We select the right combination of data platform, modelling, governance, visualisation, and analytics technologies for each Power BI project, ensuring scalable architecture, trusted insights, optimal performance, security, and long-term analytical success.
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