Mobile Development/Public Sector/QA/Software Development
Digital Visa Application and Verification Platform
Mobile and web platform for vehicle service and ownership management
View projectBusiness Intelligence
Client Challenges
Reports Exist, But Decisions Still Wait For The Numbers
Companies usually engage Solbeg for business intelligence services when existing reporting takes too much effort to produce, different teams do not trust the same numbers, or the current setup struggles to scale with the volume and complexity of the data.
Sales, finance, and operations may calculate revenue, margin, churn, or other metrics using different logic and filters. Management meetings then turn into discussions about which figure is correct rather than what action should follow. Decisions are delayed until someone reconciles the differences manually.
Analysts export data from several systems, clean it, and rebuild the same workbook every reporting cycle. The process consumes specialist time and can break when a source system changes a field, format, or export structure. If the person who maintains the workbook is unavailable, the reporting process becomes difficult to reproduce.
Existing BI solutions may have been implemented as a set of reports without agreed metric definitions, ownership, or a clear process for maintaining them. When business users cannot explain where a number comes from or why it differs from another report, they often return to asking analysts directly.
Queries that once returned quickly begin to time out, refresh windows extend into working hours, and reporting activity may compete with production workloads. Teams respond by reducing date ranges or excluding data, which can limit the historical context needed for meaningful analysis.
Service Overview
BI Delivery Across Data Sources, Modeling, And Reporting
Our business intelligence services cover data integration, modeling, governed datasets, dashboards, and analytical reporting. An engagement can begin with an existing BI environment that needs improvement or with one part of the reporting architecture that is currently missing.
Review of current reports, data sources, metric definitions, platform configuration, and known reporting issues. The result is a prioritized view of what should be retained, corrected, redesigned, or replaced.
Design of the reporting data model and reusable calculation logic that sits above it. Core metrics are defined once and made available consistently across reports instead of being recalculated independently in each dashboard.
Development of operational, analytical, and management reporting against the governed data layer, including drill-down behavior, filters, navigation, and access rules appropriate to different user roles.
Certified datasets, metric definitions, naming standards, access rules, and a controlled process for extending enterprise business intelligence solutions as more teams begin creating their own reports.
Migration from a legacy, unsupported, or unsuitable reporting platform to a new environment, including reconstruction of calculations and business logic that may currently exist only inside individual reports.
Extension of existing models, pipelines, dashboards, and reporting domains as new data sources, products, teams, or business units come into scope.
Problem & solution
The problem
Reporting challenges often start with definitions and ownership
Most companies already have some form of reporting platform. What may be missing is a single agreed definition of important metrics such as revenue, active customers, margin, churn, or returns. The calculation logic can be spread across dashboards, spreadsheet formulas, SQL queries, and undocumented knowledge held by individual employees.
Internal analytics teams are often capable of resolving these inconsistencies, but structural work competes with a constant queue of ad hoc reporting requests. Data modeling, documentation, validation, and governance may therefore remain postponed because they do not provide an immediate answer to the latest business question. Introducing another reporting tool can then create an additional set of business intelligence solutions without addressing the underlying inconsistency.
Durability is another challenge. Source systems change fields, integrations evolve, business rules are revised, and data quality can shift over time. Without clear ownership and reconciliation between the reporting layer and source systems, incorrect results may remain unnoticed until a user identifies a number that looks implausible.
Solbeg's approach
Definitions and the data model are established before dashboards become the final output
We begin business intelligence consulting by reviewing the metrics and reporting logic that already exist. Conflicting definitions are identified, calculation rules are documented, and business owners confirm the meaning of the metrics that will be used across the reporting environment.
Structural work can run as its own delivery stream so the client’s internal analysts can continue supporting daily business requests while models, transformations, pipelines, and access rules are being improved. Existing reporting does not need to be removed before the replacement or revised model has been validated.
Pipelines and models are built with reconciliation against relevant source systems and, where useful, against reports the business already trusts. Metric ownership, refresh behavior, dependencies, and known limitations are documented as part of delivery so the reporting environment can be maintained after source systems or business rules change.
When this service fits
Situations Where An External BI Team Can Provide Additional Capacity Or Structure
BI services are most useful when reporting requires coordinated work across data definitions, architecture, integrations, performance, and ownership in addition to dashboard development.
One analyst may know how the monthly reporting pack is assembled, which exports feed it, which corrections are applied, and how individual metrics are interpreted. Vacation, sick leave, or resignation then creates operational risk. The task is to turn that knowledge into a documented and repeatable process.
A vendor may be ending support, licensing conditions may have changed, or the platform may no longer fit the current infrastructure. Reports often cannot simply be exported because important logic exists inside individual dashboards rather than in a reusable data model.
Current reports may show what happened inside one business system while management questions require data from several systems. Understanding which channel produced which margin or which customer segment creates the highest service cost requires a model that connects those entities consistently.
ERP, CRM, billing, support, ecommerce, or other applications may each contain part of the information required for reporting. Work then begins with identifying shared keys, resolving mismatched identifiers, and establishing a consistent structure for combining the data.
Refresh windows may continue to grow, concurrent users may affect one another, or analytical queries may place load on operational databases. In these cases, the constraint is usually architectural and involves how data is extracted, stored, modeled, and served before it reaches the dashboard.
A self-service model requires more than giving users access to a BI tool. Certified datasets, common metric definitions, security rules, documentation, and ownership are needed so users can explore the data without creating competing versions of the same business logic.
A new business unit can introduce its own systems, chart of accounts, identifiers, and definitions. Consolidated reporting needs to incorporate those differences without changing the meaning of historical results for the rest of the organization.
Scope of Work
Roles And Responsibilities Inside A BI Engagement
BI delivery can involve several disciplines between the source systems and the reports used by business teams. The exact composition depends on the reporting scope, existing architecture, platform, and the responsibilities retained by the client’s internal team.
BI analysis
Collection of reporting requirements from business stakeholders, documentation of metric definitions, and mapping of each metric to its underlying source systems and fields. The resulting specification provides a shared basis for development and validation.
BI architecture
Definition of the reporting architecture, including where data is stored, how it is layered and modeled, how semantic definitions are organized, and how access is segmented by user role.
Data engineering
Data engineering services include the development and scheduling of pipelines that extract, transform, and load information into the reporting layer, including incremental processing, history management, and handling of corrected or late-arriving records. Business intelligence development services at this level can also include integration with source-system databases and APIs.
BI development
Implementation of semantic models, calculations, reports, and dashboards on the selected platform, including drill paths, filtering behavior, access controls, and performance optimization against real data volumes.
Data quality and validation
Reconciliation of reporting output against source systems and against relevant reports already trusted by the business. Differences are investigated, documented, and either corrected or accepted as intentional differences in definition.
BI platform administration
Management of workspaces, refresh schedules, permissions, capacity, resource usage, and relevant operational monitoring so failed refreshes or stale data can be identified before they affect users.
Documentation and user enablement
Metric dictionaries, model documentation, usage guidance, and information for report consumers and self-service authors. This reduces dependency on individual employees to explain how a metric or report works.
Custom components and integrations
Where standard BI platform functionality is insufficient, business intelligence software development services can include embedded analytics, custom components, application integration, or automated delivery of analytical data to external consumers.
Why Solbeg
What Working With Solbeg Changes On A BI Engagement
Companies evaluating business intelligence consulting companies often start by comparing experience with individual reporting tools. In practice, important differences also appear in how the underlying data layer is engineered, how metrics are governed, and what the client’s own team is able to maintain after delivery.
Data engineering and reporting can be handled within one team
Many BI issues originate below the dashboard in data pipelines, source integration, or the semantic model. Keeping data engineers and BI developers within the same engagement reduces handoffs when a missing field, inconsistent source, or transformation issue affects reporting.
Metric definitions are treated as a deliverable
Calculation logic can be documented alongside dashboards, including business decisions about how specific metrics should be interpreted. New reports can then reuse those definitions instead of creating another independent version of the same number.
Work can begin inside the platform you already use
A BI engagement does not automatically require a platform migration. Where the existing platform can support the required scope, work can focus on the data model, pipelines, metric definitions, performance, governance, and reporting rather than replacing tooling unnecessarily.
Delivery can be phased around usable business areas
The scope can be sequenced so that individual reporting domains reach production before the entire program is complete. This gives stakeholders usable output earlier and allows later priorities to change without invalidating completed work.
Handover is designed for the internal team
The BI environment is documented and structured so the client’s team can understand, maintain, and extend it after handover. Models, pipelines, definitions, permissions, and operating procedures remain clear and accessible, while Solbeg can continue supporting further development, optimization, and ongoing BI needs under the agreed engagement model.
Comparison
Direct Connections To Source Systems Or A Modeled Reporting Layer
One of the central architecture decisions in a BI project is whether the reporting platform should query operational sources directly or consume data through a separate modeled layer. BI consulting at the beginning of the engagement can help evaluate that choice against source complexity, data volume, reporting requirements, user count, and expected growth.
Engagement Process
BI Engagement Process Across Audit, Modeling, Validation, And Adoption
A BI engagement can start with a new reporting environment or an existing setup that needs restructuring. Each stage produces a reviewable result before the next part of the implementation proceeds.
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Discovery and metric audit
We review existing reports, collect reporting requirements, and identify conflicting or undocumented definitions. Relevant business stakeholders, analysts, and technical specialists participate so the result is an agreed list of metrics, priorities, and reporting requirements.
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Data and platform assessment
Source systems are reviewed for structure, extraction options, quality, volume, and integration constraints. The existing BI platform and related infrastructure are assessed against the target scope. The result is an architecture direction and a defined scope for the first implementation phase.
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Model and reporting design
The data model, semantic layer, metric logic, report structure, and relevant access rules are designed against the agreed requirements. Layouts, navigation, drill paths, and business logic can be reviewed with the intended users before full implementation.
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Build and validation
Pipelines, transformations, models, calculations, and dashboards are implemented and reconciled against source systems and relevant existing reports. Differences are investigated and either corrected or documented according to agreed business definitions.
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Rollout and enablement
Reports are released to their intended users with the required access controls, documentation, and guidance. Business intelligence consulting services at this stage also support adoption by ensuring users understand which reports and datasets should be used for particular decisions.
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Iteration and extension
New data sources, metrics, reports, and business domains are added to the existing architecture rather than automatically creating separate reporting environments. The scope and priorities for each iteration are agreed with the client.
Completed projects
View all projectsSelected projects we’ve delivered
A few examples of digital products we’ve built for our clients and the results they brought to their business.
Healthcare/Online Scheduling/Public Sector/Retail/Web Development
Online Appointment Scheduling System
Online booking system for appointments and reservations
View project
Oil & Gas/QA/Software Development/Web Development
Data Automation Platform for Oil & Gas Operations
Mobile and web platform for vehicle service and ownership management
View projectFAQ
What Companies Ask Before Starting A BI Engagement
Not necessarily. Reporting can be built directly against existing systems when the source environment, data volumes, historical requirements, and number of users make that approach practical. The assessment determines whether a separate reporting layer is justified rather than treating a data warehouse as a mandatory prerequisite.
Yes. An existing platform can remain in place when it supports the required reporting scope, performance, governance, and security requirements. Migration becomes relevant when the current tool is unsupported, technically constrained, commercially unsuitable, or missing functionality that the project depends on.
Reporting outputs are reconciled against relevant source systems and the reports that business owners already use as references. When differences appear, the underlying calculation rules are traced and documented. The business owner then confirms the intended definition, while the delivery team implements and tests it consistently.
Internal involvement is most important when business definitions, priorities, access rules, and acceptance criteria are being agreed. The amount of time varies by scope, but a business intelligence consultancy needs access to the people who understand what the numbers mean and how the reports will be used. Technical delivery can then proceed with more focused stakeholder reviews.
Access requirements are considered as part of the reporting architecture. Depending on the platform and use case, controls can include role-based access, row-level or object-level restrictions, masking, workspace segmentation, regional requirements, and audit logging. These requirements are defined early because they can affect both the model and the platform architecture.
Yes. A takeover usually begins by understanding what the existing reports, pipelines, and models actually do before changes are introduced. Undocumented calculations and dependencies are identified and documented as part of the assessment. This is particularly important when an existing setup delivered by a previous business intelligence company does not include complete technical or metric documentation.
Ownership and handover conditions are defined for the engagement, including the models, pipelines, reports, documentation, repositories, and platform assets created within the agreed scope. When evaluating business intelligence companies, it is useful to confirm in advance where these assets will reside, which licenses or services they depend on, and what the client’s team will be able to access and maintain after handover.
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