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Business intelligence architecture:
key components, benefits & tools
April 01, 2025
Business intelligence architecture represents a framework that enables users to collect, cleanse, analyze, and visualize data. It includes technologies and tools that help turn raw data into actionable business insights and user-friendly reports.
With more than 15 years of providing business intelligence services, Itransition designs, deploys, fine-tunes, and maintains high-performing BI solutions, helping companies of various sizes derive meaningful insights from high-volume business data and make informed decisions.
5 key components of a business intelligence architecture
Scheme title: Exemplar BI architecture
Data sources
These are software solutions, systems, or devices that produce structured, semi-structured, or unstructured data the BI tool ingests in real-time streams or batches. Internal sources provide data captured and maintained within a company (CRMs, relational/non-relational databases, etc.), while external sources furnish data generated outside the organization (social media, public data from government sources, market research, surveys, and statistics).
However, not all corporate data sources should be integrated with the BI solution, as some information may not be necessary to analyze for current business needs.
Data integration & data quality management layer
This layer contains tools that make data complete, accurate, relevant, and consistent so that it is suitable for storage and analysis. Depending on the information type, format, and volumes, as well as the purpose of aggregation, BI engineers set up data integration pipelines, choosing a suitable method, such as extract, transform, load (ETL), extract, load, transform (ELT), data replication, change data capture, streaming data integration, and data virtualization.
Simultaneously with data integration, the BI system should perform data cleansing processes to ensure high data quality, remove data errors and duplicates, enrich data with semantics, and audit data against the established data quality metrics.
Data storage layer
This component encompasses various repositories that structure and store data for further processing. Analytical data stores include data warehouses for consolidating and managing corporate-wide historical data organized into schemas and data marts tailored to the analytics and reporting needs of particular business departments or divisions.
Additionally, companies can employ operational data stores to keep data in a raw format until it is overwritten with the newer one. A data lake is another data repository that can be included into the BI architecture to accompany an EDW. Most commonly, its purpose is to store voluminous data in its native format for backup and archiving and serve as an interim storage before data is loaded into the EDW or further processed with machine learning and big data technologies.
BI & analytics layer
This layer encompasses solutions for data processing and visualization, such as query and reporting, online analytical processing (OLAP), data mining and data visualization tools, and AI and machine learning solutions. They allow data analysts to request specific information, slice and dice data, search for trends, model what-if scenarios, and create interactive dashboards.
If the BI solution has self-service capabilities, business users can run their analyses independently of data teams, build dashboards and scorecards, edit the existing content, and share their findings with colleagues.
Data governance layer
This layer encompasses tools and services facilitating data governance that ensure robust data flows and control data access, usage, quality, and security.
Data governance tools offer capabilities like data catalogs that use metadata to create an informative and searchable inventory of all data assets, business glossaries with definitions of common business terms, and automated data lineage documentation to track the data throughout its lifecycle. This layer should also allow for low-code or no-code creation and configuration of data governance workflows and provide data stewardship capabilities for data governance teams to manage data-related issues. Data governance also implies implementing role-based access controls for setting user permissions, automated data quality metrics generation and measurement, and centralized data policies and standards management.
BI architecture real-life examples
90x
reduced data upload time
BI for incident management
Itransition built a scalable and customizable BI solution equipped with natural language processing algorithms to help a global risk management and safety assurance firm collect and analyze incident data. The BI solution incorporates a dashboard that visualizes data, a tool for uploading client data and data models.
7x
increased data analysis speed
Power BI implementation & employee training
We implemented Power BI for an analytics and research consulting company, designed interactive and user-friendly reports, and introduced an analytics module to enable ad performance exploratory analysis. To facilitate a smooth transition from well-established business processes to a new ecosystem, we prepared a detailed training course and conducted user training sessions.
24x
faster data delivery
BI system modernization for order management
Itransition’s team has helped a global logistics business migrate to Microsoft Power BI, a more effective and robust BI system than their former data analytics software. We established an efficient ETL process, built a data warehouse, and moved 150 reports from the previous data analytics platform to the new BI tool, enabling informed decision-making.
Looking for a reliable tech partner to implement your BI architecture?
BI architecture benefits
A business intelligence architecture perfectly aligned with business needs and implemented properly brings numerous benefits to the adopting companies.
Maximized data value
By implementing a BI architecture, a company gets a high-performing data management environment, capturing all valuable information and minimizing the amount of dark data.
Reduced IT department workload
BI architecture allows for partial offloading of dedicated IT and data analytics teams, collecting, modeling, and analyzing data automatically.
Increased efficiency of business users
BI architecture helps create and streamline a comprehensive view of business operations and streamlines report sharing with other employees, boosting their efficiency. Moreover, with self-service BI in place, non-technical users operate data without having to wait for IT teams to assist them.
Cost savings
Enhanced visibility into business processes and results provided by BI tools makes it easier to spot inefficiencies and identify cost-saving opportunities, enabling companies to improve their business performance and financial KPIs.
Smarter decision-making
Business intelligence data architecture allows users to make decisions backed by actual data rather than intuition, be it forecasting future events or analyzing past data.
Real-time analytics
As the implementation of a BI architecture results in streamlined information management and analytics processes, end-users can make data-driven decisions more quickly.
Data consistency
BI architecture facilitates data quality management, which means all information gathered will be brought into a consistent format for the analytics tools to process it correctly.
Scalability & flexibility
BI architecture components like an enterprise data warehouse and data lakes can handle large volumes of historical and real-time data, making the architecture suitable for growing companies and increasing data processing needs.
Best business intelligence tools
Power BI is a comprehensive business intelligence tool for business analytics and report generation, allowing business users and developers to interact with data and embed dashboards and reports into third-party applications. Power BI supports self-service BI implementation, provides over 150 pre-built connectors with data sources, and offers a Windows desktop application, an online software as a service (SaaS) solution, and mobile apps for Windows, iOS, and Android devices.
Differentiators
- Native integration with the Microsoft ecosystem
- AI-powered features, including smart data discovery and visualization, natural language query, anomaly detection, sentiment analysis, and forecasting
- A wide range of Microsoft training courses
Limitations
- Rigid and complex formula expression language (DAX)
- Free version doesn’t provide sharing or collaborating features
- On-premises solution lacks some features compared to the cloud service
Pricing plans
- Power BI Pro, Power BI Premium Per User, Power BI in Microsoft Fabric charged per user/month
- Free Power BI Desktop app
- 60-day free trial through a Microsoft Fabric account
Tableau is a cloud-based visual analytics platform that can also be deployed on-premises. The platform comes with 80 connectors to various sources, ready-to-use and customizable dashboards, interactive functionalities, and filtering options that let you adjust what data to analyze and view. It protects system data by supporting industry-standard authentication, including Active Directory, Kerberos, OpenId Connect, SAML, trusted tickets, and certificates; provides fine-grained control over the data, content, and objects; and employs SSL/TLS protocols for encrypting data transmissions.
Differentiators
- High-performance SQL engine that supports interactive exploration, quick real-time analytics, and ETL transformations
- Proprietary VizQL (Visualization Query Language) that handles complicated aggregation and transformation operations and behind-the-scenes calculations
- AI-driven, easy-to-understand explanations and visualizations of data point values
Limitations
- A steep learning curve for advanced features like LOD calculations and complex visualizations
- Performance issues when there are too many customizations
Pricing plans
- Tableau, Enterprise, and Tableau+ editions with role-based licensing: Creator, Explorer, and Viewer
- 14-day trial for Tableau Desktop
- Free Tableau Public edition
- One-year free access to Tableau Desktop, Prep, and eLearning for teachers and students with the ability to renew it
Looker is a BI tool for analyzing, visualizing, and exploring data that offers over 1,000 data connectors. Looker allows for cloud deployment as a Google Cloud service, offers embedded analytics, and can be hosted on-premises or on a third-party cloud provider’s environment like AWS and Azure. The platform enables self-service analytics via real-time Looker dashboards and interactive, collaborative, and ad-hoc Looker Studio reports, integrates with BigQuery to store and process massive datasets, and provides a semantic modeling layer for centralizing data curation and governance.
Differentiators
- Connectivity with Google applications such as Google Sheets, Vertex AI, and Google Analytics
- A powerful SQL-based modeling language, LookML, that allows analysts to centrally define and manage business rules and definitions
- Gemini integration for faster visualization, report configuration and generation, formula development, and data modeling
Limitations
- Certain features, like BI Connectors, Google Sheets integration, and Slack integrations, are unavailable for customer-hosted deployments
- Can be too expensive for small teams
- Requires extensive user training
- Data modeling is attainable only by experienced users
Pricing plans
- Standard, Enterprise, and Embed editions requiring annual commitment
- Platform pricing for running the Looker platform on Google Cloud comprises features for semantic modeling, platform administration, and integrations
- User licensing is divided into tiers (Developer, Standard, and Viewer) based on the type of user and their permissions within Looker
- $300 in free credits and 20+ always-free products
Oracle is an all-in-one system consisting of a wide range of business intelligence solutions, including Oracle Business Intelligence Server, Oracle BI Answers, Oracle BI Interactive Dashboards, Oracle BI Delivers, and other tools, that covers a wide range of BI needs, from data integration and warehousing to reporting, analysis, and dashboarding. It can be deployed on the Oracle server or on-premises.
Differentiators
- Seamless integration with Oracle products
- Designed to be highly scalable, handling concurrent tasks and multiple users
- Provides business activity monitoring and alerting
Limitations
- Not a very intuitive and customizable interface
- Complex architecture and deployment
- Minimum user or record count requirements to purchase products
- No free option
Pricing plans
- Separate pricing for standalone applications, Enterprise Performance Management Suites, and Fusion BI applications
- One-time license fee and software update license and support fees
Qlick provides data integration, analytics, and visualization solutions powered by AI and ML and has two primary offerings: QlikView (first-generation on-premise product) and Qlik Sense (modern cloud-based software, which is the main focus of the company’s attention). The solution ensures efficient data analysis thanks to data compression, integrates AI for chart creation, association recommendations, and data preparation, and supports various deployment options (on-premises, cloud, or hybrid).
Qlik Sense is suitable for self-service analytics, providing governed data models with robust data security. Qlick’s agile data warehouse automation and transformation platform, Qlik Compose® is an alternative to traditional ETL tools that automates repetitive, labor-intensive tasks associated with ETL integration and data warehousing.
Differentiators
- Unique associative engine that provides robust calculation performance for large datasets, real-time data, and an extensive user base
- 30+ responsive visualizations that automatically summarize data shape, highlight patterns, and pinpoint outliers
- Advanced geographic calculation, generative AI, and predictive AI
Limitations
- Complex product pricing, with separate fees for AI/ML products, Qlik Cloud® Analytics, and Data Integration and Quality solutions
- Limitations on simultaneous data processing and reporting
Pricing plans
- Starter, Standard, Premium, and Enterprise editions in the Data Integration and Quality solution, with pricing available upon request
- Separate pricing for Qlik Answers™ and Qlik AutoML®, which can be requested from the sales team
- Demo Qlik AutoML® version available upon request
- 30-day free trial
- A free QlikView Personal Edition
ThoughtSpot is an easy-to-use BI tool that enables business users to work with data without using SQL or other query languages. It connects to spreadsheets, CSV files, cloud data systems, and other information sources, understands questions asked in natural language, and generates interactive graphs and reports. ThoughtSpot can be installed as a standalone tool on-premises, in the cloud (on AWS, Microsoft Azure, or Google Cloud), or on VMware. It can also be embedded into existing products as a low-code analytics solution.
Differentiators
- Natural language search
- A generative AI-powered analytics experience
- Simplified data exploration
Limitations
- The need for additional training to use advanced functionality
- The constant need to adapt to regular updates
Pricing plans
- 14-day free trial
- Developer, Pro, and Enterprise plans for ThoughtSpot Embedded with custom pricing provided if requested
- Consumption-based licensing model
- Free ThoughtSpot Embedded for developers for 1 year
Gain maximum value from your data with a suitable BI architecture
BI architecture implementation challenges & solutions
Since a BI architecture is a complex framework, its implementation can be fraught with obstacles hindering its adoption by businesses.
Poor data quality |
When connecting the BI system to data sources containing inconsistent, insufficient, or duplicate
information, it can produce erroneous results, which influence business decisions and outcomes.
| Put in place a well-defined data management strategy, internal data standards, and robust data integration processes. To ensure high data quality, establish clear and measurable data quality criteria, practice data profiling, monitor its quality levels, and regularly cleanse information. |
|---|---|---|
User pushback |
Business users who are accustomed to working with Excel and isolated analytics tools can oppose the
transition to a more complex system. Moreover, they can lack the required knowledge and skills or be
reluctant to learn them.
| Educate employees and provide accessible documentation and master classes, educating them on how to use the BI tool to prepare reports relevant to their functional area. To get their buy-in, demonstrate data analytics applications and real-life examples of how a bespoke BI architecture benefits tactical and strategic decision-making. Consider integrating self-service BI features, simplifying the work with the BI tool for users with different technical expertise. |
Security risks |
BI architecture handles vast amounts of data, such as financial, personal, and intellectual property
information, which can be sensitive, and creates higher risks of data breaches.
| Implement security measures like user authentication, data encryption, and masking, set up access controls, and conduct regular security audits. Additionally, define and improve data governance and data security practices within the organization and choose tools that have robust built-in security features and are compliant with data protection laws and regulations like GDPR, CCPA, HIPAA, and FedRAMP. |
BI architecture implementation best practices
Engage stakeholders right from the get-go
When planning BI architecture implementation, determine stakeholders at all levels of the organization who’ll directly use the BI solution (employees from different departments and analytics specialists), ensure data governance (data governance teams), or whose work will be affected by the BI solution (business department leaders). Communicate with them at different stages of the project, taking into account their requirements, suggestions, and challenges.
Choose the right tools
The choice of tools depends on your business needs. That is, rapidly growing businesses will benefit from cloud-based or hybrid solutions, while companies operating in highly regulated industries and requiring complete control over data should opt for on-premises BI deployment.
Consider incremental implementation
Incremental implementation means that the BI architecture is rolled out in manageable phases, enabling developers to timely spot and eliminate issues, business users to get used to the tool, and the organization to optimize its spending on the BI architecture.
Focus on scalability
The BI architecture should support increasing data volumes, users’ needs, and long-term company goals. To make it future-proof, opt for scalable cloud solutions with automated workflows and AI/ML capabilities, optimizing system performance for more demanding requirements, using caching, partitioning, load balancing, and other techniques.
Gather user feedback & optimize accordingly
After the BI architecture launch, monitor its performance and collect user feedback to address any issues.
Foster a data-driven corporate culture
Promote transparency, communication, and data sharing between teams, allowing employees of all levels to access relevant data insights. Lead by example, showing how you apply the information learned from the BI tool to make decisions and the results it brings.
BI services we provide
Consulting
We advise companies on the best BI implementation approach, tech stack, and data management policies, offering end-to-end strategic and implementation assistance, helping businesses design an effective BI architecture, and providing support throughout the entire BI implementation cycle.
Implementation
We carefully analyze businesses’ data management and analytics needs and, based on that, deliver individual BI components into the existing IT ecosystem or build comprehensive BI environments.
Support
We provide ongoing post-deployment services for the BI architecture implemented by us or other vendors, monitoring the software for any bugs or performance issues and updating the system if needed.
Craft a robust business intelligence architecture
Investing in data management and analytics leads to improved operational efficiency and increased profit. However, opting for individual technological solutions for data collection, transformation, and analysis can result in more silos and data leaks.
This is why an end-to-end BI architecture, when implemented and maintained with proper expert guidance, is set to bring optimal enterprise intelligence capabilities, a competitive edge, and, in the long run, better business outcomes.
Implement your business intelligence architecture with Itransition
FAQs
Business intelligence data architecture helps define the infrastructure that supports all the stages of the BI process, from data collection to data transformation, analysis, and visualization, enabling users to present data through reports and dashboards and use it for various data science tasks.
A business intelligence team is typically composed of a BI program manager, BI project manager, BI solution architect, BI developer, BI analyst, and BI systems administrator. Depending on the company’s size and allocated budget, specialists can fulfill several roles and have similar or even overlapping responsibilities.
A BI solution architect cooperates with business analysts and stakeholders to define business requirements, designs suitable BI solutions, and supervises their implementation. This specialist also evaluates the existing BI environment, creates new system requirements when the business needs change, prioritizes change requests, and manages data governance and security.
By deployment model, the BI architecture can be on-premises, cloud, or hybrid. The BI platform can also be centralized, allowing users to perform data activities on all enterprise data, and decentralized, limiting the data to what is relevant to specific departments. Moreover, a BI solution can be traditional, with most tasks handled by IT specialists, and self-service, minimizing the reliance of business users on IT teams for data-related tasks.
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