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April 01, 2025
Scheme title: Exemplar BI architecture
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. Data sources can be internal and external, with the information captured and maintained within a company (CRMs, SQL databases, NoSQL databases, etc.) or generated outside the organization (social media, public data from government sources, market research, surveys, and statistics), respectively.
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.
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, companies should choose suitable integration methods, selecting from 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.
This component encompasses various repositories that structure and store data for further processing. Analytical data stores include data warehouses for managing cleaned, consolidated enterprise data 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.
This layer encompasses solutions for data processing and visualization, such as query and reporting, online analytical processing (OLAP), data mining and data visualization solutions, and AI and machine learning tools. 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.
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 capturing data and cataloging it using categories, tags, indexes, etc., 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.
A business intelligence architecture perfectly aligned with business needs and implemented properly brings numerous benefits to the adopting companies.
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.
BI architecture allows for partial offloading of dedicated IT and data analytics teams, collecting, modeling, and analyzing data automatically.
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.
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.
BI architecture allows users to make decisions backed by actual data rather than intuition, be it forecasting future events or analyzing past data.
As the implementation of a BI architecture results in streamlined information management and analytics processes, end-users can make data-driven decisions more quickly.
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.
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.
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.
Tableau is a cloud-based visual analytics platform that can also be deployed on-premises. The platform comes with 80 connectors to various data source systems, ready-to-use and customizable dashboards, interactive functionalities, and filtering options that let you adjust what data to analyze and view.
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.
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.
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.
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.
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 a real-life example and use cases of how a bespoke BI architecture benefits department and company-wide 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. |
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.
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.
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.
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.
After the BI architecture launch, monitor its performance and collect user feedback to address any issues.
Promote transparency, communication, and data sharing between teams, allowing employees of all levels to access relevant insights. Lead by example, showing how you apply the information learned from the BI tool to make decisions and the results it brings.
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.
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.
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.
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.
Business intelligence (BI) architecture helps define the infrastructure that supports all the stages of the BI process, from data collection, cleansing, structuring, storage, and analysis to its 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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