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October 21, 2025
A BI development process can differ from one project to another depending on available resources, BI solution complexity, and the company’s priorities. Here is a typical roadmap for BI solutions development encompassing the key phases of such a project.
Discovery & solution conceptualization
At the beginning of the BI development project, a BI team gathers information on a company’s analytical and
reporting objectives, expectations from the BI solution, and issues users currently face when analyzing and
visualizing data. Then, they assess the company’s IT infrastructure, conduct an initial review of data
sources and data quality, including its completeness, timeliness, accuracy, and consistency, and evaluate
existing data security and regulatory compliance practices.
Based on preliminary research results, BI specialists outline functional and non-functional requirements for
the BI solution and identify potential project risks.
Architecture design
The BI team proceeds with a more comprehensive audit of data sources and conceptual and logical data modeling. They also design ETL/ELT processes, data storage solutions, data models for OLAP cubes, and dashboards for various user roles. Besides, they establish the BI system’s policies for data integration and quality control, data protection practices, and data governance standards and policies.
Deployment environment & technology selection
After designing the BI solution’s architecture, the BI team selects the most suitable environment for BI deployment, such as cloud, on-premises, or hybrid, and chooses an optimal tech stack for each component of the BI system.
Project implementation planning
At this point, the BI delivery team outlines the scope of BI system development and testing activities, develops an implementation strategy, plans resource distribution, formulates the approach to risk management, and calculates BI development project costs, duration, and TCO. Here, they also outline a BI development team’s roles and responsibilities, establish communication workflows and channels, and create the project’s schedule.
Components development & delivery to the staging environment
The development process covers building or configuring data connectors and ETL/ELT processes, developing
data storage solutions, including enterprise data warehouses, data lakes, and data marts, and implementing data quality management policies and mechanisms.
BI specialists also design and implement data processing and querying mechanisms, implement data security mechanisms,
including row-level security, access controls, and network monitoring, and integrate the BI solution with the
business’s broader IT ecosystem. Moreover, they create role-specific reports and dashboards, enabling users from
different departments to visualize the analysis results and present the insights in an understandable format.
Throughout BI development, the team thoroughly reviews the solution to confirm that it matches the outlined business
requirements. They use different types of QA activities to make sure the software operates as intended, including
functional, performance, usability, and compatibility testing.
Solution deployment to the target environment
At this stage, BI engineers deliver the developed BI solution to the live environment, prepare technical documentation to explain the design, development, and usage of a BI system for future reference, and educate users on how to utilize it effectively to fulfill their specific needs. After the BI system's launch, the BI team can monitor its performance, stability, and usability and troubleshoot any issues that emerge. If required, they also integrate new data sources and configure additional dashboards to improve the BI solution’s operation.
BI development involves creating an ecosystem of integrated components that cover different activities in the business intelligence lifecycle.
These solutions consolidate information from different sources utilizing various methods, such as ETL/ELT, change data capture, and data virtualization, and ensure its accuracy and completeness for further storage and analysis.
These include data warehouses and other data storage options, like data marts, data lakes, lakehouses, and operational data stores, that keep enterprise data in a ready-for-analysis format and simplify data access.
A data analytics component contains engines that process data to generate descriptive, diagnostic, prescriptive, and predictive insights, discovering trends and patterns within large datasets.
Data visualization tools enable data analysts and business users to transform complex data into an easy-to-understand format with the help of intuitive reports and dashboards.
Comprehensive data quality management policies and practices help companies maintain data accuracy, consistency, and reliability throughout its lifecycle in the BI system.
A robust data governance and security framework establishes data ownership and access controls, ensuring its integrity and safety across all BI workflows.
Enterprise-grade BI solutions help analyze data from the entire organization, enabling top-level executives to monitor key performance indicators, evaluate overall business health, compare it against set goals and competitors, and make strategic decisions.
Operational BI solutions enable front-line managers and employees to analyze real-time data on current business operations, production output, resource availability, and incidents and swiftly respond to changing conditions.
Business intelligence tools help HR specialists analyze employee productivity and satisfaction, predict employee churn, identify training needs, make better hiring decisions, and optimize staff allocation and schedules.
Processing data from supply chain operations, business intelligence software helps specialists drill down into the details about inventory levels, supplier performance, and delivery efficiency to optimize fulfillment accuracy and speed, prevent understocking, and analyze market trends to forecast demand.
BI tools for financial departments enable financial data analysis and performance tracking, providing insights into the company’s profitability, cash flow, expenses, and revenue and facilitating budget planning and financial risk forecasting.
Customer service departments use BI software to analyze client satisfaction, agent performance, and issue resolution effectiveness. BI solutions also provide insights into the issues customers face, how fast specialists resolve problems, which channels are the most effective for customer support, whether clients find the service helpful, and how to improve their experience.
Marketing and sales teams use BI tools to dive into various performance metrics and ensure the effectiveness of their promotional campaigns. BI software for marketing and sales also facilitates the analysis of customer-related metrics, such as engagement, satisfaction, and churn, helping forecast customer behavior, identify high-value clients, and personalize customer experience.
Healthcare providers use BI software to evaluate caregiver performance, analyze patient feedback, predict patient needs, and personalize care.
For store owners and employees, BI software facilitates the company’s product offering analysis, sales tracking, demand forecasting, as well as the creation of a 360-degree customer view for developing personalized offers, enabling the optimization of inventory management, product pricing, and positioning.
Financial institutions apply BI tools for activities such as analyzing trading data to predict investment risks, detecting signs of financial fraud based on transaction data, evaluating portfolio performance, budgeting and revenue forecasting, and handling regulatory compliance.
In the manufacturing industry, business intelligence solutions help monitor inventory levels, plan procurement activities, predict equipment maintenance needs, and analyze energy utilization and emission levels.
Real estate agents employ BI systems to analyze property value, sales data, and property management practices, evaluate property profitability factors, and track rent trends and property price dynamics.
Using BI systems, companies from the telecommunications, media, and entertainment sectors can determine customer profitability, lifetime value, and churn factors, timely catch indicators of customer attrition, and evaluate marketing and product success.
A business intelligence system aggregates data from the company’s systems in a single place and ensures its reliability, usability, and accessibility for analysis and informed decision-making.
Users can easily and quickly get access to the needed insights via a unified interface and generate comprehensive reports without having to switch between different tools to pull together distributed data.
Business intelligence tools automate manual data management processes, allowing companies to cut down on costs related to time-consuming report compilation, human errors, and missed cost-saving opportunities.
Able to benchmark their business performance against other market players, companies can make business decisions targeted at addressing product gaps and customer journey bottlenecks to outperform their competitors.
By providing a complete picture of business processes, BI software enables companies to identify potential risks that can lead to operational disruption and downtime and mitigate them proactively to maintain business continuity.
BI solutions streamline industry trend analysis, allowing businesses to anticipate market shifts, pinpoint underserved niches, and seize potential sales opportunities to develop new products, optimize their marketing efforts, and enter overlooked markets.
We build custom BI solutions, engineering tailored data source connectors, data warehouses, data analytics solutions, and dashboards that fully meet your unique data analysis and visualization requirements.
We deliver BI solutions based on ready-made BI platforms, adjusting them to your analytics and reporting needs and company workflows so that they fully meet your requirements.
As company needs evolve and the number of operations and tools grows, getting insights from data scattered
across business departments and systems becomes challenging. BI software helps different users, from C-suite
executives to regular employees, get accurate and easy-to-interpret insights to make better decisions.
If you want to enhance your current analytics and reporting capabilities, consider turning to Itransition to determine
how BI can support your goals, conceptualize your BI solution, and provide comprehensive BI development services.
To excel in their field, a BI developer should be proficient in the following areas:
Becoming a business intelligence developer requires specific education, i.e., a bachelor’s degree in
information technology, business intelligence, computer science, mathematics, or software development.
Also, you need to obtain real-life experience and constantly deepen your knowledge of BI tools and development
technologies by taking dedicated courses and getting a certification from recognized institutions or companies
to boost your credibility.
Business analytics is a subset of BI that focuses on analyzing data to understand past events and predict future outcomes using advanced statistical analysis, data modeling, and machine learning algorithms. Business intelligence, in turn, involves analyzing current and past enterprise data and visualizing it using descriptive dashboards and reports. This way, BI helps stakeholders better understand the company’s present-day operations and performance to make tactical and strategic decisions.
Data engineers focus on creating a data architecture that supports the company’s data analytics and reporting operations, while data scientists develop statistical models, machine learning algorithms, and predictive analytics solutions to process complex datasets and uncover trends and insights from them. A BI developer, in turn, works in collaboration with these specialists to conceptualize and implement reporting and analytical solutions in line with the company’s business needs.
Data integration & quality management | Azure Data Factory Azure Stream Analytics Azure Functions Azure Event Grid Azure Event Hubs Microsoft SQL Server Integration Services AWS Glue AWS Data Exchange Amazon MWAA Oracle Data Integrator Databricks Apache NiFi Talend Informatica PowerCenter MuleSoft Anypoint Platform IBM InfoSphere Information Server SnapLogic Syncsort Striim Pentaho Fivetran Matillion |
|---|---|
Data storage | Azure Blob Storage Azure Data Lake Storage Azure SQL Database Azure Synapse Analytics Azure SQL Database elastic pools Azure SQL Managed Instance Azure Cosmos DB Amazon S3 Amazon Redshift Amazon Aurora Amazon RDS Amazon DynamoDB Amazon ElastiCache Google BigQuery Google Cloud Storage Microsoft SQL Server PostgreSQL MySQL Oracle IBM Cloud Object Storage SAP HANA Cloud Teradata |
Data analytics | Azure Synapse Analytics Azure Analysis Services Microsoft SQL Server Analysis Services Amazon Redshift Amazon Athena Amazon Kinesis Data Analytics Google BigQuery |
Data visualization | Microsoft Power BI Tableau Qlik Sense QlikView Amazon QuickSight Looker Apache Superset Metabase Redash Datawrapper Infogram Excel Python |
Data security management | Microsoft Entra ID (formerly Azure Active Directory) Azure Firewall Azure Key Vault Azure VPN Gateway Amazon Cognito AWS KMS AWS Secrets Manager |
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