Business intelligence implementation: key steps, team, and tech options
the projected global business intelligence market by 2028
Fortune Business Insights
of the global workforce have access to business intelligence tools
Accenture
of large enterprises plan to invest more in their business analytics initiatives
MicroStrategy
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Our business intelligence implementation guide
While the process can differ from company to company depending on their needs, objectives, and BI strategy, some business intelligence implementation steps are typical for most projects. Below, we’ve formulated a common implementation roadmap for an enterprise BI solution based on our experience:
1.
Analyze your business needs
A BI implementation project starts with the elicitation of business needs and goals, expectations, risks and concerns, as well as existing issues and bottlenecks from stakeholders, including C-level executives, department managers, data analysts, business consultants, and users.
At this stage, your goal is to get a clear vision of what business problems you want to solve with the BI implementation. You can do this using a combination of various techniques, such as interviews, brainstorming sessions, workshops, observation sessions, questionnaires, and business process audits. Based on the findings, you can define the overall trajectory of the BI implementation project and use them as the foundation for a BI solution conceptualization. You can also break down the defined business needs into KPIs to measure the company’s BI implementation progress down the line.
2.
Evaluate your current environment
Next, review the current analytics and technology environment as well as processes and organizational structures to define what capabilities you have to meet the defined business goals. You should explore what data you collect and its sources, what departments and individuals contribute to the process, and your current data management practices.
On top of this, you have to conduct a preliminary data source analysis (data volumes, types, granularity, sensitivity), as well as the analysis of data security and compliance practices at your company.
3.
Conceptualize a BI solution
You can now draw up a wishlist of functional and non-functional requirements for the solution and group them into categories such as: essential, important, and desired. A detailed list of functional and non-functional requirements, together with BI solution constraints and assumptions, is then formalized into the software requirements specification (SRS) document.
Objective
Based on
Defined by
Obligation
Examples
Functional requirements
Functional requirements
Describe what the BI solution does
User requirements
Stakeholders
Mandatory
- Dashboards and data visualization
- Analytical querying
- Data management
- Data connectivity
- Data reporting
- Augmented analytics
Non-functional requirements
Non-functional requirements
Describe how the business intelligence solution works
User expectations
Tech experts
Not mandatory, but good to have
- Security
- Availability
- Performance
- Usability
- Reliability
- Scalability
4.
Design BI solution architecture
Once you’ve compiled the SRS document, you should go on with creating the blueprint of the technology environment to support the end-to-end BI workflow, from data processing to the presentation of insights to business users. Traditionally, a BI solution's architecture includes five components: data sources, data integration and quality management layer, data storage layers, BI and analytics, and a data governance layer.
Scheme title: Exemplar BI architecture
Data sources
- CRM
- ERP
- Sensors
- Flat files
- Social media
- Statistics
- Surveys, etc.
Data integration & data quality management layer
- ETL/ELT
- Change data capture
- Data replication Streaming data integration
- Data virtualization
- Data cleansing
- Data scrubbing
- Data enrichment, etc.
Data repositories
ODS
Data lake
EDW
Data marts
BI & analytics layer
- Query and reporting
- OLAP
- Data mining
- Machine learning
- Data visualization
- Self-service BI
- Reports
- Dashboards
- Scorecards
- Portals
Data governance layer
Data catalog — Business glossary — Metadata management — Data security management, etc.
Within this step, companies should perform the following:
- Comprehensive audit of all data sources (CRM, ERP, SCM, accounting and finance management software, external systems, etc.)
- Conceptual and logical data modeling
- Creation of data integration and quality management policies and rules (data cleansing, data transformation, data deduplication, etc.)
- ETL/ELT pipeline design
- Design of data repositories, which may include an enterprise data warehouse, data marts, a data lake, and an operational data store
- Design of data models for OLAP cubes
- Design of dashboards for different user roles with specific KPI sets
- Creation of data security policies and rules
- Creation of data governance standards and policies
5.
Select the deployment environment
After designing the overall architecture of the solution and its components, you have to decide on whether to deploy the solution on local servers (on-premises), in the cloud (public or private), or use a hybrid approach and deploy some components of the BI solution in the cloud and others on local servers.
On-premises
Pros
- High availability and excellent query performance
- Total control of the infrastructure
- Satisfies most deliberate compliance requirements
Cons
- Heavy upfront investments
- Delayed upscaling due to the need to expand in-house infrastructure
Cloud
Pros
- Accessible via web browser/mobile
- Fast deployment
- Instant up- and down-scaling of storage and compute resources
- Increased fault tolerance
- Infrastructure security ensured by a cloud vendor
- No hardware-related costs
- Accessible across multiple locations
- Support of serverless scenarios
Cons
- Regulatory compliance requires effort
- A more expensive option in the long run
6.
Select the technology stack
Define the optimal technology for each component of the BI solution - the ETL software, a database management system, business intelligence tools, analytical processing tools, data security software, metadata management software, and others.Â
Choosing the optimal technology requires tremendous effort, so to avoid chaos and ensure you meet your schedule and budget, prioritize the following factors:
- Prioritized business, functional, and technical requirements
- Vendor capabilities, framework, stability, and support
- Licensing and support cost models
- The enterprise’s existing infrastructure
- The in-house IT competencies
- Data security and compliance requirements
- The company's strategic approach to IT budgeting (CapEx/OpEx)
7.
Plan the BI implementation project
At this stage, you need to plan BI development and implementation, including:
- BI solution development and testing scope (key steps, deliverables, timelines, roles and responsibilities)
- Resource allocation planning
- An implementation strategy
- How to overcome BI implementation challenges
- Efforts estimation for the BI implementation project, as well as its TCO and ROI
Consequently, you’ll draw up a detailed project plan, project schedule and budget, a risk management plan, and a communication plan.
8.
Develop & deliver BI solution components
According to the chosen implementation approach, you develop and deliver the BI solution components into the staging environment. After that, you integrate the solution with all the required data sources and the rest of your technology environment.Â
- Creation of custom data connectors
- Implementation of ETL pipelines
- Data integration
- Implementation of data quality management rules
- Development of a data warehouse and data marts
- Reports and dashboards creation
- Implementation of data security rules, including row-level security, access policy, and network monitoring
9.
Perform QA and train end users
To avoid such problems as data inconsistency, wrongly calculated KPIs, and slow response times, you should test your BI solution. After all quality assurance activities are performed, you have to provide end users with training sessions and support documentation.
Functional testing
Checking if the delivered BI solution functions as required.
Performance testing
Checking if the BI solution meets the set performance metrics under normal and extreme loads.
Usability testing
Checking how intuitive and user-friendly the developed business intelligence software is.
Compatibility testing
Checking how well the BI software interacts with different types of hardware and software, operating systems and networks, devices and browsers.
10.
Deploy the BI solution to business users
Finally, you are ready to run the BI solution in production. During the first month after the solution launch, you should carefully monitor its performance and address arising issues. If you need to add new data sources or configure some additional dashboards, you have to ensure it won’t jeopardize the overall system’s performance and functionality beforehand.
Common sourcing models for business intelligence implementation
All in-house
The internal BI team carries out business intelligence implementation
Pros
- Total control over the business intelligence implementation project
- The development team has a clear vision of the company’s specific needs
- Minimized communication barriers between the development team and key stakeholders
Cons
- If you lack the required expertise, you have to hire specialists yourself
- High risks of resource overprovisioning after project completion
- Total responsibility over the project
- Possible delays due to resource unavailability
- Knowledge loss due to employee turnover
Complete project outsourcing
You assign a project sponsor from your team, while a third-party tech vendor carries out the end-to-end BI implementation.
Pros
- Quick time-to-market
- Well-established software development and project management practices
- More efficient resource allocation
- The vendor holds full responsibility over the project success, timelines, and budget
Cons
- High vendor risks
- Low control over the project
Partial project outsourcing
You augment the in-house team with additional expertise for more complex tasks like BI solution architecture design, BI components development, etc.
Pros
- The vendor guides you through the most challenging activities, while you still maintain control over the project
- The vendor closes all skill gaps themself
- You are in charge of resource allocation
Cons
- Moderate vendor risks
- High requirements for the in-house project management competencies
- Potential project team coordination issues
Common composition of a business intelligence implementation team
Business solution consultant
- Identifies new ways to increase efficiency and optimize business processes through improvement or automation
- Recommends business process changes based on industry best practices
- Creates a strategic vision for the company’s technological needs, including hardware, software, security practices, and data storage solutions
- Creates training materials for employees
Project manager
- Creates and maintains project timelines
- Holds meetings to present project progress, updates, risks, and constraints
- Collaborates with business analysts to develop plans, policies, and procedures for BI implementation
- Collaborates with the leadership and negotiates the project’s planning and implementation
- Provides feedback to the implementation team for adjusting or revising project schedules
Business analyst
- Identifies the development need for BI initiatives and business process improvements
- Analyzes business requirements and processes and translates them into software specifications
- Makes recommendations for software improvements
- Creates and maintains the documentation for BI solutions
- Reviews technical documentation to ensure business requirements are adequately reflected
BI solution architect
- Evaluates functional and technical business requirements to transform them into BI solution components
- Collaborates with a BA to identify business problems and create solutions aligned to corporate architectural standards, principals, and security requirements
- Creates BI architecture specifications and other design artifacts
- Provides educational/mentoring resources to the BI implementation teams
BI developer
- Collects and maintains business reporting requirements and needs
- Designs BI reports and dashboards
- Develops, deploys, and maintains BI interfaces
- Creates technical documentation for BI tools
Data engineer
- Creates and maintains data pipelines
- Builds infrastructure for the optimal data extraction, transformation, and loading from various data sources
- Creates new data validation methods and data analysis tools
- Ensures compliance with data governance and security policies
QA engineer
- Creates tests to identify problems with the BI solution components
- Analyzes bugs and errors found during tests and suggests solutions
- Documents test results for the development team and makes improvement recommendations
- Verifies if the final product meets the requirements
DevOps engineer
- Collaborates with the implementation team to conceptualize, develop and deploy the BI solution
- Secures software to prevent security breaches and other vulnerabilities
- Provides detailed specifications for proposed BI solution including materials, manpower and time necessary
- Mentors and trains other engineers and seeks to continually improve processes
- Automates and improves the BI solution development processes
Support engineer
- Diagnoses and troubleshoots technical issues
- Installs and configures BI solution components
- Responds to customer inquiries and assists in troubleshooting and resolving challenges
- Escalates unresolved issues to appropriate internal teams
- Prepares reports listing requests for technical assistance
Looking for a vendor to implement a cost-effective BI solution?
Best off-the-shelf BI platforms
Before we outline the most popular BI software options, here’s a checklist of must-have BI functionality:
- Support for structured, unstructured and semi-structured data located on-premises and in the cloud
- Integration with the existing systems and applications, third-party apps, etc.
- Security and compliance functionality
- Support for different types of users
- Data visualization capabilities (dashboards, data storytelling, low-code/no-code visualization)
- Collaboration and sharing (emails, alerts, content sharing, commenting, etc.)
- Mobile support
- Flexible pricing options (per user, enterprise subscriptions)
- Training and customer support
- Free trial
Key features
- Pre-built connectors for 150+ data sources, including Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure SQL database, Azure Machine Learning Studio
- Support for DAX, Power Query, SQL, R, and Python
- Self-service data preparation, analysis, reporting and visualization
- NLP capabilities
- Real-time data streaming
- Pre-build customizable visuals
- Data storytelling capabilities
- Team commenting and content subscriptions
- Row-level security
- Mobile-ready
- Embedded BI
Category
- BI and interactive data visualization software
Platform pricing
- Power BI Desktop
- free
- Power BI Pro
- $9.99 per user/month
- Power BI Premium
- $20 per user/month or $4,995 per capacity/month with an annual subscription and an unlimited number of users
- Power BI Embedded
- from $1.0081/hour
- Free trial
Key features
- Native integrations to 80 data sources, including local files, spreadsheets, relational and non-relational databases, data warehouses, big data, and on-cloud data sources
- Self-service data preparation
- No-code analytical data querying
- Real-time collaboration and sharing
- User-friendly UI with drag-and-drop functionalities and NLP
- Custom dashboard creation
- Row-level security
- Mobile-ready
- Embedded analytics
Category
- BI software
Platform pricing
- Tableau Creator
- $70/user/month
- Tableau Explorer
- $35/user/month (fully hosted by Tableau)
- Tableau Explorer
- $42/user/month (on-premises or public cloud)
- Tableau Viewer
- $12/user/month (fully hosted by Tableau)
- Tableau Viewer
- $15/user/month (on-premises or public cloud)
- Free trial
Key features
- Broad data connectivity (including file-based, on-premises, cloud-based and web sources)
- Self-service data preparation, analytics, and reporting
- Automated visual recommendations
- Data storytelling and reporting
- Group sharing and collaboration
- Support for multiple user types
- NLP support
- Row- and column-level security
- Automated ML capabilities
- Embedded analytics
- Mobile-ready
Category
- BI software
Platform pricing
- Qlik Sense Business
- $30/user/month
- Qlik Sense Enterprise SaaS
- custom pricing is available upon direct request
- Free trial
Business intelligence implementation costs
The cost of implementing a full-scale BI solution depends on multiple factors, including:
- The data you want to analyze – a number of data sources, data volume, data structure and format, data quality
- Complexity of data analysis – the need for real-time analysis, self-service analytics and ML capabilities, the number of analytics users and their roles
- Complexity of data visualization and reporting – NLP support, number of custom reports/dashboards, self-service capabilities
- Data security and compliance requirements
- BI software costs
Need help with choosing the optimal tech stack for your BI solution?
Why implement business intelligence
Single source of truth
Consolidation of all business data into a central database helps break down data silos and ensure data consistency across business departments.
Faster and smarter business decisions
Accurate and timely reporting, personal data views, and an intuitive user interface can encourage business users to make data-driven decisions, regardless of their tech expertise.
Improved organizational efficiency
A 360-degree view of the business allows decision-makers to assess corporate performance against the set goals, identify inefficiencies, and spot growth opportunities.
Enhanced customer experience
Data-driven insights can improve customer-facing activities like pricing, churn prevention, promotion optimization, cross-selling and upselling.
Decreased operational costs
Business process transparency can optimize internal processes such as supply chain management, fraud prevention, demand planning, procurement management, and more.
New revenue opportunities
Solid analytics capabilities can help discover emerging trends, study competitors, and quickly spot changes in demand, market capacity, and investment environment.
Overcoming business intelligence implementation challenges
Challenge
Solutions we offer
Analytics insights across different departments can appear inconsistent, outdated, or irrelevant.
Poor data quality may jeopardize the successful BI implementation, resulting in wasted time and resources and business stagnation. To avoid that, we help companies adopt a solid data quality management approach, which involves:
- Data set curation by data experts and IT teams before or during the upload into the data warehouse or other analytics repositories
- Overall awareness of high-quality data importance and its assurance
- Easy-to-track data lifecycle
BI adoption levels are not as high as expected.
Here’s how companies can mitigate BI adoption issues:
- Set key performance indicators in strict adherence to business needs
- Start the deployment with a use case that clearly demonstrates the tangible benefits of a BI system and motivates users to embrace it
- Deliver BI reports and dashboards that are relevant to employees at different positions
- Adopt intuitive self-service BI software
- Conduct training for users across different business departments
- Monitor users’ activity and requests to identify adoption problems and timely solve them
Deployment of self-service tools across different business units results in a chaotic data environment and overlapping KPIs.
Self-service data analytics and exploration should be regulated by robust data governance standards and policies, which should be established before the BI deployment.
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