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
Why choose Itransition
15+ years in business intelligence
20+ years in enterprise software development
Solid experience in delivering custom, platform-based, and embedded BI solutions
Strategic partnerships with Microsoft, AWS, and Google Cloud
40+ successful BI projectsÂ
Our selected BI projects
70%
faster time-to-market
Cloud BI for vehicle manufacturers
We migrated the customer’s legacy BI system to the microservices architecture to achieve a 15-20x higher system throughput, 100% process predictability and transparency, and 50% higher productivity.
7x
reduction in spending
Benchmark dashboards for ad campaign optimization
We developed an analytics optimization suite for a leading digital media company to make realistic forecasts based on the results of marketing campaigns and monitor advertising campaign performance.
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Our business intelligence implementation guide
While a BI implementation process can differ from company to company depending on their needs, objectives, and BI strategy, some steps are typical for most projects. Below, we’ve formulated a typical 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.
Common sourcing models for business intelligence implementation
All in-house
The internal IT team carries out BI 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
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
Looking for a vendor to implement a cost-effective BI solution?
Best off-the-shelf BI tools
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
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-critical 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
Challenge
Analytics insights across different departments can appear inconsistent, outdated, or irrelevant.
Challenge
Solutions we offer
Poor data quality may jeopardize the overall BI success, 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
Challenge
BI adoption levels are not as high as expected.
Challenge
Solutions we offer
Here’s how companies can mitigate BI adoption issues:
- Set KPIs 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
Challenge
Deployment of self-service tools across different business units results in a chaotic data environment and overlapping KPIs.
Challenge
Solutions we offer
Self-service data analysis and exploration should be regulated by robust data governance standards and policies, which should be established before the BI deployment.
