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Business Intelligence (BI) can potentially create stronger and smarter businesses across industries. Data profoundly impacts businesses by enabling informed decision-making, improved efficiency, and enhanced customer experiences.
Forbes reported that, according to psychologists, we make an average of 35,000 decisions on any given day. Per the publication, data analytics generally steers these decisions, whether we are conscious of that fact or not. We store most of what we need in our devices and regularly turn to them for insights on travel, work, leisure, planning, and spending. These insights come from data that helps inform us before finalizing our agenda.
Forbes claims the impact of seamless BI integration can sometimes be more significant and wider reaching, with every move ultimately affecting a company’s overall profitability and success. The same practice of unknowingly using BI on our phones can now apply to business, with many BI platforms allowing for the unintentional use of data rather than a calculated analysis to derive pointed insights and solutions.
In other words, businesses can continually make better decisions using data without even realizing that’s what they’re doing. It simply becomes automatic—after all, we are moving toward a future of automation.
The Journey of Business Intelligence (1980s-Present Day)
BI has been around for several decades, evolving and adapting as our world has become almost entirely digital. There’s a continually rising demand for data-driven insights to support business growth and innovation as we rely more fully on digitization in life and business.
Significant phases in the evolution of BI include:
Reporting and Query Tools (1980s-1990s)
The earliest BI tools focused on generating basic reports and queries from structured data. These tools helped businesses retrieve and visualize data from databases.
Online Analytical Processing (OLAP) (1990s)
OLAP tools enabled users to analyze multidimensional data, allowing for more complex insights and interactive exploration of data cubes.
Data Warehousing (1990s-2000s)
Data warehouses emerged as centralized repositories for storing and managing large volumes of data from various sources. This made it easier to retrieve and analyze data for reporting and decision-making.
Data Mining and Predictive Analytics (2000s)
BI expanded to include data mining and predictive analytics, allowing organizations to discover patterns and trends in their data to make informed predictions about future outcomes.
Self-Service BI (2010s)
Self-service BI tools empowered non-technical users to create reports and visualizations without depending on IT departments, thus democratizing data access and analysis.
Big Data and Advanced Analytics (2010s)
The proliferation of big data led to the development of tools and techniques to process and analyze large, diverse datasets. Advanced analytics, including machine learning (ML), became integral to BI.
Cloud-Based BI (2010s)
Cloud computing made BI more accessible, scalable, and cost-effective, allowing organizations to store and analyze data without significant infrastructure investments.
Embedded BI (2010s)
BI capabilities were embedded into other software applications, making data insights available within daily business operations.
Augmented Analytics (Present and Future)
Integrating AI and ML into BI tools enhances data analysis by automating insights, anomaly detection, and natural language querying.
Real-Time Analytics and IoT Integration (Present and Future)
Businesses are increasingly focusing on real-time data analysis to make instant decisions. Integration with the Internet of Things (IoT) data allows for monitoring and responding to events as they happen.
Choosing the Right BI Platform for Your Business
When choosing the right BI platform, it’s important to consider company needs, data sources, budget, and user skills. To find the best fit, business leaders must evaluate features, scalability, ease of use, integration capabilities, and customer support. It’s also helpful to try demos, gather user feedback, and consult a data expert before deciding.
Most BI platforms offer a range of core features to support data analysis, reporting, and decision-making. Here are features commonly found in many BI platforms:
- Data Integration
- Data Visualization
- Dashboards and Reports
- Ad Hoc Querying
- Data Transformation
- Data Modeling
- Predictive Analytics
- Collaboration
- Mobile Accessibility
- Data Security and Access Control
- Scheduled Reporting
- Alerts and Notifications
- Data Drill-Down
- Data Governance
- Integration with Third-Party Tools
- Cloud Integration
- APIs and Extensibility
- Data Exploration
- Performance Monitoring
- What-If Analysis
- Data Connectors
Popular BI platforms to choose from include:
- Alteryx
- BOARD
- Domo
- Hitachi
- IBM Cognos
- Looker
- MicroStrategy
- Oracle
- Power BI
- Qlik
- Salesforce
- SAP
- Sisence
- Tableau
- TIBCO
- Yellowfin
- Zoho
How Can Oxford Help
Despite the many benefits of BI and the number of platforms available to assist businesses in data analysis, many organizations have yet to capitalize on this thriving technology. Several obstacles can hinder the adoption and overall success of BI, including:
- Data quality and integration
- Lack of skills
- Resistance to change
- Cost and resources
- Complexity
- Data security and privacy concerns
- Cultural shift
- Lack of executive support
- Integration with existing systems
- Misalignment with business goals
According to 2023 estimates, data is generated at approximately 328.77 million terabytes daily. We can help you put that data to use.
Oxford is committed to success and can set up businesses to achieve success in BI and data analytics. We pre-vet our talent, so we’re ready to provide solutions before our clients even reach out. That’s how we match you with The Right Talent. Right Now.