Business intelligence (BI) and big data are two of the most talked about technologies in the data landscape today. On the surface, they may seem similar after all, they both involve analyzing data to drive business decisions. However, there are some fundamental differences between the two that are important to understand.
Defining Business Intelligence
Business intelligence refers to the processes, tools, and techniques used to collect, analyze, and report on business data to help organizations make better decisions.
BI tools access and analyze data from internal systems like CRM, ERP, financial reporting systems, etc. They transform this data into insights via dashboards, reports, and visualizations. Common BI capabilities include:
- Reporting and visualization
- Ad hoc queries
- Interactive dashboards
- Data mining
- Predictive modeling
The goal is to take raw business data and turn it into actionable information that drives strategic planning and effective decision making. BI has been around since the 1990s and remains essential for modern data driven organizations.
The Rise of Big Data
The concept of big data emerged in the early 2000s with the realization that the volume, velocity, and variety of data being generated was growing exponentially. This data explosion was driven by:
- Proliferation of digital devices and interactions
- Growth of social media and internet-of-things
- Increasingly data driven business models
Analysing this firehose of complex and multi structured data using traditional BI and database technologies was near impossible. This led to the development of new big data platforms, tools, and techniques.
Defining Big Data
Big data refers to extremely large, complex data sets made up of structured, semi structured, and unstructured data from diverse sources. Big data is often described using 4 key characteristics:
Scale of data in terabytes and petabytes
Speed at which data is generated and moves
Different formats like text, images, audio, video, etc.
Inconsistency and uncertainty around data accuracy
Analyzing this data requires massively parallel software and high performance hardware like Hadoop and Spark. The goal is to reveal patterns, trends, associations, customer preferences, and other useful business insights.
While BI and big data overlap in some areas, they have some fundamental differences:
|Internal systems like ERP, CRM, financial reporting systems, etc.
|Any digital data source including weblogs, social media, mobile devices, sensors, machine data, etc.
|Historical data from internal operational systems
|Any data that might be relevant now or in the future
|Multi-structured, semi-structured, and unstructured data
|Slice and dice data to answer predetermined questions and test hypotheses
|Explore broad data sets to uncover hidden questions, patterns, and insights
|Descriptive, diagnostic, predictive modeling, data mining, benchmarks
|Data mining, machine learning including supervised and unsupervised techniques
|Enable reporting and interactive dashboards to monitor KPIs and support tactical decisions
|Build predictive models and uncover data driven insights for competitive advantage and strategic direction
As can be seen, while BI and big data can work together, they have some fundamental differences in terms of data sourcing, scope, structure, analytics focus and techniques.
Convergence and Collaboration
In recent years, technologies like data lakes and enterprise data warehouses have allowed more unstructured big data to be combined with structured BI data. This enables integrated analytics on a single platform. According to Gartner, by 2022 over 80% of enterprise data warehouse solutions will include integrated sel sservice BI and visual data discovery capabilities.
As BI and big data converge, business leaders are realizing they complement one another:
- BI excels at descriptive and diagnostic analysis using predefined data to measure, monitor and manage performance.
- Big data exploratory analytics reveals unexpected insights and helps model future opportunities.
Together they enable organizations to not only report on what happened and why but also predict what might happen next so they can prepare accordingly.
Looking ahead, technologies like artificial intelligence and machine learning will continue to be applied to both BI and big data analytics. This will automate more data processing and analysis tasks to help humans focus on higher value activities.
As 5G networks and internet-of-things drive data explosion over the next decade, organizations need to embrace BI and big data solutions that allow them to handle huge volumes in real time. Those who leverage data and analytics effectively will gain sustainable competitive advantage.
The most data driven organizations structure their strategy, operations, processes and culture around BI and big data capabilities. They invest in skills, platforms, governance and infrastructure to fuel fact based planning and smart decision making.
Although the technologies overlap, business intelligence and big data serve different primary purposes. BI enables reporting on historical data to monitor performance vs plans. Big data explores broader data sets to uncover new opportunities. Used together, enterprises can make tactical decisions based on what happened in the past while also informing strategic direction based on emerging trends and predictive modeling for the future. Companies that learn to leverage both capabilities will be best positioned to compete in the data centric business landscape of the 2020s and beyond.
Frequently Asked Questions
What is the main difference between business intelligence and big data analytics?
The main differences are the data sources used, focus of analysis, analytics techniques leveraged, and end goals. BI accesses structured internal data to enable monitoring and reporting. Big data explores a broader universe of multi structured data to uncover new insights and opportunities.
Can business intelligence and big data be used together?
Yes, BI and big data are increasingly being integrated on shared platforms to enable holistic data analysis. This allows tactical reporting on Key metrics as well as revealing strategic insights from exploratory analytics.
Which came first business intelligence or big data?
Business intelligence solutions emerged in the 1990s to enable better reporting and dashboards for performance measurement. The concept of big data analytics appeared in the 2000s as data volumes, variety and velocity grew exponentially.
Is business intelligence enough or do you need big data?
Organizations need capabilities from both BI and big data. BI enables drill down reporting and alerts when KPIs fall out of desired ranges. Big data reveals hidden insights to inform strategy and innovation. Leading data driven companies leverage both.
What is the future of BI and big data?
AI and machine learning will automate more processes in both BI and big data analytics. As data volumes grow, techniques like predictive modeling, simulation and streaming analytics will become mainstream. Leading organizations will focus strategy around advanced data and analytics capabilities.
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