How Business Intelligence Platforms Generate Context for Data
In addition to providing simple visibility and readability into an organization's metrics, a BI platform serves the important purpose of generating context for that data.
For example: if a department is over budget, in a well-built BI platform, it's possible to drill down and see the cause of that overage - but it's also possible to see the secondary or tertiary impact throughout the company. A BI platform also allows the user to track trends that may not be visible at a lower level, and identify opportunities or remedies. Finally, a BI platform and the context it generates helps combat siloing, by helping stakeholders to understand the obstacles, opportunities, or successes being encountered by other areas of the company.
What is Data Context?
Data context refers to the background information and relevant details that surround and describe a dataset. This includes metadata, which provides answers to questions such as who collected the data, what the data is about, when and where it was collected, and why and how it was collected1. By understanding the context, businesses can better interpret the data and derive more valuable insights.
How BI Platforms Generate Context
- Metadata Management: BI platforms manage metadata, which is crucial for understanding the context of data. Metadata includes information about data sources, data lineage, and data quality, helping users trust and understand the data they are analyzing1.
- Data Integration: BI platforms integrate data from multiple sources, such as internal databases, external data feeds, and third-party APIs. This integration creates a more comprehensive view of the information, allowing for better contextualization2.
- Data Visualization: Visualization tools within BI platforms help users see data in the context of different dimensions, such as time, geography, and business units. This makes it easier to identify patterns, trends, and anomalies3.
- Advanced Analytics: BI platforms leverage advanced analytics, including machine learning and artificial intelligence, to analyze data in various contexts. These technologies can uncover hidden relationships and provide deeper insights3.
- User Collaboration: BI platforms often include collaboration features that allow users to share insights and discuss data within the context of their specific roles and responsibilities. This collaborative approach ensures that data is interpreted correctly and used effectively across the organization4.
Benefits of Contextualized Data
- Improved Decision-Making: With a clear understanding of the context, decision-makers can make more informed and accurate decisions, leading to better business outcomes4.
- Enhanced Data Quality: Contextual data helps in tracking data quality and integrity, ensuring that the data used for analysis is reliable and trustworthy1.
- Increased Efficiency: By providing context, BI platforms reduce the time and effort required to interpret data, allowing users to focus on deriving insights and taking action2.
Conclusion
Business Intelligence platforms play a crucial role in generating context for data, transforming raw information into valuable insights. By managing metadata, integrating data from multiple sources, providing visualization tools, leveraging advanced analytics, and fostering user collaboration, BI platforms enable organizations to make data-driven decisions that align with their strategic goals.
Why did your traffic drop when you converted to GA4? Here are some common reasons.
Many of our clients have come to us because they noticed tracking differentials when they switched to GA4 from Universal Analytics.
The below is a quick summary of the most common reasons why this might be happening.
- Different Data Model: GA4 uses an event-based data model instead of the session-based model used in Universal Analytics (UA). This shift can make it challenging to understand and compare metrics directly1.
- Missing or Changed Metrics: Some familiar metrics from UA, like bounce rate, have been redefined or are missing in GA4. For example, the bounce rate in GA4 is now the inverse of the engagement rate1.
- Unassigned Traffic: Traffic sources can sometimes appear as “Unassigned” in GA4 reports. This can happen due to incorrect UTM parameters, issues with the Measurement Protocol, or incomplete data processing2.
- Direct Traffic Misclassification: High levels of direct traffic might indicate tracking issues or misattributed traffic. This can occur if tracking parameters or URL tagging are not properly configured3.
- Data Discrepancies and Missing Data: Users often encounter discrepancies between GA4 and other analytics tools or even within GA4 itself. Missing data can also be a problem, affecting the accuracy of reports4.
- Integration Problems: Integrating GA4 with other tools and platforms can sometimes be problematic, leading to incomplete or inaccurate data collection4.